**1. Introduction**

The construction industry is one of the most rapidly growing industries globally. The substantial growth of the construction industry led to the increasing demand for concrete. The population in urban regions is likely to grow from around 3.4 billion in 2009 to 6.5 billion by 2050. It is estimated that the yearly concrete production withstood at 10,000 Mt and is expected to rise twice in the next 40 years [1]. As a result, with time, there is an increasing demand for green building materials and eco-friendly construction practices to reduce the environmental impact of the concrete industry [2,3]. Carbon dioxide (CO2) emission has always been a severe worldwide concern in cement manufacturing. Moreover, activities related to the processing and transportation of cement are also responsible for greenhouse

**Citation:** Khan, K.; Aziz, M.A.; Zubair, M.; Amin, M.N. Biochar Produced from Saudi Agriculture Waste as a Cement Additive for Improved Mechanical and Durability Properties—SWOT Analysis and Techno-Economic Assessment. *Materials* **2022**, *15*, 5345. https://doi.org/10.3390/ ma15155345

Academic Editor: Dumitru Doru Burduhos Nergis

Received: 14 May 2022 Accepted: 19 July 2022 Published: 3 August 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 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 (https:// creativecommons.org/licenses/by/ 4.0/).

gas emissions, which are considered a severe environmental threat. Concrete contributes to approximately 8% of the entire world's production of CO2 during the production, processing, and preparation phase [4,5]. As a result of the high CO2 emissions and environmental issues, it has become necessary to implement sustainable CO2 reduction methods relevant to cement-based materials (CBM) in the environment [2]. Environmentally friendly cementitious materials (CBM) can entirely or partially replace cement to reduce the negative environmental impacts of concrete production [6]. In recent years, various waste materials such as fly ash, silica fume, glass, rubber and tires, steel slag, etc., have been utilised in concrete to improve performance and lower carbon footprint [7–9]. The use of these materials has a two-fold benefit. It reduces carbon emissions by reducing the use of cement in concrete, but it also diverts waste materials away from landfills, which helps increase sustainability.

Biowaste, including municipal, industrial, agricultural, and other forms, is widely generated worldwide. This waste can be dumped into landfills and cause severe environmental consequences [10]. Transforming waste into value-added products for various applications via various approaches is a step toward a circular economy and an effective waste management strategy [11]. The pyrolysis technique is most commonly adopted to convert various biomass sources such as organic industrial and household waste, wood, and agricultural waste into biochar, biogas, and bio-oil [12,13]. Biochar is a carbon-rich material with high porosity obtained via thermochemical conversion of biomass in the absence of oxygen [14]. Recent studies showed that a pyrolysis temperature of > 500 ◦C releases all the organic components from biochar leading to high surface area biochars [2,15]. The biochar's excellent mechanical and thermal stability, high surface area, and porosity proved it to be favourable to use as a cement replacement as an admixture in concrete [16,17]. For instance, Choi et al. [18] partially replaced the cement by adding biochar in a mortar and reported replacing 5% biochar showed a 10% increase in the compressive strength. Correspondingly, substantial improvement in mechanical strength (16–20%), water penetration (40%), and water absorption (35–60%) by the addition of biochar produced from various feedstock were reported [3,19]. Furthermore, Restuccia et al. [16] stated that adding biochar to cement paste could enhance its fracture energy and modulus of rupture. Wang et al. [20] reported that biochar in concrete enhances the mechanical properties by reducing the microcracks in the concrete and improving cement's hydration. Nevertheless, there is still a considerable gap in investigating the effect of biochar from various feedstock on concrete's mechanical and durability properties. The Kingdom of Saudi Arabia (KSA) is one of the largest producers of date palm trees, with about 35 million trees. These trees generate large agricultural waste, either carried to landfills or burned in the open areas. This has a significant detrimental impact on humans and the ecosystem [14]. Walid et al. [21] stated an efficient and valuable use of ash obtained from date palm waste as a partial replacement of Portland cement in concrete structures. Though, the addition of biochar derived from date palm wastes for construction application has not been studied yet.

The primary aim of the presented research is to study the impact of biochar derived from date palm fronds as an additive in concrete to produce high-strength and durable concrete. The control and all other biochar-containing mixes assessed compressive and flexural strengths after 7, 14, and 28 days. Additionally, the durability properties of the concrete samples for the mixes were evaluated by measuring their electric resistivity and ultrasonic pulse velocity. Finally, the SWOT analysis and techno-economic assessment of the biochar-concrete system were performed to provide detailed insight into date palm derived biochar's potential application on a commercial level.


**2. Materials and Methods**

#### *2.1. Indicators and Data Sources*

The data used in this study include up to 10 years of subnational region estimates of childhood immunization, indicators of gender inequality, and other demographic, economic, and social characteristics. Data were available for 702 subnational regions across 57 countries. We included the 10 most recent years of available data (2010–2019); all region

years where estimates for subnational gender development and immunization outcomes were available were included, for a total of 1066 total region years of data.

#### 2.1.1. Immunization Outcomes

We examined two outcomes based on subnational coverage of the DTP vaccine. First, the prevalence of zero-dose children (zero-dose DTP), defined as the percentage of surviving one-year old children in a subnational region who have not received the first dose of the DTP vaccine series. This indicator is a proxy for children who have missed immunization services entirely. Second, the prevalence of DTP3 immunization (DTP3), the percentage of surviving one-year old children in a subnational region who have received three doses of DTP vaccine. This indicator is a proxy for children who have accessed the full series of basic immunizations. Together, these are frequently used indicators of child health more broadly as they reflect regular and timely interaction with health services (DTP3) and health equity (zero-dose DTP) [11–13].

These estimates are derived from Demographic and Health Surveys (DHS) Program data, which uses a rigorous survey design to create representative samples at the subnational level. Substantial detail on the study design and methodology of the DHS has been published elsewhere [14].

### 2.1.2. Factors Associated with Immunization Coverage

We examined variables selected a priori based on prior national-level analyses, to make findings as directly comparable as possible [9]. These factors were chosen to account for demand and supply side factors that influence vaccination and might confound the association between immunization and gender inequality [15–19]. These included subnational estimates of percent of population under 15 years of age, percent of population living in urban areas, and a number of human development indicators (described below). We also utilized national estimates of average annual rate of population change; estimates corresponding to study subnational regions were not readily available.

To capture human development in adjusted models, we utilized the subnational human development index (SHDI). The SHDI is a summary measure of development in three dimensions, namely education, health, and standard of living, with an index normalized between 0 and 1 created for each dimension [20]. The education index based on mean expected years of schooling for children and mean years of schooling for adults ages 25 years and older, the health index is based on life expectancy at birth, and the standard of living index is based on gross national income per capita (2017 purchasing power parities [PPP] in USD). We utilized the three dimension-specific indices in analyses. Each of these indices are calculated both for the total population, as well as disaggregated by sex. All human development indicators were available at the subnational level.


**4.2. Green composites of TPS and cellulose fibers**

*4.1.1. Extraction by chemical hydrolysis*

92 Composites from Renewable and Sustainable Materials

A commonly used extraction methodology of CNF is acidic hydrolysis of the amorphous regions surrounding the embedded CNF and cleavage of the bundles, followed by filtration or centrifugation to exclude dissolved noncrystalline elements [46, 47]. The methodology is beneficial in that it can be performed on very small quantities of cellulose, it requires only the simplest laboratory equipment, and the CNF can be obtained without any induced imperfec‐ tions caused by mechanical processing. The conditions typically involve the use of aqueous solutions of sulfuric acid, stirred at 45–60°C at atmospheric pressure until a homogeneous beige solution is obtained [48, 49]. After acid hydrolysis, the suspension is diluted with water in order to stop the chemical reaction. This suspension is subjected to centrifugation to obtain the cellulose and eliminate excess acids. The resulting cellulose is washed with water using

centrifugation and is finally dialyzed using membranes to reach a neutral pH [50].

microcrystalline cellulose (MCC) by Battista in 1975 [52].

for hydrolysis.

nm [59].

*4.1.2. Extraction by mechanical force*

This procedure results in CNF having anionic groups on the surfaces (leading to electrostatic stabilization of the nanocrystals in suspension) with the ability to form chiral nematic liquid crystalline phases in concentrated solutions [51]. The form of cellulose obtained was denoted

Exaggerated hydrolysis can typically be noted as the solutions turn dark or black in color as the degradation of the CNF occurs. This phenomenon was reported by Roman et al. in 2004 [53], who assigned the crystal degradation to potential induced thermal degradation related to the sulfate groups, introduced as a functional surface on the CNF when sulfuric acid is used

The mechanical methods to extract CNF from wood pulp and parenchyma cells typically involve a high‐pressure homogenizer treatment [42, 54], a microfluidizer [55, 56], a high‐ pressure refiner, a super‐grinder treatment [57], or ultrasonication [58]. The form of cellulose obtained was denoted microfibrillated cellulose (MFC) by Herrick et al. and Turbak et al. in 1983 [42, 43]. These processing methodologies have in common the fact that they rely on the application of high shear forces on cellulose fiber suspensions in order to mechanically liberate the CNF from the original plant cell wall structure. In a high‐pressure homogenizer this is achieved by allowing a cellulose suspension to pass under high pressure through a thin slit where it is subjected to high shear forces. The shear forces serve to disintegrate the microfibrils or microfibril bundles in the plant cell wall, resulting in CNF with diameters of about 5–100

High‐intensity ultrasonication consists of a combination of chemical pretreatment and high‐ intensity ultrasonication. To obtain the cellulose fiber, first, the lignin is eliminated from the samples by immersion for 1 h at 75°C in a solution of sodium chlorite, previously acidified [60, 61]. After the bleaching, the samples are treated with potassium hydroxide (3%) at boiling point for 2 h. After which, they are subjected once again to a treatment with potassium hydroxide at a different concentration (6%), the aim being to eliminate hemicellulose, residual starch, and pectin. After the application of the chemical treatment, the samples are washed Human beings are known to be highly dependent on synthetic polymers derived from petroleum for the elaboration of diverse packaging and utensils, giving rise to environmental problems [45]. However, reports have been published of the combination of natural fibers, which are completely biodegradable, for the development of green composites. These mate‐ rials are environmentally friendly and their use can reduce contamination on a global level [45].

