*2.2. Batteries*

The electric vehicle batteries using the parking lot for charging were assumed to be representative of the current Dutch EV fleet, including BEVs and Plug-in Hybrid Electric Vehicles (PHEVs). The battery energy capacities considered therefore range from the 8.8 kWh Audi A3 PHEV to the 100 kWh Tesla Model X BEV. The solar parking lot also included a stationary Li-ion based battery storage system, for storing excess energy to further reduce the peak demand. The battery had a rated power of 50 kW and a capacity of 50 kWh, of which 80% was usable. The total number of batteries, *Nb*, is at all times less than 41, since the maximum occupancy of the parking lot is 40 EVs and there is always the stationary battery.

The charging efficiency, *ηchg*, and discharging efficiency, *ηdis*, in the battery, were each assumed to be 95%, leading to overall roundtrip losses of 9.75%. Coulomb counting was used to infer the state of charge (SoC) of the battery and changes to it. The rectification stage in the vehicle was assumed to lead to losses of about 6% in charging [15].

#### *2.3. Electric Vehicle Supply Equipment*

The system includes 40 charge points, each rated at 32 A (7.4 kW) for both AC and DC. While this rating is commonly found as AC level 2 charging [16], it is a lower current capacity than commercial DC charge points. The reason for this choice was to enable the slow charging of EV batteries on the DC bus without multiple rectification-inversion stages, as is expected in the future. The losses in the EVSE, which are primarily resistive in nature, were assumed to be around 0.2% [15].

#### *2.4. Electric Vehicle Load Profile*

The electric vehicle load profile was built based on two submodels—first, the EV arrival and departure model and the second, the estimation of the state of charge at the point of entry. In addition, the load is also determined by the extent to which the battery is to be charged by the time of departure. In this work, it is assumed that the EV drivers wish for their EVs to be charged to 100% SoC whenever possible.

#### 2.4.1. EV Arrival and Departure

Direct Use of Observed Activity-Travel Schedule (DUOATS) models are a common method applied in smart charging and demand response studies [17]. Such a model was used in this work, whereby observed vehicle patterns were used to simulate EV behavior. The parking location considered was a workplace, and the model was based on data from the EV Project, a project run by the United States Department of Energy. It included data related to 8228 electric vehicles and hundreds of thousands of trips and charging events [18]. Since the data was collected from a large number of participants and geographical locations across the USA, it was assumed to be generalizable.

This historical parking data was used to determine the arrival and departure rates at both parking lots for each timestep of the day, considering weekdays and weekends separately. Based on these rates, a Monte Carlo approach was taken to determine the number of EV arrivals in each timestep. The duration of parking and the time of day were used to determine the number of departures in each timestep, after which an occupancy profile was built. A representative week of occupancy of vehicles at the workplace is shown in Figure 2. There are noticeable daily patterns of arrivals and departures during weekdays, with weekends having lower arrival rates.

**Figure 2.** Simulated occupancy at the workplace parking lot over a week.

#### 2.4.2. EV State of Charge on Arrival

Truncated normal distributions were used for assigning the SoCs on arrival. The coefficients for the normal distribution were inferred by fitting data from the EV Project [18], which collected data for over 8000 EVs in the USA. They are shown in Table 2.

**Table 2.** Coefficients describing the assignment of EV battery states of charge on arrival.

The lower mean SoC and greater standard deviation for PHEVs are explained by the lower concern of PHEV drivers about depleted batteries in comparison with BEV drivers.

Based on the time of arrival of an individual EV, the battery capacity of the EV and its SoC at the time of arrival, the expected charging demand of the EV was calculated. This demand (or as high a fraction as possible) needed to be met within the plug-in duration. The control system was informed about the time of departure at the time of arrival in all cases.

With knowledge of the available solar energy, the forecasted solar energy over the forecast horizon and the EV charging demand, the optimal charging of EVs in the parking space was to be determined in a manner that minimized the peak electricity demand in the solar parking lot. The following section describes the methods used for this scheduling.

#### **3. Methods for Charge Scheduling**

For an investigation into the effect of uncertainty of EV demand on peak loads, two reference cases and three scenarios are considered. One reference case is unscheduled charging, where EVs charge at maximum power as soon as they are plugged in. In addition, a case is simulated with perfect forecasting of solar production and EV demand, which may be considered as another reference case. The three scenarios investigated lie between these two extreme cases:

In all these three scenarios, the schedule was designed to be robust across a range of possible solar forecasts. These scenarios thus differ only in their approach to EV demand forecasting. The objective function for peak reduction under perfectly accurate forecasts is described in Section 3.1. The introduction of uncertainty in the forecasts is described in Section 3.2, after which each of the three scenarios and its optimization formulations are described. The commercial solver, Gurobi, was used with MATLAB in each case on a Windows PC with an Intel i5 1600 MHz quad-core processor and 32GB RAM.

#### *3.1. Problem Formulation with Perfect Forecasting*

In the scenario with perfect forecasting, the future solar production, as well as future electric vehicle demand over the 24 h horizon in the future, are assumed to be accurately known in advance. This is not a practically feasible scenario since neither of these can be accurately known in advance. However, this scenario clearly defines the best possible performance of the scheduling approach, with reference to which other scenarios may be compared. In addition, it also provides an idea of the performance of the scheduling algorithm independent of the degree of accuracy of the forecast.

To optimize the scheduling of EVs within the carport, we apply Model Predictive Control (MPC). MPC is a control technique used for determining the optimal behavior of complex multivariate problems. The control action is determined at each time step by solving an open-loop optimal control problem over a finite time horizon. MPC is used to solve problems during the operation of the system, taking the state of the system into account at each time step. These characteristics make MPC suitable for the control of EV charge scheduling at the solar parking lot.

The goal of the scheduling of EV charging is to reduce the peak demand of the solar parking lot over the time horizon under consideration. Thus, the objective function is:

$$\text{minimize} \quad \max \left( E\_{\text{grid}}(k), \dots, E\_{\text{grid}}(k + N\_{\text{p}} - 1) \right), \tag{1}$$

where *Egrid*(*k*) is the net energy exchange between the parking lot and the grid at time *k* and *Np* is the time horizon, which is 24 h. A sensitivity analysis on the duration of the horizon revealed that longer forecasts had no increased benefits to the simulation. Further, with reducing the accuracy of the length of the simulation, the reliability of longer horizon forecasts is lower upon the introduction of forecasting uncertainty. An auxiliary variable, *Emax grid* (*t*) is introduced, which represents the local maxima or peak in grid exchange of the parking lot with *Nb* batteries and lighting load, *Eload* over the considered horizon, *Np*.

The objective function is thus rewritten as:

$$\underset{E^{\max}\_{\text{grid}}, E, \delta, z}{\text{minimize}} \quad E^{\max}\_{\text{grid}} \tag{2}$$

with the decision variables

$$E\_{\text{grid}}^{\text{max}}, E\_i(t), \delta\_i(t), z\_i(t) \quad \text{for } i \in \{1, \dots, N\_b\}, \quad t \in \{k, \dots, k + N\_p - 1\}, \tag{3}$$

subject to a number of constraints, described below.

At any given time, *t*, the energy exchanged with the grid, *Egrid*(*t*), depends on the PV production, *EPV*(*t*), the lighting load, *Eload*(*t*), and summed load of each battery, *E*(*t*). It is ensured that the energy exchange peak, which is subject to minimization, is the highest peak within the considered horizon both in purchase as

$$E\_{grid}^{\text{max}} \ge E\_{load}(t) - E\_{PV}(t) + \sum\_{i=1}^{N\_b} E\_i(t) \,\,\forall \, t\_\prime \tag{4}$$

as well as in feed in as

$$E\_{grid}^{\min} \le E\_{load}(t) - E\_{PV}(t) + \sum\_{i=1}^{N\_b} E\_i(t) \,\,\forall \, t. \tag{5}$$

The stored energy in the *ith* battery in the (*k* + 1)*th* timestep, the state variable, *Si*(*k* + 1), is described as

$$S\_i(k+1) = \begin{cases} S\_i(k) + \eta\_{c\lg, i} \cdot E\_i(k), & E\_i(k) > 0 \\ S\_i(k) + \frac{1}{\eta\_{dis, i}} E\_i(k), & E\_i(k) \le 0 \end{cases} \tag{6}$$

In order to formulate the battery behavior linearly, the Mixed Logical Dynamics (MLD) formalism is used [19]. A binary decision variable *δi*(*t*) is introduced, defined as:

> [*δi*(*t*) = 0] ↔ [*Ei*(*t*) > 0] (EV or battery is charging) (7)

$$\left[\delta\_{i}(t) = 1\right] \leftrightarrow \left[E\_{i}(t) \le 0\right] \text{(battery is discharged)},\tag{8}$$

and leads to the constraint:

$$
\delta\_i(t) \in \{0, 1\} \; \forall \; i, \; t. \tag{9}
$$

This binary decision variable, *δi*(*t*), is then used to reformulate Equation (6) as

$$S\_{i}(k+1) = S\_{i}(k) + \eta\_{\text{clg},i} \cdot E\_{i}(k) \cdot (1 - \delta\_{i}(k)) + \frac{1}{\eta\_{\text{dis},i}} E\_{i}(k) \cdot \delta\_{i}(k). \tag{10}$$

However, this formulation is still nonlinear because it contains the product of two decision variables, *Ei*(*k*) and *δi*(*k*). An additional set of continuous decision variables is introduced as

$$z\_i(t) = \delta\_i(t) \cdot E\_i(t). \tag{11}$$

The minimum energy, which can be stored in the *ith*, battery in the (*t* + 1)*th* timestep, is limited by the lowest possible energy which can be delivered to it with the objective of maximimizing the SoC at departure. This is formulated as

$$S\_i^{\min}(t+1) \le S\_i(t) + \eta\_{\text{clg},i} E\_i(t) + \left(\frac{1}{\eta\_{\text{dis},i}} - \eta\_{\text{clg},i}\right) z\_i(t) \,\,\forall \, i,\tag{12}$$

The maximum energy which can be stored in the *i*-th, battery in the (*t* + 1)-th timestep is limited by the physical constraints on the battery capacity and the power rating of the chargepoints. It is formulated as

$$S\_i^{\max}(t+1) \ge S\_i(t) + \eta\_{\text{chg},i} E\_i(t) + \left(\frac{1}{\eta\_{\text{dis},i}} - \eta\_{\text{chg},i}\right) z\_i(t) \,\,\forall \, i,\tag{13}$$

The constraints on the energy exchanged with the *ith* battery in each time step is given as

$$E\_i(t) \le M\_i \cdot (1 - \delta\_i(t))\tag{14}$$

$$E\_i(t) \ge \varepsilon + (m\_i - \varepsilon) \cdot \delta\_i(t) \tag{15}$$

where *Mi* is the maximum allowable value of *Ei*(*t*) and *mi* is the minimum allowable value. An overestimate of *Mi* or an underestimate of *mi* is acceptable, but values close to the true maximum and minimum are preferred to lower computational time. These values are taken as

$$M\_{\bar{l}} = P\_{\bar{l}}^{\text{max}} \cdot \Delta t \tag{16}$$

$$m\_i = -P\_i^{\max} \cdot \Delta t\_\prime \tag{17}$$

where *Pmax i* and *Pmin i* are the power limits of the *ith* battery and Δ*t* is the length of the time step i.e., 15 min. The tolerance, *ε* is a small value, typically the machine precision of the solver.

