**6. Conclusions**

48 Landscape Planning

moment provided larger accuracy and the most appropriate window size was 7X7 and 9X9. Multiscale texture measures should be incorporated with original spectral wavebands to improve classification accuracy (Shaban and Dikshit 2001, Podest and Saatchi 2002, Butusov 2003). Recently, the geostatistic-based texture measures were found to provide better classification accuracy than using the GLCM-based textures (Berberoglu et al. 2000). For a specific study, it is often difficult to identify a suitable texture because texture varies with the characteristics of the landscape under investigation and the image data used. Identification of suitable textures involves determination of texture measure, image band, the size of moving window, and other parameters (Chen et al. 2004). The difficulty in identifying suitable textures and the computation cost for calculating textures limit the extensive use of textures in image classification, especially in a large area (Lu and Weng

To test the texture data on classification accuracy, five different GLCM was derived such as, variance, contrast, dissimilarity, homogeneity, entropy. These measurements incorporated with Landsat spectral wavebands in Eastern Mediterranean region. Overall accuracy was unchanged, however accuracies of settlement and agricultural land classes were increased 4- 5%. However, accuracy of bareground and sand dunes decreased using DT classifier.

Vegetation metrics are another ancillary data for more accurate LUC mapping. Deriving the metrics is dependent on the spectral resolution of an optical image. A vegetation index derived from combination of image wavebands. The most used vegetation indices are normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), normalized difference water index (NDWI), green vegetation index (GVI) and perpendicular vegetation index (PVI) at the literature. Vegetation indices indicate health condition (NDVI) and water content (NDWI) of the vegetation canopy. There are many textbooks and papers about calculation of vegetation indices. These indices provide extra information for LUC

For instead, NDVI calculated using red and near infrared band (NIR) combination as shown

NDVI data was included to Landsat TM data to show the effect of a vegetation index on LUC mapping. Overall accuracy was unchanged significantly, but sand dunes, baregrounds,

Besides, there are some indices specifically designed for sensors. For example; Envisat MERIS data has own chlorophyll index called MERIS terrestrial chlorophyll index (MTCI). Additionally, vegetation metrics such as fraction of photosynthetically active radiation (fPAR), leaf area index (LAI) and fraction of green vegetation covering a unit area of horizontal soil (fCover) can be obtained using specific equations from MERIS data. Berberoglu et al. (2009) used this vegetation metrics to improve RT soft classification accuracy using MERIS data. When only MERIS wavebands used to determine the tree cover

NIR – RED / NIR + RED (5)

2007).

**5.3 Vegetation indices**

classification to discriminate subtle classes.

in following equation (Rouse et al 1974);

deciduous classes were classified more accurately.

This chapter has demonstrated various issues in LUC classification including, ability of optical remotely sensed data, different classifiers, training data size and ancillary data in the example of Eastern Mediterranean region. Parametric, non-parametric hard and soft LUC mapping techniques in local scale were assessed. Main findings of this chapter are:

Selection of a classification scheme and the optical data are vital for a reliable result in LUC mapping. Remotely sensed data must be defined according to the mapping scale and study purpose. LUC classification scheme and level should be defined based on optical data ability such as spatial and spectral resolution. Image pre-processing such as, geometric registration, atmospheric correction, geometric correction and radiometric calibration are essential parts in change detection studies.

Training data size, quality and mapping details are also important to select suitable classifier for LUC mapping. MLC, LDA, and DT techniques are useful for hard classification outputs. On the other hand, to derive a continuous map like cover percentage of each LUC or probability of each LUC needs soft classifiers such as RT and LMM. Training data size and quality affect the classification accuracy and classifier selection. Although model based classifiers has potential when strong training data set was used. In this case, data dependent classifiers can be chosen for better accurate LUC map. Linear techniques are suitable if mixture degree is small in a pixel. LMM is ideal if there are enough training data and pure pixel for each LUC. However, if training pixel size and pure pixels are weak, non-linear techniques like RT or ANN are suggested.

Hard classifiers were performed inaccurately with coarse spatial resolution images (e.g. MODIS, MERIS, NOAA, SPOTveg) because of mixed pixel problem. Fuzzy classifiers are reduced this problem and provided better accuracy than hard classification. Hard pixel based mapping techniques were successful using medium spatial resolution data (e.g. Landsat TM/ETM, Aster and Alos AVNIR) in regional and local scale, however, for the specific purposes like detailed crop pattern mapping or urban pattern mapping, object based classification approach was recommended for more reliable LUC mapping. Object based classification is appropriate when using very high spatial resolution data (e.g. rapid eye, Ikonos, Aerial photos, Geoeye). In segmentation stage of object based classification, pixels were merged to create each segment or object according to spectral, structural and textural similarities. This method is tolerated the pixel misclassification if there is a pixel noise in an area (Figure 15).

In this chapter ancillary data integration were also discussed using several data from satellite remote sensing sensors. Three types of ancillary data were integrated to the DT hard classifier. DEM resulted the largest improvement in overall classification accuracy among others. Surface texture and vegetation indices were improved the accuracy of specific land cover types. When all data used together, overall classification accuracy were reduced. Additionally, more ancillary data is not important to enhance classification accuracy. Success of the ancillary data varies based on classification target, study area characteristics and remotely sensed data.

Land Use/Cover Classification Techniques

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**1. Introduction**

**3**

Matthias Pietsch

*Germany*

**GIS in Landscape Planning**

Landscape planning supports sustainable development by creating planning prerequisites that will enable future generations to live in an ecological intact environment (Bfn, 2002). It breeds to a full-coverage strategy with the aim of maintaining landscape and nature as well as facilitating municipal and industrial development (von Haaren, 2004; BfN 2002). Contrary to the design approach (McHarg, 1969) it has been developed to an institutionalized planning system based on analytical processes (Schwarz- v. Raumer & Stokman, 2011). Objectives will be derived from scientifically based analysis and normative democratically legitimized goals (Riedel & Lange, 2001; Jessel & Tobias, 2002; von Haaren, 2004 a.o.).

Existing Geographic Information Systems (GIS) offer the needed capabilities concerning the whole planning cycle (Harms et al., 1993; Blaschke, 1997; von Haaren, 2004; Pietsch & Buhmann, 1999; Lang & Blaschke, 2007; a.o.). Data capturing for inventory purpose, scientific-based analysis, defining objectives, scenarios and alternative futures and planning measures can be carried out by using GIS (Schwarz-v. Raumer, 2011; Ervin, 2010; Steinitz, 2010; Flaxman, 2010). For the implementation and sometimes necessary updates environmental information systems can be developed for specific purpose (Zölitz-Möller, 1999; Lang & Blaschke, 2007). Nowadays required models (e.g. process, evaluation, decision) can be defined and interchanged for different scopes (Schaller & Mattos, 2010). The technical evolution of hard- and software enable planners and designers to improve participation processes and decision-making using visualization and WebGIS-technologies (Warren-Kretzschmar & Tiedtke, 2005; Paar, 2006; Lange, 1994; Wissen, 2009; Bishop et al., 2010; Buhmann & Pietsch, 2008a and b; Pietsch & Spitzer, 2011; Richter, 2009; Lipp, 2007). Transforming the existing planning process to a process-oriented one with new ways of interaction technical enhancements are necessary as well as a new planning and design style (Ervin, 2011). Therefore teaching methods must be changed to a more process- and workflow oriented thinking (Steinitz, 2010; Ervin, 2011) using the advantages of the different software tools like GIS, CAD, visualization and Building Information Models (BIM) (Flaxman, 2010). GeoDesign as a "new" term had been discussed the last years. While some planners contribute that they are doing this for years (Schwarz-v. Raumer & Stokman, 2011) requirements had been defined for a more collaborative and process-oriented planning

(Francica, 2012; Ervin, 2011; Steinitz; 2010; Flaxman, 2010; Dangermond, 2009, 2010).