Nanocomposites contain materials with a nanometric dimension ranging between 1 and 100 nm. These materials surpass conventional composite materials due to their superior thermal, mechanical, and barrier properties [62, 63]. Biodegradable polymers, in particular, may require improvement in terms of brittleness, low thermal stability, and poor barrier properties [63].

The physical and mechanical properties of the polymeric material reinforced with cellulose are strongly dependent on their structure, relaxation, and morphological processes, as well as a good dispersion of the fiber in order to achieve the minimization of holes. Indeed, a good dispersion of fibers in the polymer matrix has been reported as something very difficult to achieve [64].

The processing of TPS reinforced with cellulose fibers processing is similar to most conven‐ tional synthetic thermoplastic processing [65]. Most thermoplastic operations involve heating and forming into desired shapes, and then cooling. Processing techniques used on thermo‐ plastics can also be used in the TPS reinforced with cellulose fibers. These include extrusion, injection molding, internal mixing, compression molding, and others [66].


**6.2.1 Example of the stopwatch behavior**

14 Will-be-set-by-IN-TECH

To support servers, we add an additional *server queue* for *each* server *σi*, denoted by *σi*.*sq*, to keep track of the events local to the server, i.e. delays and periodic arrival of tasks *τj* ∈ *γ*(*σi*). At any time at most one server can be active; all other servers are inactive. The additional server queues make sure that the events local to inactive servers do not interfere with the

When a server *σi* is switched in its server queue is activated by calling *ActivateQueue(σi.sq)*. In

1. the *system queue*, keeping track of system events, i.e. the replenishment of periodic servers, 2. the *server queue* of the *active* server, keeping track of the events local to a particular server,

When the active server is switched out (e.g. a higher priority server is resumed, or the active server gets depleted) then the active server queue is deactivated by calling *DeactivateQueue(σi.sq)*. As a result, the queue of the switched out server will be "paused", and the queue of the switched in server will be "resumed". The system queue is never deactivated. To keep track of the time which has passed since the last server switch, we introduce a *stopwatch*. The stopwatch is basically a counter, which is incremented with every tick. In order to handle time overflows discussed in Section 4.1, we represent the stopwatch as a RELTEQ

During the time when a server is inactive, several other servers may be switched in and out. Therefore, next to keeping track of time since the last server switch, for each server we also need to keep track of how long it was inactive, i.e the time since that particular server was switched out. Rather than storing a separate counter for each server, we multiplex the stopwatches for all servers onto the single stopwatch which we have already introduced, exploiting the RELTEQ approach. We do this by inserting a *stopwatch event*, denoted by *σi*.*se*, at the head of the stopwatch queue using *InsertEvent(stopwatch, σi*.*se)* whenever server *σi* is switched out. The event points to the server and its time is initially set to 0. The behavior of the tick handler with respect to the stopwatch remains unchanged: upon every tick the head

During runtime the stopwatch queue will contain one stopwatch event for every inactive server (the stopwatch event for the currently active server is removed when the server is switched in). The semantics of the stopwatch queue is defined as follows: the accumulated time from the head of the queue until (and including) a stopwatch event *σi*.*se* represents the

When a server *σi* is switched in, its server queue is synchronized with the stopwatch using *SyncQueuesUntilEvent(σi.sq, stopwatch, σi.se)*, which handles all the events in *σi*.*sq* which might have occurred during the time the server was switched out. It accumulates the time in the stopwatch queue until the stopwatch event *σi*.*se* and handles all the events in *σi*.*sq* which have expired during that time. Then *σi*.*se* is removed from the stopwatch queue. When *σi* is

this new configuration the hardware timer drives two event queues:

queue and use *IncrementQueue(stopwatch)* to increment it.

time the server *σi* was switched out.

i.e. the delays and the arrival of periodic tasks belonging to the server.

event in the stopwatch queue is incremented using *IncrementQueue(stopwatch)*.

switched out, *σi*.*se* with time 0 is inserted at the head of the stopwatch queue.

**6.2 Limiting interference of inactive servers**

currently active server.

The stopwatch queue is a great example of RELTEQ's strength. It provides an efficient and concise mechanism for keeping track of the inactive time for *all* servers. Figure 6 demonstrates the behavior of the stopwatch queue for an example system consisting of three servers *A*, *B* and *C*. It illustrates the state of the stopwatch queue at different moments during execution, before the currently running server is switched out and after the next server is switched in.

Fig. 6. Example of the stopwatch queue.

Initially, when server *σi* is created, a stopwatch event *σi*.*se* with time 0 is inserted into the stopwatch queue. At time 0 server *A* is switched in and its stopwatch event is removed. While server *A* is running, the tick handler increments the head of the stopwatch queue, which happens to be the stopwatch event of server *B*. At time 3, when server *A* is switched out and server *B* is switched in, server *B* synchronizes its absolute queue with the stopwatch queue until and including *B*.*se*, *B*.*se* is deleted, and *A*.*se* with time 0 is inserted. Note that when *B*.*se* is deleted, its time is added to *C*.*se*.

At time 7 server *C* is switched in, its absolute queue is synchronized with time 4 + 3 = 7, after which *C*.*se* is deleted, and *B*.*se* with time 0 is inserted.

At time 9, since no server is switched in, no synchronization is taking place and no stopwatch event is deleted. Only stopwatch event *C*.*se* with time 0 is inserted, since server *C* is switched out.

At time 16, when server *B* is switched in and its stopwatch event *B*.*se* is deleted, the time of *B*.*se* is added to *C*.*se*.

**6.4 Switching servers**

*ActivateQueue(σi.sq)*; *ActivateQueue(σi.vq)*; **if** *σi*.*we* ∅ **then**

*σi*.*we* = ∅*;*

**end if**

**end if**

*DeleteEvent(σi.vq, ej)*.

larger than 0.

*DeleteEvent(system, σi.we);*

*DeleteEvent(stopwatch, σi.se)*; *σi.se = NewEvent(stopwatch, 0, σi)*; *InsertEvent(stopwatch, σi*.*se)*; *DeactivateQueue(σi.sq)*; *DeactivateQueue(σi.vq)*; **if** *σi*.*readyTasks* = ∅ **then**

*InsertEvent(system, σi.we);*

Fig. 8. Pseudocode for *ServerSwitchOut(σi)*.

**6.6 RELTEQ tick handler with support for servers**

**6.5 Server replenishment and depletion**

Fig. 7. Pseudocode for *ServerSwitchIn(σi)*.

*σi.we = NewEvent(wakeup, Head(σi.sq).time, σi);*

*SyncQueuesUntilEvent(σi.sq, stopwatch, σi.se)*;

The methods for switching servers in and out are summarized in Figures 7 and 8.

An Efficient Hierarchical Scheduling Framework for the Automotive Domain 83

We introduce two additional RELTEQ event kinds to support servers: *budget replenishment* and *budget depletion*. When a server *σi* is created, a replenishment event *ej* is inserted into the *system queue*, with *ej.data* pointing to *σi* and *ej.time* equal to the server's replenishment period

Upon replenishment, the server's depletion event *ej* is inserted into its *virtual server queue*, with *ej.data* pointing to *σi* and *ej.time* equal to the server's capacity Θ*i*. If the server was not depleted yet, then the old depletion event is removed from the virtual server queue using

An example of the RELTEQ queues managed by the tick handler in the proposed RELTEQ extension with servers is summarized in Figure 9. Conceptually, every tick the stopwatch queue is incremented and the heads of the system queue, the active server queue and the active virtual server queue are decremented. If the head of any queue becomes 0, then their head event is popped and handled until the queue is exhausted or the head event has time

Actually, rather than decrementing the head of each active queue and checking whether it is 0, a *CurrentTime* counter is incremented and compared to the *Earliesttime*, which is set whenever the head of an active queue changes. If they are equal, then (i) the *CurrentTime* is subtracted from the heads of all the active queues, (ii) any head event with time 0 is popped and handled,

Π*i*. When *ej* expires, *ej.time* is updated to Π*i* and it is inserted into the system queue.


**Abstract**

Selenium is a mineral element that is essential for both animal and humans and can also serve as an environmental toxicant. A narrow margin exists between an ideal and toxic intake of selenium. It is a useful microelement existing in minute amounts in animals, plants, microorganisms and humans. Although it is beneficial to both animals and humans as an antioxidant, it can be toxic at high concentrations as a result of it competing and replacing sulfur in amino acids leading to inappropriate folding of protein and eventually creating a nonfunctional protein and enzymes. Selenium exists in organic forms as SeMet and SeCys and inorganic forms as selenide, selenite and selenite in the environment. It is translocated in plants via the sulfate transporters in the plasma membrane of the plant root. Its translocation and distribution however depends on the plant species, their different developmental phases, forms, concentration and other physiological conditions like pH. Inorganic selenium is first converted to selenite via the action of two different enzymes (ATP sulfurylase and APS reductase), selenite is further converted to selenide by sulfite reductase. Selenide eventually couples with O-acetyl serine via the action of cysteine synthase to form SeCys. SeCys can either be methylated to methyl-SeCys through the action of selenocysteine methyltransferase or to elemental selenium via SeCys lyase or converted by a series of enzymes to selenomethionine. Selenium toxicity or Selenosis can occur when the optimal concentration of selenium is exceeded. Two major mechanism of selenium toxicity exists; either by induction of oxidative stress or malformation of selenoproteins. Selenium uptake, metabolism and toxicity in tropical plants are hereby discussed in this chapter.