The following constraints then limit the auxiliary MLD variables *δi*(*t*) and *zi*(*t*) to ensure they will be equivalent to their stated definitions [20]

$$z\_i(t) \le M\_i \cdot \delta\_i(t) \tag{18}$$

$$\forall z\_i(t) \ge m\_i \cdot \delta\_i(t) \tag{19}$$

$$z\_i(t) \le E\_i(t) + M\_i \cdot (1 - \delta\_i(t)) \tag{20}$$

$$z\_i(t) \ge E\_i(t) + m\_i \cdot (1 - \delta\_i(t))\tag{21}$$

#### *3.2. Inclusion of Uncertainty in Forecasting*

Since historic data of the PV yield and modeled data for the EV demand were used, the values of the future PV yield and EV demand were known. In order to simulate an inaccurate forecast, errors were introduced to the known PV production and the EV demand over the relevant horizon.

#### 3.2.1. Uncertainty in PV Forecasting

*EPV*(*t*) is known to be the PV production over the relevant horizon at the timestep, *t*. The forecasted value of PV production is the sum of *EPV*(*t*) and an additional solar forecasting error term, *ωPV*(*t*), as

$$E\_{fest}(t) = E\_{PV}(t) + \omega\_{PV}(t). \tag{22}$$

Monte Carlo methods are used in this case to generate a finite but large number of error vectors, Ω∗*PV*, which are then considered.

$$
\Omega\_{PV}^\* = \{\omega\_{PV}^{(1)}, \dots, \omega\_{PV}^{(N\_\ell)}\} \subseteq \Omega\_{PV\_\ell} \tag{23}
$$

where *Ne* is the number of error vectors considered. If *Ne* is large enough, Ω∗*PV* may be considered to be a reasonably good approximation of Ω*PV*, the set of all possible error vectors. 10,000 is chosen for *Ne* in this case. Though forecasting error is normally distributed, for robust optimization, a bounded distribution is required. The distribution is therefore truncated such that

$$-3\sigma\_{PV}(t) \le \omega\_{PV}^j \le 3\sigma\_{PV}(t),\tag{24}$$

for all *t* in *k*,..., *k* + *NP* − 1 and *j* in 1, . . . , *Ne*. The upper and lower bounds are determined by *σPV*(t) the standard deviation of the forecasting error at time, *t*. The forecasting error is also truncated so that the forecasted power generation cannot be less than zero or greater than the clear sky generation. The choice of three standard deviations as a limit, rather than more conservative values of five or seven, is justified based on simulation results.

#### 3.2.2. Uncertainty in EV Forecasting

Two approaches were considered here—the average approach and the Monte Carlo approach, as used in the PV uncertainty introduction. In the average approach, the terms *S*min *i* and *S*max *i* , defined in Equations (12) and (13) are taken to be their average values based on data collected from EVs in the parking lot at the timestep, *t*. Thus, uncertainty in a number of variables like arrival time and numbers, departure times, the energy capacity of the vehicle, the SoC of the vehicle on arrival are all clustered together to be dealt with through the optimization formulation. The nature of the formulation implies a single forecast is available in each timestep, which is based upon average values in the past, thus satisfying the aim of this method of introduction of uncertainty.

In the second approach, error terms are introduced through Monte Carlo simulation, as with the errors in PV generation forecasts. However, as in the average case, the errors are introduced in the terms *S*min *i* and *S*max *i* , influencing the state constraints rather than the state variables. This formulation thus considers a range of possible forecasts, treated as equally probable, over which the problem needs to be solved.

#### *3.3. No EV Demand Forecast*

No EV forecasting is the simplest strategy where the charging of EVs plugged-in at the parking lot at the current time step are optimized over the period they are expected to be plugged in. The arrival of additional EVs in the near future and their demands are not considered—a drawback of the approach.

The schedule is designed to deal with uncertainty in the PV forecast. It does this by considering a range of possible PV forecasts, over all of which it reduces peaks. In other words, it operates robustly over a range of PV uncertainties.

To ensure robustness, we use min max optimization, which minimizes the cost function over the decision variables for the worst case i.e., highest peak load. In this case, the objective function is

$$\underset{E^{\text{max}}\_{\text{grid}'}, E\_i(t), \delta\_i(t), z\_i(t)}{\text{minimize}} \quad \underset{\omega \in \Omega\_{PV}(t)}{\text{maximize}} \quad E^{\text{max}}\_{\text{grid}'} \tag{25}$$

where *ω* is a vector representing a random possible value for the forecasting errors at each time step *t*, and Ω*PV*(*t*) is the bounded set of all possible forecasting errors. The errors at each time step are therefore drawn randomly from the uniform distribution given by

$$
\omega\_{PV}^{\bar{j}}(t) \in \mathcal{U} \left( -\mathfrak{A}\sigma\_{PV}(t), \mathfrak{A}\sigma\_{PV}(t) \right). \tag{26}
$$

A new auxiliary variable, *T*, is now defined as the maximum value for the objective function under all the forecasting errors considered. Because the objective function is not directly dependant on the forecasting uncertainty, *T* can simply be defined as being equal to the original objective function. If *J* is the maximum peak in each case of forecasting error considered, and *u*˜ the decision variables, *T* is given by

$$T = \max\_{\mathcal{A}} (f(\omega\_{PV}^{(1)}), \dots, f(\omega\_{PV}^{(N\_t)}) \ \ = \mathbb{E}\_{\text{grid}}^{\text{max}} \tag{27}$$

where *Emax grid*is independent of the uncertainty variables.

The objective function is redefined as

$$\underset{T, E\_{grid}^{\max}, E, \delta, z}{\text{minimize}} \quad T\_{\prime} \tag{28}$$

with the decision variables:

$$T.E\_i(t), \,\delta\_i(t), \,z\_i(t) \qquad \text{for } i \in \{1, \dots, N\_b\}, \quad t \in \{k, \dots, k+N\_p-1\}. \tag{29}$$

The constraints in Equations (4) and (5) are changed to include the solar forecasting error as

$$E\_{grid}^{\text{max}} \ge E\_{load}(t) - E\_{fcst}(t) + \omega\_{PV}^{\text{max}}(t) + \sum\_{i=1}^{N\_b} E\_i(t) \tag{30}$$

$$E\_{grid}^{\min} \le E\_{load}(t) - E\_{fct}(t) + \omega\_{PV}^{\min}(t) + \sum\_{i=1}^{N\_b} E\_i(t) \tag{31} \tag{31}$$

The optimization problem is then solved to be robust across the errors in PV forecast, *ωPV*(*t*), which lie within the bounded range of uncertainty Ω*PV*(*t*). As proven in [20], a constraint cannot be active at some intermediate value of the disturbance without violating the constraint at the extreme value. In this model, the greater-than constraint will be active only at the maximum value of the disturbance and a less-than constraint will be active only at the minimum. Hence, only the minimum value, *ωmin PV* (*t*), and maximum value, *ωmax PV* (*t*), of the errors were considered, which are sufficient for the all intermediate error values.

Although there is no EV forecasting, vehicles which are not plugged in at the charging station at the relevant timestep, would still begin charging at some point in the future. These limits are then included in constraints in Equations (12) and (13), which are modified to

$$\mathcal{S}\_{i}^{\min}(t+1) \begin{cases} = 0, \text{ when a vehicle is not present at the current timestep, } t, \text{ in space, i} \\ \leq \mathcal{S}\_{i}(t) + \eta\_{\text{clg},i} E\_{i}(t) + \left(\frac{1}{\eta\_{\text{cli},i}} - \eta\_{\text{clg},i}\right) z\_{i}(t) \,\forall \, i, \\ \text{when a vehicle is present at the current timestep, } t, \text{ in space, i} \end{cases} \tag{32}$$

and

$$\mathcal{S}\_i^{\text{max}}(t) \begin{cases} = 0, \text{when a vehicle is not present at the current timestep, } t, \text{ in space, } i \\ \ge \mathcal{S}\_i(t) + \eta\_{\text{clg},i} \mathcal{E}\_i(t) + \left(\frac{1}{\eta\_{\text{cla},i}} - \eta\_{\text{clg},i}\right) z\_i(t) \; \forall \; i, \\ \text{when a vehicle is present at the current timestep, } t, \text{ in space, } i \end{cases} \tag{33}$$

The constraints in Equation (15) through Equation (21) remain unchanged.

