ABSTRACT

A machine learning-based fast seismic risk assessment framework is proposed to ease the computational burden in estimating the potential earthquake-induced loss of a building during its intended life. Both the hazard parameters of sites and structural parameters of buildings were incorporated as inputs. The continuous risk values and discrete risk levels were used as outputs for the regression and classification tasks of supervised learning, respectively. A proof-of-concept study of steel frames proved the feasibility of the proposed approach. Artificial neural networks achieved the lowest root mean square error of 0.0051 for regression and the highest accuracy of 96.8% for classification.

摘要

提出了一种基于机器学习的快速地震风险评估框架，以减轻估计建筑物在其预期寿命期间潜在地震引起的损失的计算负担。场地的危险参数和建筑物的结构参数都被纳入作为输入。连续风险值和离散风险水平分别用作监督学习的回归和分类任务的输出。钢框架的概念验证研究证明了所提出方法的可行性。人工神经网络的回归均方根误差最低，为 0.0051，分类准确率最高，为 96.8%。