**Keywords:** selenium, distribution, toxicity, tropical plants

#### **1. Introduction**

Selenium (Se) is a widely distributed trace metalloid found in the crust of the Earth. Jacob Berzelius, Chemist first isolated selenium in 1817 and it has been known for its toxic effect. However, in 1957, some importance of selenium was discovered. It is mostly linked to sulfur and an essential nutrient for human, animals and microorganisms. Many enzymes such as thioredoxin reductase and glutathione peroxidase are mostly composed of selenium which helps the enzymes to perform roles like reproduction, tumor prevention and antioxidation [1]. Selenium can also

promote growth of lettuce seedlings by delaying senescence [2]. It can be toxic at large concentrations and can lead to pro-oxidative reactions. Also deficiency of selenium can occur in soils where selenium bioavailability is low leading to health risks for animals and humans. Supplementing fertilizers with sodium selenate has been shown to improve the food chain from soil to animals and then to humans [3]. The recommended daily intake of selenium should be adhered to for maximum utilization of its benefits. Within the plant and soil environment, selenium is converted to another chemical form [4]. The metabolism and mechanisms through which plants cope with high selenium concentrations are explained by the transformations of selenium form one form to another. The bioavailability, biotransformation, speciation, metabolism and functions all have great implications for both human and animal health. This chapter presents the physicochemical properties of selenium, its sources in the environment and locations, role of selenium in the body, metabolism, uptake and accumulation in plants.

### **2. Physicochemical properties of selenium**

Selenium being a metalloid from the same family of sulfur and oxygen, has it name derived from the word "Selene" that is, moon goddess since it is mostly linked to tellurium [5]. It has six isotope coexisting in nature with mass numbers 74, 76, 77, 78, 80 and 82 [6]. It is similar to sulfur in terms of bond energies, oxidation state, atomic size and ionization potentials [7]. Selenium possesses properties of both non-metal and metal hence it is referred to as a semi metal. It is considered stable as it does not oxidize at room temperature. It produces selenium dioxide and blue flame when it burns which is followed by an unpleasant smell. Selenium can form compounds with elements (fluorine, bromine, hydrogen and phosphorus) having a close analogy to those of sulfur [8, 9]. It has a lower affinity for oxygen than sulfur with only two oxides known; SeO3 and SeO2. Combustion of selenium in air produces dioxide which dissolves in water to give selenious acid (H2SeO3); a solution that can oxidize most metals except platinum, palladium and gold [10–20].

Selenic acid (H2SeO4) is a hygroscopic diacid with a higher oxidizing potential than H2SO4. It is produced by the reaction of oxidizing agents such as chlorine, fluorine, bromine with Se, SeO2, H2SeO3 in the presence of H2O. Reaction of selenium with hydrogen and reaction of metal selenides with acids (or water) releases hydrogen selenide (H2Se), a highly reactive compound. At about 160°C, it starts to decompose to Se and H2, it also forms a deposit of red selenium in moist air [21].

### **3. The physical and chemical forms of selenium**

Selenium exists in nature and in organisms in organic and/or inorganic forms. The organic form includes selenocysteine (Secys) and selenomethionine (Semet), while the inorganic forms includes selenate (SeO4 −2 ), selenide (Se−2 ), selenite (SeO3 −2 ) and selenium (Se) (**Figure 1**) [22].

Selenium exists in a solid state at room temperature and can take up various physical forms [8, 23]. Precipitation from aqueous solution produces amorphous selenium (red brick powder) with a density of 4.26 with photoconductive properties. At very high temperature between 110 and 180°C, the color turns gray, this is a variety of selenium that is thermodynamically stable and it is obtained by cooling liquid selenium hence it is used for its semiconducting properties.

*Uptake, Metabolism and Toxicity of Selenium in Tropical Plants DOI: http://dx.doi.org/10.5772/intechopen.90295*

**Figure 1.** *Selenocysteine and selenomethionine.*


**1. Introduction**

With the advent of Industry 4.0 and Internet Plus, new concepts such as smart mine and digital mine have been continuously proposed. Cloud computing, mixed reality, big data, Internet of Things, and information physics systems have gradually merged with modern industries [1]. Consequently, coal-mining equipment has been continuously transformed into automated, intelligent, and unmanned equipment [2]. Virtual reality is a technology, developed in the 20th century, which involves a computer, electronic information, and simulation technology. Its basic implementation method is to create a simulated environment by computer technologies so as to give people a sense of environmental immersion [3,4]. Due to the unique working conditions of the coal production process, its complexity, and uncertainty, most of the traditional simulation tools and simulation methods are used to simulate a certain process or a single device in the production process, and it is difficult to achieve an overall and real effect, the integration of virtual reality technology into the traditional coal-mining field also presents new demands and broad prospects. Combined with the information from the actual production equipment, assembly conditions, geological environment, and operation data, a realistic three-dimensional (3D) scene was constructed to show and simulate the production process of the fully mechanized mining face [5,6].

The shearer is one of the key equipment for the fully mechanized mining face. It is an important technological equipment to achieve high-e fficiency intensive coal mining, reduce major malignant accidents in coal mines, and improve the working conditions of the working face. It is, therefore, of great significance to the virtual simulation of the shearer's cutting operations. Several studies on the problems of virtual assembly [7], operation demonstration, and attitude analysis [8–10] of the shearer in the virtual environment have been carried out. However, most of these studies were carried out under the condition of a horizontal floor. In the real mining process, there are often complex geological conditions such as coal seam dip and undulations in the roof and floor, which leads to the disparity between the equipment operation state presented in the virtual simulation and that in the actual situation.

The planning of the cutting path of the shearer is an important means to achieve intelligent coal mining. At present, the researches on the cutting path planning of the shearer are mainly based on the accurate positioning of the shearer to obtain memory cutting [11,12], and automatic adjustment of drum based on intelligent decision-making or coal rock recognition [13]. Li Wei [14] proposed a hidden Markov model (HMM) memory cutting method for the shearer, which prevented large residual errors and frequent adjustments of the shearer cutting drum. Gospodarczyk Piotr [15] focused on the coal mining process for di fferent variants of the shearer construction and kinematic parameters. Xu Jing [16] proposed an online cutting-pattern recognition method with high accuracy and speed. The cutting sound was collected as the recognition criterion. Although there are many reported studies on the cutting path planning of shearer, those on the demonstration and simulation using virtual reality technology are limited.

The 3D coal seam model is an important aspect to study the virtual operation of the shearer under complex geological conditions. With the help of a 3D visualization platform, the spatial geometry of the geological body in the mining area can be described more accurately and comprehensively by the real 3D coal seam model. Alan M. Lemon [17] developed a 3D geological model based on the drilling data using the horizons method. The user-defined profile method was further used to dynamically modify the geological model. Wu Lixin [18] proposed a generalized triangular prism (GTP) data model for coal seam modeling to solve the problem of geometric cracks. Most of the existing coal mine virtual environment systems are developed by independent GIS Engine [19,20], which makes it di fficult for the virtual coal seam to carry out simulation analysis jointly with the shearer and other equipment. In the Unity3D, the 3D coal seam and the fully mechanized mining equipment model are built for joint analysis; all elements of the production process are digitized, and a more comprehensive and real virtual scene is restored for simulation, so as to enhance the actual production.

In an attempt to solve the above-stated challenges, this study was carried out to improve the authenticity and accuracy of simulation. Based on the analysis of the shearer walking and heightening motion model, the equipment modeling and 3D coal seam modeling, combined operation of the shearer and the scraper conveyor were further obtained in the virtual reality engine Unity3D. A cutting path control strategy of the shearer based on adjusting the direction of the drum movement is proposed. Finally, the simulation and the fuzzy comprehensive evaluation [21,22] of the simulation results were carried out.

#### **2. An Idea of Cutting Path Planning Technology of Shearer Based on Virtual Reality**

The main technical equipment in the fully mechanized mining face includes shearer, scraper conveyor and hydraulic support. The hydraulic support is mainly used for face advance and supporting the roof of the working face. The shearer and scraper conveyor jointly complete the circular coal cutting. Among them, the scraper conveyor is arranged on the coal seam floor. When the coal seam floor is undulating, the scraper conveyor section by section adapts to the terrain to produce slight bending. The shearer is meshed in the chute of the scraper conveyor through the traveling wheel, which is connected with the scraper conveyor to move forward smoothly, and the shearer sends the drum to the designated position by adjusting the rocker arm to complete the cutting work.

In the selection and design stage of comprehensive mining projects, geological conditions, coal storage, equipment selection, production capacity, and other factors are often considered. Therefore, the selection and design cycle of comprehensive mining face is long and difficult, and coal mining enterprises need to select quickly, and previewed the pre-selected schemes as much as possible, so as to reduce the problems that may occur in the future.