#### *3.4. Average EV Demand Forecast*

The objective function with robust solar PV forecast and a single average EV forecast remains the same as Equation (28). The stored energy terms, *S*min *i* (*t*) and *S*max *i* (*t*) in the constraints in Equations (12) and (13) are replaced by average values of these variables. The new constraints are therefore:

$$\overline{S\_i^{\min}(t+1)} \le \overline{S\_i(t)} + \eta\_{\text{clg},i} E\_i(t) + \left(\frac{1}{\eta\_{\text{dis},i}} - \eta\_{\text{clg},i}\right) z\_i(t) \tag{34} \\ \qquad \qquad \forall \, i, \\ \tag{34}$$

$$
\overline{S\_i^{\text{max}}(t+1)} \ge \overline{S\_i(t)} + \eta\_{\text{clg},i} E\_i(t) + \left(\frac{1}{\eta\_{\text{dis},i}} - \eta\_{\text{clg},i}\right) z\_i(t) \tag{35}
$$

where *S*min *i*(*t*) and *S*max *i*(*t*) are the average values for the variables *S*min *i*(*t*) and *S*max *i*(*t*) respectively.

#### *3.5. Robust EV Demand Forecast*

The system is meant to be robust in the sense of reducing peak grid exchange across a wide range of EV forecasting errors as well as errors in the solar forecast. The maximum value, *ω*max *i* (*t*) and minimum value, *ω*min *i* (*t*), in the range of errors introduced in the EV demand forecast through the Monte Carlo simulation, as described in Section 3.2.2, were used to define the state constraints for *S*min *i*(*t*) and *S*max *i*(*t*) as

$$S\_i^{\min}(t) + \omega\_i^{\max}(t) \le S\_i(t) + \eta\_{\text{clg},i} E\_i(t) + \left(\frac{1}{\eta\_{\text{dis},i}} - \eta\_{\text{clg},i}\right) z\_i(t) \tag{36}$$

$$S\_i^{\min}(t) + \omega\_i^{\min}(t) \ge S\_i(t) + \eta\_{\text{clg},i} E\_i(t) + \left(\frac{1}{\eta\_{\text{dis},i}} - \eta\_{\text{clg},i}\right) z\_i(t). \tag{37}$$

However, constraints risk making the problem infeasible if *ω*max *i* (*t*) > *ω*min *i* (*t*), which is highly likely. In order to enable a feasible solution to the problem, the state constraints for vehicles which are forecasted to arrive, are expressed as soft constraints:

$$S\_i^{\min}(t) + \omega\_i^{\max}(t) \le S\_i(t) + \eta\_{\text{clg},i} E\_i(t) + \left(\frac{1}{\eta\_{\text{dis},i}} - \eta\_{\text{clg},i}\right) z\_i(t) + \varepsilon\_i^1(t) \tag{38}$$

$$S\_i^{\min}(t) + \omega\_i^{\min}(t) \ge S\_i(t) + \eta\_{\text{clg},i} E\_i(t) + \left(\frac{1}{\eta\_{\text{dis},i}} - \eta\_{\text{clg},i}\right) z\_i(t) - \epsilon\_i^2(t). \tag{39}$$

for all *i* ∈ {1, ... , *Nb* − 1}, *t* ∈ {*k*, ... , *k* + *Np* − 1}. The slack variables 1*i* (*t*) ≥ 0 and 2*i* (*t*) ≥ 0 correspond respectively to the constraints for the minimum and maximum stored energy, and are added to the optimization problem as an auxiliary decision variables. For the stationary battery as well as the vehicles plugged in at the parking lot at a given timestep, *k*, the state constraints remain hard since the minimum and maximum stored energies are known with certainty.

The complete optimization formulation is then given by:

$$\underset{T, \underset{\text{prim}}{\text{minimize}}}{\text{minimize}} \quad T + \frac{1}{(N\_b - 1) \cdot N\_p} \sum\_{i=1}^{N\_b - 1} \sum\_{t=k}^{k + N\_p - 1} c\_1 \epsilon\_i^1(t) + c\_2 \epsilon\_i^2(t),\tag{40}$$

where *c*1 and *c*2 are penalty constants. The values *c*1 = 1 and *c*2 = 1 were found empirically to lead to lowest values of peak demand. Increasing the penalty constant values did not however lead to large changes in peak demand. The decision variables are:

$$T, E\_{\rm grid}^{\rm max}, \, E\_i(t), \, \delta\_i(t), \, z\_i(t), \, \epsilon\_i^1(t), \, \epsilon\_i^2(t) \qquad \text{for } i \in \{1, \ldots, N\_b\}, \quad t \in \{k, \ldots, k + N\_p - 1\}, \tag{41}$$

with the additional constraint

$$
\epsilon\_i^1(t), \epsilon\_i^2(t) \ge 0 \quad \forall \, i, \, t. \tag{42}
$$

#### **4. Results and Discussion**

A year of operation of the solar parking lot was simulated for each of the three described scenarios to determine the performance of the system at peak reduction in each case. During the comparison, the reference cases of unscheduled charging and perfect forecasts are also included to provide additional insights.

## *4.1. Example Simulations*

Figure 3 shows the energy flows within the system over a representative period, in this case, a week. The two reference cases, unscheduled charging and charging with perfect forecasting, are compared. The characteristics of the scheduling seen here are also valid for longer simulations over the year.

(**a**) Energy flows (unscheduled charging).

(**b**) Energy flows (perfect forecasting).

**Figure 3.** Comparison of energy flows in the solar parking lot with unscheduled charging as opposed to charging with perfect forecasts.

In accordance with thermodynamic system conventions, the energy entering the electrical system (PV production, grid purchase and battery discharge) is taken as positive and energy leaving the system (EV and battery charging, lighting load and feed-in to the grid) as negative. The shape of the positive and negative sides are similar, showing the energy balance in each timestep, excluding losses. The solar energy production is shown in yellow, battery charge and discharge in red and EV charging in blue. The lighting loads, which are very small in comparison with the others, are shown in green and residual grid load (after solar self-consumption and battery charge/discharge) in gray. The highest peak in the week is highlighted in each figure.

The largest peak in the residual grid load (110 kW) as a result of unscheduled charging, seen on Thursday in Figure 3a, is considerably reduced in magnitude to 50 kW as a result of scheduled charging with perfect forecasts. In addition, the frequency of these peaks is found to decrease considerably and a uniformly flat load profile achieved. The arrivals of many EVs on Tuesday, Wednesday and Thursday mornings, all of which lead to peaks in energy demand if unscheduled are all adequately shaved. Although the perfect forecasting scenario is not an applicable case, it demonstrates the success of the method at reducing the peak residual load in the week considered.

Figure 4, similarly, shows the energy flows in the system for the three scenarios. As seen in Figure 4a, where no EV forecast is available, the magnitude of the highest peak in this case (100 kW) is lower than that in the unscheduled case. Relative to the perfectly forecasted case, though, the peaks are considerably higher. A drawback of the scenario can be seen in the situation leading to the Wednesday afternoon peak. The EVs in the parking lot on Tuesday night was charged slowly to reduce the peak load at night. This led to a larger number of vehicles in need of simultaneous charging on Wednesday morning, leading to a high peak in electricity demand in the afternoon. On the other hand, the Thursday peak seen in the unscheduled charging case was effectively shifted.

The single average forecast seen in Figure 4b already provides a considerable reduction in the magnitude of the highest peak (85 kW) relative to the case without an EV forecast. On the same Wednesday, the vehicles parked overnight are charged in the morning in anticipation of future arrivals, thus lowering the peak on Wednesday. However, a drawback of the system is seen on Monday, which has a lower than average EV charging demand. A large number of EVs are charged in the morning in anticipation of demand later in the day. However, the demand in the afternoon was lower than expected, making the morning peak unnecessary in hindsight. This leads to Monday having the highest peak in the week.

The robust treatment of forecasting seen in Figure 4c results in even further reduction in the magnitude of the highest peak in the week, which is about 75 kW. The Wednesday load is lowered even more, and there is a better performance in the case of the lower demand on Monday. Similar to the average forecast, there are many peaks with similar magnitude across the week, but the height of the highest one is lower than in the case without the demand forecast.

(**a**) Energy flows (no electric vehicle (EV) demand forecast).

#### **Figure 4.** *Cont*.

(**b**) Energy flows (average EV demand forecast).

(**c**) Energy flows (robust EV demand forecast).

**Figure 4.** Comparison of energy flows in the solar parking lot with no EV demand forecast, a single average EV demand forecast and a robust consideration of EV demand forecasting.

It can be seen that in the system, the peak loads on the grid are always the result of EV charging and never the result of solar feed-in. While solar peaks did occur under low occupancy of the parking lot during summer when the stationary battery was full, they were generally lower than the EV charging peaks. Further, it was assumed that curtailment would be the strategy for peak shaving of solar feed-in peaks.

The power flows also reveal that the use of fixed storage remains low in all cases. A sensitivity analysis was conducted on the battery size used. It revealed that small batteries had a considerable impact on peak reduction, but the returns diminished with increasing battery size. Increasing the battery size beyond 50 kWh had no further effect on peak reduction.

#### *4.2. Maximum Annual Peak Exchange with the Grid*

The scenarios are compared in terms of a few metrics, which provide insight into the simulation results. The first metric considered is the highest annual peak in each scenario. These peaks have a magnitude which is generally close to the transformer rated capacity and occur rarely—a few times in the transformer lifetime. They lead to a type of transformer loading known as short term emergency loading, involving a very high demand occurring for periods of half an hour or less. However, despite the short duration and relative rarity of their occurrence, these overloads can cause considerable damage. Increased hot-spot temperatures, resulting in the evolution of free gas from insulation and insulating fluid, reduced mechanical strength and deformation of conductors and structural insulation, and high internal pressures resulting in leaking gaskets or loss of oil can all be results of short term

emergency loading. These loads can considerably shorten the lifespan of these assets [21]. A reduction in the highest annual peak indicates a reduction in the intensity of these events. Figure 5 illustrates the peak annual power demand compared across the three scenarios and the two reference cases.

**Figure 5.** Comparison of annual peak power exchange with the grid.

The comparison shows that smart charging for peak reduction in the absence of EV demand forecasting is effective at the reduction of short term peak loads, but this effect is limited (about 16% peak reduction relative to unscheduled charging). Further, the magnitude of peak reduction in the absence of EV demand forecasting is considerably less than that possible with perfect forecasting of future EV demand (about 54% reduction), which is the case often assumed in the literature. The availability of single forecasts based on average demands in the past result in an increase in the effectiveness of reducing short term peak loads. Peaks are reduced by an additional 20% relative to unscheduled charging. Consideration of multiple possible forecasts across which the system works in a robust manner further reduces the peak power exchange of the system. Such a system had an annual peak of 39% lower than that found in unscheduled charging.