In this paper the different terms will be explained and the special German definition of landscape planning will be described. Based on this definition the use of GIS in the different

*Anhalt University of Applied Science*


Antimicrobial Resistance in Staphylococci Special Emphasis on Methicillin Resistance among Companion Livestock and Its Impact on Human Health in Rural India

*Sweta Jangra, Sandhya Khunger and Debasish Chattopadhya*


**4. The leadership expectation in social work organization**

Generally, diverse and complex social work organizations require social workers that are both willing and able to practice dynamic and successful leadership in organizations. The social work practice as a people cantered profession or pursuit has a relatively modern origin which acknowledges that many organizations involve themselves in different and complex activities such as developmental work, humanitarian relief work; advocacy, legal, human rights, and health among others [13]. The process in social work leadership is analysed in the context of these formal and informal organizations which require social workers to use different leadership styles such as the authoritarian, democratic, laissez-faire and the charismatic leadership skills. In essence, what it means is that social work practitioners have to be willing to contribute to the effective leadership in various organizations using the required models or theories. Previous studies by Harworth et al. demonstrate that the implementation of social work leadership requests understandable and relevant as well as definition of models of practice [5]. So far, social workers have been employed in assorted organizations with varied organizational structures and objectives in which failure to exercise the acceptable theories and models result in the breakdown of the organization to dispense adequate social services. Haworth et al. [5] underscore that the significance of using effective leadership styles has been referenced by Torgersen well as a number of international organizations, scholars and reports repeating that case reviews which agree to the significance of effective leadership in organizations. Social work organizations' mandates have been to provide quality services for the people to restore or enhance their capacity for social functioning while creating societal conditions favourable to their goals using suitable leadership styles. The authoritarian leader for example will decide by him/her and the laissez-faire leader to

#### *Perspective Chapter: Fostering Effective Leadership in Social Work Organisations DOI: http://dx.doi.org/10.5772/intechopen.106281*

let the subordinates make decisions, hence take no leadership role other than assuming the position [3]. Khan [3] further proffers that the democratic leader assesses his/ her subordinates and then makes the decisions. Moyo et al. [13] provides that some of the organizations are developmental in nature whilst a good number focus on different areas of specialty such as health, humanitarian work, clinical social work, schools social workers. Given the diversity and complexity of the context in which leadership is used, a number of studies have proved that inadequate use of leadership models and theories in organizations is a nagging problem which requires careful interrogation from social work perspectives.


**Abstract**

In this study, in order to clarify the relationship between health and physical strength, athletic capability, school life, and lifestyles, we collated the results of physical fitness tests with questionnaire surveys of 102 elementary school fifth-grade males and females. In the school life aspect, children who scored high on the physical fitness test, like Physical Education, enjoy learning, play well with friends while in the lifestyle aspect, they clean up their surroundings and rooms, study even without being told and there is a tendency for them to make their own plan. In addition, it was confirmed that children with high physical fitness test scores were in good condition, such as being healthy every day and hardly getting sick. On the other hand, in relation to the physical fitness test scores and the body shape by BMI, there were many high scorers among the children whose body figure was standard. These results suggest that physical fitness and athletic capability are related to children's school life and health, and that high physical fitness has a positive impact on children's school life and health.

**Keywords:** physique, physical fitness, school life, health, elementary school student

## **1. Introduction**

Physical strength is the source of human activity and an important element of "the power to live" such as abundant humanity, the ability to learn by oneself and the ability to think for themselves [1, 2].

In recent years, changes in the social environment and lifestyle have led to great changes in the environment surrounding children. There is a decrease in walking opportunities for children due to the development of transportation, the neglect of adults to disregard children's play and sports, a decrease in helping children's housework, and the development of information equipment, etc.

As a result, children's human relationships become weak, and there are fewer opportunities to have more friends and to move their bodies [1, 2].

Along with this, the current physical strength of children around 1985, has been declining over the long term in all ages, such as their running power, throwing power and gripping power, etc. [3].

Also, recently, the bipolarization of physical strength between children who are actively engaged in exercise and sports and those who lack exercise has been pointed out [4]. Children's lack of exercise is not just a decline in physical strength, but also has an adverse effect on the improvement of mental and physical health [5].

In a questionnaire survey on children's experience activities conducted by the Ministry of Education, Culture, Sports, Science, and Technology in 1998, a total of 33% of elementary school second graders answered that they "often" or "sometimes" feel tired on a daily basis and it is reported that it reaches 60% in the second grade of junior high school [6].

Also, there are quite a few children who have stress in their school life resulting in disturbed lifestyles such as spending a considerable amount of time studying at school, studying at cram school or at home, having insufficient sleeping time, being unable to sleep at night, being easy to get tired, having no desire for breakfast, wanting to speak out loud somehow and being frustrated with nothing.

Furthermore, disruption of children's lifestyles can have a negative impact on reduced energy and motivation and a lack of concentration [7]. It may be said that the lack of exercise in children greatly affects the development of their strength and growth.

Therefore, it is thought to be extremely important to examine athletic ability and physical strength which is the basis for living.

Up to now, many research endeavors have been conducted from various angles to solve this problem, and various improvement measures have been tried on how children's physical fitness and exercise capacity are related to school life and lifestyle and it is deemed necessary to conduct further verification.

Therefore, the purpose of this study is to examine in detail the physical strength and athletic capability of elementary school students such as running ability, jumping ability, throwing ability and endurance and to clarify the relationship between health and physical strength, athletic capability, school life and lifestyle.

#### **2. Method**

#### **2.1 Participants**

A self-administered questionnaire survey was conducted for fifth graders of a public elementary school in Tokyo.  The results were recorded on a 10-point scale according to the score table for each item set by the Sports Bureau of the Ministry of Education, Culture, Sports, Science, and Technology (MEXT). In addition, the total result was calculated by adding the scores of each item. Based on the comprehensive

#### *A Study on the Relationship between Health and Physical Fitness and School Life… DOI: http://dx.doi.org/10.5772/intechopen.110164*

evaluation criteria table of the new physical fitness test, comprehensive evaluation was obtained in five stages from A to E. A and B were set to "high," C was set to "standard," while D and E were set to "low."

#### *2.2.2 Figure*

At the school, the BMI was calculated from the height and weight of body measurements. Based on that, we used Kato's [8] age-specific BMI percentile curves up to 18 years old by classifying obesity, standard, and lean body types. The body type is determined by setting the 10 to 90 percentile value of each age in the BMI percentile curve as "standard" (15.0 to 18.5). In addition, "lean" is defined as less than this range, and "obesity" is defined as exceeding this range.

#### *2.2.3 Analysis method*

The physical fitness test and body type and each question item were examined by gender. Furthermore, we examined the relationship between physical strength tests and school life and health; and between physical strength tests and body types.

All analyzes were performed with SPSS19.0 for Windows, with a significance level of 5%. The χ2 test was used to test the ratio bias, and the unmatched t-test was used to test the difference between the average values of BMI and physical strength test results.

### **3. Result**

#### **3.1 Gender differences in physical characteristics and physical strength/athletic capability test results**

In terms of physical characteristics, the sitting height was 75.6 3.6 for boys and 77.3 3.7 for girls, with girls significantly higher than boys (p < 0.05) (**Table 1**). In the physical strength test, for softball throwing: the score was 21.72 6.97 m for boys and 11.87 3.32 m for girls (p < 0.001). In repeated sideways jumps, the score was 36.36 7.05 for boys and 33.51 33.51 for girls (p < 0.05). In the 20 m shuttle run, the boys had 43.60 20.84 times while the girls were 30.98 12.77 times (p < 0.001).

On the other hand, in the sit and reach test the score was 31.00 7.94 cm for boys and 38.00 8.73 cm for girls, with girls showing significantly higher results compared to boys (p < 0.000). The overall fitness test score was slightly higher for girls than for boys, but there was no significant difference. In addition, boys have low scores and little higher standard scores, while girls had slightly higher scores.

