Cooperative problem-based learning experience and coaching strategies of engineering course

The problem-based learning (PBL) methodologies are considered adequate for core engineering courses. The integration between cooperative learning and PBL methodologies establishes an encouraging environment for the students. However, for effective implementation of cooperative problem-based learning (CPBL) environment, close supervision of students’ experiences is vital, and deficient areas are to be improved, as PBL is a dynamic process. A study was conducted for the first-year undergraduate engineering class taught under the PBL environment. The objective was to evaluate the course by the preview of students, for highlighting weak domains in the teaching methodology for future improvements. A course experience questionnaire was designed considering PBL implications, with 35 question items, and 31 responses were collected by the end of the semester. Three different analyses were performed on the collected data, i.e., descriptive statistics and Cronbach’s alpha, Student's t-test, and Pearson Chi-square test. The achieved results supported the effective adoption of the PBL system by the students. However, few areas were highlighted requiring special consideration, such as PBL workload, pressure due to extra course content, and assessment opportunities under the PBL system. It was proved that maximum students considered PBL methodologies convenient and effective for learning than the traditional learning approach

Impact of Zero Energy Building: Sustainability Perspective

In an era with major developments in the energy sector, along with many benefits of energy consumption, it is also showing adverse effects on the end-users and the environment due to emission of various harmful gases mainly carbon dioxide (CO2). To deal with these issues, the zero energy building emerges to bring constructive developments through the construction industry. The concept of zero energy building is to develop a structural building which can generate its own required energy and have zero negative effects. The energy will be enough to fulfill all the requirements of the building operations and can save natural quarries. By increasing the numbers of zero energy buildings, major reforms can be brought in the construction industry and thus stabilizing the economy and the climate

Material Classification via Machine Learning Techniques: Construction Projects Progress Monitoring

Nowadays, the construction industry is on a fast track to adopting digital processes under the Industrial Revolution (IR) 4.0. The desire to automate maximum construction processes with less human interference has led the industry and research community to inclined towards artificial intelligence. This chapter has been themed on automated construction monitoring practices by adopting material classification via machine learning (ML) techniques. The study has been conducted by following the structure review approach to gain an understanding of the applications of ML techniques for construction progress assessment. Data were collected from the Web of Science (WoS) and Scopus databases, concluding 14 relevant studies. The literature review depicted the support vector machine (SVM) and artificial neural network (ANN) techniques as more effective than other ML techniques for material classification. The last section of this chapter includes a python-based ANN model for material classification. This ANN model has been tested for construction items (brick, wood, concrete block, and asphalt) for training and prediction. Moreover, the predictive ANN model results have been shared for the readers, along with the resources and open-source web links

Data Processing Using Artificial Neural Networks

The artificial neural network (ANN) is a machine learning (ML) methodology that evolved and developed from the scheme of imitating the human brain. Artificial intelligence (AI) pyramid illustrates the evolution of ML approach to ANN and leading to deep learning (DL). Nowadays, researchers are very much attracted to DL processes due to its ability to overcome the selectivity-invariance problem. In this chapter, ANN has been explained by discussing the network topology and development parameters (number of nodes, number of hidden layers, learning rules and activated function). The basic concept of node and neutron has been explained, with the help of diagrams, leading to the ANN model and its operation. All the topics have been discussed in such a scheme to give the reader the basic concept and clarity in a sequential way from ANN perceptron model to deep learning models and underlying types

Road Accident Data Collection Systems in Developing and Developed Countries: A Review

The road accidents trigger major financial loss and casualties to the individual as well as the state as a whole. The intelligent safety systems are developed to provide all road users with a safe transport system. This approach acknowledges the sensitivity of individuals to extreme injury in road accidents and recognizes the need for the system for improvement. To establish a proper system for road accident prevention, records from prior accidents play a key role in the evaluation and prediction of the accident, damage, and consequences. Therefore, this study was performed to evaluate and comparing existing practices in developing and developed countries for collecting road accident data. Moreover, the manual and digital approaches of data collection are highlighted. Keeping this in mind, this review provides an overview of how developing countries currently collect their data and their data dissemination methods to extract such useful information, which could prove beneficial in deciding the road safety programs for the well-being of end-users

Key Enablers of Resilient and Sustainable Construction Supply Chains: A Systems Thinking Approach