#### *4.3. Duration of Peak Loads*

While the previous metric considered short term load intensity, it did not consider the frequency of occurrence of peaks of marginally smaller magnitude, whose impact is similar to that of the annual peak. A comparison of load duration curves can provide further insight. Figure 6 focuses on the leftmost section of the curve, where high peaks are visualized:

The downward shift of the y-intercept through smart charging represents the reduction in the magnitude of annual peak loads, whereas the leftward shift near the y-intercept represents the reduction in the number of peaks. Smart charging without EV demand forecasting, in addition to lowering the magnitude of peaks, is also found to reduce the number of peaks. The curve reveals that the times for which loads are greater than 100 kW are reduced by more than half while the times for which loads are greater than 80 kW are reduced by about half.

The provision of EV demand forecasting further reduces these durations. Loads above 100 kW are avoided altogether and the number of peaks greater than 80kW are greatly reduced. There is no clear advantage of robust forecasting over average forecasting in this case, with both methods providing similar reductions in the number of peaks that the asset is exposed to.

**Figure 6.** Load duration curves.


3.3.2. Co-institution Analysis

Figure 5 presents a co-institution network consisting of 167 nodes and 218 links. It can be seen from Figure 5 that there is a certain amount of collaboration and exchange between institutions in the world, but the cooperation between domestic institutions is closer than that between international institutions.

**Figure 5.** Knowledge map of co-institution collaboration network.

Combined with Figure 5, the top 20 institutions with published papers are listed in Table 3. The London Sch Hyg and Trop Med (433 articles, Harvard Univ (409 articles), Univ Toronto (396 articles), Univ Washington (385 articles), and Univ Oxford (327 articles). All of them made central contributions to the research on global health. It is worth noting that 12 of the top 20 institutions are from the USA, with 11 schools and one government agency. The second is the UK, with three institutions (London Sch Hyg and Trop Med, Univ Oxford, and UCL) in the top 20. It further demonstrates the outstanding contributions and leadership role of the USA and the UK in the field of global health research.

**Table 3.** The top 20 institutions.


**2.1 Model description**

confess that we, not only engineers, scientists, but product managers, or even government officers, have underestimate the power of novel numerical methods and how much they forge the manufacturing process in modern industries.

*Solid State Physics - Metastable, Spintronics Materials and Mechanics of Deformable…*

developed and scattered in worldwide universities and industries.

cies and vibrational responses of the structures can be altered [4, 5].

numerical uncertainties.

**210**

As for finite element method, it is one of the most successful numerical methods in high fidelity modeling of the dynamic behaviors of complex structures. To the best of our knowledge, SAP is the first commercial software. Soon after, other software like ADINA, ANSYS, ABAQUS, NASTRAN, and DYNTRAN have been

However, like any other numerical methods, FEM has many inherent drawbacks due to the way it discretizes the structures. For instance, to address the vibrational responses in high frequencies, the mesh size must be as tiny as 1/6, or less, of the structural waves so that it can accurately reproduce the dynamics of the structures. However, such a meshing strategy is not always successful since too much finer meshes need not only excessive computational costs, but also lead to unexpected

As for ship structures, the vibration of fluid-loaded plates or shells composes as a very important part in the studies of many engineering structures [1–3]. One of the major reasons lies in the fact that the dynamics of these structures depends on the structures and the fluid simultaneously. The vibrating structures can induce pressure disturbances in their surrounding fluid, and, in return, the resonance frequen-

Recently, dynamic stiffness method (DSM) has won great interests and received intense studies [6–10] from research and design engineers because it can overcome the above issues without too much geometrical discretization requirements. Various DSM elements have been developed for transverse or in-plane vibrations of plates. In the beginning, more research works were mainly focused on transverse vibrations since bending modes are easily excited, especially in low frequencies. Dozens of investigator [6–15] made comprehensive contributions on DSM that only accounts for transverse vibrations of a plate with two opposite edges simply supported. Later, Bercin and Langley [8, 9] proposed a DSM that incorporates both in-plane and bending vibrations. It is reasonably expected that all these works are only applicable to few specified cases due to oversimplified modeling assumptions. To address the vibrations of more practical engineering structures, Casimir et al. [7] developed DSM elements for a plate with completely free boundary conditions, in which Gorman's superposition method was employed to obtain the exact transverse displacements. Banerjee and his colleagues [10–12] proposed the dynamic stiffness matrix for a rectangular plate with arbitrary boundary conditions. Similarly to DSM for bending plates with arbitrary boundary conditions, the dynamic stiffness matrix for in-plane vibrations of plates is developed by Ghorbel et al. [15, 16], Nefovska-Danilovic and Petronijevic [17, 18] in which all the four edges can be prescribed with any arbitrary conditions by adopting Gorman's superposition method.

Since the year 2016, Yin and his associates [19–21] have conducted comprehen-

The main objective of this work is to formulate the vibration analysis of ship structures based on dynamic stiffness method that accounts for both in-plane and bending vibrations within plate itself, all possible motions in stiffened beams,

sive studies on developing dynamic stiffness method and its application to the dynamics of ship structures. Li et al. [19] proposed a dynamic stiffness formulation accounting for both in-plane and bending vibrations of plates with two opposite edges simply supported. This method was then employed for modeling vibration transmission with built-up plate structures [22] and a ship cabin with complex hulls. To consider the dynamics of stiffened plates, Yin et al. [21] extended Li's formulations and developed a dynamic stiffness method that considers torsion, bending,

and extension vibrations in beams with eccentric cross-sections.

**Figure 1** shows multiple rectangular plates in global coordinates OXYZ, which are rigidly joined along their common edges. Each plate has dimension of *Lx* � *Ly* and thickness of *h*. Its two opposite edges marked by the symbol 'S-S' denote simply supported boundary conditions while the other two edges are arbitrary. In addition, each plate is reinforced by uniform eccentric beams, and in contact with acoustic fluid on its one side.


*2.6. Betalain Content*

Betalains are composed of red-violet betacyanins and yellow-orange betaxanthins [6]. The distribution of betalain pigment differs not only according to the beetroot parts, but also the extraction method (Figure 4). Generally, OEM extracted the highest betalain content compared to IEM in all parts. On the other hand, beetroot peels, pulps, and juice exhibited the highest betalain content compared to of leaves and stems, regardless of the used solvent. Methanol (II) OEM extracted the highest betalain content from peel (0.81 mg/g) compared to extracted by methanol (I) IEM (0.39 mg/g) (Figure 4). While ethanol (II) extracted the highest amount of betalain from pulp (0.81 mg/g), leaves (0.48 mg/g), and juice (0.79 mg/g) compared to 0.40, 0.18, and 0.40 mg/g obtained by ethanol (I) from pomace, leaves, and juice, respectively. The obtained betalain from beetroot peel, pulp, and juice was higher than 274.4 mg/kg detected in ethanolic extract (50%) of beetroot puree treated at 120 ◦C for 60 min [30]. Methanol (II) extracted 0.5 mg of betacyanin from peel and pulp, while 0.31 and 0.29 mg/g of betacyanin were detected in peel and pulp, respectively. The obtained result of betacyanin and betaxanthin was higher than 37.22 mg/100 g of betanin and 0.71 mg/100 g of vulgaxanthin I detected by Vulic et al. [20] in fresh beetroot pomace ethanolic extract. However, the obtained betalain from peel and pulp (0.81 mg/g) agreed with the betalain content of Redval and Forono varieties from Australia 853 and 826 mg/L, respectively [31]. On the other hand, Wruss et al. [31] detected another betalain concentrations ranged from 767 to 1309 mg/L in the different Austrian beetroot varieties. The findings in the current invistigation showed that the different parts of Egyptian beetroots are rich betalain sources particularly peels, pomace and juice. At the same time methanol is the best solvent for the extraction of betalain from different parts of beetroots.

**Figure 3.** Total flavonoids of beetroot extract (mg catechin/g) based on the dry weight; (I): IEM; (II): OEM at −20 ◦C.


**3. Laparoscopic surgery in spleen-preserving surgery**

Carroll et al reported the laparoscopic splenectomy for the first time in 1992. Since then, the laparoscopic surgery has extended to the traditional fields covered by laparotomy, e.g. spleen adhesion, splenorrhaphy, artery ligation, partial splenectomy and the like, and has been combined with such new techniques as LigaSure, splenic arterial embolization, CUSA, radiofrequency ablation, thus adding a new vitality to the spleen-preserving surgery. The laparoscopic spleen-preserving surgery is somehow difficult, time-consuming, and costly. However, when compared to laparotomy, this surgery has more advantages, for example, clear operative field, minimal invasion, rapid recovery, and short hospital stay.

Laparoscopic inspection: To determine the extent and scope for splenic injuries or lesions; to understand injuries or lesions in the surrounding tissues or organs of spleen; to judge the extent for the bleeding area and vascular injuries; to carry out the pathological examination for the spleen or the surrounding tissues and organs under direct vision biopsy.

The laparoscopic spleen-preserving surgery has the following indications: Grade I-II splenic injuries with hemodynamic and vital sign stability; local benign lesions in the spleen, e.g. splenic cyst, splenichemangioma, echinococcosis, and etc.; hypersplenia, e.g. portal vein hypertension, hereditary spherocytosis and etc.; perisplenic tumors, e.g. pancreatic tumor, gastric cancer and etc.; splenic congenital diseases, e.g. splenectopia, accessory spleen and so on. Contraindications: Grade IVsplenic injuries; severe portal hypertension; splenomeglia; severe coagulopathy.

The spleen-preserving surgery is similar to the laparotomy:

In the laparoscopic spleen-preserving surgery, the complications include hemorrhage, visceral injury, infection, splenic vein thrombosis and so on.

The laparoscopic spleen-preserving surgery is still in trial stage, and its efficacy is uncertain. Clinically, we should not blindly pursue new technology ignoring its efficacy; instead, we should never forget the damage control principles for splenic surgery, always save life first, and then deal with the injury.