#### **3.2 Gender differences in physical fitness and athletic capability test evaluation**

The percentage of "high" scores for both boys and girls exceeded 50%, but the percentage of girls was slightly higher than that of boys. However, the percentage of "low" scores was slightly higher for boys than for girls, but no significant difference was observed (**Table 2**).

*Bridging Science and Practical Appliance in Resistance Training*

**6**

*Gender differences*

 *in physical*

*characteristics*

 *and physical fitness test results.*

**Table 1.**

**Table 2.**

*Gender differences in physical fitness test evaluation.*

## **3.3 Gender differences in school life aspect, lifestyle aspect, health aspect, degree of obesity, and sleep time**

At school, many children answered that they were having fun. In particular, a high percentage of male respondents answered the items "I like physical education classes" and "play well with friends when playing" compared to girls. Compared with boys, girls responded more frequently to items such as "school is fun", "study is fun", and "has good friends" (**Table 3**). In terms of lifestyle habits, boys and girls accounted for more than 70% of the children who answered "yes" in the "staying at home alone" item. For most items, more children answered "No" than "Yes".

In the items "I have decided what I want to be in the future" and "I have something I want to do when I grow up" (p < 0.01), the percentage of "Yes" for girls was higher than that for boys.

*A Study on the Relationship between Health and Physical Fitness and School Life… DOI: http://dx.doi.org/10.5772/intechopen.110164*

#### **Table 3.**

*Gender differences in school life aspect, lifestyle aspect, health aspect, degree of obesity, and sleep time.*

In terms of health, most of the boys and girls were "very well" and "healthy" in the item "I spend time well every day". On the other hand, there were about 50% of children were feeling heavy or tired and sometimes get sick.

In terms of obesity, the percentage of leanness was slightly higher for girls than for boys, and the percentage of obesity was 27.7% for boys and 18.2% for girls, which was higher for boys. In the sleeping hours on weekdays, both boys and girls had a high rate of 8 hours to less than 9 hours. On holidays, boys had the highest rate of 8 hours to less than 9 hours, and girls had a slightly higher rate of 9 hours to less than 10 hours.

### **3.4 Relationship between physical strength and athletic capability test evaluation and school life**

Many children with high physical strength and athletic capability test evaluations answered that they enjoyed studying (p < 0.05), liked physical education at school (p

#### **Table 4.**

*Relationship between physical fitness test evaluation and school life.*

< 0.01) and played well with friends during play time (p < 0.001) (**Table 4**). Although it was not a significant difference, 80% of the children who answered "high" in the physical strength/athletic capability test said that school was fun. In addition, in the "I have good friends" item, there were almost no children answered it regardless of the high or low score of physical strength and physical capability test.


**4. Examples**

#### **4.1 An artifical example**

The synthesis/analysis paradigm can be useful for understanding a problem. This means synthesizing (simulating) a dataset, so that you know the model and parameter values, and then applying your analysis method to see how well it performs. In the present context, it is interesting to simulate a dataset of measurements of rectangles, with variables length (L) and width (W) and also some functions of those such as perimeter = 2 L + 2 W and difference = L–W. In one synthesis, we took L to be Normal with a mean of 10 and a variance of 1, W was Normal with a mean of 10 and a variance of 1, PERI = 2 L + 2 W plus N(0,1) error, and DIFF = L–W plus N(0,1) error. The eigensystem was computed, and as expected, it is noted that there are two large eigenvalues, with subsequent ones dropping off a lot in value and being close to zero. The eigenvalues of the correlation matrix were 1.91, 1.83, 0.21, and 0.05.

#### **4.2 A real example**

Next, we consider the principal component analysis of a sample from the Los Angeles (LA) Heart Study. This was a long-term study, 1947–1972. It was a study among Civil Servants of Los Angeles county. LA civil servants, 2252, randomly selected, ages 21–70, received a battery of examinations for "routine" cardiovascular disease (CVD) risk factors.

The variables include age, systolic blood pressure (SYS), diastolic blood pressure (DIAS), weight (WT), height (HT), and coronary incident, a binary variable indicating whether the individual had a coronary incident during the course of the study. Blood pressure is reported as a bivariate variable, (SYS, DIAS). SYS is the pressure when the heart pumps, and DIAS is the pressure when the heart relaxes.

In the textbook [9], data for a sample of *n* ¼ 100 men were studied. (Data on the same variables for another sample of 100 men are also given in [9]. Results can be compared and contrasted between the two samples.) Although, of course, the emphasis in the Heart Study was on explaining and predicting the coronary incident variable, here, we focus on the first five variables, their representation in terms of a smaller

number of PCs, and the interpretations of the PCs. we did the PC analysis; it was not in the LA Heart Study or the textbook.

We used Minitab statistical software for the analysis. Aspects of the analysis are shown as follows.

The lower-triangular portion of the correlation matrix for the five variables is shown in **Table 1**. The highest correlation is 0.835, between SYS and DIAS. The next highest correlation, 0.426, is between HT and WT.


**1. Introduction**

Ascorbic acid (AA; Vitamin C) is a remarkable water-soluble antioxidant concentrated predominately in citrus fruits, strawberries and vegetables (e.g., spinach and broccoli) and found in

many supplement formulations (LPI Micronutrient Information Center). The adequate functioning of the human organism in general, and of the brain in particular, is highly dependent on AA, but humans are completely dependent on dietary sources due to the evolutionary process leaving us a gene incapable of producing the enzyme gulonolactone oxidase needed to yield AA from glucose [1]. Thus, humans require a constant stream of AA from the diet and rely on robust "carrier" transport and "barrier" integrity mechanisms to meet the brain's demand. AA is the most powerful water-soluble antioxidant of the organism, and key to preventing oxidative lipid damage in biological systems [2]. It forms the first line of antioxidant defence under many types of oxidizing conditions. It can rapidly intercept free radicals in the aqueous phase before they attack lipids [3]. As an antioxidant, AA also provides protection against oxidative stress-induced cellular damage by neutralization of lipid hydroperoxyl (LHP) radicals and by protecting proteins from alkylation by electrophilic lipid peroxidation activity [4].

Several pathological processes can involve the production of free radicals, antioxidant depletion, oxidative and nitrosative stress including vascular disease and cognitive impairments seen in aging older adults (Dementia of Alzheimer's type). Dementia, with its most prevalent form, Alzheimer's disease (AD), is characterized by an insidious progressive nature that usually begins with memory deficits followed by disturbances in other cognitive domains that eventually reach a level that impacts functions of daily life. Age–related dementias also disrupt the family unit, which must often scramble to meet day-to-day care requirements to compensate for loss of independence. The economic conditions associated with this phenomenon of disability and dependence is an enormous and pressing threat to public health. The major risk factor for dementia and AD remains to be advanced age; therefore the projected impact of AD can be estimated by frequency in strata of the population. For example, in the US about 14% of the older adult population age 65 and older carry a diagnosis of dementia and age 85 and older is about 47%. In either case, these cohort effects will stress the value of anti-AD strategies while general practitioners, geriatricians, neurologists and health care professionals all around the globe are projected to face over 115 million people with dementia from all causes by 2050 [5].

The pathological hallmarks of AD include a loss in synaptic function and accumulation of extracellular amyloid-β plaques and intraneuronal neurofibrillary tangles. Oxidative stress displays chronological primacy in the onset of AD, and in its prephase, mild cognitive impairment (MCI) [6–9]. Thus, preventive therapy that is safe and effective for reducing oxidative damage seen early in neurodegenerative disorders and applied before the onset of dementia is a public health priority.

This overview will focus on the biological rationale for the avoidance of AA deficiency in the promotion of healthy brain aging. We include discussion on the biochemistry and transport of AA, much of which has been clarified through basic research using genetically modified mouse models, and also the clinical studies designed to better understand the influence over cognitive health in our aging populations.


