摘要
针对现有高铁桥梁施工风险评估模型存在的主观性强、环节繁琐及需要大量样本等不足,提出基于层次分析法(AHP法)+BP神经网络的高铁桥梁安全风险评估模型。首先对高铁桥梁施工安全进行风险因素的识别,建立风险评估体系;然后采用AHP法对专家经验评估法获取的样本数据进行预处理,结合BP神经网络对处理后的样本进行训练,构建评估模型;最后将该评估模型应用于某高铁桥梁施工安全风险评估。结果表明:该模型能够较全面地评估主要风险因素对高铁桥梁施工的影响,预测得到的风险等级与专家评审结果一致,且该方法更加简洁实用,便于编制标准化的算法程序,可为高铁工程项目的施工安全风险等级评估提供参考。
The conventional construction safety risk assessment models of the high-speed railway bridge are considered relatively subjective in identifying safety risks,incorporating complex and cumbersome procedures and needing a large amount of samples.In this paper,a construction safety risk assessment model based on analytical hierarchy process(AHP)and BP neural network is proposed.The model can first identify the safety risk factors in high-speed railway bridge construction and develop a risk assessment system.Then,the AHP method is used to pre-process the sample data gained by expert evaluation method,which are subsequently trained using the BP neural network to build up the assessment model.The assessment model has been employed for the construction safety risk assessment of an existing high-speed railway bridge.It is shown that the proposed model can comprehensively assess the impact of the main risk factors on the construction of high-speed railway bridge.The risk grade predicted agrees well with the conclusion of expert review.In addition,the proposed model is easier for utilization,facilitates the compilation of standardized algorithm procedure.The application case can be referenced by the similar high-speed railway bridge project.
作者
刘永莉
席铭洋
朱方一
柏华军
LIU Yong-li;XI Ming-yang;ZHU Fang-yi;BAI Hua-jun(School of Civil Architecture and Environment,Hubei University of Technology,Wuhan 430070,China;Wuhan Railway Bridge Vocational College,Wuhan 430050,China;China Railway Siyuan Survey and Design Group Co.,Ltd.,Wuhan 430063,China)
出处
《世界桥梁》
北大核心
2023年第3期66-73,共8页
World Bridges
基金
国家自然科学基金项目(51708188)。
关键词
高铁桥梁
桥梁施工
安全风险评估
层次分析法
BP神经网络
风险因素
风险等级
railway bridge
bridge construction
safety risk assessment
analytical hierarchy process
BP neural network
risk factor
risk grade