In the current study, the spleen function is not very clear, but we begin to know it can play an important role in human body. Spleen-preserving surgeries have been widely implemented. Moreover, the extensive laparoscopic application has brought new opportunities, making the future splenic surgery more scientific and reasonable.


2.1.1. Methods for Synthesis of Magnetic Nanomedicines

The various types of synthesis strategies for MNP can be separated into two approaches: top-down and bottom-up. The "bottom-up approach" starts from metal ions in solution via chemical methods and is probably the most commonly used strategy. The top-down approach starts with bulk material, which is further processed, i.e., by laser ablation [72–76] or lithography [77–79] including that of nanospheres [80]. The lithography techniques have broad control on the shape of nano or microstructures; however, the scaling of this method to large-scale production was reported to be challenging [81]. The laser ablation method offers the building of quite complicated structures such as core-shell MNPs and has a lot of degrees of freedom for adjustment by variation of the environment, material of target, laser regime, as well as external stimulus, for example, magnetic or electrical field to change the shape and structural properties of MNPs [72,75]. This method beats some drawbacks of more common chemical methods; for example, it does not require high temperature, pressure, or organometallic precursors to obtain MNPs with excellent magnetic properties [76,82]. Thus, the laser ablation method has great potential to set higher standards in nanoparticle production.

However, many methods are combinations of both types of methods. For example, the known ball milling method of the MNP preparation is popular for permanent magnet fabrication [83]. It allows one to scale up the synthesis to an industrial scale, although the control of particle shape and size is difficult. Potential agglomerations of nanoparticles can occur which makes the particles unsuitable for biomedical applications [83]. Nevertheless, in combination with chemistry, the ball milling equipment can be used in the so-called mechanochemical process. Here, for instance, a nanocomposite of MNPs in the benzene-1,3,5-tricarboxylic acid matrix was obtained via a mechanochemical process [84]. The obtained material was porous and defined as a metal-organic framework. This nanocomposite was tested for a drug delivery application to release doxorubicin as a model drug. The authors noted that the high surface area of such porous materials favors an increased loading rate, while the magnetic properties of this material offer novel perspectives for diagnostic systems [84].

Typical synthesis steps of chemical methods consist of different steps, particularly burst nucleation, and the following nanocrystal grow, which is called Ostwald ripening [85,86]. Control of reaction kinetics by varying temperature, solvent or other conditions, and operation with the Ostwald process by pH control and electrostatic repulsion of nuclei allow us to systematically vary the size of the particles [86]. It is of utmost importance to obtain nanoparticles with precise and predefined size, shape, and phase composition [87,88]. It was suggested to evaluate the most commonly used synthesis strategies by the four-word strengths, weaknesses, opportunities, and threats (SWOT) analysis for applications in molecular recognition [88]. The research group evaluated the co-precipitation, thermal decomposition (HTD), microemulsion, and microfluidic synthesis method and studied dual-particles consisting of several materials. The first one, co-precipitation, is an easy to use technique to obtain large amounts of MNPs by alkalization of metal salt solutions. First demonstrated in 1981 by Massart, this method is beneficial and allows us to produce well-crystallized iron oxide or ferrite MNPs in the size range of 10−30 nm [89–91]. The drawbacks of this method are the poor control of shape and size distribution; moreover, for smaller particles, less than 10 nm, the quality of crystals decreases and the number of disordered spins leads to a change in the magnetic properties [64,92]. Advanced co-precipitation methods are performed at high temperature and pressure, by hydrothermal surface treatment, or hydrothermal routes [93–95], as well as in non-aqueous medium by solvothermal methods [96–98]. The polyol process is another interesting method which is a cost-effective and easily scalable method to produce MNPs of high quality and variety morphology, from simple pseudo-spherical to multi-core nanoflowers of core-shell MNPs [99–101]. In the polyol process, solvents also play the role of a reducing agent and a surfactant.

Invented in 2004 for the synthesis of MNPs, the HTD method allows us to obtain MNPs with a narrow size distribution and high crystallinity [102–104]. MNPs produced with this method have a high value of magnetization, favoring their use in many biological applications, including sensors and detection [92,105]. According to the SWOT analysis above described [88], a drawback of this method is that it is time consuming and expensive. Precise shape control can be achieved by varying the experimental conditions. For instance, a variation of ligands and surfactants offers advantages for both magnetic properties and the related behavior in biological environments [87,106,107]. The group of Jinwoo Cheon [108] demonstrated higher magnetization values of cubic MNPs (165 emu/gFe+Zn) compared to spherical (145 emu/gFe+Zn) particles. The difference can be attributed to the lower amount of disordered spins on the surface. It was also reported that cubic shaped MNPs on the sensor's surface exhibit a higher binding ability because of the higher contact area of planar interface compared to the spherical one [106]. Furthermore, cubic MNPs exhibited stronger signals, as evaluated by giant magnetoresistive sensing (GMR) and force-induced remnant magnetization spectroscopy (FIRMS).

There are various methods for co-precipitation with different modifications, which, together with HTD, are the most frequently used methods for MNP synthesis [60,88]. Co-precipitation represents the most important production method: it is easy, cheap, and enables a rapid synthesis. The generated particles have hydrophilic surfaces, which can be functionalized in situ. The second most important method, HTD, allows one to produce MNPs with well-defined shape and narrow size distribution. However, the low amount of reaction products, high-cost reagents, and hydrophobic surfaces, which can be functionalized in the multi-step process, make this method currently mostly interesting only for research activity.


**2.10 Molecular mass determination**

Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) was performed with a 12.5 % gel in accordance with the Laemmli method (Laemmli, 1970). M. W. Marker "Daiichi" II (Daiichi Pure Chemicals, Tokyo, Japan) was used as a molecular mass marker. After electrophoresis, the gel was stained with Coomassie brilliant blue R-250 (CBB).

#### **2.11 N-terminal sequence analysis**

SDS-PAGE of xylanases was performed according to the above-described method and then the xylanases on the gel were electroblotted to a commercial membrane (Immobilon-P;

Characterization of Enzymes Associated

cellulose and lignin.

not enhance the iturin A production.

diamonds, carboxymethyl cellulose.

**fermentation**

with Degradation of Insoluble Fiber of Soybean Curd Residue by *Bacillus subtilis* 529

Fig. 1. Analysis of insoluble fiber contents in raw soybean curd residue and soybean curd residue cultured with *B. subtilis* RB14-CS (N=3). Gray bars, hemicellulose; Open bars,

**3.2 Iturin A production by** *B. subtilis* **RB14-CS using insoluble fibers in submerged**

Fig. 2. Iturin A production during submerged fermentation in liquid medium containing fibers (N=3). Symbols: open circles, no additional carbon sources (control 1); open triangles, glucose (control 2); solid circles, pectin; solid triangles, xylan; solid squares, avicel; solid

To investigate the effect of insoluble fibers on iturin A production of RB14-CS, each of insoluble fibers was added to a liquid medium as a carbon source and RB14-CS was cultivated in the medium. Results are shown in Figure 2. Xylan exhibited iturin A production at the same level with glucose which has been used as a carbon source for iturin A production in the previous reports (Asaka & Shoda, 1996; Tsuge et al., 2001). Other insoluble fibers, avicel and carboxymethyl cellulose, showed the similar level of iturin A production with control where no additional carbon was added. Pectin, a hardly-soluble or sometimes insoluble fiber which is contained in soybean curd residue (Kasai et al., 2004) did

Millipore, Tokyo, Japan) with a horizontal blotting apparatus (ATTO, Tokyo, Japan). For the blotting of pure enzyme of Fraction II, 0.01 % of SDS was added to transfer buffer to improve protein transfer efficacy. Parts of the membrane blotted with xylanases were cut out and then amino acid sequencing analysis was performed with an amino acid sequencing apparatus (PPSQ-21; Shimadzu, Kyoto, Japan) according to the standard method (Edman, 1949).

Searches for homologous amino acid sequences were performed by a *B. subtilis* database BSORF (http://bacillus.genome.jp/) and the nonredundant database at The National Center for Biotechnology Information (http://www.ncbi.nlm.nih.gov/) with the BLASTP.


**Liangyi Yao, Xiangyuan Zou, Shuqi Zhou, Hongxiang Zhu, Guoning Chen, Shuangfei Wang, Xiuyu Liu and Yan Jiang**

Cationic Lignocellulose Nanofibers from Agricultural Waste as High-Performing Adsorbents for the Removal of Dissolved and Colloidal Substances Reprinted from: *Polymers* **2022**, *14*, 910, doi:10.3390/polym14050910 ................. **149**


*3.2.1 Current of economic and managerial research influence*

The emergence of the managerial and sociological approach of knowledge management comes, according to [11], in three phases:

*First phase*: a change of paradigm of the corporate strategy called "the approach based on the resources," to which Edith Penrose strongly contributed. She was the first one to begin this change of paradigm in 1959, with the publication of her book entitled "*The theory of the growth of the firm"* [30]. She explains in this work that the company undergoes a loss of capital when a capable employee, who is an employee whose services interfere in the process of production, leaves the firm. By conferring on the knowledge an economic value, in the same way as any other

material resource being a part of the capital, Edith Penrose opened the way to a new economic theory which has to place the knowledge in the center of the process of creation of the wealth.

*Second phase*: a new vision of the company, through the notions of directory of knowledge and of organizational routines expressed by [31]. In their work *An Evolutionary Theory of Economic Change*, the authors define the notion of skill as a capacity to coordinate a sequence of behavior to reach goals in a given context. Besides, they define the notion of organizational routine as a predictable and regular behavioral plan. These routines are the siege of the knowledge of the organization, because beyond any formalization, the best way of storing the knowledge of the organization lies in the exercise of these. So, all the routines of an organization constitute its directory of knowledge [30].

*Third phase*: an organizational change taking care of the problem of capitalization of the knowledge of the company [1, 31, 32]. Concretely, the company has to learn to establish connections between her members. This means connecting people whose cooperation will generate new and useful knowledge for themselves and for the company. These connections can take place as well at the individual level as at the level of a team or at the level of the whole organization.


*Article* **E**ff**ects of BIM-Based Construction of Prefabricated Steel Framework from the Perspective of SMEs**

#### **Mooyoung Yoo, Jaejun Kim \* and Changsik Choi**

Department of Architectural Engineering, Hanyang University, 220 Wangsimni-ro, Seongdong-gu, Seoul 04763, Korea<EMAIL_ADDRESS>(M.Y<EMAIL_ADDRESS>(C.C.)

