Introduction Construction delays remain a challenge in projects, often resulting in cost overruns, disputes, productivity losses, and schedule inefficiencies. Growing project complexity has increased the need for data-driven approaches to delay analysis and management. This study reviews the application of artificial intelligence in construction delay analysis and management by examining application areas, techniques, project contexts, implementation maturity, and research gaps. Methods A structured literature review was conducted using a PRISMA-guided search and screening process. Scopus and Web of Science were searched for studies published between 2021 and 2025. Following database filtering, title and abstract screening, and full-text eligibility assessment, 42 studies were retained for qualitative synthesis. Results The reviewed studies applied artificial intelligence to delay prediction, delay-cause identification, risk prioritisation, progress monitoring, schedule recovery, and optimisation using machine learning, deep learning, ensemble learning, and hybrid modelling approaches. Several studies reported predictive accuracy above 90%, particularly for artificial neural networks, ensemble techniques, and hybrid optimisation models. However, evidence remains constrained by limited datasets, project-specific applications, weak real-time data integration, and insufficient consideration of contractual and external factors. Discussion The reviewed studies indicate that Artificial Intelligence (AI) models have improved predictive capability in construction delay management. However, practical implementation remains limited by data availability, model interpretability, and weak integration with project decision-making processes. Conclusion Artificial intelligence can improve construction delay management through transparent and data-driven approaches. Further research is needed to develop integrated frameworks linking predictive analytics with practical delay-management decisions in dynamic construction environments.
Introduction/Objective This study compared six reinforced-concrete slab systems for a four-story residential building in Kuwait to identify priority-dependent performance trade-offs. Methodology Six ETABS models represented one-way slabs with beams in the x- and y-directions, a two-way solid slab, and three flat-slab configurations. Models shared geometry, materials, supports, loads, and shear walls; member sizing followed ACI 318-19, and loading followed ASCE/SEI 7-16. Outputs comprised reinforcement and concrete quantities, structural weight, concrete-and-reinforcing-steel material cost, material-associated embodied CO 2 , and comparative vertical natural frequency. A CRITIC-weighted multi-criteria analysis ranked alternatives across material efficiency, cost, embodied CO 2 , and frequency; sensitivity scenarios tested priority changes. Results The y-direction one-way slab required the least reinforcement (26,780 kg), had the highest vertical natural frequency (6.40 Hz), and ranked first under CRITIC and equal weighting. The drop-panel flat slab without edge beams had the lowest concrete quantity (298.39 m 3 ), structural weight (7,161.37 kN), material cost (12,992.87 KD), and material-associated embodied CO 2 (233,906.41 kg), but the lowest frequency (4.86 Hz). It ranked first in the material, cost, and emissions priority scenario. Discussion Results reveal a case-specific trade-off between material-based performance and vertical modal response. Natural frequency alone does not establish vibration acceptability. Conclusion For this case, the y-direction one-way slab was the highest-ranked overall compromise under CRITIC weighting; the drop-panel flat slab without edge beams was preferable under material-, cost-, and emissions-focused priorities. No system was universally optimal; selection should reflect project priorities and assessment boundaries.
Introduction Early-age cracking in mass concrete structures, driven by thermal stresses from cement hydration, remains a critical durability concern. One effective way to reduce the risk of early cracking is to select a concrete mixture formulation that reduces heat emission. This study considers, for the first time, the inverse problem of preventing early crack formation: determining the maximum allowable concrete heat release (Qmax) for the given geometric characteristics of the structure and concreting parameters using machine learning methods. Materials and Methods A dataset of 39,200 numerical experiments was collected via thermo-mechanical modeling, considering variables like slab thickness, heat transfer coefficient, concrete grade, ambient temperature, and concreting duration. The target value Q max was identified using the bisection method, ensuring the tensile stress-to-strength ratio remained below unity. A feedforward Artificial Neural Network (ANN) with two hidden layers was developed and trained on this dataset. Results The ANN model achieved exceptional prediction accuracy, with a correlation coefficient of 0.99955 between target and predicted Q max values. Analysis revealed that the concrete compressive strength grade had a minimal effect on the maximum permissible heat release. Discussion Feature importance analysis showed that the curing rate and slab thickness are the most significant parameters influencing the Q max value. The negligible impact of compressive strength stems from its competing effects on tensile strength and elastic modulus. Conclusion The developed ANN model provides a highly accurate tool for predicting permissible concrete heat release, enabling optimized mix design to mitigate early thermal cracking in massive foundation slabs.
