Aidi Hizami Alias’s research while affiliated with Universiti Putra Malaysia and other places

Occupational health risks such as falls from height, electrocution, object strikes, mechanical injuries, and collapses have plagued the construction industry. Deep learning algorithms are exploding due to their outstanding analytical capabilities and are believed to improve safety management significantly. Therefore, this paper systematically reviewed the literature on DL algorithms from 2015 to 2024 in modular construction. It found that the six most popular DL algorithms in this area are “Convolutional Neural Network (CNN),” “Recurrent Neural Network (RNN),” “Generative Adversarial Network (GAN),” “Auto-Encoder (AE),” “Deep Belief Network (DBN)” and “Transformer.” However, in addition to each algorithm's limitations, problems like data constraints, talent gaps, and a lack of guidance frameworks also exist. To address these issues, three strategies are proposed. They are “establishing a multi-modal data sharing platform,” “proposing a paradigm framework for the application of DL algorithms,” and “constructing a compound construction talent training mechanism,” which provide researchers with future references.

Driven by global sustainability trends, Building Information Modelling (BIM) technology is increasingly becoming a key tool in the construction industry to improve efficiency and sustainability. This study aims to identify the key factors affecting BIM implementation in the context of Environmental, Social, and Governance (ESG) and Sustainable Development Goals (SDGs) and to construct a theoretical framework for BIM implementation based on these factors. To achieve this objective, this study used a systematic literature review (SLR) method to systematically review the relevant literature between 2009 and 2024 and identified 16 key factors from the selected 406 studies through keyword co-occurrence analysis (using VOSviewer 1.6.20) and data coding. These key factors include top management support for ESG and SDGs, alignment of SDGs, ESG integration, technical support, BIM software, BIM hardware, structural adjustment and collaboration, capacity building, change management, skill and attitude, educational training and development, incentive mechanism, roles and responsibilities, sustainable construction practices, policies and regulations, and resource efficiency. This study categorises these factors under the Strategy, Technology, Organisation, People, Environment (STOPE) framework and proposes a theoretical implementation framework for BIM accordingly. The findings not only provide a practical guiding framework for the sustainable development of construction companies in the context of ESG and SDG integration but also lay a solid theoretical foundation for future empirical research.

Driven by global sustainability trends, Building Information Modelling (BIM) technology is in-creasingly becoming a key tool in the construction industry to improve efficiency and sustaina-bility.This study aims to identify the key factors affecting BIM implementation in the context of Environmental, Social and Governance (ESG) and Sustainable Development Goals (SDGs) and to construct a theoretical framework for BIM implementation based on these factors.To achieve this objective, this study used a systematic literature review (SLR) method to systematically review the relevant literature between 2009 and 2024, and identified 16 key factors from the selected 406 studies through keyword co-occurrence analysis (using VOSviewer) and data coding.These key factors include:Top Management Support for ESG and SDGs,Alignment of SDGs,ESG Integra-tion,Technical Support,BIM Software,BIM Hardware,Structural Adjustment & Collabora-tion,Capacity Building,Change Management,Skill and Attitude,Educational Training and Devel-opment,Incentive Mechanism,Roles & Responsibilities,Sustainable Construction Practic-es,Policies and Regulations,Resource Efficiency.This study categorises these factors under the STOPE framework (Strategy, Technology, Organisation, People, Environment) and proposes a theoretical implementation framework for BIM accordingly. The findings not only provide a practical guiding framework for the sustainable development of construction companies in the context of ESG and SDG integration, but also lay a solid theoretical foundation for future empiri-cal research.

Hoisting is essential for large-scale construction projects, including urban viaducts, high-rise buildings, and undersea tunnels. However, this critical process is subject to frequent safety accidents in China, which result in many casualties and asset losses. The lack of a practical risk framework has contributed to poor safety management in this field. Most of the limited risk frameworks in this field focus only on the direct causes leading to accidents, ignoring the systematic and complex nature of lifting risks. In this study, a new risk framework for lifting is constructed by combining the STAMP (Systems-Theoretic Accident Model and Processes) theory and the quantitative analysis capability of the PLS-SEM (Partial Least Squares Structural Equation Modeling) to effectively identify, assess, and manage various potential risks in the lifting construction process. The factors were then analyzed for importance through the independence weight coefficient method. The study found that “Failure to conduct pre-operational inspections of lifting equipment and rigging components,” “Physical or mental impairment of operators, such as intoxication or distraction,” and “The hoisting program was not prepared under the actual working conditions at the project site and did not adequately plan for emergencies,” were the factors with the top 3 highest weight. Ultimately, the framework is validated by 200 real cases from 2019 to 2024 in China. This proposed STAMP-HC framework can accurately identify the risk transfer paths in accidents, and the results of risk factor weighting can also provide a reference for risk management, with the potential to be extended to other countries.