**\*** Correspondence<EMAIL_ADDRESS>Tel.: +82-2-2220-0307; Fax: +82-2-2296-1583

Received: 11 February 2019; Accepted: 24 April 2019; Published: 26 April 2019

**Abstract:** Small- and medium-sized enterprises (SMEs) are part of the building construction industry. Although many effect analyses of applying building information modeling (BIM) to projects have been conducted, analyses from the perspective of SMEs are lacking. We propose a BIM-based construction of prefabricated steel framework from the perspective of SMEs. We derive the essential functions of the system from the viewpoint of SMEs and verify the qualitative effect through a case analysis of prefabricated steel frame construction that is based on BIM. The following system functions and qualitative effects are analyzed according to project stages that are based on interviews of working groups participating in system development and case projects. (1) Preconstruction stage: extraction of fabrication drawing and review of shop drawing, (2) fabrication stage: prefabrication review, steel member removal, and field loading review, and (3) construction phase: integrated management of cost and schedule and quality management. The expected effects of applying the system are qualitatively and quantitatively analyzed through expert group interviews and surveys. For the quantitative analysis, an evaluation index is used for the end-user computing satisfaction survey. Further analysis of the finishing and installation work is required. Future research should also analyze the effect of system application on human resource management.

**Keywords:** SMEs; BIM; construction management system; steel frame construction

#### **1. Introduction**

Building information modeling (BIM) enables integrated design [1], and it can be applied to various fields (e.g., design verification, quantity calculation, and prefabrication) throughout the construction life cycle by utilizing the three-dimensional BIM model that is built for each construction type [2]. In the preconstruction phase, performing collision detection and design review using BIM can help to reduce the losses that occur in the construction phase by identifying the problems that can be solved beforehand at the design phase (e.g., design errors) [3,4]. Doing so improves the design quality by reducing design errors, design changes, and rework [5,6]. In the fabrication phase, the procurement process of construction frame members through BIM-based prefabrication can be simplified, and the productivity of workflow between the designers and constructors can be improved [7]. In addition, in terms of production productivity, the workers that are involved in the production of frame members can utilize the information inputted to BIM objects to support the manufacturing process; such information is parametric and it is not provided in the existing two-dimensional (2D)-based production work [8]. In the construction phase, a four-dimensional (4D) simulation can be implemented by linking the BIM model with the schedule [9]. A BIM-based 4D simulation combines information that is related to the process, equipment, and space to identify various uncertainties that arise during the process, and provides information that can be reviewed in advance (Moon et al., 2014). Thus, the effect of BIM on each phase of a construction project becomes clear.

However, previous studies on the effects of BIM on construction projects were conducted for large-scale projects [10]. Previous studies analyzed the return on investment (ROI) that was attained by general contractors for BIM-based construction projects [11–13]. Of course, many studies have analyzed the effects of BIM application from the viewpoint of small- and medium-sized enterprises (SMEs) [14–17]. However, studies on developing a BIM-based construction management system from the viewpoint of SMEs are lacking [18]. Due to the nature of SMEs, the sales and profits are not as large as those of large-scale enterprises, and many of them abandon the application of BIM, which involves high initial costs [19,20]. Therefore, we begin this study by analyzing the difficulties of managing a construction project from the viewpoint of SMEs. We propose a BIM-based construction management system framework to support SMEs. We also conduct a case study to verify the major functions that will be installed in the system for the potential SME users and their perceived performance.

#### **2. Literature Review**

#### *2.1. Characteristics of SMEs in Construction Management*

In 2016, 99.5% of the 5.5 million businesses in the United Kingdom (UK) were classified as SMEs, accounting for 60% of all private sector employment in the UK and a huge proportion of the 240,000 construction service providers that are operating within the UK [21]. The latest set of government business population estimates shows that the number of construction companies with less than 50 employees has increased to over 1,005,290, up from 972,475 in 2016. The data also indicate that these small businesses are responsible for an approximate total turnover of over £185 Bn a year, up from £172 Bn in 2016. The total turnover of all the businesses operating in the construction sector reached £296.8 Bn in 2017, up from £271.9 Bn in 2016 [22]. The Department for Business, Enterprise, and Regulatory Reform (BERR) [23] defines the size of a firm in terms of the number of employees (see Table 1). The BERR defines

A previous study outlined the characteristics of SMEs, as follows [24]. Many SMEs do not record procedures in a clear format, unlike large-scale companies [25]. Thus, SMEs' management style is flexible and informal. For example, one of the main methods for communicating information is through informal interviews between individuals. Therefore, a fundamental limitation in identifying human resources to be used in construction projects and undertaking construction management based on quantitative information exists. Moreover, SMEs may face difficulties in raising funds and maintaining adequate cash flow, which thus limits continued capital investment in employee education and technology development [26]. As such, SMEs that participate in construction projects have common limitations with regard to improving their business systems. However, owners/managers of SMEs play a key role in the decision-making process [27,28]. Thus, if the outcomes of business process reengineering (BPR) are clear, the SME may be more likely to change and in a more flexible manner, when compared to their large-scale counterparts. Therefore, this study proposes a BIM-based construction management system framework from the perspective of SMEs.

#### *2.2. Limitations of BIM Adoption*

Many previous studies have analyzed BIM application and its effects [1–9]. However, most of this research is limited to case studies, such as loss prevention due to design errors for a single project [3,4]. Moreover, the studies focused on improving design quality, which is difficult to quantify [5,6]. Some works analyzed the effect of BIM application on interference checking and conducted a 4D simulation for a specific task [9]. Ideally, BIM supports all of the project participants using data on

the construction life cycle. However, it is difficult to improve the process and achieve the business integration effect using BIM in large organizations [7].

**Table 1.** Functions and effects of building information modeling (BIM).

Moreover, it is difficult to manage the inputted manpower by only using BIM data for the building. As a result, research on the application of the internet of things to manage workers by attaching wireless sensors to helmets is also being conducted [29]. In addition, BIM does not immediately reflect implementation via a virtual model. As a result, laser scan and image scan technologies have been introduced as tools for creating and integrating real models. Additionally, the concept of digital twin, which combines virtual models and real models to pre-simulate specific issues, may be used. Indeed, recent studies have tended to integrate not only BIM, but also various other technologies (e.g., three-dimensional (3D) laser scanning, augmented reality, mobile computing, wireless connection, quick response codes) with building construction management [30].

Existing BIM-based construction management systems (e.g., a project management information system; PMIS) should be accompanied by BPR, because the system is often constructed at the enterprise level [31]. In addition, the scope of the system construction is very wide, because the project characteristics of various business areas, rather than a single project, should be considered. Each division of the company should devise the system to address all of the diverse functions that are required. Some companies report issues that are caused by the presence of several project-related databases and the fact that security concerns limit system accessibility for the users. In this paper, we propose a BIM-based construction management system from the viewpoint of SMEs. In addition, we qualitatively verify the functioning of the system through a case analysis.

#### *2.3. BIM for Prefabricated Steel Frame Construction*

A general contractor manages the various specialty contractors that are involved in a construction project. A steel frame construction is implemented through a specialty contractor. Steel frame construction involves prefabricating materials at a factory that are based on a preconstruction blueprint [32]. Generally, in the preconstruction phase, the design drawings and specifications are reviewed, the construction companies are selected, and the construction plans and details are prepared [33]. The factory processing of the steel frame members is conducted in the order of full-size drawing, prototyping, deformation inspection, marking gauge, cutting and machining, drilling, joining, riveting and welding, metal cutting, rustproofing, and loading at the site. When the steel frame members are brought to the laydown area, they are then assembled by bolting or welding, depending on the installation drawing. Lastly, a crane is used to lift the steel frame members, and the workers complete the installation process [34,35].

However, if any design changes are made to the steel frame, the following problems may arise [33]: (1) Increase in construction costs and a delay in construction period due to the need to place new orders for materials and/or additional production, (2) surplus of preordered materials, and (3) duplicate production of steel frame members. These costs and risks due to the delay can impact the profits of the specialty contractors. It is necessary to introduce BIM and other recent technologies (e.g., laser scanning) to solve these problems.

Table 1 lists the main functions and effects of BIM. A 3D BIM model with object information is created from a 3D BIM conversion design based on the drawings that were generated at the design stage, and the 3D BIM model is visualized for collaboration among experts in different fields [36]. In addition, the clash check function helps to improve the quality of the 2D design drawings by examining the inter-member clash and reducing errors therein in terms of potential errors that may occur at the preconstruction site [13]. Parametric modeling—a popular BIM technique—is used to project and automatically change the information that is associated with each member when the design is changed. Thus, the design process is more quickly and accurately completed. Table 1 lists the effects of information utilization using BIM in the design and construction phases.

Table 2 lists the research highlights of previous studies that used BIM for modeling productivity, shop drawing calculation, and process management in steel frame construction.

Previous studies focused on improving the productivity of the unit tasks of steel frame construction (e.g., automatic modeling of joints and drawings) and managing the process. However, the SMEs need to derive the effects of BIM for tasks that are related to the design, fabrication, and construction, rather than applying the technology to the unit tasks. This study presents a case study on steel frame construction to verify the main BIM-based construction management function to be installed in the system for an SME.

#### *2.4. Evaluation of Information System*

Information systems (e.g., PMIS) that were used in existing enterprises possess different characteristics depending on their purpose and support tasks. Information systems are classified according to their purpose and functionalities that are based on the requirements of the company [48,49]. Other works have analyzed the characteristics of information systems according to their contribution to efficiency improvement and relationship improvement in the internal and external aspects of the company [50,51]. Research regarding the success of information systems is based on the work of Shannon and Weaver and Mason [52,53], and the results suggest that the level of information can be measured in terms of the technical, semantic, and effectiveness levels [54]. However, the usefulness of such evaluations is limited for the following reasons. First, it is difficult to quantify the measurement factors and objectively separate the effects of the information system from other aspects [55–57]. As a result, attempts have been made to evaluate the information systems in terms of their use as well as the end user's satisfaction (i.e., considering the user's cognitive aspect). However, Lucas [55] argued that, while indicators pertaining to an actual information system (such as measured time) are somewhat objective and easy to quantify, they are not a useful measure, because the use of the information system may be compulsory. Thus, in many studies, end-user satisfaction, rather than evaluation of use, is perceived as a key factor in measuring the success of an information system [58–62].

