Toward using explainable data-driven surrogate models for treating performance-based seismic design as an inverse engineering problem
This addresses computational inefficiencies in seismic design for engineers, though it is incremental as it applies existing methods to a specific domain.
The study tackled performance-based seismic design by treating it as an inverse engineering problem, using explainable machine learning models to map design variables to performance metrics, achieving high accuracy with R2 > 90% in optimizing member properties to minimize seismic repair costs for steel and concrete frames.
This study presents a methodology to treat performance-based seismic design as an inverse engineering problem, where design parameters are directly derived to achieve specific performance objectives. By implementing explainable machine learning models, this methodology directly maps design variables and performance metrics, tackling computational inefficiencies of performance-based design. The resultant machine learning model is integrated as an evaluation function into a genetic optimization algorithm to solve the inverse problem. The developed methodology is then applied to two different inventories of steel and concrete moment frames in Los Angeles and Charleston to obtain sectional properties of frame members that minimize expected annualized seismic loss in terms of repair costs. The results show high accuracy of the surrogate models (e.g., R2> 90%) across a diverse set of building types, geometries, seismic design, and site hazard, where the optimization algorithm could identify the optimum values of members' properties for a fixed set of geometric variables, consistent with engineering principles.