**5. Depression**

Depression is one of the five most incapacitating illnesses, and by 2030, it is expected to be one of the major issues in industrialized countries. It's a common reaction to a rapid deterioration in living conditions, which involves isolation and uncertainty. When people are subjected to unpredictable circumstances, they feel helpless and unmotivated, which can lead to sadness [40]. During past epidemic outbreaks (SARS and Ebola), rates of depression in the general population have been reported with a prevalence approaching even 73.10 percent [41]. Past epidemics were more quickly contained, and infection rates were lower despite greater fatality rates, which might explain why depressive symptoms were less common [42]. Furthermore, during the SARS pandemic in Canada, Hawryluck et al. discovered that the length and unpredictability of the lockdown contributed to greater levels of depression [43]. As a result, the present global lockdown measures might explain the increased incidence of depressive symptoms reported during the COVID-19 epidemic. According to a research that looked at 69 million health data from over 62,000 persons diagnosed with COVID-19, 6% of COVID-19 patients suffered mental health problems including sadness and anxiety within three months of diagnosis, compared to 3.4 percent of non-COVID-19 patients [44]. The addition of a psychological burden to an already devastating physical burden affects the prognosis in a number of ways, with long-term consequences. Furthermore, those who are depressed are less likely to seek treatment for physical or mental problems; as a result, depression, like anxiety, can act as a barrier to reasonable medical and mental health interventions during a pandemic.

#### **5.1 Depression among patient population**

Earlier outbreaks such as SARS and MERS have displayed the presence of depressed mood among those with acute infection [45]. The COVID-19 pandemic is no different. The reported prevalence of depression at 6% in COVID-19 patients is higher to general population [44]. Females are more likely to experience depression than males [46]. According to one study, nearly one-fourth patients had intended to commit suicide or harm oneself [27]. Individuals who were married or underwent a divorce exhibited greater levels of depression than single individuals [27]. This could in part be explained by worries for one's family and grief of separation respectively. Having a family member with confirmed COVID-19, and having two current physical symptoms are independent risk factors for depressive symptoms [28]. Suicide has become a more pressing concern as the pandemic evolved [47]. Those with psychiatric disorders experience worsening symptoms and others are predisposed to develop depression, are all associated with increased suicide risk. Media and other news platforms also affect mental health and psychological behavior [35]. Despite the fact that receiving regular updates on COVID-related health information appears to reduce sadness, it is also claimed that social media exposure is linked to depression and mixed anxiety and depression [48–50]. Unemployment, low social status, a lack of social support, and financial losses are among socioeconomic variables that might contribute to greater incidence of depression [48, 49, 51, 52]. The added impact of quarantine has led to high occurrence of depression and even self-reported suicidal thoughts [29, 53].

#### **5.2 Depression among HCW population**

The COVID-19 pandemic has significant negative impacts on healthcare workers' psychological health, fostering anxiety, depression, and sleep disturbance. Studies conducted during the SARS have reported a prevalence of depression among the front-line HCWs to be 38.5% [54]. The factors in play causing depression are pretty similar to those contributing to anxiety. Increased workload, burnout, inadequate PPE, the risk of contracting the disease, and the challenge of making difficult moral decisions about care priorities during the pandemic have exposed healthcare workers to severe psychological pressures leading to depression. The high infectivity and mortality rates also contribute to depression among healthcare workers around the world. Furthermore, factors such as a high-risk workplace, a lack of clinical experience, young age, and a history of psychological disorders can contribute to depression among healthcare workers. Working on the front lines was found to be an independent risk factor for poor mental health outcomes across all aspects studied, including the prevalence of depression among HCWs. Between doctors and nurses, the latter have been studied to harbor a greater level of depression [55–57]. This may be partially confounded by the fact that nurses are primarily female but could also be attributed to the fact they may face a greater risk of exposure to COVID-19 patients as they spend more time onwards, provide direct care to patients and are responsible for the collection of sputum for virus detection [55]. Emotional exhaustion, depersonalization, being a nurse, 12- or 24-hour shifts or on-call hours, those who live with people who are at risk, and being very concerned about a possible infection of a family member they do not live with are all positively and significantly related to having depression symptoms [58]. Depression is also significantly higher among HCWs who did not know the latest COVID-19-related research/information. Lack of information may precipitate mental health concerns, and prior studies have suggested that updates and knowledge about COVID-19 may have psychosocial impacts, possibly as they represent an active way of coping and dealing with pandemic-related issues [59, 60]. Research also indicates that healthcare workers who take vacation days experience lower levels of depression [26]. Unfortunately, the COVID-19 pandemic crisis witnessed doctors along with other healthcare workers question their choice of profession, in part due to rising infection rates, unavailability of adequate personal protective equipment and other unexpected pandemic related experiences [60].

#### **6. Sequelae**

Long COVID is a term used to describe a condition in COVID-19 patients who have symptoms over an extended period [61]. These patients report prolonged, multisystem involvement and significant disability, which can last for more than six months in 93.2% of patients after the acute phase of illness [62]. Musculoskeletal, cardiovascular, gastrointestinal, pulmonary, and neuropsychiatric symptoms are prevalent in >85% of participants. Fatigue, breathing problems and cognitive dysfunction are among the most debilitating symptoms [62]. Such prolonged physical sequelae are associated with and often are a harbinger of psychological

#### *Mental Impact of COVID-19 – Fear, Stress, Anxiety, Depression and Sequels DOI: http://dx.doi.org/10.5772/intechopen.102754*

sequelae. Multiple studies conducted during previous pandemics also support the development of psychiatric sequelae in survivors. SARS-CoV-1 survivors exhibited posttraumatic stress disorder (PTSD) with an incidence of up to 55%, depression was observed in 39%, pain disorder in 36.4%, panic disorder in 32.5%, and obsessive–compulsive disorder in 15.6% of SARS-CoV-1 survivors [63]. Long term psychiatric complications such as depressed mood, anxiety and insomnia were also reported in 10-20% of patients following SARS and MERS infections [45]. These complications could result from central nervous system involvement of the virus or perhaps a consequence of fear associated with the infection and isolation itself.

Among the psychological sequelae, the most prominent and popular condition is posttraumatic stress disorder [64]. Surviving a critical illness is known to induce PTS symptoms [65]. Data suggests that as many as 43% of COVID-19 patients suffered posttraumatic stress symptoms [66], 'not though true prevalence of PTSD' prevalence. The severity of COVID-19 poses a significant risk factor for PTSD, supported by evidence of higher incidence among ICU patients juxtaposed to non-ICU patients [67, 68]. Even measures of quarantine and isolation, which help to contain the infection from the spread, can have psychological consequences leading to PTSD [2]. Healthcare workers, too, experience significant PTS symptoms. Emotional fatigue, depersonalization, working in a hospital, being highly concerned that someone they live with may become infected, and believing that becoming infected with COVID-19 is very likely are all positively and substantially associated with posttraumatic stress symptoms in the HCW population [56].

Aside from PTSD, COVID-19 infection survivors are more likely to experience depression and anxiety, similar to the acute phase of the disease [64]. The incidence of these disorders is linked to the severity of the disease and the length of hospitalization [69–71]. The baseline systemic inflammation index (SII) is strongly correlated with anxiety and depression [45, 59]. At follow-up, the prevalence of baseline comorbidities, such as mental disorders and female sex, is also linked to depression and anxiety [71, 72]. Although anxiety and depression symptoms generally go away within 1-3 months after infection, their presence increases the risk of developing PTSD later on [61].