Using the virtual reality engine Unity3D, we studied the cutting path planning method of the shearer drum and obtained the operation simulation of the virtual shearer on the 3D terrain. The overall research framework is shown in Figure 1, which was realized through the following steps.

**Figure 1.** Overall framework.

#### **3. Kinematics Analysis and Cutting Path Planning Method of Real Shearer**

#### *3.1. Kinematics Model of Shearer Height-Adjusting*

The simplified model for the height-adjusting system of the drum shearer is shown in Figure 2. A MG/TY 250/600 double-drum shearer (TZ coal mine whole set equipment Company Ltd, Taiyuan, Shanxi, China) was used in this research. The hydro-cylinder and the cylinder rods are articulated to the shearer fuselage and the rocker arm, respectively. When the cylinder rod moves, the short arm of the rocker arm rotates hence, drives the long arm to obtain the change in the drum position. In the process of lifting and descending the shearer drum, the drum has the same trajectory and law of movement, that is, the movement parameters of the drum in lifting and descending are only distinguished by positive and negative signs. Therefore, the kinematic analysis and height-adjusting strategy of shearer in this paper only focus on the process of drum lifting.

**Figure 2.** Height-adjusting system of the drum shearer.

#### *3.2. Kinematics Analysis of Shearer Height-Adjusting and Walking*

While the shearer is working, it performs two main functions; traction speed regulation and drum-height regulation. The analysis of the movement of the shearer is mainly the analysis of the above two functions. The traction motion and the drum-height adjustment motion of coal mining on the horizontal and uneven ground are analyzed as follows:

#### • Stationary shearer and the drum lifts

Setting the expansion speed of the height-adjusting cylinder rod to *v*c and the running time to *t*, the rotation angle of the rocker arm can be calculated as follows:

$$\alpha = \arccos\left[\frac{r^2 + a^2 - \left(l + v\_{\rm c} \cdot t\right)^2}{2ra}\right] - \arccos\left[\frac{r^2 + a^2 - l^2}{2ra}\right].\tag{1}$$

The rotation angle of the height-adjusting cylinder is given by:

$$\gamma = \arccos\left[\frac{l^2 + a^2 - r^2}{2ra}\right] - \arccos\left[\frac{\left(l + v\_{\mathbb{C}} \cdot t\right)^2 + a^2 - r^2}{2\left(l + v\_{\mathbb{C}} \cdot t\right)a}\right].\tag{2}$$

The tangential velocity of the drum at any time is:

$$\upsilon\_{\mathsf{T}} = \upsilon\_{\mathsf{C}} \cdot \sin \left[ \arcsin \left( \frac{r^2 + \left(l + \upsilon\_{\mathsf{C}} \cdot t\right)^2 - a^2}{2 \cdot r(l + \upsilon\_{\mathsf{C}} \cdot t)} \right) \right] \tag{3}$$

where *r* and *p* are the lengths of the short and the long arms (values are 543 and 1962 mm), respectively; *a* is the distance between the joint of the cylinder body and the fuselage and the joint of rocker arm and fuselage, its value is 1303 mm; *l* is the total initial length of the cylinder rod and the cylinder body, its value is 1701 mm. It can be observed that the expansion speed of the cylinder and the rotation angle of the rocker arm have a non-linear relationship.

• Shearer walks on the flat floor and the drum lifts

Under this condition, the rotation angle of the shearer rocker arm and that of the cylinder remain unchanged. The horizontal velocity of the shearer drum is expressed thus:

$$v\_{\rm h1} = -v\_{\rm c} \times \sin\left[\cos^{-1}\left(\frac{r^2 + \left(l + v\_{\rm c} \cdot t\right)^2 - a^2}{2r(l + v\_{\rm c} \cdot t)}\right)\right] \times \frac{p}{r} \times \sin\left(0.0872 + a\right) + v\_{\rm s} \tag{4}$$

The vertical velocity of the shearer drum is:

$$w\_{\rm v1} = v\_{\rm c} \times \sin\left[\cos^{-1}\left(\frac{r^2 + \left(l + v\_{\rm c} \cdot t\right)^2 - a^2}{2r(l + v\_{\rm c} \cdot t)}\right)\right] \times \frac{p}{r} \times \cos(0.0872 + a). \tag{5}$$

The angle between the direction of the drum movement and the horizontal direction is:

$$\theta\_1 = \tan^{-1} \left( \frac{v\_{\forall 1}}{v\_{\text{h1}}} \right) \tag{6}$$

where the angle (in rad) between rocker arm and fuselage of shearer at initial state is 0.0872, *v*s is the traction speed (in mm/s) of the shearer.

• Shearer walks on the unflat floor and the drum lifts

The horizontal velocity of the shearer drum is given by:

$$
\upsilon\_{\mathbf{h}} = \upsilon\_{\mathbf{h}1} \times \cos \beta - \upsilon\_{\mathbf{v}1} \times \sin \beta. \tag{7}
$$

The vertical velocity of the shearer drum is:

$$
\upsilon\_{\rm V} = \upsilon\_{\rm h1} \times \sin \beta + \upsilon\_{\rm v1} \times \cos \beta. \tag{8}
$$

The angle between the direction of the drum movement and the horizontal direction is:

$$\theta = \tan^{-1} \left( \frac{v\_{\text{v}}}{v\_{\text{h}}} \right) \tag{9}$$

where β is the inclination angle (in rad) of shearer fuselage.

• Joint analysis of shearer traction speed, cylinder expansion speed, and direction of drum movement

Set the simplified expansion speed of the height-adjusting cylinder to *v*c = 5 mm/s and the range of traction speed of the shearer to 0–12.9 m/min (0–215 mm/s). In the process of lifting the drum from the horizontal floor, the image of the angle between the direction of the drum movement and the horizontal is as shown in Figure 3 (the upward direction is the positive direction).

As can be seen from the figure, although there is an obvious non-linear relationship between the expansion speed of the height-adjusting cylinder and the direction of the drum movement, the direction of the drum movement does not fluctuate much with increase in the running time when the traction speed of the shearer and the expansion speed of the cylinder are fixed.

**Figure 3.** Relationship between the direction of drum movement, running time and traction speed of the shearer: (**a**) oblique view; (**b**) Y–Z view.

#### *3.3. Automatic Height-Adjusting Strategy*

In the traditional memory cutting, the driver controls the shearer to cut a knife along the working face of the coal seam first, and the information such as the travel position of the sampling point, the height position of the drum, the working attitude of the shearer and the traction speed is stored into the computer. The later cutting process of the shearer drum is automatically controlled by the computer according to the stored parameters. When the geological structure of coal seam changes greatly, if the shearer continues to work in the original cutting direction using the parameters recorded by the memory cutting, the shearer drum may interfere with the roof and floor of the coal seam, thus causing damage to the comprehensive mining equipment or requiring manual adjustment of the Shearer to reduce the mining efficiency. The most fundamental way to solve this problem is to achieve self-adaptive cutting in the process of each cutting operation. When the geological structure of coal seam changes, the shearer drum can adjust the cutting posture timely and accurately, instead of keeping the same as the traditional memory cutting.

During the cutting process of the shearer, the inclination angle of the fuselage changes with the dip angle of the coal seam while the cutting height of the drum changes accordingly. Therefore, the lack of suitable positioning reference standards for the shearer while running on real terrain is one of the difficulties in achieving accurate and smooth control of the cutting path. In this study, a method of automatic height-adjusting of shearer based on the fine modeling of the coal seam is proposed. The mine itself is a real 3D static geological environment in which all mine activities are carried out. The position of the drum, any position on the coal seam, and the roof and floor are represented by absolute 3D coordinates. The direction of the drum movement, which integrates the information from the shearer traction speed, expansion speed of the height-adjusting cylinder, and inclination angle of the fuselage, are calculated. According to the analysis in 3.2, in the process of drum height-adjusting, the direction of the drum movement mainly changes with the change of haulage speed of shearer. When the roof and floor of the coal seam fluctuate, the posture of the shearer and the position of the coal to be cut will change accordingly. By adjusting the haulage speed of the shearer, the direction of the drum movement can be adjusted to move to the predetermined target position, so as to achieve self-adaptive cutting.

Based on the geological survey data and 3D coal seam modeling, the roof and floor curves of the current working face are obtained (as shown by the curve in Figure 4). The roof and floor curves are composed of a series of key cutting points with known coordinates. In the actual coal mining process, the key cutting point generally refers to the point where the roof and floor curves of the coal

seam have significant fluctuations. In this study, the key cutting points are obtained by building a fine three-dimensional coal model based on geological exploration data of a coal mine [23]. As shown in Figure 4, P*i* and P*i*+1 are selected as the two key cutting points. When the right drum of the shearer runs to the horizontal position of P*i*, the direction of the drum movement can be changed by increasing the traction speed of the shearer and by lifting the drum, so that it moves to the next key cutting point P*i*+1 (In the figure, β is the inclination angle of the shearer fuselage, and K is the angle between the direction of the drum movement and the horizontal at the key cutting point (K is the specific value of θ in formula 9 in 3.2 at the key cutting point). Q is the angle between the line connecting the next key cutting point and the instant apex of the drum and the horizontal).

