基于数据库与支持向量机的施工升降机安全风险预测  

Safety risk prediction of construction elevator based on database and SVM

作　　者：赵挺生[1] 庞奇志[2] 姜雯茜 ZHAO Tingsheng;PANG Qizhi;JIANG Wenxi(School of Civil Engineering and Mechanics,Huazhong University of Science and Technology,Wuhan Hubei 430074,China;School of Engineering,China University of Geosciences(Wuhan),Wuhan Hubei 430074,China)

机构地区：[1]华中科技大学土木工程与力学学院,湖北武汉430074 [2]中国地质大学(武汉)工程学院,湖北武汉430074

出　　处：《中国安全科学学报》2021年第4期11-17,共7页China Safety Science Journal

基　　金：国家重点研发计划项目(2017YFC0805500)。

摘　　要：为预防施工升降机安全事故的发生,利用数据库和支持向量机(SVM)算法预测施工升降机的安全风险。首先依据相关理论和施工升降机的特点,初步定性分析施工升降机的安全风险因素;然后利用施工升降机安全事故数据库管理系统统计分析施工升降机安全事故案例,细化安全风险因素,确定施工升降机安全风险预测指标;最后运用SVM算法构建施工升降机安全风险预测模型,利用网格搜索法、遗传算法、粒子群算法优化模型参数,确定施工升降机安全风险最佳预测模型。结果表明:可以利用安全事故数据库建立施工升降机安全风险预测指标体系,通过构建SVM预测模型划分施工升降机安全风险等级,从而采取相应的风险防控措施,降低施工升降机的安全风险,保障人员安全、减少财产损失。In order to prevent safety accidents of construction elevator,database and SVM algorithm were used to predict its safety risks.Firstly,based on relevant theory and characteristics of construction elevators,preliminary theoretical qualitative analysis on their risk factors was carried out.Then,safety accident cases were statistically analyzed by utilizing accident database management system,risk factors were defined and risk prediction indicators were determined.Finally,SVM algorithm was applied to construct a safety risk prediction model,and its parameters were optimized by grid search method,genetic algorithm and particle swarm optimization algorithm respectively to determine the best prediction model.The results show that safety accident database can be used to establish a risk prediction index system,and risk level can be divided by constructing an SVM prediction model,which is helpful for us to take corresponding prevention and control measures to reduce risk of construction elevators,ensure personnel safety,and reduce property losses.

关 键 词：数据库 支持向量机(SVM) 施工升降机 安全风险因素 风险预测 

分 类 号：X948[环境科学与工程—安全科学]