**6.1 Population**

Pakistan has sustained significant population growth in the past century. In early 1891, the population of the country was 18.4 million, which dropped to 16.6 million in 1901. Then the population was estimated at 19.4 million in 1911, which increased to 21.2 million in 1921 and 23.6 million in 1931, while in 1941 it again increased up to 28.3 million. According to the first well-organized census in 1951, Pakistani population was 33.7 million having 6 million urban and 27.8 million rural population. In 1961, this figure was increased to 42.9 with 9.6 million urban and 33.3 million rural inhabitants. Onward, in 1971, it i up to 65.4 million with 7.6 million urban and 48.8 million rural population. The increase in population continues in 1981, where population of the country reached to 84.3 million having 23.9 million urban and 60.5 million rural population. As per 1998 census, the population had quadrupled to 132 million inhabitants and almost double (207.8 million in 2017) (**Figure 5**, **Table 1**).

**105**

**Figure 5.**

*Pakistan population 1890–2017.*

**Year Population Year Population**

*Arid region of Pakistan demographic characteristics.*

**density**

 18.4 1890 NA 1890 NA 1890 NA NA 16.6 1901 20.8 1901 1.6 1901 9.8 90.2 19.4 1911 24.4 1911 0.8 1911 8.7 91.3 21.2 1921 26.5 1921 1.1 1921 9.8 90.2 23.6 1931 29.6 1931 1.9 1931 11.8 88.2 28.3 1941 35.9 1941 1.8 1941 14.2 85.8 33.8 1951 42.4 1951 2.4 1951 17.7 82.3 42.9 1961 53.9 1961 2.3 1961 22.5 78.0 65.4 1972 82.1 1972 3.3 1972 28.3 71.7 84.3 1981 105.8 1981 3.3 1981 28.3 71.7 132.4 1998 166.3 1998 2.7 1998 32.5 67.5 207.8 2017 256.0 2017 2.4 2017 36.4 63.6

**Year Annual**

**growth**

**Year Urban Rural**

programs (**Table 1**).

*Source: GoP [17].*

**Table 1.**

populations by the year 2010.

*Environment, Agriculture, and Land Use Pattern DOI: http://dx.doi.org/10.5772/intechopen.83850*

In early 1994, the population of Pakistan was estimated to be 126 million, making it the ninth most populous country in the world. Its land area, however, ranks 32nd among nations. Thus, Pakistan has about 2% of the world's population living on less than 0.7% of the world's land. The population growth rate is among the world's highest, officially estimated at 3.1% per year but privately thought to be closer to 3.3% per year by many planners involved in population

The total population of the arid region in 1998 was 60.2 million comprising 31.5 million male and 28.7 million female. It is estimated that the projected population of the arid zone will be 78.7 million including 41.2 million male and 37.5 million female

*Environment, Agriculture, and Land Use Pattern DOI: http://dx.doi.org/10.5772/intechopen.83850*

#### **Figure 5.**

*Climate Change and Agriculture*

**6. Human environment**

*Natural vegetation of the arid region, Pakistan, Khan [3].*

**Figure 4.**

**6.1 Population**

Agriculture is practiced in diverse economic, social, and environmental conditions. Hence, great diversity has developed in agriculture practices and products. In some areas of the arid region, farming is carried out to meet the local demand; in others it is primarily cultivated for commercial purposes. "The different types of farming in the arid region of Pakistan are intensive subsistence farming, dairy farming, plantation farming, truck farming, and extensive herding" [16]. Among the factors that have influenced the type of farming include distribution of popula-

Pakistan has sustained significant population growth in the past century. In early 1891, the population of the country was 18.4 million, which dropped to 16.6 million in 1901. Then the population was estimated at 19.4 million in 1911, which increased to 21.2 million in 1921 and 23.6 million in 1931, while in 1941 it again increased up to 28.3 million. According to the first well-organized census in 1951, Pakistani population was 33.7 million having 6 million urban and 27.8 million rural population. In 1961, this figure was increased to 42.9 with 9.6 million urban and 33.3 million rural inhabitants. Onward, in 1971, it i up to 65.4 million with 7.6 million urban and 48.8 million rural population. The increase in population continues in 1981, where population of the country reached to 84.3 million having 23.9 million urban and 60.5 million rural population. As per 1998 census, the population had quadrupled to 132 million inhabitants and almost double (207.8 million in

tion and human settlements, as well as socioeconomic conditions.

**104**

2017) (**Figure 5**, **Table 1**).

*Pakistan population 1890–2017.*

#### **Table 1.**

*Arid region of Pakistan demographic characteristics.*

In early 1994, the population of Pakistan was estimated to be 126 million, making it the ninth most populous country in the world. Its land area, however, ranks 32nd among nations. Thus, Pakistan has about 2% of the world's population living on less than 0.7% of the world's land. The population growth rate is among the world's highest, officially estimated at 3.1% per year but privately thought to be closer to 3.3% per year by many planners involved in population programs (**Table 1**).

The total population of the arid region in 1998 was 60.2 million comprising 31.5 million male and 28.7 million female. It is estimated that the projected population of the arid zone will be 78.7 million including 41.2 million male and 37.5 million female populations by the year 2010.

#### *Climate Change and Agriculture*

The population density of the arid region is 137 persons/km2 area in 1998, while it is expected that the projected population will be 179 persons/km2 by the year 2010.


**4. Discussion**

#### *4.1. Remotely Sensed Estimation of LAI and TSA*

Our field investigation suggests that the LAI is highly correlated with TSA in early post-fire landscapes. Given that surveying TSA in the field is time-consuming and labor intensive, utilizing a DHP system can be a more efficient way to monitor post-fire tree sapling recovery. We found that all Landsat-derived spectral indices exhibited statistically significant correlations with both LAI and TSA, but the variance explained was usually less than 50%. EVI2 and NBR were the most predictive spectral indices for LAI and TSA respectively, but we found that NBR performed consistently well for simulating LAI and TSA in two Landsat analysis cases. This suggests that NBR may be the best choice for monitoring post-fire vegetation dynamics in our study area. A similar finding was also reported by Chen, et al. [75], who found that NBR is the most sensitive indicator of post-fire vegetation recovery in the Black Hills National Forest of the USA, suggesting that the value of NBR extends beyond our boreal forest study system. Previous studies have also shown that NBR is better than NDVI for quantifying burn severity [26,76], further supported by Kennedy, Yang and Cohen [54] and White, Wulder, Hermosilla, Coops and Hobart [53], who concluded that the NBR is a very useful indicator for detecting fire disturbance and monitoring post-fire vegetation trajectory.

Although Landsat-derived spectral indices exhibited significant correlations with the LAI and TSA, we found that the WorldView-2 (VHR) imagery performed even better. PPCT had a stronger explanatory power for LAI than for TSA because our DHP measurement of LAI consisted of canopy foliage of both surviving trees and tree saplings. The in-field upward canopy photograph results closely match PPCT derived from WorldView-2 imagery, further confirming the value of this technique. However, PPCT cannot wholly reflect TSA as there were surviving trees that exhibited disproportionately large canopy projection areas in the imagery when compared to tree

saplings. The low spectral separability of surviving trees and tree saplings limited the applicability of WorldView-2 imagery for monitoring TSA.

Image textures are commonly used as auxiliary inputs to provide spatial features that spectral information cannot characterize. Many previous studies have reported that including image textures as predictors can improve the estimation of forest structural parameters such as tree diameter [77], stand density [61] and crown diameter [78]. However, image textures did not exhibit strong explanatory power to either LAI or TSA in our study. One reason is that crown shadow is an important factor in representing spatial variation as reflected in the imagery [61]. In contrast to the coarse surface of mature forests, the post-fire landscape where dominated by tree sapling that did not represent very strong shadow effects (contrasts) in the imagery (Figure 3c). In addition, the value of textural features has no explicit relationship to spectral features and the same value of textural attributes may represent very different land surface properties. For example, post-fire landscapes usually exhibit high homogeneity, however similar homogeneity values may be corresponding to different land cover compositions.

