Dfma-oriented rebar design optimization for reinforced concrete structures using graph neural network

Reinforced concrete (RC) structure is the most commonly used structure format in buildings and civil infrastructures, wherein steel reinforcement (also called “rebar”) is embedded into concrete to enhance the tensile strength of concrete. Rebar detailing design is a critical stage of RC building design in terms of structural integrity, serviceability, durability and construction cost. In industrial practice, structural engineers first conduct structural analysis for the building based on a preliminary structural layout, using structural analysis software. The sizes of RC components are then adjusted, while the required steel areas for these components are identified based on the structural analysis results for rebar design.Currently, the rebar design is carried out manually or semi-automatically with some customized programs, which is time-consuming and relies heavily on structural engineers’ expertise and experience to achieve the optimal solution. Some research efforts have been made to automate this process, but some major limitations exist that hinders their applications in reallife projects: (1) impractical formulation of rebar design optimization problem, (2) lack of considerations on buildability of rebar design, (3) low computational efficiency, (4) lack of pplicability to different component types, and (5) lack of consideration on interference between RC components.Echoed with the above limitations in current practices and existing studies, this research aims to develop an automatic and practical rebar design optimization approach with high computational efficiency considering buildability and interference between RC components. Design for Manufacture and Assembly (DfMA) and graph neural network (GNN) are the two key techniques identified from literature review to realize the above objective. Contributions lie in the following four aspects.(1) Chapter 3: Developed a DfMA-oriented rebar design optimization approach considering both material efficiency and buildability.Firstly, the rebar DfMA principles are first summarized through a review of rebar-related activities. Then, the rebar design optimization problem is explicitly formulated with detailed definition of design variables, constraints and objective functions for elongated RC components (including beams and columns). A quantitative approach for evaluation of buildability is developed and incorporated into the objective function for multiobjective optimization. The implement details are then introduced, including the method for rebar layout searching and the Exploratory Genetic Algorithm (EGA) for robust optimal solution searching.(2) Chapter 4: Developed a GNN-based approach for rapid rebar design optimization.Firstly, graph representations for elongated RC components are developed according to the characteristics of their typical rebar layouts and design methods to enable the adoption of GNN. Then, the rapid rebar design proposal using GNN is developed, which could immediately provide a near-optimal rebar design for a given design case. Since the rebar design proposed by GNN may not satisfy all the code requirements and the optimality of it is also not guaranteed, a post-processing algorithm is designed to check and optimize the initial design from GNN.(3) Chapter 5: Extended the DfMA-oriented and GNN-based rebar design optimization approach for RC flat components.Firstly, the DfMA-oriented optimal design formulation is extended for flat components (including slabs and walls) considering strip-based design method and finite element analysis. Secondly, the graph representations for the rebar design of flat components are constructed, based on which the GNN-based rebar design proposal approach is presented.