**Figure 4.** Automatic height-adjusting diagram.

The specific process of automatic height-adjusting is as follows:

• Reading data and calculating parameters

If the right drum of the shearer runs to the horizontal position of P*i*, the coordinates of the center of the shearer drum at this time (*Xa*1,*Ya*1,*Za*1) are recorded. The coordinates of the next key cutting point *Pi*(*j*+1) (*Xb*1,*Yb*1,*Zb*1) are then read, after K (the angle of the direction of drum movement) is recorded. This is followed by the calculation of Q (the angle of the target direction):

$$\mathbf{Q} = \tan^{-1} \left( \frac{\mathbf{Y}\_{b1} - (\mathbf{Y}\_{a1} + \mathbf{9})}{Z\_{b1} - Z\_{a1}} \right) \tag{10}$$

where 9 is the radius of the drum, (in decimeter). The height compensation value of the inclination angle of the shearer fuselage given M = *L* × sin β (where *L* is the length of a scraper conveyor, in dm; β is the inclination angle of the shearer fuselage, in rad) is calculated. When the shearer fuselage is inclined at an angle, the absolute height of the drum changes if not adjusted.

• Judgment of action

After reaching a key cutting point, the drum can experience three actions; up, down, and no adjustment. The concrete action is determined by the height difference between the apex of the drum and the next key cutting point. The thresholds of the height difference are determined by the inclination angle of the fuselage and the feasible region of the shearer traction speed. These thresholds include:

In the above equations, H is the height di fference between adjacent key cutting points, β is the inclination angle of the shearer fuselage, θ*max* and θ*min* are the maximum and minimum angle, respectively, between the direction of the drum movement and the horizontal direction during the lifting process, ε indicates the range of the relative height di fference between the adjacent key cutting points within which the drum may not be adjusted. In this study, we set ε to 60 mm.

When the adjustment direction is determined, the K is compared with the Q. The haulage speed of the shearer is then adjusted to redirect the drum toward the target point. Specific execution actions at the key points include acceleration-up, deceleration-up, acceleration-down, deceleration-down, constant speed-up, constant speed-down, and no adjustment. When matching the moving direction and the target direction of the drum, considering that there is still a slight change in the process of drum lifting, the analysis law of the shearer drum movement obtained in 3.2 is taken as the judgment knowledge and included in the strategy. The specific action judgment is shown in Table 1.

**Table 1.** Strategy of action judgment.

#### *3.4. Selection of Operation Parameters of the Shearer*

In the coal production process, the selection of the operating parameters of the shearer is key to achieving high-e fficiency and high-quality cutting under various working conditions, and as well, meeting the production capacity of the working face. When the model of the fully mechanized mining equipment is selected, the performance of the shearer is mainly a ffected by the traction speed and the drum cutting speed; this study focuses on the traction speed of shearer. Under the condition of guaranteed normal coal falling and conveying, the higher the traction speed of the shearer, the better the coal quality, the lower the specific energy of cutting, and the higher the coal production e fficiency. However, the traction speed is restricted by the traction and the cutting powers of the shearer motor. On the other hand, the higher the traction speed, the lower the recovery rate of the coal seam with larger fluctuation. In actual production, the mining replacement arrangement, mining technology, and mechanization degree vary in each mining operation. In most cases, higher production capacity of the working face and recovery rate of the coal seam is required.

With reference to the mine production capacity verification standard, the coal mining face capacity is calculated as follows:

$$A\_{\mathbb{C}} = 10^{-4} l \cdot h \cdot r \cdot b \cdot n \cdot \text{N} \cdot \text{c} \cdot a,\tag{11}$$

where *AC* is the annual production capacity of the coal mining face, 10,000 tons/year; *l* is the average length of coal mining face, m; *h* is the average mining height of coal seam in coal mining face, m; *r* is the raw coal density, m3; *b* is the average daily advancement of coal mining face, m/d; *n* is the annual working days, d; *N* is the normal cycle operation coe fficient, %, according to the technical

performance of coal mining equipment, the production organization, staff quality, and other factors, generally taking 0.8; *c* is the recovery rate of coal mining face, %; *a* is the average number of coal mining face. It can be seen that for annual production capacity, the speed of coal-mining equipment needs to be adjusted when other conditions are fixed. With reference to the actual production experience of a coal mine, the minimum average traction speed of the shearer may be up to 3 m/min for higher production capacity.

#### **4. Virtual Cutting Path Planning Method of Shearer and Design of Prototype System**

Comparing the characteristics of the traditional CAD software and that of the Unity3D engine, the following main processes are needed to simulate the working process of shearer under the 3D coal seam (Figure 5):

**Figure 5.** Algorithm of the main process of the virtual cutting of shearer.

#### *4.1. Key Technology for Modeling Coal Seam Roof and Floor*

Developing a 3D model that accurately expresses the coal seams is the key to achieving digital, visual, and intelligent production management in the fully mechanized mining face in coal mines. In this study, the discrete detection points are transformed into a series of continuous data using the geological exploration data of a coal mine, through the Kriging [20] interpolation. After processing, the 3D coordinates of 16 × 40 × 2 points are obtained, so as to build the coal seam roof and floor model in Unity3D.

The mesh grid component in Unity provides a way to form a surface closely through a large number of small triangles. The MeshFilter records the grid data whereas the MeshRenderer components set the grid material. Since only one side of the triangle generated by the mesh grid component is visible (visible on one side of the clockwise connection), the opposite vertex order is required to make the coal seam top and bottom plates visible under normal viewing angles. During the implementation of the mesh grid of the coal seam roof, the vertices of the triangle are selected according to the specific rules with reference to Figure 6. As shown in Figure 6, assuming that there are altogether 640 3D geological coordinates of the coal seam roof (No. 0–639), the three vertices of the first triangle are 0, 40, 1 in turn, the three vertices of the second triangle are 1, 41, 2 in turn. Similarly, the three vertices of the last triangle were 639, 598, 638 in turn.

**Figure 6.** Rules for sorting triangular vertices in roof and floor mesh: (**a**,**c**) connection order of triangle vertices; (**b**) clockwise and counterclockwise connections.

The processed data of the coal seam roof and floor are stored as an XML file, and the mesh is created by a c# script. According to the above method, the final effect in Unity3D is as shown in Figure 7:

**Figure 7.** Mesh of virtual coal seam roof and floor.

#### *4.2. Key Technology for Combined Operation of Shearer and Scraper Conveyor*

This paper presents a method of combined operation of the shearer and scraper conveyor on the real terrain of Unity3D.

The collider component in Unity3D provides a physical collision between the colliding bodies under gravity. Based on the above-mentioned modeling of coal seam, the mesh collider component is added to the GameObject of the coal seam floor and scraper conveyor to measure the position and the inclination of each scraper conveyor on the floor. Using the measured parameters of each scraper conveyor, they are arranged according to the actual arrangement rules. In the course of the shearer walking on the scraper conveyor, the attitude of its fuselage is determined by the left and right supporting slippers. Therefore, it is necessary to obtain the shape function of the scraper conveyor so that the slippers can move strictly along the obtained shape while the virtual walking of the shearer is controlled by coordinates. The shape function of the scraper conveyor can be calculated as shown in Figure 8.

**Figure 8.** Shape function of the scraper conveyor.

Where (*Xi*,*Yi*) is the coordinate of the pin of the ith scraper conveyor, mm; α*i* is the inclination angle of the ith scraper conveyor, rad; *i* is the serial number of the scraper conveyor where the slipper of the shearer is located; *L* is the length of a scraper conveyor, mm; *v* is the traction speed of the shearer, mm/s; *t* is the walking time of the shearer, s; *x* and *y* are the corresponding coordinate values on the shape function.

The ultimate effect in Unity3D is as shown in Figure 9:

**Figure 9.** Effect diagram of combined operation of shearer and scraper conveyor.

#### *4.3. Key Technology of Virtual Cutting of Shearer*

In the Hierarchy interface of Unity3D, the hierarchical relationship (parent-child relationship) of each component is established. By defining the Transform property of each component through C# script, the parameters such as position, rotation, and size of each component can be obtained and varied through the script. The virtual shearer can be controlled by a programmed C# script based on the collaborative mathematical model, obtaining the start and stop, variable speed walking, positioning of the shearer, expansion, and rotation of the cylinder, adjustment of the drum height, and rotation of the drum. The components are moved using the 3D coordinate, whereas, they are rotated by the quaternion. The parent-child relationship of shearer components is shown in Figure 10.

**Figure 10.** Parent–child relationship of shearer components.

In the process of creating a coal seam roof and floor, the cutting point data was stored as an XML file. As the shearer runs, the current number of the cutting feed is *i*, and the cutting point number within the range of one knife is *j*. The key cut point of the path in the entire coal seam is therefore, the set *Pij*(*i* = 1, 2, 3 ··· , *j* = 1, 2, 3 ···). When the system starts to run, the current cutting point of the working face can be read and the LineRender component in Unity3D is used to draw the curve of the coal roof and the cutting curve from time to time. The cut point data on the cut curve is also stored in the XML file for subsequent analysis of the simulation results. The schematic diagram of the cut point and the effect obtained in Unity3D are shown in Figure 11a,b, respectively.

**Figure 11.** Schematic diagram: (**a**) the map of cutting points (**b**) the effect of cutting points in Unity3D.

## **5. Simulation Experiment**

#### *5.1. Setting of Simulation Conditions*

Two di fferent height-adjusting modes were designed in the experiment, namely; the automatic height-adjusting mode and the manual operation mode. In the planning stage of the fully mechanized mining face, the production capacity of the working face is often verified. In this study, the influence of shearer traction speed on production capacity was considered most. The lowest traction speed of the cutting process was set to ensure that the production capacity meets the requirements of di fferent working faces. The feasible range of the haulage speed of the shearer was 2.5–12.9 m/min, 3.5–12.9 m/min, 4.5–12.9 m/min, and 0–12.9 m/min (operated by experienced workers).