#### *4.2. The Response of Forest Recovery to Spatial Controls*

We found that including surrogates of intra-specific competition (i.e., understory coverage) and density of surviving trees (i.e., shadow coverage) greatly improved model fit. According to partial dependence curves, we found a negative effect of understory (grass and shrub species) coverage on tree sapling regeneration (Figure 7a). Understory species usually act as pioneers in post-disturbance landscapes and rapidly occupy available spaces and resources. In addition, some understory species are resistant to wildfire and recover rapidly. By comparison, tree saplings may require longer timespans to establish; they will compete with understory species for available resources and may be limited by seed availability.

Tree sapling regeneration (estimated by LAI) is very sensitive to shadow coverage, suggesting that even a few surviving trees can strongly promote post-fire recovery (Figure 7b). The nearest distance to unburned area, which reflects the importance of seed inputs from out of the burned area, significantly impacted LAI recovery with areas near the edge of boundaries having a higher opportunity to receive seeds from unburned areas. Our results showed that edge effects extend for a distance of approximately 700 m for this fire (Figure 7h), which can partially explain why the core area exhibited poor tree recovery. Trees that survive fire are thus critical for supporting reestablishment of tree community post-burn [79,80]. Nonetheless, if the number of surviving trees was over a certain threshold, canopy closure was negatively affected because our study area was dominated by mature larch forests, which have wide tree spacing and relatively open canopies. Surviving trees may prevent the establishment of saplings, likely through resource competition.

Burn severity is often described as a legacy effect as high severity fires have a long-term impact on forest ecosystems through removing surface organic layers and creating canopy openings through killing nearly all large trees. Burn severity is well-reported to have adverse effects on forest restoration in boreal forests [81] and subalpine forest ecosystems [82]. Our results suggest that high severity burns decrease tree sapling regeneration post-fire, consistent with previous findings in similar ecosystems. In our study wildfire exerted limited effects on tree recovery when dNBR was lower than 0.730 (Figure 7c), which is related to low severity (dNBR < 0.418) and moderate severity (dNBR < 0.942) levels according to published thresholds from Fang and Yang [26]. Surviving trees contributed a large part of canopy foliage at low and moderate burn severity areas and combining with tree sapling foliage to result in high post-fire LAI under low and moderate burn severities. We found that LAI recovery decreases dramatically with dNBR increasing, implying that moderate to high severity fires have profound effects on diminishing forest resilience. Severe fire behavior may cause crown fires in our study area [45], which will destroy areal seed banks in serotinous cones of coniferous trees [8]. At the same time, severe surface fires will consume fine fuels such as litters and soil organic materials, create exposure of soil and base rock, and destroy surface seed beds. Given the key roles of coniferous tree species on maintaining the unique functions of boreal forest communities [9,14,23], recovery of coniferous tree species has raised increasing concerns [10,21]. Coniferous tree species have relatively shorter seed dispersal distance (e.g., larch < 150 m, and Scotch pine ~ 500 m) than broadleaf trees (e.g., birch and aspen > 1000 m) due to heavier seed mass [83,84]. Many studies found that Great Xing'an boreal forests will experience severe fire activities that were characterized as more frequent and large burns with higher severity along with climatic changes and fuel accumulation [18,85,86]. It is not difficult to speculate that coniferous forests may experience more recovery limitations, which has been validated in boreal forests of Central Siberian [84] and North America [8,11]. Besides, very high severity fires may have also killed broadleaf tree species and understory species whose roots would otherwise provide a source of asexual reproduction even if above-ground portions are killed, creating opportunities for primary succession.

**Figure 7.** Partial dependence plots of a-h show influences of eight selected spatial controls (variable names see x-axis) on forest recovery in terms of leaf area index (LAI). Variable abbreviations were described in Table 3.

Topography is usually considered an environmental filter because it theoretically determines the drainage, thermal, solar radiation, surface roughness and other site factors which are usually exert considerable influence on tree species distribution and growth [87,88]. Total solar radiation during the growing season is typically the most important topographical factor and we found that LAI increased significantly when solar radiation exceeded 90,000 WH/m2 (Figure 7d). At our study site, southern slopes and flat areas favored broad leaf tree species, such as white birch and aspen, that can regenerate through roots sprouting [21,47]. The TPI, TWI and elevation shared similar relative importance. Higher TPI indicates higher slope position (Figure 7e), and thereby sites that tend to have good drainage and open space to accept seed rain, especially low weight seeds of white birch and aspen dispersed via wind.

Our results suggest that TWI is inversely related to LAI (Figure 7f) while elevation has a positive influence LAI (Figure 7g). TWI is known to have a strong direct relationship with soil moisture in boreal forests ecosystems [89] and our findings are consistent with Cai, Yang, Liu, Hu and Weisberg [21], who reported similar relationships between soil moisture, elevation and broad leaf tree sapling abundance through field investigation. However, while Cai, Yang, Liu, Hu and Weisberg [21] found moist soil valley bottoms favorable for larch saplings, our study finds them to be more limited in terms of tree recovery than more elevated locations. One explanation is that at our site permafrost is usually present in those moist valleys [90], and the seasonal thaw may prevent seed germination. In addition, moss (*Sphagnum cuspidatulum*) and grass (*Carex appendiculata*) is often thick in moist valley bottoms and can prevent larch seed from encountering the mineral soil necessary for their establishment.


**5. Asphalt concrete strength**

#### **5.1. Bitumen**

Bitumen of grade BND 100/130, produced by Pavlodar Petrochemical Plant (PPCP), was used for the preparation of fine-grained dense asphalt concrete in laboratory conditions in this work. Bitumen complies with the requirements of Kazakhstan standard ST RK 1373-2013 [29]. Standard indicators for bitumen are represented in **Table 1**. Content of bitumen in the asphalt concrete was 4.8% by the mass of the dry filler.

#### **5.2. Asphalt concrete**

Hot dense fine-grained asphalt concrete of Type B was adopted for test, which satisfies the requirements of the Kazakhstan standard ST RK 1225-2013 [30], and it was prepared with the use of aggregate of fractions 5–10 mm (20%), 10–15 mm (13%), 15–20 mm (10%) from Novo-Alekseevsk rock pit (Almaty oblast), sand fraction 0–5 mm (50%) from the plant "Asphaltconcrete-1" (Almaty city) and mineral powder (7%) from Kordai rock pit (Zhambyl oblast). The main standard indicators for asphalt concrete are represented in **Table 2**. The grading curve of mineral part of hot mix asphalt concrete is shown in **Figure 8**.

#### **5.3. Test methods**

In this study, asphalt sample tests have been performed according to the following methods:

**Table 1.** Main standard indicators for bitumen.

Stationary state is achieved at *R* = \_\_\_ 3*B*

enon for the asphalt concrete pavement.

the asphalt concrete strength.

**5. Asphalt concrete strength**

concrete was 4.8% by the mass of the dry filler.

*Rcr* > \_\_\_ 3*B*

68 Modified Asphalt

of new cells is bigger.

**5.1. Bitumen**

**5.2. Asphalt concrete**

**5.3. Test methods**

sions of 5 × 5 × 16 cm.

