Shi Fengting, Li Ling, Wu Xueling, Wang Yueyue, Niu Ruiqing
School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China.
School of Future Technology, China University of Geosciences, Wuhan 430074, China.
Sensors (Basel). 2024 Jul 5;24(13):4366. doi: 10.3390/s24134366.
This study develops a model to assess building vulnerability across Xinxing County by integrating quantitative derivation with machine learning techniques. Building vulnerability is characterized as a function of landslide hazard risk and building resistance, wherein landslide hazard risk is derived using CNN (1D) for nine hazard-causing factors (elevation, slope, slope shape, geotechnical body type, geological structure, vegetation cover, watershed, and land-use type) and landslide sites; building resistance is determined through quantitative derivation. After evaluating the building susceptibility of all the structures, the susceptibility of each village is then calculated through subvillage statistics, which are aimed at identifying the specific needs of each area. Simultaneously, different landslide hazard classes are categorized, and an analysis of the correlation between building resistance and susceptibility reveals that building susceptibility exhibits a positive correlation with landslide hazard and a negative correlation with building resistance. Following a comprehensive assessment of building susceptibility in Xinxing County, a sample encompassing different landslide intensity areas and susceptibility classes of buildings was chosen for on-site validation, thus yielding an accuracy rate of the results as high as 94.5%.
本研究通过整合定量推导与机器学习技术，开发了一个模型来评估新兴县建筑物的脆弱性。建筑物脆弱性被表征为滑坡灾害风险与建筑物抗性的函数，其中滑坡灾害风险使用一维卷积神经网络（CNN）针对九个致灾因素（海拔、坡度、坡形、岩土体类型、地质构造、植被覆盖、流域和土地利用类型）以及滑坡地点进行推导；建筑物抗性则通过定量推导确定。在评估所有建筑物的易损性之后，通过子村统计计算每个村庄的易损性，目的是确定每个区域的具体需求。同时，对不同的滑坡灾害等级进行分类，建筑物抗性与易损性之间的相关性分析表明，建筑物易损性与滑坡灾害呈正相关，与建筑物抗性呈负相关。在对新兴县建筑物易损性进行全面评估之后，选择了一个涵盖不同滑坡强度区域和建筑物易损性等级的样本进行现场验证，结果准确率高达94.5%。