Introduction The adoption of Building Information Modelling (BIM) in Latin American Architecture, Engineering, and Construction (AEC) organisations remains uneven, as implementation barriers extend beyond technical proficiency to include organisational readiness and capability development gaps. Although leadership is recognised as a driver of digital transformation, the relationship between transformational leadership dimensions and BIM maturity stages remains underexplored. Methods This study conducted a scoping review using the Scopus database and PRISMA-ScR-guided screening procedures. Of the 576 initial records, 72 publications were retained for bibliometric mapping with Bibliometrix and VOSviewer, and 14 studies met eligibility criteria for qualitative synthesis through iterative coding. Bass and Riggio’s transformational leadership dimensions were cross-mapped against Succar’s three-stage BIM maturity framework. Results The review identified stage-sensitive leadership patterns across BIM maturity. Idealised influence and individualised consideration were salient in early adoption; intellectual stimulation became critical for process redesign and collaboration; and inspirational motivation gained strategic importance in advanced integration contexts. Bibliometric findings showed increasing interest after 2021 and a thematic shift from technical to organisational and human factors. Discussion These findings suggest that BIM maturity progression should be understood not only as a technological transition but also as a leadership-mediated organisational change process. Leadership behaviours appear to vary by maturity stage, particularly in contexts marked by institutional asymmetries and uneven capability development. Conclusion This study proposes an exploratory conceptual framework linking transformational leadership dimensions to BIM maturity stages. The framework provides a theoretically grounded basis for aligning leadership with maturity and supports future empirical validation across diverse AEC contexts.
This study aims to predict the peak axial load capacity (PALC) of steel-reinforced concrete-filled square steel tubular (SRCFSST) columns under elevated temperatures using a machine learning-based approach. The motivation arises from the limitations of traditional experimental and numerical methods, which are often time-consuming, costly, and computationally intensive. A hybrid predictive framework was developed by integrating Principal Component Analysis (PCA) with Extreme Gradient Boosting (XGB). The dataset, comprising 135 instances from prior experimental studies, underwent PCA for dimensionality reduction, retaining 99% of the variance. The PCA-transformed data was used to train XGB models, with hyperparameter tuning conducted via Grid Search, Random Search, and Bayesian Optimization. A 5-fold cross-validation technique was employed to enhance model generalizability, and performance was evaluated using R2, RMSE, and WMAPE. Among the three tuning strategies, the Bayesian-optimized PCA-XGB model demonstrated the highest predictive performance with an R2 of 0.928, MAE of 2.3%, and RMSE of 3.5% on the test dataset. Statistical analyses, including paired t-tests and Wilcoxon signed-rank tests, confirmed the superiority of this model with significant improvements over other configurations (p < 0.05). The use of PCA notably reduced multicollinearity and computational complexity. The findings underscore the value of combining dimensionality reduction with advanced hyperparameter tuning to develop efficient, accurate, and interpretable models for structural fire engineering. The PCA-XGB-BO framework offers a viable alternative to traditional modeling approaches, particularly for complex prediction problems involving high-temperature effects on structural components. This study establishes a robust data-driven methodology for estimating PALC in SRCFSST columns exposed to high temperatures. The integration of PCA and Bayesian Optimization within an XGB modeling framework delivers high accuracy while reducing computational burden. Future research should focus on extending this framework to other structural systems, incorporating physics-informed constraints, and validating performance through large-scale experimental testing.