*A* , that is, *ds* = 0. And at *R* > \_\_\_ 3*B*

*A* (*Rcr*: critical size of the cell) the cell should be separated, otherwise, it will die. The volumes of the mother cell and two daughter cells are similar, and the total area of the surfaces

The abovementioned examples for self-organization in thermodynamics systems—Benar's cells and cell separation can be used further for the explanation of the fatigue failure phenom-

Fatigue failure of the asphalt concrete pavement, of course, has been directly connected with

Bitumen of grade BND 100/130, produced by Pavlodar Petrochemical Plant (PPCP), was used for the preparation of fine-grained dense asphalt concrete in laboratory conditions in this work. Bitumen complies with the requirements of Kazakhstan standard ST RK 1373-2013 [29]. Standard indicators for bitumen are represented in **Table 1**. Content of bitumen in the asphalt

Hot dense fine-grained asphalt concrete of Type B was adopted for test, which satisfies the requirements of the Kazakhstan standard ST RK 1225-2013 [30], and it was prepared with the use of aggregate of fractions 5–10 mm (20%), 10–15 mm (13%), 15–20 mm (10%) from Novo-Alekseevsk rock pit (Almaty oblast), sand fraction 0–5 mm (50%) from the plant "Asphaltconcrete-1" (Almaty city) and mineral powder (7%) from Kordai rock pit (Zhambyl oblast). The main standard indicators for asphalt concrete are represented in **Table 2**. The

In this study, asphalt sample tests have been performed according to the following methods:

**1.** Determination of asphalt concrete strength at direct tension at various temperatures has been performed in thermal chamber of TRAVIS, manufactured by Infratest GmbH (Germany). Sample tests have been performed at deformation with constant rate 1 mm/ min in accordance with European standard pr EN 12697-46 [31]. Samples had dimen-

**2.** Cyclic (fatigue) asphalt concrete strength at various temperatures has been determined by sample testing with dimensions 5 × 5 × 38 cm in thermal chamber of four-point bending device under European standard EN 12697-24 [32]. Loading frequency was f = 10 Hz. The

stress, equal to σ = 1400 kPa, was kept as constant prior to the sample failure.

grading curve of mineral part of hot mix asphalt concrete is shown in **Figure 8**.

*A* , that is, *ds* > 0, therefore, at

**Table 2.** Main standard indicators for asphalt concrete.

**Figure 8.** Asphalt mixture grading curve.

#### **5.4. Sample preparation**

Asphalt concrete samples of cylindrical shape, designed for direct compression, were prepared under Kazakhstan standard ST RK 1218-2003 [33] by compaction of the asphalt concrete mix in special mold. The samples of rectangular shape and in the shape of beam of various dimensions were prepared in the following way. First, the asphalt concrete samples in the shape of square slab with dimensions 5 × 30.5 × 30.5 cm were prepared by roller compactor (model CRT-RC2S, company Cooper, UK) in accordance with European standard EN 12697-33 [34]. Then, the samples with the shape of rectangular prism of various dimensions were obtained from square slabs.

which at any other equal conditions, the more is the ratio of elasticity modulus of the asphalt concrete layers to the elasticity modulus of the under layers of the pavement base and subgrade soil [10, 40]. It is considered that the abovementioned ratio of elasticity moduli in pavement structure is the biggest one in the spring season, when the upper part of subgrade has been defrosted and loosened, and the asphalt concrete pavement has a big stiffness due to the

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**Figure 9.** Strength of fine-grained asphalt concrete (BND 100/130, PPCP) at various types of stressed condition.

As it is seen from **Figure 10**, namely within the temperature interval 0 and +10°С, the difference in the asphalt concrete tensile strength and bending strength (compression) is the biggest one!

**Figure 10** shows the graphs of cyclic strength for fine-grained dense asphalt concretes at bending and tension at the temperature of 20°С. The upper graph has been constructed at testing of asphalt concrete samples under scheme of bending on the four-point bending device in Kazakhstan Highway Research Institute, and the bottom one has been constructed according to the experimental data, obtained in the University of North Carolina State (USA) at direct tension [44]. It is clearly seen that the cyclic strength of the asphalt concrete at bending is considerably higher than at tension. Similar regularity can be seen on the curves of the long-term

Thus, single loading, cyclic and long-term strength of the asphalt concretes at tension is con-

To answer the question: "Does the asphalt concrete strength depend on the tested sample dimensions at compression?", the test of samples for the fine-grained dense asphalt concrete of type B (BND 100/130) has been performed for direct compression. The thickness of all the samples was similar and equal to 5 cm, and the length and the width of the samples had the values

relatively low air temperature, which is within 0 and +10°С [2, 41–43].

strength of the asphalt concrete, as shown in **Figure 11** [45, 46].

siderably less, than at bending and compression.

#### **5.5. Single loading, cyclic and long-term strength of asphalt concrete**

**Figure 9** represents the graphs, showing the dependence of the asphalt concrete strength at various types of loading—tension, compression and bending. As it is seen, in the considered temperature interval (0–50°С), the asphalt concrete has the least tensile strength, and the largest—at compression. Bending strength occupies the intermediate location between tension and compression. Meanwhile, compression and bending strength of the asphalt concrete with the temperature increase decreases nearly with the similar rate within the whole temperature interval considered, and at tension, the rate of decrease is higher than two times compared with compression and bending strength. It is also seen that the difference between temperature curves of bending strength and compression strength is kept as constant in the whole temperature interval considered and it is equal to, at average, 1.0 MPa. The maximum difference between temperature curves of tensile strength and bending (compression) strength occurs at low temperatures (0–10°С), which is equal to 2.5 MPa (3.5 MPa) and decreases with the temperature increase; these differences at temperature 50°С are equal to 0.8 and 1.6 MPa, respectively.

It is generally accepted that the fatigue cracks occur due to the repeated impact of the tensile stress in the bottom surface of the asphalt concrete pavement [2, 35–39], the more the value of Fatigue Destruction of Asphalt Concrete Pavement: Self-Organization and Mechanical… http://dx.doi.org/10.5772/intechopen.75536 71

**Figure 9.** Strength of fine-grained asphalt concrete (BND 100/130, PPCP) at various types of stressed condition.

**5.4. Sample preparation**

70 Modified Asphalt

**Figure 8.** Asphalt mixture grading curve.

were obtained from square slabs.

Asphalt concrete samples of cylindrical shape, designed for direct compression, were prepared under Kazakhstan standard ST RK 1218-2003 [33] by compaction of the asphalt concrete mix in special mold. The samples of rectangular shape and in the shape of beam of various dimensions were prepared in the following way. First, the asphalt concrete samples in the shape of square slab with dimensions 5 × 30.5 × 30.5 cm were prepared by roller compactor (model CRT-RC2S, company Cooper, UK) in accordance with European standard EN 12697-33 [34]. Then, the samples with the shape of rectangular prism of various dimensions

**Figure 9** represents the graphs, showing the dependence of the asphalt concrete strength at various types of loading—tension, compression and bending. As it is seen, in the considered temperature interval (0–50°С), the asphalt concrete has the least tensile strength, and the largest—at compression. Bending strength occupies the intermediate location between tension and compression. Meanwhile, compression and bending strength of the asphalt concrete with the temperature increase decreases nearly with the similar rate within the whole temperature interval considered, and at tension, the rate of decrease is higher than two times compared with compression and bending strength. It is also seen that the difference between temperature curves of bending strength and compression strength is kept as constant in the whole temperature interval considered and it is equal to, at average, 1.0 MPa. The maximum difference between temperature curves of tensile strength and bending (compression) strength occurs at low temperatures (0–10°С), which is equal to 2.5 MPa (3.5 MPa) and decreases with the temperature increase; these differences at temperature 50°С are equal to 0.8 and 1.6 MPa, respectively. It is generally accepted that the fatigue cracks occur due to the repeated impact of the tensile stress in the bottom surface of the asphalt concrete pavement [2, 35–39], the more the value of

**5.5. Single loading, cyclic and long-term strength of asphalt concrete**

which at any other equal conditions, the more is the ratio of elasticity modulus of the asphalt concrete layers to the elasticity modulus of the under layers of the pavement base and subgrade soil [10, 40]. It is considered that the abovementioned ratio of elasticity moduli in pavement structure is the biggest one in the spring season, when the upper part of subgrade has been defrosted and loosened, and the asphalt concrete pavement has a big stiffness due to the relatively low air temperature, which is within 0 and +10°С [2, 41–43].

As it is seen from **Figure 10**, namely within the temperature interval 0 and +10°С, the difference in the asphalt concrete tensile strength and bending strength (compression) is the biggest one!

