Seismic vulnerability and risk assessment at the urban scale using support vector machine and GIScience technology: a case study of the Lixia District in Jinan City, China

Liu, Yaohui; Zhang, Xinyu; Liu, Wenyi; Lin, Yu; Su, Fei; Cui, Jian; Wei, Benyong; Cheng, Hao; Gross, Lutz

doi:10.1080/19475705.2023.2173663

Cited by 12 publications

(1 citation statement)

References 58 publications

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“…Consequently, the validation of the assessment results based on accident cases is difficult to realize, and it is still necessary to build a quantitative risk assessment model for urban road collapse accidents based on the coupling relationship between road collapse risk factors. Machine learning models have been widely used in disaster risk assessment [37,38], and models such as Convolutional Neural Networks (CNNs) [39,40], Artificial Neural Networks (ANNs) [41,42], and Support Vector Machines (SVMs) [43,44] are beginning to be used in risk assessment for natural disasters with large amounts of monitoring data, such as earthquakes and floods. However, the process of modeling quantitative risk assessment for urban road collapse accidents is not yet directly applicable to the process of modeling natural hazard risk due to its characteristics of multiple risk factors and difficulty in capturing accident data.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Model for Risk Assessment of Urban Road Collapse Based on China Accident Data

Zhang,

Qi,

Cheng

et al. 2024

Sustainability

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With the deepening development and utilization of urban underground space, the risk of urban road collapse is becoming increasingly prominent, which is a serious threat to the safety of life and property. Therefore, the risk assessment of urban road collapse has vital significance for the safety management of cities. The main idea is to predict ongoing accidents by analyzing historical accident cases in depth. This paper explores the combination of Interpretative Structural Modeling (ISM) and Bayesian Networks (BNs) to construct a risk assessment model of road collapse. First, the main risk factors of road collapse and their coupling relationships are identified, which is used to increase the low reliability of complex systems. Then, the risk factors of road collapse are logically divided by ISM to obtain the BN hierarchy. Finally, the BN node probabilities are evaluated by the Expectation–Maximization (EM) algorithm using the collected 92 real road collapse accident cases. The model can be used to quantify the coupling strength and influence degree of each risk factor on the occurrence of road collapse accidents, which in turn can predict the probability of road collapse accidents in a given scenario. This study can provide a theoretical basis for urban safety management and reduce the risk of road collapse and potential loss of life and property, which is important for the sustainable development of societies.

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