结合机器学习算法最新研究进展,提出一种基于改进遗传算法优化BP神经网络的单体建筑物震害评估方法。以四川地区为例,通过改进遗传算法优化BP神经网络建立评估模型,输出评估区域内不同结构类型单体建筑物在各震害影响因素综合作用下的破坏等级,并通过实际算例分析对模型的有效性进行验证。结果表明,该方法可快速、准确地评估单体建筑物震害情况。

This paper proposes a single building damage assessment method based on improved genetic algorithm optimized back propagation(BP)neural network,after reviewing the latest research progress in machine learning algorithms. Taking the Sichuan area as an example,the improved genetic algorithm optimized BP neural network is used to establish an assessment model and to output the damage levels of single buildings with different structural types in the assessment area under the combined effect of various seismic damage influencing factors. The validity of the model is verified through the analysis of practical examples,and the results show that the method can quickly and accurately assess the seismic damage of single buildings.

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“十三五”国家重点研发计划课题(2020YFA0710603)、四川地震科技创新团队专项(201901)共同资助