Several models have been proposed to measure the end-user satisfaction with information systems [63,64]. The model of Doll and Torkzadeh [64] presented 12 evaluation indicators, which consisted of five factors (content, accuracy, format, ease of use, and timeliness) that are based on existing results (Table 3). Other researchers have consistently verified this model [65–67]. In this study, we use these indicators to evaluate the system to be developed. In doing so, we intend to customize the system for SMEs.

**Table 3.** Evaluation index for measuring End-User Computing Satisfaction (EUCS).

#### **3. Research Method**

#### *3.1. BIM-Based Construction Management System Framework*

Figure 1 shows the proposed BIM-based construction management system framework from the perspective of SMEs, based on the research problem and theoretical considerations.

**Figure 1.** Conceptual diagram of BIM-based construction.

The system framework has the following features. First, it should be able to input and output various information (e.g., BIM model, wireless sensor data, laser scan, or image scan data). Even if the system only needs to manage the construction of a single process (e.g., steel frame construction), the as-planned model for the plan, the as-built model for the execution result, and the information about the construction labor can differ. Information on buildings can be obtained from the BIM Model and laser scan data, but that manpower needs to be collected using equipment, such as wireless sensors (e.g., safety helmets, safety vests, and safety shoes) [29]. Construction management and human resource management should be undertaken by utilizing this information. Second, from the viewpoint of SMEs, the system should only be equipped with the functions (e.g., 3D visualization, cost planning, quantity take-offs (QTOs), and clash detection) that are necessary for a BIM-based construction management system. It is necessary to supervise the construction progress while using this information and to establish accurate planning, input, and management of the workforce. Moreover, the system should be realistic, usable, and compatible in various system operating environments (e.g., Off-site and Web- and app-based on-site management environments). Third, the system must improve accessibility for all users in various formats (e.g., html, pdf, image, dwfx, etc.), which are typically used for the Web and apps. It should be possible to access the information that is necessary for construction management with smart device operation technology rather than the BIM software, which is difficult to access and operate.

#### *3.2. Main Function Derivation for the System*

We analyze a typical steel frame construction in order to derive the necessary functions for the system [35]. Table 4 summarizes the main work phases and tasks, the authorities that are responsible for each task, work location, and BIM usage in steel frame construction. For on-site and off-site steel frames constructions, BIM authoring creates the 3D model, visualizes the quality review, produces a shop drawing, conducts a 4D simulation for process management, and checks QTOs for cost management.

In the preconstruction phase, the drawings and specification reviews are used to check whether the design requirements are reflected and whether the object information that is related to the material and dimension of the steel frame is correctly entered. After reviewing the design information, a 2D steel frame shop drawing is extracted from the BIM model for factory production.

In the prefabrication phase, accurate quantity calculation data are used when ordering the steel material for frame member fabrication. In addition, the cutting work from the manufacturing process is numerically managed using the BIM model. As a result, it is possible to find not only the gross weight of the member, but also its net weight, excluding the cutting part. To minimize the loss of steel material, the BIM model can be used to efficiently cut the members. During this process, the installation of the

steel frame is reviewed to reduce the work, and the site conditions, such as fabrication, transportation, lifting, and ease of installation, are considered. When the fabrication and inspection of the steel members are completed, the prefabricated steel members are procured on-site.

**Table 4.** Main tasks involved in steel frame construction and BIM use mapping.

\*: GC and SC denote general contractor and specialty contractor, respectively

In the construction phase, the steel frame members that were procured from the factories are laid down when considering the order of installation. They are then temporarily assembled through steel bolting and welding. Thereafter, the construction is sequentially done using the shop drawing and 4D simulation. The installation procedure involves installing the column, large beam, small beam, and brace members.

#### *3.3. Definition of System Database*

#### 3.3.1. Building Objects of Prefabricated Steel Frame

Figure 2 shows the objects that make up the BIM model of the prefabricated steel frame. The part that constitutes the prefabricated steel frame as per the BIM model is divided into the main part and the secondary part. Each part is automatically categorized with a number (double or int) or a string and stored as a BIM object. In addition, the bolts that are connected to the steel frame members and welded members are grouped, and the respective components are assembled. The number and dimensions of the holes for cutting and bolting the steel frame members are included [68].

#### 3.3.2. Connecting Building Objects to Create a Prefabricated Steel Frame

Table 5 lists the numerical data regarding the BIM model of the steel frame joints for the case study project that is discussed in this paper. It shows the data structure of the connections of the steel frame parts, namely the main part, secondary part, and bolts, as well as welding lengths. The materials of the end plate and bolts and the hole types are stored as strings, whereas the size and dimension of the bolts, tolerance distance, and notch length are stored as the numerical values. The strings and numerical data can be utilized to numerically control the machines that are used to produce the steel frame members [70]. In addition, the integration of the components of each steel member results in the complete BIM model for the steel frame construction. It includes most of the building components that are used in the steel frame construction based on the attribute values of the steel members. The BIM model constructed using such an object-oriented modeling can be quickly and accurately modified if any changes to the design and/or information are made.

**Table 5.** Connecting building objects to create a prefabricated steel frame.

#### **4. E**ff**ects of BIM-Based Construction Management System for SMEs**

#### *4.1. Case Study*

#### 4.1.1. Project Description

To analyze the effect of BIM on the prefabricated steel frame construction from an SME perspective, the project, as summarized in Table 6, is chosen. The total construction cost was less than ₩1 Bn and the total load acting on the prefabricated steel frame was 91.78 t. The design phase of this case study project was based on a 2D Computer-Aided Design (CAD) drawing.

**Table 6.** Overview of the case study project.

The project client majored in architectural engineering and has worked as a BIM manager. Thus, the client is aware of the usefulness of the BIM model. However, the client does not have firsthand experience of the effect of BIM on actual projects. After interviewing the client, we found that the accuracy of the information regarding the quantity of prefabricated steel frame, which accounted for a large portion of the total construction cost, significantly influenced the decision-making process. Table 7 summarizes the total construction cost (of the three bidding projects), the prefabricated steel frame construction, and whether BIM was applied.

**Table 7.** Comparative analysis for general contractor/ specialty contractor (GC/SC) who participated in the case study project bidding.

The specialty contractor (SC) used TEKLA to create a BIM model of the steel frame based on the design drawings for the general contractor (GC). Accordingly, we calculated the quantity of the major components required for the steel frame construction. Using this information, the GC calculated the material, labor, and overhead costs required to estimate the total construction cost. As for the other companies, the total construction cost was higher, and no information on the cost of steel frame construction, including material, labor, and overhead costs, was provided to the client. Therefore, the client selected the contractor who presented the lowest total construction cost with the exact quantities for steel construction.

The selected GC has 25 employees, with a capital of less than ₩1 Bn and a revenue of ₩14.6 Bn as of 2016. The SC, who is responsible for the steel frame construction, is engaged in the construction of steel frames and related structures, with a capital of ₩10 Mn and a revenue of ₩1,810.33 Mn as of 2015. The GC and SC are both SMEs.

#### 4.1.2. Description of the BIM Model of a Prefabricated Steel Frame

Figure 3 shows the main and secondary parts of the BIM model based on the contents of the case study that is shown in Figure 2. Figure 4 shows the BIM model, including the number of bolts, welding length, cutting parts, grouting, and surface treatment related to the steel joints. Based on these data, the standard, quantity, grade, area, weight, and unit weight of the steel members can be automatically calculated, as shown in Figure 5.

**Figure 3.** BIM model of a prefabricated steel frame.

**Figure 4.** Detailed information regarding the joints of the steel frame members.

**Figure 5.** Information regarding the quantities of the steel frame members.

#### *4.2. E*ff*ects of BIM-Based Construction Management System for SMEs in the Preconstruction Phase*

#### 4.2.1. Extraction of Fabrication Drawing

The BIM model of the prefabricated steel frame, which was developed before the construction, is used to create various shop drawings for factory construction. As listed in Table 7, the types of drawings are general arrangement (GA) drawings, which show information regarding the planes, elevations, and cross-sections; a single part drawing, which shows a single part without the welded parts, such as anchor bolts and plates; an assembly drawing, which contains information regarding the steel frame members; and, a cast unit drawing, which is used for the foundation and steel frame plinth. The design changes are automatically reflected in the shop drawings as the BIM model and shop drawings are created based on the objects, and they are linked by parameters. For this case study, a total of 107 detailed drawings were generated to construct the prefabricated steel frame from the BIM model (Table 8).

#### 4.2.2. Review of Shop Drawing

In the final phase, the BIM model was used to conduct a clash check before fabricating the members using the shop drawings. As a result, a total of 39 clashes occurred, which was similar to the ones shown in Figure 6. The clashes mainly occurred

**Figure 6.** Clash check step applied to the BIM model: (**a**) Steel part members and bolts; (**b**) Steel part members and steel plate members; and, (**c**) Steel part members and stairs.

The members that were included in the clash check result can be classified into type, object ID, assembly ID, and object name. The members were modified prior to fabricating at the factory based on this information.

#### *4.3. E*ff*ects of BIM-Based Construction Management System for SMEs in the Fabrication Phase*

#### 4.3.1. Prefabrication Review

In the fabrication phase, the necessary steel frame members should be accurately produced. Therefore, a fabrication review should be performed based on the information extracted from the BIM model. The following considerations are important in the manufacture of steel members:

The visual and quantity-related information regarding the unit steel frame members, including mark (Mark), specification (Description), quantity (Quantity), length (Length), material (Remark), and number of bolts, were extracted from the BIM model. Thus, the review could be performed smoothly. Table 9 lists the information required for steel frame fabrication.

