Seismic risk prioritization of masonry building stocks using machine learning

IF 4.3 2区 工程技术 Q1 ENGINEERING, CIVIL Earthquake Engineering & Structural Dynamics Pub Date : 2024-09-04 DOI:10.1002/eqe.4227
Onur Coskun, Rafet Aktepe, Alper Aldemir, Ali Erhan Yilmaz, Murat Durmaz, Burcu Guldur Erkal, Engin Tunali
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Abstract

The seismic risk mitigation plans are vital since vulnerable structures are prone to partial or total collapse under the effect of future major earthquake events. Therefore, vulnerable structures in large building stocks should be determined using robust and accurate methods to prevent loss of lives and property. In the current state-of-the-art, the risk states (i.e., whether risky or not) of structures completely depend on the experience of the reconnaissance team of engineers, which could not result in standardized decisions. In this study, machine learning has been integrated into the decision-making algorithm to classify more precise and reliable seismic risk states of masonry buildings, categorizing them into up to four risk categories. For this purpose, a large database, including 12 features and detailed seismic risk analysis results of 4356 masonry buildings, is formed. Firstly, the input variables are preprocessed using feature engineering methods. Then, several machine learning algorithms are utilized to produce a network to estimate the risk state of masonry buildings in association with the risk states obtained from the detailed analysis results. As a result of the analysis of these algorithms, the correct prediction percentages for the testing database of the proposed method for two, three, and four risk states classification are predicted as approximately 87.5%, 86.6%, and 79.0%, respectively. This new approach makes it possible to produce risk color maps of large building stocks and reduce the number of buildings that require immediate action.

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利用机器学习确定砌体建筑群的地震风险优先次序
地震风险缓解计划至关重要,因为在未来大地震事件的影响下,易损结构很容易部分或全部倒塌。因此,应采用稳健、准确的方法确定大型建筑群中的易损结构,以防止生命和财产损失。在目前最先进的技术中,结构的风险状态(即是否有风险)完全依赖于勘察工程师团队的经验,无法形成标准化的决策。本研究将机器学习融入决策算法中,对砌体建筑的地震风险状态进行更精确、更可靠的分类,最多可分为四个风险类别。为此,建立了一个大型数据库,其中包括 12 个特征和 4356 栋砌体建筑的详细地震风险分析结果。首先,使用特征工程方法对输入变量进行预处理。然后,利用几种机器学习算法生成一个网络,结合从详细分析结果中获得的风险状态来估计砌体建筑的风险状态。通过对这些算法的分析,所提出方法的测试数据库对两种、三种和四种风险状态分类的预测正确率分别约为 87.5%、86.6% 和 79.0%。这种新方法使绘制大型建筑群的风险颜色图成为可能,并减少了需要立即采取行动的建筑数量。
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来源期刊
Earthquake Engineering & Structural Dynamics
Earthquake Engineering & Structural Dynamics 工程技术-工程:地质
CiteScore
7.20
自引率
13.30%
发文量
180
审稿时长
4.8 months
期刊介绍: Earthquake Engineering and Structural Dynamics provides a forum for the publication of papers on several aspects of engineering related to earthquakes. The problems in this field, and their solutions, are international in character and require knowledge of several traditional disciplines; the Journal will reflect this. Papers that may be relevant but do not emphasize earthquake engineering and related structural dynamics are not suitable for the Journal. Relevant topics include the following: ground motions for analysis and design geotechnical earthquake engineering probabilistic and deterministic methods of dynamic analysis experimental behaviour of structures seismic protective systems system identification risk assessment seismic code requirements methods for earthquake-resistant design and retrofit of structures.
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