基于机器学习方法的地震破坏预测

地震破坏预测研究对于建筑结构减灾规划、震前风险预测、震后应急评估有着重要意义。传统的方法因为受到震害资料数量的限制以及计算精度和能力的制约，一般基于经验只考虑少数因素对结构破坏的影响。通过使用随机森林算法，设计了一个综合考虑地震动、结构和场地等多维度信息的分类方法，用以预测建筑物的地震破坏程度。本文研究框架基于新西兰国家地震委员会所收集的27次地震详细破坏资料，处理得到14.2万条高质量建筑物损失数据，考虑了谱加速度、建筑形状、层数等16个影响因素，将4种不同的损伤状态作为模型的学习标签进行地震破坏预测训练。结果表明，随机森林算法在6种分类算法中性能最佳，经过学习曲线法调参、代价敏感学习之后，经过优化得到的随机森林模型对于测试集的整体预测精确率可以达到75.4%,对4种损伤标签的召回率分别达到了88.2%、55.0%、60.7%和70.8%,远好于其他方法。当只考虑对结果影响最重要的前12个因素，该模型的预测精度仍然能够达到73.7%。可见，基于此框架所训练的预测模型具有良好的精度与适用性，同时该框架对于中国震害资料大数据库的研究具有较高的参考价值。

Earthquake damage prediction based on machine learning

Seismic damage prediction studies are of great importance for disaster mitigation planning, pre-earthquake risk prediction, and post-earthquake emergency assessment. Traditional methods usually consider the impacts by a limited number of influencing factors based on empirical experience due to the constraint of limited detailed damage data and computing resources. In this paper, based on the random forest algorithm, a classification model that integrates multidimensional information including ground shaking, structural information, and site conditions is designed to predict structural earthquake damage. Based on the detailed damage information of 27 earthquakes assembled by the Earthquake Commission of New Zealand, 142,000 high-quality post-processed building damage records were used for training the prediction model, which included 16 influencing factors such as spectral acceleration, building shape, and number of stories. Four damage state are used as learning labels of the model. The results show that the random forest algorithm has the best performance among six classification models used in the comparison study. After fine tuning the learning curve parameters and cost-sensitive learning, the overall prediction accuracy of the optimal random forest model can reach 75.4% in the test set, and the recall rates for the four damage labels reach 88.2%, 55.0%, 60.7% and 70.8%, respectively, demonstrating the efficacy of the proposed approach based on the large detailed damage data sets. When the first 12 factors are used based on their contribution to model accuracy, the overall classification accuracy of the model can still reach 73.7%. It can be seen that the prediction model trained based on this research framework has good accuracy and applicability, and the framework has a high reference value for future research on the domestic earthquake damage.