In the globalized world, one significant challenge for organizations is minimizing risk by building resilient supply chains (SCs). This is important to achieve a competitive advantage in an unpredictable and ever-changing environment. However, the key enablers of such resilient and sustainable supply chain management are less explored in construction projects. Therefore, the present research aims to determine the causality among the crucial drivers of resilient and sustainable supply chain management (RSSCM) in construction projects. Based on the literature review, 12 enablers of RSSCM were shortlisted. Using the systems thinking (ST) approach, this article portrays the interrelation between the 12 shortlisted resilience enablers crucial for sustainability in construction projects. The causality and interrelationships among identified enablers in the developed causal loop diagram (CLD) show their dynamic interactions and impacts within the RSSCM system. Based on the results of this study, agility, information sharing, strategic risk planning, corporate social responsibility, and visibility are the key enablers for the RSSCM. The findings of this research will enable the construction managers to compare different SCs while understanding how supply chain characteristics increase or decrease the durability and ultimately affect the exposure to risk in the construction SCs

A Bibliometric Review of Research Trends on Kenaf Fiber Reinforced Concrete

To prevent the excessive depletion of natural resources, sustainable development requires using alternate sustainable materials. Researchers in the field of advanced construction materials are increasingly paying attention to kenaf fibers as a "green" material because of their possible application in composites to advance sustainable development. However, there has been no attempt of scientometric analysis to investigate the comprehensive understanding of the present state of applications of kenaf fibers in reinforced concrete. The study aims to perform a bibliometric analysis of the existing kenaf fibers reinforced concrete literature and to provide a picture of the research status during the last ten years from 2013 to September 2022. There were 303 articles extracted from the Scopus database. The “VOSviewer” tool was employed to visualize the literature containing the most active scientific journals, countries, and highly used keywords in the field of fibers reinforced concrete. The outcomes showed that “Hybrid Composites”, “Impact Strength”, “Water Absorption”, “Scanning Electron Microscopy”, “Polypropylenes” and “Polymer Composite” have recently emerged as themes related to the applications of KFRC, and grabbed the interest of academics, may also offer future research opportunities. Additionally, according to the frequency of the keywords used, three important research domains associated with kenaf fibers within the concrete in the construction materials field have been identified, including “Mechanical Properties”, “Fiber Reinforced Plastics”, and “Tensile Strength”. Furthermore, the recent studies on the impact of kenaf fiber utilization on the structural performance of reinforced concrete are reviewed. Accordingly, the explanations related to research findings, suggestions for future studies have been provided on the incorporation of kenaf fibers reinforced concrete in civil engineering applications

COORDINATION FACTORS AND PROJECTS PERFORMANCE: AN EVALUATION MODEL FOR CONSTRUCTION

In a rapidly changing business, the construction industry is under a great pressure to improve its performance in line with other industries. It is acknowledged that construction industry still suffers from uncoordinated and interdependent processes due to fragmentation and projects' complexity

COORDINATION FACTORS AND PROJECTS PERFORMANCE: AN EVALUATION MODEL FOR CONSTRUCTION

In a rapidly changing business, the construction industry is under a great pressure to improve its performance in line with other industries. It is acknowledged that construction industry still suffers from uncoordinated and interdependent processes due to fragmentation and projects' complexity

E-learning versus face-to-face civil and environmental engineering education: A case study of the COVID-19 pandemic

The Malaysian Government implemented a countrywide lockdown due to COVID-19, which also affected the educational institutes. Under these circumstances, the e-learning educational strategy was initiated for the resuming of educational activities. A need for the study was devised and performed to assess the students’ perspective on this transition from face-to-face learning to e-learning for the effective implementation of the system. Students’ feedback data was evaluated for the January 2020 semester, which was collected before the pandemic and the September 2020 semester, which was collected during the pandemic by the end of the semester. Both data were analysed by adopting parametric Student’s t-test and non-parametric Mann-Whitney U test. Overall, it is concluded that students were comfortable with the e-learning educational system. However, the effectiveness of the e-learning system is dependent on the course type and requirements. This study will help the instructors to evaluate and improve their teaching strategy for the e-learning educational system by the preview of students for the deficiencies, in comparison to face-to-face learning, as the current lockdown situation is uncertain due to the COVID-19 pandemic