用可解释模型把抗震设计变反向优化，省时又精准。
Toward using explainable data-driven surrogate models for treating performance-based seismic design as an inverse engineering problem
- 用可解释机器学习建代理模型，直接映射设计参数与性能指标。
- 模型R²超90%，在多种建筑类型中精准预测最优截面尺寸。
- 适合做抗震设计优化的工程师或研究人员参考。
本研究提出一种将基于性能的抗震设计视为逆工程问题的方法，直接推导出满足特定性能目标的设计参数。通过构建可解释的机器学习代理模型，实现设计变量与性能指标的直接映射，克服了传统性能设计计算效率低的问题。该模型被集成至遗传优化算法中求解逆问题。方法应用于洛杉矶和查尔斯顿的钢与混凝土框架结构库存，优化得到在固定几何条件下使年均修复成本最小化的构件截面属性。结果表明，代理模型在多样化建筑类型、几何形态、抗震设计及场地地震危险性下均保持高精度（如R² > 90%），优化算法所识别的构件最优属性值符合工程原理。
原文摘要 · Abstract (English)
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.
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