Construction industry is one of the significant contributors to the economic growth and development of Cambodia. Two major challenges are limiting the performance of the construction industry in Cambodia, which are poor cost and schedule performance of the construction projects. Therefore, the aim of this study is to fill an important knowledge gap by identifying the various attributes for construction project delay, using the residential building projects as a starting point. Feedback from a survey administered to the contractors and consultants was analysed using Relative Importance Index (RII). Results showed that shortage of materials on site; unrealistic project scheduling; late delivery of material; shortage of skilled labour; complexity of project; labour absenteeism; late payment by the owner for the completed work; poor site management; delay by subcontractor; accidents due to poor site safety are ranked by the contractors and consultants as the main causes of project delays in Cambodia. Construction frontline players are recommended to put their efforts on the identified key factors in relation to their magnitudes of influence. By doing so, the causes of project delays in the Cambodia’s construction and real estate sector could be significantly reduced or controlled, which will ultimately lead to the on time project completion.

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.

Offsite manufacturing (OSM) is believed to be an alternative means of improving construction productivity. However, OSM has attracted limited attention from researchers and industry stakeholders, particularly in developing countries. Therefore, this study considers the key factors hindering the industry-wide adoption of OSM in the Cambodian context. A survey conducted with 75 construction practitioners asked respondents to quantify the level of influence from 30 constraints pertaining to OSM. Multi-attribute utility and correlation coefficient analyses were used to statistically analyze the data. Six broad categories of constraints to adopting OSM underlie the challenges facing the local industry: project planning and methodology, construction cost, construction industry and culture, skill and shortage, supply chain and logistics, and construction site operations. The findings may be useful for construction engineering professionals seeking ways to improve construction productivity performance by implementing OSM as an alternative technique in developing countries.

The construction industries’ unsafe conditions require increased efforts to improve safety performance to prevent and reduce accident rates. Safety performance in the Iraqi construction industry is notoriously poor. Despite this condition, safety research has so far been neglected. Implementing a safety program is a proven initial step to improve safety. Therefore, the aim of this study is to identify the key elements of a safety program in the Iraqi construction industry. To verify and validate a list of safety program elements identified in the literature review, a mixed method approach was used by using interviews and questionnaire surveys. A final list of 25 elements were then analyzed using exploratory factor analysis. The analysis found that these elements can be grouped into four interrelated dimensions: management commitment and employee involvement, worksite analysis, hazard prevention and control systems, and safety and health training. This study contributes to the body of knowledge on safety in the Iraqi construction sector, a research area which has not been adequately investigated previously. They also help decision-makers focus on key elements that are needed to start improving safety performance in this context.

The utilization of Internet-of-Things (IoT)-based technologies in the construction industry has recently grabbed the attention of numerous researchers and practitioners. Despite the improvements made to automate this industry using IoT-based technologies, there are several barriers to the further utilization of these leading-edge technologies. A review of the literature revealed that it lacks research focusing on the obstacles to the application of these technologies in Construction Site Safety Management (CSSM). Accordingly, the aim of this research was to identify and analyze the barriers impeding the use of such technologies in the CSSM context. To this end, initially, the extant literature was reviewed extensively and nine experts were interviewed, which led to the identification of 18 barriers. Then, the fuzzy Delphi method (FDM) was used to calculate the importance weights of the identified barriers and prioritize them through the lenses of competent experts in Hong Kong. Following this, the findings were validated using semi-structured interviews. The findings showed that the barriers related to “productivity reduction due to wearable sensors”, “the need for technical training”, and “the need for continuous monitoring” were the most significant, while “limitations on hardware and software and lack of standardization in efforts,” “the need for proper light for smooth functionality”, and “safety hazards” were the least important barriers. The obtained findings not only give new insight to academics, but also provide practical guidelines for the stakeholders at the forefront by enabling them to focus on the key barriers to the implementation of IoT-based technologies in CSSM.