#### *5.2. Analysis of Simulation Results*

In scheme 1, the actual cutting curve coincides with the preset roof curve, and the maximum error was obtained to be 12 cm. However, in schemes 2 and 3, an increase in the minimum traction speed of shearer decreased the coincidence, and the maximum error was obtained to be 48 cm. This could be attributed to the fact that the direction of the drum movement cannot meet the changing trend of the preset roof curve when the shearer is at the minimum traction speed. During the height-adjusting of the shearer while walking, if the shearer is running at a relatively large traction speed, K (the angle of the direction of drum movement) is relatively small, and the drum height-adjusting cannot be performed in a large scale. At the position where the roof and floor of the coal seam fluctuates greatly, the cutting curve will produce a large error. The e ffect in Unity3D is shown in Figure 12a and the cutting errors of the four schemes are shown in Figure 12b.

(**a**)

(**b**)

**Figure 12.** Simulation results: (**a**) three-dimensional graph of cutting curve (**b**) error of the actual cutting points and the preset points.

In production, scientific research and our daily life, we need to evaluate the quality of goods, the reasonable degree of some engineering design parameters, etc. to determine the best way to deal with them. In the cutting process of the fully mechanized mining face, there are many indexes to measure the quality of the cutting results, including the coal quality, recovery rate, production efficiency, economic benefit, and safety degree. Evaluating coal production is not an easy task, as it involves dimensionless index, human decision making, which are imprecise, fuzzy, and uncertain. Hence, using a scientific method to evaluate coal production comprehensively and e ffectively plays a crucial role in the analysis of simulation results.

The evaluation that needs to be made according to many factors is called comprehensive evaluation. When some specific evaluation factors are fuzzy, the comprehensive evaluation is called a fuzzy comprehensive evaluation. The fuzzy theory bridges the gap between the precise classical mathematics and the fuzzy real world. Fuzzy comprehensive evaluation method is a method which transforms qualitative evaluations into quantitative evaluations through the membership degree theory of fuzzy mathematics. It, therefore, has a good theoretical basis for the overall evaluation of things or objects having many constraints. Hence, the fuzzy comprehensive evaluation method is suitable for evaluating the cutting results. The general steps of fuzzy comprehensive evaluation include the construction of evaluation indexes, the determination of the weight of each index, the construction of the evaluation matrix, the synthesis of evaluation matrix and weight.

The specific steps for evaluating the cutting results using the fuzzy comprehensive evaluation are as follows:

• Determine the evaluation object

$$X = \langle \mathbf{x}\_1, \mathbf{x}\_2, \mathbf{x}\_3, \mathbf{x}\_4 \rangle = \text{(scheme1, scheme2, scheme3, scheme4)}. \tag{12}$$

• Determine the index set

$$\mathcal{U} = \langle u\_1, u\_2, u\_3 \rangle$$

$$= \langle \text{recovery rate}, \text{ production efficiency}, \text{ others}(\text{safety degree}, \text{ labor cost}, \text{ etc}) \rangle. \tag{13}$$

When the index set is determined, each of the indicators of each scheme is analyzed quantitatively. The recovery rate is expressed by the sum of the squares due to the error of the cutting points thus: *SSE* = *n i*=1 *wi*(*yi* − *y*) 2, where *yi* and *y* are the heights of the actual cutting point and the preset roof point, respectively. The e fficiency of production is represented by the time used for cutting one knife, however, other factors are scored according to the production experience of the general enterprises (0−9 point). The scores of each index are shown in Table 2.

**Table 2.** Evaluation index.

• Determine the weight vector of the evaluation index

Set the weight vector to *A* = (*W*1, *W*2 ... *Wn*), where *Wi* indicates the *i*-th index weight coe fficient. The matrix gives di fferent weights to each index according to the di fferent functions of each index in the overall evaluation. Under the condition that the safety level and other production costs are guaranteed, coal production pays more attention to the production e fficiency directly linked to economic benefits

and the recovery rate that can maximize the use of resources. According to the general enterprise production experience [24], *A* = (0.4, 0.4, 0.2).

#### • Determine the fuzzy evaluation matrix

After determining the weights of the indicators, the evaluation index needs to be normalized and quantified one by one. A row in the fuzzy evaluation matrix R reflects the degree of membership of di fferent schemes on this indicator. For the evaluation index with a specific value, the value of the optimal evaluation object can be set to 1 whereas, it is set according to the scoring level (0 ≤ E ≤ 1) for other objects. For the index whose value is bigger and whose evaluation is better, the calculation method of evaluation value is: Evaluation value = specific value/optimal value. For the index whose value is smaller and whose evaluation is better, the calculation method of evaluation value is: Evaluation value = optimal value/specific value. When calculating the evaluation value of the recovery rate, a higher SSE indicates a lower recovery rate, and the evaluation value should be smaller. Among the four schemes, the one with the highest recovery rate is scheme 1, and the corresponding SSE value is 13.89. Then, the evaluation value of scheme 1 is set to 1, the evaluation value of scheme 2 is set to 0.15 = 13.89 92.5 , the evaluation value of scheme 3 is set to 0.11 = 13.89 125.73 , the evaluation value of scheme 4 is set to 0.339 = 13.89 40.96 .

Finally, the fuzzy evaluation matrix is obtained thus:

$$R = \begin{bmatrix} 1 & 0.15 & 0.11 & 0.339 \\ 0.7 & 0.808 & 1 & 0.646 \\ 1 & 1 & 1 & 0.25 \end{bmatrix} \tag{14}$$

#### • Comprehensive evaluation

Comprehensive fuzzy evaluation vector B is calculated by the combination of the fuzzy multiplication and fuzzy evaluation matrix *R*; *B* = *A R*. The common fuzzy multiplications mainly include single-factor prominent type and weighted average type, in order to be able to objectively evaluate the impact of each index, the fuzzy multiplication chosen in this paper is the weighted average method [25]; *M*( ·, v), *bj* = *ai*·*rij* (*j* = 1, 2 ··· *m*). Hence, the final evaluation was calculated as follows:

$$B = A \odot R$$

$$B = \begin{bmatrix} & 0.88 & 0.5832 & 0.644 & 0.444 \end{bmatrix}$$

It can be observed that the four schemes above are sorted as schemes 1, 3, 4, and 2. The higher value of the test has a better comprehensive evaluation. When the minimum traction speed of the shearer is small, more accurate trajectory tracking can be achieved. With an increase in the minimum traction speed, the production e fficiency of the shearer improves, whereas the accuracy of trajectory tracking decreases. On this basis, according to the requirements of di fferent enterprises, the production process of di fferent working parameters can be previewed and evaluated in a virtual environment.


**1. Introduction**

#### *1.1. Muscle Protein Metabolism and Skeletal Muscle*

The maintenance of and optimizing the accretion of skeletal muscle mass are critical outcomes for athletic-minded individuals, whether the goal is increased performance, improved muscularity, or enhanced recovery. Furthermore, while skeletal muscle mass accretion is often a goal of active individuals, there are direct clinical applications and benefits for the general public as well, especially for aging adults. Skeletal muscle is regulated through a near-continual ebb and flow between rates of muscle protein synthesis (MPS) and breakdown [1]. Muscle mass loss occurs during a net negative balance (breakdown > synthesis) while muscle gain occurs when synthesis rates outweigh breakdown. Rates of

**Citation:** Kerksick, C.M.; Jagim, A.; Hagele, A.; Jäger, R. Plant Proteins and Exercise: What Role Can Plant Proteins Have in Promoting Adaptations to Exercise? *Nutrients* **2021**, *13*, 1962. https://doi.org/ 10.3390/nu13061962

Academic Editor: David C. Nieman

Received: 24 April 2021 Accepted: 5 June 2021 Published: 7 June 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 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 (https:// creativecommons.org/licenses/by/ 4.0/).

MPS and muscle protein breakdown are highly sensitive to physical activity and dietary intake, namely protein and essential amino acid intake [2], with evidence available indicating that rates of MPS are more sensitive to changes in exercise status and dietary intake [3]. As a result, observed changes in MPS rates are viewed to be primarily responsible for the changes in muscle mass in response to exercise and nutrition experienced over time [4].

#### *1.2. The Importance of Added Protein to Optimize Exercise Training Adaptations*

Supplementing the diet with added protein beyond the recommended dietary allowance (RDA) has long been a well-supported tactic for exercising athletes to optimize exercise training adaptations. In this respect, multiple review articles and position stands have advocated for a greater intake of dietary protein to support increased physical training volumes, heighten exercise training adaptations, and promote health and recovery [5–9]. Previously, Cermak and colleagues [10] completed a meta-analysis of studies that employed some form of protein supplementation while completing resistance training. Results from this analysis included data from over 680 subjects and concluded that protein supplementation led to a significantly greater increase in fat-free mass (mean difference: 0.69 kg, 95% CI: 0.47–0.91 kg, *p* < 0.001) and maximal lower-body strength (mean difference: 13.5 kg, 95% CI: 6.4–20.7 kg, *p* < 0.005) when compared to a placebo. These results were extended by Morton and investigators [7] who used a meta-analysis and meta-regression approach to establish the efficacy of protein supplementation while also identifying the minimum amount of daily protein needed to maximize efficacy. In this study, 49 studies were included that represented 1863 participants and the authors reported that protein supplementation was responsible for significant increases in strength (1 RM), fat-free mass, and muscle crosssectional area. Moreover, results from this study highlighted that a daily protein intake beyond 1.62 g/kg/day offered no further impact in facilitating improvements in fat-free mass. It is important to note, this is well above (~2×) the RDA for protein, indicating that active individuals benefit from consuming greater amounts of protein. Whether or not higher amounts facilitate improvements in other outcomes such as strength, recovery, mitigation of fat-free mass loss seen while dieting was not identified in their analysis. Notably, this amount of protein is consistent with the protein recommendation set forth by Jäger and colleagues [5] in the position stand published by the International Society of Sports Nutrition as well as the position stand endorsed by the American College of Sports Medicine, Dietitians of Canada and Academy of Nutrition and Dietetics (the former American Dietetic Association) [11]. Dietary proteins are well known to serve as the primary supplier of amino acids that can be used as building blocks to make larger proteins, such as those produced during MPS. Previous studies have highlighted the importance of the essential amino acids [12,13] at stimulating rates of MPS. In addition, extensive research continues to explore the role of leucine in its ability to stimulate the initiation of protein translation [14,15]. All things considered, exercising individuals require greater amounts of dietary protein to support their training needs, which creates a need for these individuals to purposefully include various sources of protein that deliver optimal amounts of the essential amino acids.

