The construction industry is a major contributor to global carbon emissions, making the decarbonization of its supply chain a critical goal for sustainable development. This study aims to identify key drivers of decarbonization within the construction supply chain and analyze their interrelationships using causal and structural modeling techniques. A bibliometric analysis is conducted to highlight theoretical gaps in the field, followed by the application of the Fuzzy Decision-Making Trial and Evaluation Laboratory (Fuzzy-DEMATEL) method to identify key decarbonization factors, and the Adversarial Interpretive Structural Modeling (AISM) method to construct a causal model that reveals their interactions. The results identify 17 key factors, categorized into five levels, with low-carbon policy guidance and consumer preferences emerging as the most influential. Based on these findings, a three-tier strategy is proposed to guide decarbonization efforts within the construction supply chain. This research provides theoretical insights into decarbonization and offers actionable recommendations for future industry practices and policy development.

Psychological hazards within organizational structures of construction sites are difficult to detect and can have significant negative impacts on safety performances when such hazards erupt. At present, most safety performance assessment models for construction sites ignore psychological factors. Therefore, in order to reveal psychological hazards within construction site organizations and to avoid damage caused by psychological hazards to safety performances, this paper evaluates the safety performances of construction sites by focusing on leader–member exchange ambivalence as the main trigger point. The evaluation system and evaluation criteria are established through three aspects: building scale, emotional orientation, and stability factors. The hierarchical analysis method, game theory, and extension cloud model are combined to make evaluation results more objective and credible. Moreover, a construction project with high technical requirements, high investment, and complex construction conditions (defined as a complex project) and an ordinary construction project with low technical difficulty and simple construction conditions (defined as a general project) were selected for analysis. The evaluation results indicate that both complex projects and general projects have safety hazards regarding psychological orientations. Finally, this paper makes some suggestions from three aspects: management system and corporate culture, building site intelligence, and social opinion to improve the safety performances of construction sites. The evaluation results are the same as actual operation results, which verify that models proposed in this paper can be used for safety performance evaluations of actual construction projects and provide help for managers to grasp overall safety levels.

Under China’s dual-carbon strategy, the construction sector still lacks a systematic quantitative view of what drives its shift to a circular economy. This study couples the Decision-Making Trial and Evaluation Laboratory (DEMATEL) with Total Adversarial Interpretive Structural Modeling (TAISM) to build a weighted, multi-layer model of the policy–market–organization–technology chain. DEMATEL measures causal strengths, and TAISM arranges the variables into five levels without subjective thresholds, revealing a five-stage activation pathway. Fiscal incentives and regulations start the cascade; market demand amplifies their effect into a “resonant resilience” mechanism that improves cost performance. Robustness tests show 87% hierarchy stability and causal variation within ±0.6%. Sensitivity checks indicate that policy support must supply at least 30% of total network weight, because market capital alone cannot meet circular-construction costs. A three-tier intervention—policy incentives, financial amplification, and digital decomposition via green finance, BIM, and material passports—is therefore recommended.

Multi-project staff scheduling optimization is a critical challenge in engineering management. However, traditional methods often overlook employee welfare, resulting in scheduling solutions that lack a people-centered approach. This study develops a multi-objective model that incorporates employee welfare to balance project performance and staff well-being. A mixed-integer programming model is proposed, integrating task allocation, resource scheduling, income growth, and workload balance as core optimization objectives. An improved Spider Wasp Optimization Algorithm (SWOIM) is employed to solve this model. The experimental results demonstrate that SWOIM outperforms conventional optimization algorithms in convergence speed, stability, and solution quality. It enhances staff scheduling efficiency while ensuring fair compensation and balanced workload distribution. A case study further validates the practical applicability of the proposed approach, showing that the optimized schedule not only improves task allocation but also enhances employee satisfaction and overall organizational performance. This study presents a people-centered scheduling framework aligned with sustainable workforce management principles and offers practical insights for improving multi-project coordination.

Occupational accident severity is often modeled as a single ordered outcome, potentially obscuring differences between fatal and severe non-fatal injuries. This study develops a dual-pathway framework that separately analyzes fatal versus non-fatal outcomes and severe versus minor injuries among non-fatal cases. Using 48,909 accident records from the CSDataset, structured attributes were integrated with narrative-derived scenario-exposure and work-activity variables obtained through rule-constrained LLM coding; expert validation yielded Cohen’s κ = 0.742. Logistic and Firth logistic regressions were combined with machine learning models, SHAP analysis, and scenario–activity co-occurrence analysis. The two pathways shared several explanatory variables but differed in association direction and magnitude. In the fatality pathway, vehicle and mobile-equipment exposure (OR = 1.813, 95% CI: 1.668–1.971) and workplace violence (OR = 5.184, 95% CI: 4.190–6.415) showed positive associations. Among non-fatal cases, driving operation showed the strongest positive activity-level association with severe injury (OR = 2.188, 95% CI: 1.715–2.793). Co-occurrence analysis showed that frequent combinations were not necessarily outcome-enriched; vehicle and mobile-equipment exposure combined with driving operation was enriched in both pathways. These findings support pathway-specific interpretation and work-context-oriented safety management and prevention.

Supplementary cementitious materials (SCMs), particularly ground granulated blast-furnace slag (GGBS) and fly ash, are widely used in concrete, but the effect of training-set SCM composition on prediction of GGBS–fly ash blended mixtures remains insufficiently examined. Six regression algorithms were compared using formulation-grouped nested cross-validation, after which the retained algorithm was optimized on the complete development set and evaluated on a formulation-disjoint hold-out set. The retained model was then examined under six training-set compositions on the same GGBS–fly ash blended hold-out subset, with additional analyses of training-set size, blended-formulation exposure, hyperparameter sensitivity, and SHAP attribution under alternative feature representations. XGBoost achieved a mean outer-fold RMSE of 5.396 MPa and a hold-out RMSE of 5.095 MPa; on the blended subset, training on all mixture types and on blended mixtures only yielded RMSEs of 5.372 and 5.346 MPa, respectively, compared with 7.095 MPa when blended formulations were excluded, while SCM SHAP attributions varied across feature representations. The retained XGBoost model showed that blended-mixture prediction performance within this dataset varied with training-set SCM composition and blended-domain representation, while SCM feature attributions varied with input representation.