支持向量机
基础(证据)
施工现场安全
工程类
安全监测
风险分析(工程)
鉴定(生物学)
决策支持系统
风险评估
计算机科学
建筑工程
人工智能
计算机安全
历史
生物
医学
结构工程
生物技术
考古
植物
作者
Ying Zhou,Wan-Jun Su,Lieyun Ding,Hanbin Luo,Peter E.D. Love
出处
期刊:Journal of Computing in Civil Engineering
[American Society of Civil Engineers]
日期:2017-09-01
卷期号:31 (5)
被引量:80
标识
DOI:10.1061/(asce)cp.1943-5487.0000700
摘要
Accurately predicting risks during the construction of deep foundation pits is pivotal to ensuring the safety of the workforce of public and adjacent structures. Existing methods for assessing such risks are cumbersome and are unable to accurately provide the certainty required to ensure safety levels. This paper presents a novel prediction method that utilizes the support vector machine (SVM) to determine the safety risks that can materialize during the construction of deep pit foundations in subway infrastructure projects. The development of the SVM risk prediction model involves the following steps: (1) identification of risk factors from industry experts; (2) processing the sampled data; and (3) training and testing. A case study is used to demonstrate the predictive capability of the developed SVM approach. By inputting data on a daily basis, the safety risks associated with deep foundation pits can be monitored; this enables decision-makers to formulate appropriate control measures.
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