**Figure 10** shows the graphs of cyclic strength for fine-grained dense asphalt concretes at bending and tension at the temperature of 20°С. The upper graph has been constructed at testing of asphalt concrete samples under scheme of bending on the four-point bending device in Kazakhstan Highway Research Institute, and the bottom one has been constructed according to the experimental data, obtained in the University of North Carolina State (USA) at direct tension [44]. It is clearly seen that the cyclic strength of the asphalt concrete at bending is considerably higher than at tension. Similar regularity can be seen on the curves of the long-term strength of the asphalt concrete, as shown in **Figure 11** [45, 46].

Thus, single loading, cyclic and long-term strength of the asphalt concretes at tension is considerably less, than at bending and compression.

To answer the question: "Does the asphalt concrete strength depend on the tested sample dimensions at compression?", the test of samples for the fine-grained dense asphalt concrete of type B (BND 100/130) has been performed for direct compression. The thickness of all the samples was similar and equal to 5 cm, and the length and the width of the samples had the values

**Figure 10.** Cyclic strength of the asphalt concrete at bending and direct tension at the temperature of 20°С.

with the length of sample side equal to 7 cm, and the strength increases almost linearly at the

**Figure 12.** Compression strength of the fine-grained asphalt concrete samples of type B (BND 100/130, PPCP) of various

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These results serve as the reliable explanation for gradual decrease of the horizontal dimen-

The fatigue crack on the asphalt concrete pavement occurs when it almost completely lost its tensile strength (tension at bending). Let us raise the question: can such asphalt concrete have residual compression strength? To clarify the issue, we have carried out a special experiment. The same fine-grained dense asphalt concrete of type B (bitumen grade BND 100/130) has been adopted. First, the asphalt concrete samples with dimensions 5 × 5 × 38 cm have been tested on the four-point bending device for bending fatigue to failure (stiffness reduction to 10% of the initial one) at the temperatures of 10, 20 and 30°С. Then, the samples with dimensions

**Parallel 1 Parallel 2 Parallel 3 Average**

**Table 3.** Test results of the fine-grained dense asphalt concrete of type B (BND 100/130, PPCP, f = 10 Hz, σ = 1400 kPa) at

+10 5187 4965 10,180 6777 +20 568 512 534 538 +30 565 219 222 335

sions of the asphalt concrete pavement cells with progressing of its fatigue failure.

temperature of 0°С with the decrease of sample dimensions.

**5.6. Residual strength of asphalt concrete**

**Т, °С Number of cycles to failure Nf**

fatigue on the four-point bending device.

dimensions at different temperatures.

**Figure 11.** Long-term strength of the fine-grained asphalt concrete of type B (BND 60/90) at transverse bending and direct tension.

equal to 2, 5, 7, 10, 12 and 15 cm. The test has been performed at the temperatures of 0, 10 and 20°С. Three parallel tests have been performed at each temperature and dimensions of samples.

As it is seen from **Figure 12**, the asphalt concrete strength at compression depends considerably on sample dimensions. The biggest strength occurs at the temperatures of 10 and 20°С Fatigue Destruction of Asphalt Concrete Pavement: Self-Organization and Mechanical… http://dx.doi.org/10.5772/intechopen.75536 73

**Figure 12.** Compression strength of the fine-grained asphalt concrete samples of type B (BND 100/130, PPCP) of various dimensions at different temperatures.

with the length of sample side equal to 7 cm, and the strength increases almost linearly at the temperature of 0°С with the decrease of sample dimensions.

These results serve as the reliable explanation for gradual decrease of the horizontal dimensions of the asphalt concrete pavement cells with progressing of its fatigue failure.

#### **5.6. Residual strength of asphalt concrete**

equal to 2, 5, 7, 10, 12 and 15 cm. The test has been performed at the temperatures of 0, 10 and 20°С. Three parallel tests have been performed at each temperature and dimensions of samples. As it is seen from **Figure 12**, the asphalt concrete strength at compression depends considerably on sample dimensions. The biggest strength occurs at the temperatures of 10 and 20°С

**Figure 11.** Long-term strength of the fine-grained asphalt concrete of type B (BND 60/90) at transverse bending and

**Figure 10.** Cyclic strength of the asphalt concrete at bending and direct tension at the temperature of 20°С.

direct tension.

72 Modified Asphalt

The fatigue crack on the asphalt concrete pavement occurs when it almost completely lost its tensile strength (tension at bending). Let us raise the question: can such asphalt concrete have residual compression strength? To clarify the issue, we have carried out a special experiment. The same fine-grained dense asphalt concrete of type B (bitumen grade BND 100/130) has been adopted. First, the asphalt concrete samples with dimensions 5 × 5 × 38 cm have been tested on the four-point bending device for bending fatigue to failure (stiffness reduction to 10% of the initial one) at the temperatures of 10, 20 and 30°С. Then, the samples with dimensions

**Table 3.** Test results of the fine-grained dense asphalt concrete of type B (BND 100/130, PPCP, f = 10 Hz, σ = 1400 kPa) at fatigue on the four-point bending device.

**7. Bifurcation**

into two saves it from "death."

is bigger than at bending.

(**Figure 14**).

follow further.

tion under the scheme of bending (tension).

time ("life cycle") of separate parts of the asphalt concrete pavement.

The principle of consequential change of deformation types at fatigue failure for an asphalt concrete pavement formulated earlier can be explained on the basis of provisions for thermo-

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In short description of the examples for dissipative structure occurrence—Benar's cells and cell separation, it has been mentioned earlier that the action of systems in critical conditions in both cases is a benefit for them: liquid flow along the hexagonal cells allows including additional convective mechanism of heat exchange with environment; separation of the cell

We also consider that the realization of the fatigue failure according to the consequent stages, changing deformation type from "tension" into "bending" and from "bending" into "compression" at the continuous mechanical impact is a benefit to the asphalt concrete pavement, as:

**1.** The asphalt concrete strength at bending is bigger than at tension, and at compression, it

**2.** Residual strength of asphalt concrete at compression is relevant after its failure at deforma-

Such staged failure with consequential change of deformation type prolongs the existence

The formulated principle can be visually demonstrated by the bifurcation scheme proposed

In thermodynamics and synergetics, it is accepted to consider that the system away from the equilibrium condition acquires new properties. The system in the strong nonequilibrium condition becomes more active and all substructural elements of the system work jointly, consistently, fluctuations are synergized and new structures occur at the critical moment [18–26, 48]. In addition, the system has a choice in critical conditions—what scenario of evolution to

In accordance with the proposed bifurcation scheme, the asphalt concrete pavement works as continuous medium under scheme of volumetric stressed-deformed condition since the moment of starting of operation to the moment of losing of the tension resistance (0–1). At the time moment of the complete losing of the tension resistance (point 1), the thermodynamics system (substructural elements of the asphalt concrete pavement) has a choice—which thermodynamics branch (branch А and branch B)—to function further. If the system in point of bifurcation chooses the thermodynamics branch A, the parallel cracks occur on the patch lines in point 1, and the asphalt concrete strips work as a long beam between points 1 and 2, and they are deformed under the scheme of bending. The transverse cracks occur in point 2, long asphalt concrete strips are divided into more short parts, each of the obtained parts for the period of

dynamics of irreversible processes and nonlinear dynamics (synergetics).

**Figure 13.** Residual strength of the asphalt concrete at compression after cyclic bending at various temperatures.

5 × 5 × 5 cm have been prepared from these samples, and they have been tested for direct compression at the same temperatures. Three asphalt concrete samples have been tested at each temperature. The results of the initial tests of the asphalt concrete samples at cyclic bending have been represented in **Table 3**, and their further test at direct compression—in **Figure 13**. As it is seen, the asphalt concrete has a high residual compression strength after the cyclic bending to failure, comparable with the strength of a new asphalt concrete (**Figures 9** and **12**).