Intell. Infrastruct. Constr., Volume 1, Issue 1 (June 2025) – 4 articles

Fissured expansive soils exhibit pronounced moisture-induced swelling, posing significant risks to the stability of geotechnical structures such as dam foundations and core zones. To improve predictive capacity in such environments, this study developed a back-propagation (BP) neural network model to estimate the swelling behavior of fissured expansive soils. The model incorporated four key geotechnical parameters—fissure ratio, dry density, initial moisture content, and overburden pressure—and was implemented in MATLAB using a three-layer feedforward architecture with four inputs, five hidden neurons, and a single output neuron to predict the swelling ratio (increase in specimen height due to water-induced expansion). The model was trained on 81 laboratory-tested samples, with all variables normalized to the range [−1, 1] to ensure numerical stability. Two training algorithms were evaluated: gradient descent with momentum (traingdm) and the Fletcher–Reeves conjugate gradient method (traincgf). The optimal network configuration achieved a mean squared error (MSE) below 0.01, indicating strong predictive accuracy for expansive soil swelling behavior. Comparative results showed that the conjugate gradient algorithm converged nearly 30 times faster than the gradient descent method, while maintaining similar prediction accuracy. Validation on an independent dataset confirmed high agreement with measured swelling ratios. The proposed BP model demonstrates robust generalization and computational efficiency, offering a practical decision-support tool for expansive soil deformation control in dam engineering. Its rapid and accurate predictions make it valuable for Smart City applications such as embankment stabilization, intelligent dam core design, and real-time geotechnical risk assessment. Full article

The nexus between sustainability and safety in construction is crucial for improving a resilient and responsible built environment. By adhering to sustainable principles, construction practices can not only mitigate the environmental impact but also prioritize the health and safety of workers and communities. To prevent accidents and enhance safety in construction, unmanned aerial vehicles (UAVs) are utilized for aerial inspection, site monitoring, surveying, emergency response, and training purposes. However, no systematic review has yet identified UAV deployment’s adoption, challenges, and prospects. UAVs have emerged as promising technologies for improving safety through applications such as aerial inspections, site monitoring, surveying, emergency response, and training. However, a comprehensive review of UAV adoption, challenges, and prospects in the construction industry is still lacking. To address this gap, this study conducts a systematic literature review and bibliometric analysis to examine the current state of UAV implementation in construction safety management. The analysis reveals the interconnectedness between construction, engineering disciplines, and safety management, providing a holistic overview of influential contributors and prevalent themes. Content analysis further uncovers significant barriers hindering widespread UAV implementation, emphasizing technical, regulatory, and safety concerns. This study highlights strategies for overcoming these challenges and optimizing UAV deployment to enhance safety in construction, aligning with broader principles of social sustainability. Full article

Thermal stress control is crucial for massive concrete structures during construction. The cooling strategies directly determine the safety of structures, material quality, construction efficiency, and project cost. However, precise spatiotemporal thermal stress regulation and management are difficult to achieve due to the lack of balanced discriminant criteria and multi-objective optimization methods for the selection of traditional strategies. Therefore, an intelligent optimization method for thermal stress management strategy in massive concrete structures, considering the balance of safety, quality, efficiency, and cost (SEQC-TSOM), is proposed. Initially, a Thermal Stress Simulation Mechanism Model (TSSM) is constructed to accurately evaluate the structural state throughout the entire process. Subsequently, a mechanism data-driven surrogate model (MD-SM) is constructed to quickly evaluate the structural response under different cooling strategies. Furthermore, a multi-objective intelligent optimization model and a multi-criteria decision-making model are proposed to filter the intelligent optimal strategy from the Pareto solution set. Finally, a case study based on the Baihetan arch dam project is conducted, and the results show that the safety, quality, efficiency, and cost (SEQC)-balanced strategy increases safety by 42%, improves cooling efficiency by 36%, and reduces cooling costs by 20.6% compared with traditional strategies. Full article