As China's pillar industry, the property market has suffered a considerable impact in recent years, with a decline in turnover and many developers at risk of bankruptcy. As one of the most concerned factors for stakeholders, housing prices need to be predicted more objectively and accurately to minimize decision-making errors by developers and consumers. Many prediction models in recent years have been unfriendly to consumers due to technical difficulties, high data demand, and varying factors affecting house prices in different regions. A uniform model across the country cannot capture local differences accurately, so this study compares and analyses the fitting effects of multiple machine learning models using February 2024 new building data in Changsha as an example, aiming to provide consumers with a simple and practical reference for prediction methods. The modeling exploration applies several regression techniques based on machine learning algorithms, such as Stepwise regression, Robust regression, Lasso regression, Ridge regression, Ordinary Least Squares (OLS) regression, Extreme Gradient Boosted regression (XGBoost), and Random Forest (RF) regression. These algorithms are used to construct forecasting models, and the best-performing model is selected by conducting a comparative analysis of the forecasting errors obtained between these models. The research found that machine learning is a practical approach to property price prediction, with least squares regression and Lasso regression providing relatively more convincing results.

Net-zero energy building (NZEB), an initiative to address energy conservation and emission reduction, has received widespread attention worldwide. This study aims to systematically explore recent challenges in NZEB retrofit research through a mixed-method approach and provide recommendations and future directions. A review of 106 documents (2020–2024) retrieved from the Web of Science and Scopus databases found that the globalization of NZEB retrofit research is unstoppable. Assessment methods are diverse, ranging from modeling energy efficiency (using different software such as DesignBuilder 7.0, PVsyst 7.4, EnergyPlus 24.1.0, etc.) to multi-attribute decision-making methods (e.g., DEMATEL-AHP/ANP-VIKOR) and comparative analysis. Current assessment metrics are dominated by economic benefits (e.g., net present value, dynamic payback period, and total operating cost) and energy consumption (e.g., electricity consumption and generation), with less consideration of environmental impacts (e.g., carbon reduction), as well as comfort (e.g., thermal comfort and indoor comfort). The study found that current challenges mainly include “Low economic feasibility of retrofitting”, “Building retrofit energy code irrationality”, and “Insufficient understanding, communication, and trust between stakeholders”. To overcome these challenges, the study also proposes a framework of strategies to address them, including (1) maximizing natural space, (2) introducing a tenant equity system, (3) upgrading waste management, (4) strengthening energy monitoring, (5) establishing complete life cycle mechanisms, (6) providing systemic solutions; (7) promoting the use of low-carbon building materials, and (8) increasing policy support.

Purpose Hoisting is an essential construction work package, but there is still a high incidence of accidents due to insufficient attention to coping strategies. This study aims to provide decision support to practitioners on safety protocols by developing a multi-stakeholder risk response model and a novel evaluation method. Design/methodology/approach Firstly, the study summarizes the hoisting risk response strategies system through a literature review and stakeholder theory. Secondly, the study constructed a quantitative theoretical model based on GLS-SEM and questionnaires. Third, the EWM-VA evaluation method was developed to determine the value coefficients of strategies. Findings The strategic interaction between government and consultants, consultants and builders, and government and builders are in the top three pronounced. Three coping strategies, “Increase funding for lifting equipment and safety devices,” “Improve the quality of safety education and training on lifting construction,” and “Conduct regular emergency rescue drills for lifting accidents,” have the optimal ratio of benefits to costs. Originality/value The hoisting risk strategy model from the perspective of multi-interested subjects proposed by the study is based on the global thinking of the project, which reduces the troubles such as the difficulty of pursuing responsibility and the irrational allocation of strategies that were brought by the previously related studies that only considered a single interested subject. In addition, the EWM-VA evaluation method developed in the study also provides new options for evaluating risk strategies and has the potential to be extended to other fields.

Occupational injuries in the construction industry have plagued many countries, and many cases have shown that accidents often occur because of a combination of project participants. Assembled construction (AC) projects have received extensive attention from Chinese scholars as a future trend, but few studies have explored the interrelationships and potential risks of various stakeholders in depth. This study fills this research gap by proposing a multi-stakeholder AC risk framework. The study surveyed 396 stakeholders, then analyzed the collected data and created a risk framework based on Structural Equation Modelling (SEM) and the CRITIC weighting method. The results revealed that factors like "regular supervision is a formality," "blindly approving the wrong safety measures," and "failure to organize effective safety education and training." are vital risks in AC of China. Finally, the study validates the risk factors and the framework with 180 real-life cases, which shows that the proposed framework is theoretically grounded and realistic. The study also suggests multi-level strategies such as introducing AI-based automated risk monitoring, improving the adaptability of normative provisions to technological advances, and advancing the culture of project communities of interest to ensure AC’s safe practices.

Machine learning, a key thruster of Construction 4.0, has seen exponential publication growth in the last ten years. Many studies have identified ML as the future, but few have critically examined the applications and limitations of various algorithms in construction management. Therefore, this article comprehensively reviewed the top 100 articles from 2018 to 2023 about ML algorithms applied in construction risk management, provided their strengths and limitations, and identified areas for improvement. The study found that integrating various data sources, including historical project data, environmental factors, and stakeholder information, has become a common trend in construction risk. However, the challenges associated with the need for extensive and high-quality datasets, models’ interpretability, and construction projects’ dynamic nature pose significant barriers. The recommendations presented in this paper can facilitate interdisciplinary collaboration between traditional construction and machine learning, thereby enhancing the development of specialized algorithms for real-world projects.