Infrastructures

Construction of a long-span concrete arch in a V-shaped canyon requires coordinated control of the temporary falsework, erection stays and closure geometry. This study examines the temporary angle-steel truss falsework used for the Nujiang River reinforced-concrete box arch bridge, which has a 100 m clear span. A towerless rear-anchorage arrangement, construction-stage finite element analysis and a BIM-assisted geometric-control framework are presented. The preloading calculation uses a maximum design water-bag mass of 2480.6 t and gives a maximum displacement magnitude of 101 mm and a peak model stress magnitude of 218.4 MPa. Separately, preloading survey records at 20 points show downward elevation changes of 23–136 mm after three days and unloading recoveries of 14–71 mm. A nominal anchorage comparison is formulated using the adopted pile geometry and shaft-resistance parameters. Numerical closure-length cases illustrate the influence of geometric compatibility on the formed falsework, while three initial-tension histories illustrate the redistribution of erection-stay forces and final deflection profiles. The proposed control framework relates measurements to construction-stage targets through explicit reference states and local force–geometry sensitivities. The study provides a case-based procedure for organising geometric control and the associated structural checks during segmental falsework erection.

Machine learning (ML) has emerged as a transformative tool in geotechnical engineering, offering data-driven alternatives to traditional empirical and analytical approaches. This paper presents a critical narrative review of recent advances in theory-guided machine learning (TGML) applications in geotechnical engineering, including soil-property prediction, constitutive modelling, pile capacity prediction, seismic soil–structure interaction, foundation performance, and slope stability. The review organizes the literature according to the physical mechanisms through which theoretical knowledge is incorporated into machine-learning methods, including physics-guided features, hybrid constitutive models, physics-informed neural networks, physics-constrained surrogates, probabilistic hybrid frameworks, and multiscale models. Emerging trends such as hybrid physics-informed models, explainable artificial intelligence (AI) frameworks, and integration with numerical simulations, real-time monitoring and data assimilation, and digital-twin-enabled infrastructure assessment are also discussed. The paper identifies key research gaps and proposes future directions for improving model robustness, reliability, and practical implementation in engineering design and infrastructure management. This review provides a structured roadmap for advancing AI-driven methodologies in geotechnical engineering, and their integration into smart and resilient infrastructure systems, while highlighting the critical issues that must be addressed for broader adoption in practice.

Worker responses to automated safety warning (ASW) systems have been widely examined across construction work environments to improve worker safety. However, the evidence of worker response is fragmented because individual studies only examine different types of ASW systems, hazards, work environments, response measures, and review scopes. Therefore, this review synthesizes evidence on worker responses to ASW systems to guide the evaluation and design of future ASW systems. To support this synthesis, five databases, including Scopus and Web of Science, were searched for articles published between 2015 and 2026. The screening process involved two domain experts and three large language models (LLMs) and resulted in 40 included studies. The review found shorter reaction times, hazard avoidance, and improved situation awareness with some ASW systems. Limited comparisons found that auditory-only warnings produced slower responses than vibrotactile warnings under controlled conditions. The review also found that perceiving or reacting to a warning does not always lead to protective action. Therefore, ASW effectiveness should be evaluated across the full worker response chain rather than by warning perception or reaction time alone. It is suggested that future research should examine protective action, repeated exposure, and ASW performance with real workers under realistic work conditions.