Imran Mehmood

Construction activities impose substantial cognitive demands on workers, making mental fatigue assessment critical for occupational health and safety (OHS) management. Portable wearable electroencephalography (EEG) devices offer practical advantages in mental fatigue identification. However, they are typically limited to a few channels, restricting the spatial coverage of brain activity and potentially limiting the ability to capture fatigue-related spatial patterns. To address this limitation, a sensor-level scalp EEG representation mapping framework was developed to enhance spatial information in fatigue assessment. First, a stacked long short-term memory (LSTM)-based regression model is developed to approximate multichannel scalp EEG representations from preprocessed single-channel in-ear EEG and electrocardiography (ECG) signals. Quantitative evaluation demonstrates stable predictive performance, with mean absolute error ( MAE ) and root mean square error ( RMSE ) both below 24    μV across fatigue states. Second, the predicted scalp EEG signals are transformed into EEG topographic maps to enhance spatial interpretability. The proposed framework enables portable yet spatially informative fatigue assessment and provides methodological support for future OHS-oriented monitoring systems.

The construction industry remains one of the most perilous, where hazard identification is often inconsistent. Hazards are still missed when teams rely mainly on traditional approaches like checklists, Job Safety Analysis (JSAs)/Job Hazard Analysis (JHAs), and individual experience. This study evaluated whether a GEN-AI-assisted approach improved hazard identification performance compared with traditional approaches, using expert-verified ground truth for scoring. A quantitative within-subjects experiment was conducted with 51 participants. Each participant completed hazard identification in four conditions: traditional–pre, GEN-AI–pre, traditional–post, and GEN-AI–post, with a short training session on hazard identification delivered between the pre- and post-stages. Effectiveness was measured using the F1 score, combining both precision and recall. For analysis, traditional and GEN-AI performance were compared at each stage using paired-sample t-tests, and the overall pattern was tested using a 2 × 2 repeated measures ANOVA. The results showed that GEN-AI support produced significantly higher performance than the traditional approaches at both stages (p < 0.05). The repeated measures ANOVA confirmed a strong overall method effect. However, the overall intervention effect was small, and the method × intervention interaction was negligible, with no statistically significant change over time (p > 0.05). Overall, the findings indicated that GEN-AI support improved hazard identification accuracy relative to traditional approaches in this dataset, with limited evidence of additional gains from the training intervention. This study contributes towards providing empirical evidence that GEN-AI improves hazard identification and strengthens proactive prevention, but final outputs need human validation.

Artificial intelligence (AI) has emerged as an effective solution to alleviate excessive carbon emissions in sustainable building projects. Although there are numerous applications of AI, there is no state-of-the-art review of how AI applications can reduce net-zero carbon emissions (NZCEs) for sustainable building projects. Therefore, this review study aims to conduct a systematic literature and science mapping review of AI applications in NZCEs for sustainable building projects, thereby expediting the realization of NZCEs in building projects. A mixed-method approach (i.e., systematic literature review and science mapping) consisting of four comprehensive stages was used to retrieve relevant published articles from the Scopus database. A total of 154 published articles were retrieved and used to conduct science mapping analyses and qualitative discussions, including mainstream research topics, gaps, and future research directions. Six mainstream research topics were identified and discussed. These include (1) life cycle assessment and carbon footprint, (2) practical applications of AI technology, (3) multi-objective optimization, (4) energy management and energy efficiency, (5) carbon emissions from buildings, and (6) decision support systems and sustainability. In addition, this review suggests six research gaps and develops a framework depicting future research directions. The findings contribute to advancing AI applications in reducing carbon emissions in sustainable building projects and can help researchers and practitioners to realize its economic and environmental benefits.

Purpose: Blockchain has the potential to facilitate a paradigm shift in the construction industry toward effectiveness, transparency and collaboration. However, there is currently a paucity of empirical evidence from real-world construction projects. This study aims to systematically review blockchain adoption barriers, investigate critical ones and propose corresponding solutions.Design/methodology/approach: An integrated method was adopted in this research based on the technology–organization–environment (TOE) theory and fuzzy decision-making trial and evaluation laboratory (DEMATEL) approach. Blockchain adoption barriers were first presented using the TOE framework. Then, key barriers were identified based on the importance and causality analysis in the fuzzy DEMATEL. Several suggestions were proposed to facilitate blockchain diffusion from the standpoints of the government, the industry and construction organizations.Findings: The results highlighted seven key barriers. Specifically, the construction industry is more concerned with environmental barriers, such as policy uncertainties (E2) and technology maturity (E3), while most technical barriers are causal factors, such as “interoperability (T4)” and “smart contracts' security (T2)”.Practical implications: This study contributes to a better understanding of the problem associated with blockchain implementation and provides policymakers with recommendations.Originality/value: Identified TOE barriers lay the groundwork for theoretical observations to comprehend the blockchain adoption problem. This research also applied the fuzzy method to blockchain adoption barrier analysis, which can reduce the uncertainty and subjectivity in expert evaluations with a small sample.