Off-site construction has been increasingly employed due to its advantages, for instance, improved quality control, reduced skills labour, faster construction time, decreased material wastage and safe working environment. As the most cutting-edge off-site construction, modular buildings have been utilised for residential building, student accommodation, and hotel projects. However, because of existing and underlying constraints, the adoption of modular buildings is still relatively low. To reveal factors hindering the development of high-rise modular buildings, a comprehensive literature review, coupled with a focus group study, were conducted. A questionnaire survey inquiring about all stakeholders was implemented to quantify constraints. The results were further examined according to a real-life case study. This paper manifested that “Lack of coordination and communication among stakeholders”, “Higher cost”, “Lack of government support”, “Lack of experience and expertise”, “Lack of building codes and standards”, “Poor supply chain integration”, and “Complexity of connection” are the top barriers curbing the uptake of modular buildings. The findings should provide a valuable reference for stakeholders adopting modular buildings, whilst mitigating risks amid modular construction. Future research is expected to exploit building information modelling and design for manufacture and assembly to alleviate these existing constraints and promote the performance of modular construction as well.

This paper investigated the confinement mechanism of steel-confined concrete in steel-reinforced concrete (SRC) columns. Six axially loaded SRC-core columns were tested and the corresponding structural performance data were collected and assessed. Considering the confinement mechanism of both steel-confined and hoop-confined concrete, the concrete in the SRC columns was divided into four regions: highly steel-confined concrete (HSCC), partially steel-confined concrete (PSCC), partially confined concrete (PCC), and unconfined concrete (UCC). Based on Mander’s model, a theoretical stress–strain model for each concrete region was proposed and implemented in an analytical model to predict the mechanical behaviors of axially loaded SRC columns from the results of the tests described in this paper and of previous tests; satisfactory agreement was observed.

Fracture is an important mode of failure in steel structures, whereas traditional fracture mechanics is difficult to apply in predicting ductile fracture in the presence of large-scale yielding or in flaw-free geometries. This study offers a means for numerical simulation of ductile fractures of constructional steels. Experimental investigations on the conventional smooth round bar specimens are carried out with special focus on the postnecking strain hardening and fracture properties, and a new experimental procedure is proposed to explicitly obtain the yield-to-fracture true stress–strain relationship, as well as the fracture strain and corresponding stress triaxiality. A fracture criterion is proposed by means of finite-element unit cell–based micromechanical studies, in which the most significant microscopic features of fracture including both void growth and coalescence are considered. To calibrate and validate the proposed fracture criterion, tests of notched round bar specimens representing high stress triaxiality are also carried out. The numerical method for material fracture simulation in implicit time integration analyses is addressed, and matters needing attention when using such a method are discussed, including the countermeasures of convergence difficulties caused by material softening and determination of mesh sizes.

Truss layout optimization under complex constraints has been a hot and challenging problem for decades that aims to find the optimal node locations, connection topology between nodes, and cross-sectional areas of connecting bars. Monte Carlo Tree Search (MCTS) is a reinforcement learning search technique that is competent to solve decision-making problems. Inspired by the success of AlphaGo using MCTS, the truss layout problem is formulated as a Markov Decision Process (MDP) model, and a 2-stage MCTS-based algorithm, AlphaTruss, is proposed for generating optimal truss layout considering topology, geometry, and bar size. In this MDP model, three sequential action sets of adding nodes, adding bars, and selecting sectional areas greatly expand the solution space and the reward function gives feedback to actions according to both geometric stability and structural simulation. To find the optimal sequential actions, AlphaTruss solves the MDP model and gives the best decision in each design step by searching and learning through MCTS. Compared with existing results from the literature, AlphaTruss exhibits better performance in finding the truss layout with the minimum weight under stress, displacement, and buckling constraints, which verifies the validity and efficiency of the established algorithm.

Progressive collapse incidents of truss structures are often reported; however, studies on the collapse resistance of truss structures are comparatively few. In order to investigate the collapse-resisting mechanisms of truss structures, this paper presents a comprehensive and detailed study on the collapse-resisting performance of planar trusses subjected to local damage at various locations, using both finite-element (FE) and analytical approaches. An improved FE analysis procedure is proposed to improve the computational efficiency. The main conclusions include the following: (1) in the case of a sudden loss of a top chord member or a diagonal member, catenary action will be the primary mechanism to provide the bridging-over capacity of the remaining structure, and severer damage can result when the removed member locates in the midspan; and (2) if a bottom chord member is suddenly lost, arch action will be the main mechanism to provide the bridging-over capacity of the remaining structure, and severer damage can result when the removed member locates next to the support or in the midspan.