A Pipeline for Topology Optimization of Reinforced Concrete Frames: A Systematic Approach for Ground Structure Generation, Selection and Optimization

The topology optimization of reinforced concrete (RC) building frames is relatively underexplored compared to steel structures, partly due to the lack of a systematic approach to generate and select ground structures (GS). Existing methods often use less systematic GS strategies, limiting efficient exploration of the vast and sparse design space shaped by large bay widths and story heights. This work addresses this gap by providing a comprehensive and systematic pipeline tailored for RC frames. The key contributions are: (1) development of a GS generation framework that systematically enumerates all feasible RC frame configurations within user-defined constraints, (2) introduction of a candidate GS selection map, a surrogate-based tool employing graph-based Latin Hypercube Sampling (LHS) and sparse Gaussian Process (GP) models, which predicts compliance early and strategically guides candidate selection, significantly reducing computational cost while serving as a reference for understanding design parameter influences; and (3) implementation of an integrated topology optimization pipeline applying particle swarm optimization (PSO) to selected candidates, achieving efficient compliance minimization with reduced computational effort. The complete workflow - which spans GS generation, surrogate-based candidate selection, and iterative optimization - is implemented and validated in two design domains with width-to-height aspect ratios of 1:1 and 1:1.5 and generates 438,984 and 104,032 different frame configurations respectively. The selected candidates undergo PSO-based optimization, yielding designs with volume fractions below 0.55 and preserving critical floor beams, demonstrating the framework’s ability to enable the design of structurally efficient RC frames. The framework is designed to be extensible, with direct applicability to broader RC design scenarios including three-dimensional frames and nonlinear analysis in future work.