The catalytic potential of calcium oxide synthesized from mud clam shell as a heterogeneous catalyst for biodiesel production was studied. The mud clam shell calcium oxide was characterized using particle size analyzer, Fourier transform infrared spectroscopy, scanning electron microscopy, and BET gas sorption analyzer. The catalyst performance of mud clam shell calcium oxide was studied in the transesterification of castor oil as biodiesel. Catalyst characterization and transesterification study results of synthesized catalyst proved the efficiency of the natural derived catalyst for biodiesel production. A highest biodiesel yield of 96.7% was obtained at optimal parameters such as 1 : 14 oil-to-methanol molar ratio, 3% w/w catalyst concentration, 60°C reaction temperature, and 2-hour reaction time. Catalyst reusability test shows that the synthesized calcium oxide from mud clam shell is reusable up to 5 times.

The widespread use of artificial intelligence (AI) in civil engineering has provided civil engineers with various benefits and opportunities, including a rich data collection, sustainable assessment, and productivity. The trend of construction is diverted toward sustainability with the aid of digital technologies. In this regard, this paper presents a systematic literature review (SLR) in order to explore the influence of AI in civil engineering toward sustainable development. In addition, SLR was carried out by using academic publications from Scopus (i.e., 3478 publications). Furthermore, screening is carried out, and eventually, 105 research publications in the field of AI were selected. Keywords were searched through Boolean operation “Artificial Intelligence” OR “Machine intelligence” OR “Machine Learning” OR “Computational intelligence” OR “Computer vision” OR “Expert systems” OR “Neural networks” AND “Civil Engineering” OR “Construction Engineering” OR “Sustainable Development” OR “Sustainability”. According to the findings, it was revealed that the trend of publications received its high intention of researchers in 2020, the most important contribution of publications on AI toward sustainability by the Automation in Construction, the United States has the major influence among all the other countries, the main features of civil engineering toward sustainability are interconnectivity, functionality, unpredictability, and individuality. This research adds to the body of knowledge in civil engineering by visualizing and comprehending trends and patterns, as well as defining major research goals, journals, and countries. In addition, a theoretical framework has been proposed in light of the results for prospective researchers and scholars.

Risk decision matrix has widely been favoured by the researchers in the area of construction safety risk assessment. Although it provides the construction safety professionals with the final illustration of the risks magnitude, it suffers from major shortcomings, including inability to considering the importance of probability and severity, impaired analysis resulting from the use of raw numbers for ratings, and the limited range of classifications for assessing the risks. All these shortages give an impaired insight to the concerned parties, deteriorating the involved workers' safety. As such, this paper aims to develop a novel Risk Assessment Model (RAM) through the integration of the Fuzzy Best Worst Method (FBWM) with the Interval-Valued Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (IVFTOPSIS). Based on the application of RAM to a real-life case study, it was observed that the developed RAM contributes to the body of construction safety risk assessment in five unique ways: (1) computing the importance of the two risk parameters (i.e. probability and severity) using fuzzy-reference-based comparisons, (2) obviating the needs for having statistical data, (3) prioritizing the identified risks using the combination of interval-valued triangular fuzzy numbers with TOPSIS, (4) providing the safety analysts with wider ranges of classifications for conducting risk assessment, and (5) providing the safety professionals with appropriate evaluation strategies for controlling the analysed risks. The developed model in the study can be applied to any projects, giving a conclusive plan to the concerned safety professionals for adopting the further prudent mitigation measurements.

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Construction and demolition waste (CDW), as the main consequence of construction and demolition (C&D) activities, has severely affected our sustainability needs. However, construction and demolition waste management (CDWM) lacks the integration of sustainability concepts. Thus, there is a great need to include sustainability dimensions in CDWM to reach sustainable construction and demolition waste management (SCDWM). This study aims at empirically investigating SCDWM by analyzing the impacts of factors that contribute to sustainability aspects of CDWM on waste management hierarchy (WMH), including reduce, reuse, recycle, and disposal strategies. According to the literature, 26 factors were initially identified and grouped under four categories, namely environmental, economic, social, and administrative, that contribute to sustainability aspects of CDWM. Subsequently, a pilot test was performed to assess the significance and applicability of these factors in the Malaysian construction industry by implementing the express analytic hierarchy process (EAHP). Then, a questionnaire survey was performed to collect data from 132 construction companies involved in CDWM. Partial least squares-structural equation modeling (PLS-SEM) was used to test the hypothetical relationships by applying SmartPLS software. Results demonstrated that the economic aspect of CDWM (main category) and “public environment contamination due to illegal waste dumping” (sub-category) were the most influential factor in SCDWM in Malaysia.

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