#### *1.3. The Case for Plant Proteins*

Many sources of protein are available for consumption in the human diet. For years, heavy emphasis was placed on consuming complete protein sources, or any protein source that provides all of the essential amino acids in both the needed amount and in adequate proportion to support cellular needs across the body as well as production of nonessential amino acids [8]. Consequently, great focus has been placed on consuming animal protein sources, namely because of their high amino acid contents and favorable protein quality ratings [16]. At the same time, plant proteins were deemed inferior for these outcomes and not until recently has interest in plant proteins begun to accelerate. Several reasons are commonly associated with consuming greater amounts of plant proteins. Most commonly, plant-based diets are routinely linked with reductions in the risk of developing cancers, type

2 diabetes, and cardiovascular diseases [17]. In addition, many plant protein advocates highlight a greater level of economic sustainability than what is observed with diets that are predominantly animal protein. Finally, approximately 60% of dietary proteins consumed worldwide come from plant sources with an estimated 4 billion people across the globe consuming a primarily plant-based diet [18]. While such health considerations are unquestionably important, the aim of this review will center upon the implications of plant protein consumption and plant-based diets on outcomes linked to exercise performance, associated exercise training adaptations, and recovery.

#### *1.4. Quality Considerations for Both Animal and Plant-Based Protein*

Many factors contribute to the anabolic potential of a protein source, which often include the amounts of total amino acids, essential amino acids, and branched-chain amino acids, respectively in addition to the protein's digestibility, digestion rate, and kinetics observed during absorption. In this respect, dietary protein quality is commonly assessed based upon the essential amino acid composition provided by the protein source as it relates to human needs, against the ability of the protein to be digested, absorbed, and assimilated by various tissues in the body [19]. Several approaches have been used to assess protein quality including biological value, net protein utilization, and protein digestibility corrected amino acid scores (PDCAAS) [20], while digestible indispensable amino acid score (DIAAS) have been more recently proposed. As seen in an excellent review by Berrazaga et al. [16], biological values for common plant sources range from 56–74 while ranges of 77–104 are reported for various animal sources on theoretical 100-point scales. A similar dichotomy is observed for net protein utilization values, whereby plant sources range from 53–67 while animal sources range from 73–94 on a 100-point scale. One of the most commonly used quality comparators is that of Protein Digestibility Corrected Amino Acid Scores (PDCAAS) [21]. When using this approach, a score of 100 suggests that after considering its fecal digestibility, a given protein source can fully deliver all of the essential amino acids required by the body. In this respect, animal protein sources such as casein, whey, milk, and eggs all have scores of 100 while red meat has a score of 92. In contrast, all other common sources of plant proteins have PDCAAS values below 100 (commonly reported range of 45–75 per Barrazaga et al. [16]), with soy protein being the only exception, which has a score of 100. Similarly, if the DIAAS approach is used to assess protein quality, a similar trend is observed in that animal sources are commonly above 100 while nearly all plant sources are below 100. In this respect, Gorissen et al. [22] compared the amino acid contents of various sources of plant-based isolates against common sources of animal proteins and human skeletal muscle samples. Again, it was illustrated that many plant protein sources have inadequate amounts of certain amino acids (e.g., lysine, methionine) while also consistently having lower amounts of the essential and branchedchain amino acids, particularly when compared to animal protein sources as well as the amino acid content found in human skeletal muscle. To further reiterate this point, van Vliet and colleagues [23] have indicated previously that essential amino acid composition of a protein source was predictive of skeletal muscle's anabolic potential and that all essential amino acids should be present in optimal amounts. For these reasons, higher quality sources of protein (at least when viewed in the context of amino acid profiles) should serve as more effective protein sources in terms of anabolic potential and its innate ability to facilitate skeletal muscle accretion and promote other desired adaptations. Finally, leucine content of a protein source continues to get interest for its role in initiating the translation of muscle proteins [14,15]. Towards this end, a general acceptance has suggested the leucine content of a protein source functions as a vital and reliable predictor of MPS rates. When leucine contents are compared across different protein sources, whey protein is the highest (~12–14%) [22], which aligns with whey protein's superior ability to stimulate MPS rates when compared to isocaloric and isonitrogenous amounts of other protein sources [24]. Moreover, animal protein sources generally have higher amounts of leucine (8–9% for

non-dairy animal sources) and >10% for dairy protein sources while plant sources routinely have a leucine content of 6–8% [22,23].

Beyond amino acid content, digestibility and absorption kinetics can also influence the value of a protein. In terms of digestibility, it is well documented that the digestibility of many sources of plants is much lower (45–80%) than what is observed with various animal proteins (>90%) [25]. While somewhat beyond the scope of this review, the observed differences in digestibility are largely thought to be due to structural differences that exist within the actual protein molecule found in many plant and animal proteins. For example, many sources of plants have compounds (i.e., anti-nutritional factors such as phytic acid, protease inhibitors, tannins, etc.) that compromise their digestibility. Another key factor related to the impact of consuming different sources of protein is the absorption of amino acids in plasma followed by the utilization rates exhibited by various proteins. In this respect, several studies have illustrated divergent utilization rates when comparing animal to plant sources of protein. For example, the classic work of Boirie [26,27] and Dangin [28,29] clearly demonstrated different absorption and utilization rates for two milk proteins, whey and casein. Moreover, the observed differences in rates of muscle protein metabolism have been shown to be inextricably linked to differences in utilization rates whereby whey absorbs faster and robustly stimulates rates of MPS while casein absorbs at a slower rate and consequently functions more to attenuate protein breakdown. When considering differences observed for various plant proteins, previous work has shown that soy ingestion is absorbed at a slower rate than what is observed from whey [24,30], which helps to explain the lower rates of myofibrillar protein synthesis observed by Yang and colleagues [4] after graded doses (0–40 g) of soy isolate at rest and after exercise in elderly men. While rates of myofibrillar protein synthesis were observed to increase with an increase in the dose of soy protein, the observed rates were less than what had been previously observed with equivalent doses of whey [24]. Additional research involving wheat proteins demonstrated them to have higher deamination rates when compared to milk proteins (25% vs. 16%, respectively) [31–33]. These differences are important as they are thought to be directly related to the lower observed net protein utilization rates between wheat (66%) when compared to milk (80%) proteins. Furthermore, other studies have illustrated a greater degradation of amino acids from soy protein when compared to degradation rates observed for casein and whey [24,30,34,35]. Towards this end, measured nitrogen losses (either via deamination or intestinal loss) and splanchnic nitrogen retention are higher when plant proteins are consumed when compared to ingestion of animal proteins. In effect, these outcomes illustrate that the availability of amino acids to peripheral tissues and locations from plant proteins is lower than that of animal protein [36,37] and these differences are thought to be key drivers to the post-prandial protein synthetic response observed in various tissues. Importantly, the reader should understand that these reasons effectively function as the basis for why different sources of protein exhibit varying degrees of anabolic potential, in regards to stimulating muscle protein accretion and promotion of exercise training adaptations over time.


**4. Determination of the thermal diffusivity of the samples (without black layer)**

In this section, we will study the thermal diffusivity of samples (MOC without and with *PVAc*) using the Photothermal Deflection technique PTD. Thermal diffusivity is measured by the coincidence between the experimental curves of photothermal signal to the corresponding theoretical ones.


**ATHEROGENESIS**

**INTECHOPEN.COM**

Edited by **Sampath Parthasarathy**

http://dx.doi.org/10.5772/1447 Edited by Sampath Parthasarathy

#### **Contributors**

L. R. Ritter, Akif Ibragimov, Jay Walton, Catherine McNeal, Agius, Dorien M Schrijvers, Guido De Meyer, Wim Martinet, Alexandra Krettek, Helen Marie Fink, Anders Sellborn, Katsuri Ranganna, Adrian Manea, William Boisvert, Xinbing Han, Rita Rezzani, Luigi Fabrizio Rodella, Sánchez-Quesada, Sandra Villegas, Zlata Flegar-Mestric, Mirjana Marijana Kardum Paro, Sonja Perkov, Marija Grdic-Rajkovic, Vinko Vidjak, Daniela Gradinaru, Claudia Borsa, Cristina Ionescu, Christian Albert Gleissner, Christian Erbel, Jasmina Dragan Dimitrova-Shumkovska, Jehuda Arieh (Leo) Veenman, Moshe Gavish, Jacek Jawien, Robert Lawrence Taylor Jr., Janet Anderson, Samuel Smith, Rocio Abia, Peter C. Lucas, Jerome Sullivan, Petr Nachtigal, Jana Rathouska, Lenka Vecerova, Zbynek Strasky, Shobha Ghosh, Katja Lakota, Snezna Sodin-Semrl, Claudia Monaco, Jennifer Cole, Damjana Rozman, Gregor Lorbek, Galkina, Matthew James Butcher, Dov Lichtenberg, Tomomi Kouguchi, Youzuo Zhang, Yoshihisa Takahata, Fumiki Morimatsu, Mikako Sato

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**3. Results**

#### *3.1. Characteristics of Selected Studies*

Firstly, a total of 402 articles were retrieved through databases searching (PubMed (*n* = 349), Scielo (*n* = 6), ProQuest (*n* = 29) and OVID (*n* = 18)), from which 68 papers were discarded by duplicity. After title, abstract, and full-text screening, a total of 314 articles were excluded following the selection criteria. Twenty studies were included in this review (Figure 1).

