Multi-fidelity Bayesian optimization of shear-wall building costs

Abstract

Structural optimization has the potential to save significant costs for buildings. However, most approaches require new algorithm implementations or a significant computational cost, which is often out of reach in terms of software and computational budget for structural engineers. This work addresses these issues through Bayesian optimization, where the structural analysis, design, and normative constraints are considered black-box functions. In this work, we optimize the shear-wall thicknesses of two finite-element building models in OpenSEES to reduce the total cost of concrete and steel reinforcement. We start by running a design of experiments phase to create initial datasets with different parameters. Then, we run the optimization by iteratively training Randomized Prior Networks as surrogate models for the cost and constraint and use Thompson sampling to estimate a batch of candidate optimal points at each iteration. Later, we propose multiple low-fidelity models to feed Bayesian optimization through multi-fidelity surrogate modeling to enhance the optimization performance. We analyze different combinations of dataset sizes and low-fidelity models, and we obtain lower costs when comparing single-fidelity methods with traditional metaheuristic methods. In addition, we found that multi-fidelity modeling can achieve similar or even better optimization results than high-fidelity-based optimization in a fraction of time. This work opens the door to increase the usage of structural optimization, leading to more efficient buildings.