期刊
JOURNAL OF COMPUTING IN CIVIL ENGINEERING
卷 31, 期 5, 页码 -出版社
ASCE-AMER SOC CIVIL ENGINEERS
DOI: 10.1061/(ASCE)CP.1943-5487.0000700
关键词
Deep foundation pit excavation; Risk prediction; Safety; Support vector machine
资金
- National Science Foundation of China (NSFC) [71471072]
- NSFC
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. (C) 2017 American Society of Civil Engineers.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据