#### 4.3.2. Carrying Steel Frame Members from Off-Site to On-Site

The length, width, volume restriction of vehicles, and entrance road conditions around the site should be considered based on the surrounding traffic situation when carrying the steel frame members to the site from the factory in the order of construction. Furthermore, the order in which the steel frame members are to be unloaded and their exact arrival times should be known to the site supervisors. With the BIM model, it is possible to simulate the position of the laydown area in advance, as shown in Figure 7, and to estimate the quantity, depending on the construction progress.

**Table 9.** Prefabrication review of beams.

**Figure 7.** Preliminary review of building and member laydown area.

The BIM model helps construction companies and professional construction enterprises to efficiently lay the steel frame members by considering the type of construction. In addition, it is possible to minimize errors that may occur during the construction by accurately estimating the information regarding the steel frame members. Thus, the BIM model helps to save time and money, given that it optimizes the number of workers that are required in the field. In this project, the total

weight of the steel frame members was approximately 92 t. It took two days (August 4th and 6th) to carry the steel frame members to the laydown area, as shown in Figure 8.

**Figure 8.** Transportation of the steel frame members to the laydown area.

*4.4. E*ff*ects of BIM-Based Construction Management System for SMEs in the Construction Phase*

#### 4.4.1. Integrated Management of Cost and Schedule

After the steel members are carried to the laydown area, the installation work begins by referring to the shop drawings. To manage the cost and schedule associated with the prefabricated steel frames, these aspects of the BIM model are integrated, as seen in Table 10. Given that the project was small-scale, the construction that was carried out during the period of August 4th, 2017 to August 9th, 2017 was selected for the study. On August 4th, 2017, the steel frame members were carried to the laydown area, and the anchor bolts were installed. Based on the information that was provided in the BIM model and shop drawings, the materials were carried in, and the next phase of the work was prepared. Regarding the equipment, one crane was used for the member laydown, with five people working on-site. On August 5th, the main processes comprised building the steel columns and non-shrink grouting, and the total weight of the installed steel frame was 31.46 t. On August 6th, the welded plates and the steel frame beam members were carried to the area, and the total weight of the installed frame was 15.36 t. A total of 91.78 t of steel frame members were installed by August 9th.

#### 4.4.2. Quality Management Using BIM and Laser Scanning

The construction errors in this case study project were considerably low, because the construction was small-scale, and the pre- and post-processes had little effect on the prefabricated steel frame construction. The construction errors can be measured by comparing the BIM model, which is an as-planned and already constructed model, with a laser scanning equipment, which is an as-built model that is created by a construction company and a professional construction enterprise. Figure 9 shows the construction errors that were identified in the case study project using the BIM model and laser scanning equipment. The errors ranged from 16 to 70 mm in the web or flange direction of the steel frame member.

Although not found in this case study project, a large risk may arise if there is a significant error in the pre-installed steel frame members. If the reference steel frame member, which is assembled with many members, is twisted or severely tilted toward a specific direction, the construction of the steel frame materials, which need to be installed through subsequent works, can be considerably affected. The damages can be significant due to the increase in the construction cost and any delay in the construction from new member orders or additional fabrications, disposing of pre-order members, and duplicated fabrication of steel frame members. Therefore, an as-built model for the pre-process should be created while using the laser scanning technology, the BIM model should be reviewed, and the prefabrication should be implemented by reflecting the actual conditions of the site.

Equipment Crane 1

EA

of daily progress

**Table 10.** Four-/five-dimensional (4D/5D) model in terms of construction schedule.

₩24,196,437 Equipment Crane 1

EA

of daily progress ₩24,713,486

**Figure 9.** Column construction deviation.

#### **5. Discussion**

The features of the system framework described above are as follows: (1) capability to input and output various types of information, (2) the inclusion of only the necessary functions from the viewpoint of SMEs, and (3) improved accessibility, such that anyone may use the system online or with an app (Figure 10). Construction managers require access to detailed information on buildings, and clients need an environment that can utilize various types of reliable information to support decision-making.

**Figure 10.** Web and app-based operational samples using the proposed system.

In order to verify the effectiveness of the BIM-based construction management system framework that is proposed in this study, we interviewed three researchers (e.g., M-design, Hanyang University, Seoul National University of Science & Technology) who participated in the system development, and four participants (e.g., Client, GC, and SC) who implemented the case project. Table 11 shows the expected effects of applying the system according to the information that was provided during the interviews.

**Table 11.** Expected effects of application of the BIM-based construction management system.

It is difficult to ensure the reliability of results that are only based on data from interviews with research groups and project groups participating in small-scale projects. Therefore, we also conducted an end-user computing satisfaction (EUCS) survey. The evaluation of the information system under development was based on five factors (Content, Accuracy, Format, Ease of Use, and Timeliness) and 12 indicators (Table 3). The survey covered the potential users of the system under development, namely 40 BIM experts, including architects, construction, construction, and the BIM consultant, all of whom have more than three years of experience in using BIM. The questionnaire was completed by 26 people. Table 12 shows the results of the survey.

**Table 12.** Results of the end-user computing satisfaction (EUCS) survey.

The results of the analysis indicate that the satisfaction levels with regard to the content and accuracy categories were relatively high. This is because the system under development is based on the database (DB) of commercial BIM software (SW). Since the information that is needed for the task is extracted from the BIM model, the reliability of the information content and accuracy is ensured. On the other hand, the satisfaction levels for the ease of use and timeliness categories were relatively low. These results suggest that the pre-service training of the system to be developed is necessary from the perspective of SMEs. In addition, additional expert assistance is needed for tasks that involve a high degree of difficulty. We need to carefully consider user experience and user interface when considering the fact that the system should be usable on the Web and via an app. Finally, the survey

results indicated that the format of the system should be improved in conjunction with ease of use and timeliness. The proposed BIM-based construction management system framework can then effectively contribute to improving the work productivity, as well as the cost effectiveness of the project unit from the viewpoint of SMEs. In order to avoid construction risks, such as rising labor costs in Korea, SMEs can utilize this system framework as a technology strategy to combat the rising trend of relatively low wages paid to overseas construction laborers (e.g., those from Vietnam).

#### **6. Conclusions**

In this paper, we analyzed the BIM-based construction effect from the perspective of SMEs. The main research results of the study are summarized, as follows. First, the BIM-based Construction Management System Framework was conceptually proposed from the viewpoint of SMEs to improve the reliability of analysis. Second, detailed work analysis of the steel frame construction was conducted to derive the main functions of the system. Through this, we derived the application areas of BIM that can be used step-by-step. Third, we analyzed the BIM-based construction effect through the case project of SMEs. We analyzed the effects of BIM on SMEs stakeholders and system development researchers. The case study analyzed the effects of BIM application on the preconstruction, fabrication, and construction phases. The fabrication documents were extracted from the BIM model, and a clash detection review process in the preconstruction phase reviews the quality of the fabrication documents. In the fabrication phase, the information in the fabrication documents was checked before the prefabrication. Furthermore, the process of carrying the steel frame members to the site was reviewed by considering the surrounding buildings and the available space, and the approximate location of the laydown area was confirmed. In the construction phase, the cost and schedule were linked based on the BIM model, such that the construction of the building could be requested by the building owner based on quantitative data depending on the construction progress. After the construction was completed, comparing the BIM model with the measurements that were performed with laser scanning equipment could control the quality. Most SMEs working on construction projects obtain qualitative results when the BIM is applied to a specific work process in the preconstruction, fabrication, and construction phases. Fourth, the end user's satisfaction with the system function was investigated to supplement the qualitative analysis result. In order to overcome the limitations of the single case analysis that was performed in this study, a EUCS survey was conducted among potential system users. The results of the analysis confirmed the direction for system development and improvement. In the future, we plan to analyze the effect of BIM application on structures other than steel frames (e.g., mechanical, electrical, and plumbing engineering) from the perspective of SMEs.

**Author Contributions:** Conceptualization, M.Y. and J.K.; methodology, M.Y. and J.K.; validation, M.Y., J.K. and C.C.; formal analysis, M.Y.; investigation, M.Y.; resources, M.Y.; data curation, M.Y.; writing—original draft preparation, M.Y. and J.K.; writing—review and editing, M.Y., J.K. and C.C.; visualization, M.Y.; supervision, M.Y.; funding acquisition, M.Y.

**Funding:** This work (Grant No. C0505520) was supported by the Business for Cooperative R&D between Industry, Academy, and Research Institute funded by the Korea Small and Medium Business Administration in 2019.

**Conflicts of Interest:** The authors declare that there are no conflicts of interest regarding the publication of this paper.

**Data Availability:** Data generated or analyzed during the study are available from the corresponding author upon request.

#### **References**

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

#### *Article*


**Abbreviations**

AP1, activator protein 1; Ask1, apoptosis signal-regulating kinase 1; ATF6, Activating Tran‐ scription Factor 6; C/EBP, CCAAT/enhancer binding protein; CHOP, C/EBP homologous protein; CREBH, Cyclic-AMP-responsive-element-binding protein H; eIF2α, eukaryotic ini‐ tiation factor 2α; ER, Endoplasmic Reticulum; ER stress, Endoplasmic Reticulum stress; iNOS, inducible nitric oxide synthase; IRE1, inositol-requiring enzyme 1; IRS-1, insulin re‐ ceptor substrate-1; JIK, c-Jun N-terminal inhibitory kinase; JNK, c-Jun N-terminal kinases; NRF2, nuclear factor-erythroid-derived 2-related factor 2; PERK, pancreatic endoplasmic re‐ ticulum kinase; RER, rough endoplasmic reticulum; ROS, reactive oxygen species; SER, smooth endoplasmic reticulum; TLR, Toll-like receptor; TRAF2, TNF receptor-associated factor 2; TRIF, Toll-IL-1R-containing adaptor inducing IFN-β; UPR, unfolded protein re‐ sponse; XBP-1, X box protein-1.


*2.5. Determination of Protein Concentrations*

Protein concentrations were determined as described by Bradford et al. using bovine serum albumin (BSA) as a standard [20]. Protein samples were diluted to fit the absorption range of the standard concentrations to a final volume of 100 μL and mixed with 900 μL of Bradford reagent (Sigma, B6916). After vigorous mixing and incubating at room temperature for 10 minutes, absorbance was measured at 595 nm using UV-visible spectrophotometer (Biochrom–Libra S22, Cambridge, UK).