All trials and patients' characteristics are summarized in Table 1. On the whole, all individuals were treated using conventional cancer therapy methods, such as radiotherapy (*n* = 11, 55%), chemotherapy (*n* = 6, 30%), or both (*n* = 3, 15%). In some studies, sex was not specified (*n* = 2, 10%), and only women were included in studies dealing with specific carcinomas of the female reproductive tract (*n* = 4, 20%), such as endometrial, vaginal, uterine, and cervical cancers. The age range of the patients ranged from 18 to 75 years old (with a mean age of 57.41 years), enrolling a total of 2508 participants. All studies included were published between 1988 and 2020, and 15 studies (75%) were not registered in any clinical trial registry. Most of these studies were conducted in Asia (*n* = 9), Europe (*n* = 8), but also in America (*n* = 2) and Oceania (*n* = 1). As regards the use of probiotics, 10 of the selected studies (50%) used a single probiotic strain, while the remaining 10 (50%) used two or more probiotics combined. The presentation and forms of administration varied from study to study, with the most commonly used forms being capsules, gelatine, and yoghurt. The time of administration as well as the dose administered to the patients were also varied, which ranged from 1 to 24 weeks and 106 to 1011 CFU/day, respectively. Finally, 17 studies (85%) revealed predominantly positive results when using probiotics to reduce the incidence of treatment-related side effects in oncology patients, while three studies (15%) reported no impact in their findings.

**Table 1.** *Cont.*

**Table 1.** *Cont.*

**Figure 1.** Flowchart depicting the article selection process.

The data synthesis revealed four categories related to the use of probiotic supplements for treatment-related side effects in clinical oncology. For that matter, these categories would study the effects of probiotic treatments for different treatment-related side effects in oncology such as gastrointestinal side effects, immune-related side effects, inflammatory side effects, and performance status-related side effects. These categories are described below.

#### *3.2. Gastrointestinal Side Effects*

Probiotics have been shown to be effective in the treatment of some common oncology treatment-related gastrointestinal adverse reactions, as demonstrated in 11 of 20 trials (55%) [25–31,33–36]. The main adverse effects identified and treated were mainly diarrhea, with other drawbacks being abdominal pain, nausea and vomiting, constipation, bloating, abdominal distension, and lactose intolerance caused by chemotherapy. The most commonly probiotic strains used along these studies were *Lactobacillus acidophilus*; *L. rhamnosus* GG ATCC 53103; and *L. casei* var. *rhamnosus*. Likewise, other probiotic strains used in combination were (*L. acidophilus* LA-5 along with *Bifidobacterium animalis* subsp. *lactis* BB-12), (*L. acidophilus* BMC12130, *L. casei* BCMC12313, *L. lactis* BCMC12451, *B. bifidum* BCMC02290, *B. longum* BCMC02120 and *B. infantis* BCMC02129), (*B. infantis*, *L. acidophilus*, *Enterococcus faecalis* and *Bacillus cereus*), (*L. acidophilus* LAC-361 and *B. longum* BB-536), (*L. acidophilus* plus *B. bifidum*), and (*L. casei*, *L. plantarum*, *L. acidophilus*, and *L. delbruekii* subsp. *thermophilus*; *B. longum*, *B. breve*, and *B. infantis*; *Streptococcus salivarius* subsp. *thermophilus*). The duration of treatment ranged from 1 to 24 weeks.

Conversely, 2 trials (10%) showed inconclusive results for their benefits to control stool constituency and flatulence, although their findings were promising to prevent radiotherapy-induced diarrhoea [32,37]. The probiotic strains used in these studies included: *L. acidophilus* NCDO1748, and (*S. thermophilus*, *L. delbrueckii* subsp. *bulgaricus*, and *L. casei* DN-114 001). The treatment for these studies ranged from 1 to 6 weeks and were observed only in women.

#### *3.3. Immune-Related Side Effects*

Despite having only one study [38], positive results of the probiotics on immunerelated side effects have also been observed. A combination of probiotic strains (*Bifidobacterium*, *Lactobacillus* and *S. thermophilus*) was used for 1 to 2 weeks, in which patients improved their immune and nutritional status as well as rehabilitation, showing improved cellular immune parameters and tolerance to abdominal pain, bloating and diarrhoea. These authors used glutamine along with fish oil in their treatment as it has been shown to enhance epithelial cell growth and repair of intestinal mucous membrane, prevent bacterial translocation and reduce barrier injury, among others, which may actually be able to work synergistically with probiotics to protect the intestinal mucosa barrier and reduce permeability. In this manner, radiation-induced injuries may be alleviated by these probiotic strains, while other eco-nutrients feed the intestinal membrane.

#### *3.4. Inflammatory-Related Side Effects*

Impact on inflammatory-related side effects such as oral mucositis was also reported in three trials (15%). Among these studies, two trials [20,40] showed positive and effective results in reducing the severity of oral mucositis when using different probiotic strains: (*B. longum*, *L. lactis,* and *E. faecium*) and *L. brevis* CD2. The treatment period for these studies was from 1 to 7 weeks. However, De Sanctis et al. (2019) [39] did not notice any significant changes in the severity of oral mucositis with *L. brevis* CD2, although their treatment lasted only 1 week due to premature closure of patient accrual. While it is true that radiochemotherapy-induced mucositis is a complex process and further prospective studies are needed to explore oral microbiota modulation in reducing its incidence, the findings of Jiang and collaborators (2019) [20] and Sharma and collaborators (2012) [40] strongly underpinned the probiotics used as a plausible strategy to manage mucositis-associated pain and reduce its incidence.

#### *3.5. Performance Status-Related Side Effects*

Concerning to the impact of probiotics in patients' general well-being and activities of daily life, three trials (15%) evaluated their effects over a 4-week treatment period [41–43]. Two of these studies used a single probiotic strain, *S. salivarius* M18 and *L. casei* LC9018 respectively, and the remaining study used a combination of *L. acidophilus*, *L. rhamnosus*, *B. longum,* and *Saccharomycesboulardii*. In line with the findings of Shao and collaborators (2014) [38], not only did Doppalapudi and collaborators (2020) [42] and Vesty and collaborators (2020) [41] observe clinical improvements driven by probiotic-induced changes in the oral microbiota but also a potential mechanism to improve these performance status-related side effects throughout other modulating host immune response and microbial interactions. Having said that, only one study [43] assessed the effect of probiotics in malignant pleural effusion, which is one of the most common complications in lung cancer. This complication can have a severe impact on patient performance and shortened survival, but interestingly, *L. casei* LC9018 has been shown to be a useful adjuvant in the treatment of this type of cancer and to prevent this complication.


**1. Introduction**

One of the challenges for infrastructure owners of large national networks of highway and railroad bridges is maintaining the performance of the bridges over their designed service life without the risk of failure. But bridges do fail for a number of reasons. A bridge failure is most often the result of some failure in the life cycle of a bridge from concept through design, construction and maintenance during its service life. The direct failure of bridges is the result a number of factors to include structural over loading, material failures, poor designs, faulty construction, inadequate oversite and control of bridge construction, inadequate inspections, delayed maintenance, earthquakes, and flooding.

In research work on the frequency and causes of bridge failures K. Wardhana, et al., studied over 500 failures of bridge structures in the United States between 1989 and 2000 with the age of the failed bridges ranging from 1 year (during construction) to 157 years, having an average age of 52 years. They state "The most frequent causes of bridge failures were attributed to floods and collisions. Flood and scour, with the major flood disaster in 1993, contributed to the frequency peak of bridge failures

(almost 53% of all failures). Bridge overload and lateral impact forces from trucks, barges/ships, and trains constitute 20% of the total bridge failures. Other frequent principal causes are design, detailing, construction, material, and maintenance. Comparison made among three periods of similar studies (1977–1981, 1982–1988, and 1989–2000) revealed almost similar trends, with most failures occurring during the bridge's service life. Also, human-induced external events occurred frequently in all three periods, but were most dominant in the first and third periods [1]. While Wardhana studied three periods of bridge failures in the United States the statistics are representative of bridge failures around the world in all years.

This chapter examines bridge failures and structural deterioration for the purpose of understanding the elements that cause deterioration in bridge structures and the factors that lead to the structural failures. There are a number of elements that cause the material in bridge structural components to degrade and deteriorate over time, in both steel and RC (reinforced concrete) bridges. The two dominate elements causing deterioration are deicing chemicals (chlorides) and vehicle over loading. These two elements affect all types of bridges and their components. In extreme cases extensive deterioration potentially leads to structural failure. Of the bridge failures presented in this chapter each failure has one or more of these factors which contributed to the bridge collapsing.