As the construction industry shifts from an extensive development model to one characterized by intelligent structural systems, the imperative to enhance productivity and management efficiency has emerged as a critical challenge. Conventional rebar construction processes heavily rely on manual operations—such as on-site rebar cutting, manual transcription of material lists, and decentralized processing—which are susceptible to subjective errors and often result in significant material waste. This issue is particularly pronounced in large-scale projects, where disorganized management of rebar quantities and placements exacerbates inefficiencies. To address these challenges, this study proposes an integrated approach that synergistically combines a genetic algorithm-based rebar-cutting optimization model with BIM technology, thereby optimizing rebar management throughout the construction process. The research is structured into two primary components. Firstly, a one-dimensional mathematical model for rebar-cutting optimization is developed, incorporating an innovative real-number encoding strategy within the genetic algorithm framework to maximize material utilization. A case study conducted on a pump station project reveals that the utilization rates for 32 mm and 16 mm rebar reach 86.76% and 93.90%, respectively, significantly exceeding the industry standard of 80%. Secondly, an automated batch modeling tool is developed using C# and the Revit API, which enables the efficient generation of rebar components; a unique coding system is employed to establish a bidirectional mapping between the digital model and the physical rebar, ensuring precise positioning and effective information management. Overall, this integrated method—encompassing rebar-cutting optimization, digital modeling, and on-site intelligent management—not only mitigates material waste and reduces production costs but also markedly enhances construction efficiency and accuracy in complex projects, thereby providing robust technical support for the seamless integration of intelligent construction and industrialized building practices.

This research aims to study the deformation and fracture behavior of shale by a novel anisotropic regular lattice spring model (ARLSM). The novel ARLSM applies the normal and tangential coupling spring to release the Poisson's ratio limitation in the traditional regular lattice spring model. Meanwhile, a nonlinear strength criterion is introduced into ARLSM to simulate the fracture failure of shale. Two benchmark problems are tested to implement the research. The study shows that ARLSM has larger range of Poisson's ratio and better effects comparing with the existing anisotropic lattice spring model. Moreover, ARLSM can accurately predict the deformation and fracture behavior of shale under different conditions.

In this study, an ABAQUS-PFC3D coupling program framework based on the Computational Fluid Dynamics (CFD)-Discrete Element Method (DEM) coupled fluid-solid analysis method is constructed, which can be run in Windows computer system and Python language environment, and takes into account the high efficiency, simplicity, and computational stability of the bi-directional interaction of fluid-solid field information. The coupled program framework is validated using a classical particle-fluid coupling model case to assess its stability and accuracy. Further, the ABAQUS-PFC3D coupling program is used to study the evolution characteristics of internal erosion of gap-graded soil-rock mixtures of different fines contents (indoor test scale) and reproduce the whole process of water and mud-surge disaster in the Yonglian water-rich fault tunnel in Jiangxi, China, including the disaster-causing stages of palm collapse, accumulation of broken rock inside the tunnel, collapse within the fault, and surface collapse (engineering scale). The results show the stability of the coupling program framework, which improves a good reference for the popularization and application of the CFD-DEM fluid-solid coupling model.