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爆炸作用下RC柱损伤快速评估模型
引用本文:李忠献,钟波,师燕超.爆炸作用下RC柱损伤快速评估模型[J].天津大学学报(自然科学与工程技术版),2014(11):973-978.
作者姓名:李忠献  钟波  师燕超
作者单位:滨海土木工程结构与安全教育部重点实验室 天津大学,天津,300072
基金项目:国家科技支撑计划资助项目(2012BAJ07B05);国家自然科学基金资助项目(51238007,51021004,51008209).
摘    要:基于BP神经网络,以7个结构参数和2个荷载参数作为输入,损伤值作为输出,建立了爆炸作用下钢筋混凝土(RC)柱损伤的快速评估模型.在模型中,采用以精细化有限元模拟得到的1,032组RC柱损伤的数据训练网络,采用遗传算法和粒子群算法优化网络的初始权值和阈值,并以216个新损伤样本对模型的预测能力进行测试.研究表明,所建立的评估模型能够较准确地用于爆炸作用下RC柱损伤的快速评估;所得到的损伤分区图可以有效并直观地用于RC柱的爆炸风险评估.

关 键 词:爆炸作用  钢筋混凝土柱  损伤评估  风险评估  BP神经网络

Fast Assessment Model for Damage of RC Columns Under Blast Loading
Li Zhongxian,Zhong Bo,Shi Yanchao.Fast Assessment Model for Damage of RC Columns Under Blast Loading[J].Journal of Tianjin University(Science and Technology),2014(11):973-978.
Authors:Li Zhongxian  Zhong Bo  Shi Yanchao
Institution:Li Zhongxian;Zhong Bo;Shi Yanchao;Key Laboratory of Coast Civil Structure Safety of Ministry of Education(Tianjin University);
Abstract:A fast assessment model for damage of RC columns under blast loading is proposed based on BP neuralnetworks,by defining 7 structural parameters and 2 loading parameters as input,damage values as output. In themodel,the network is trained using 1,032 groups of damage values of RC columns obtained from refined finite elementsimulation,and the initial weights and bias are optimized by genetic algorithm and particle swarm optimization.The predictive ability of the model is verified by 216 new samples of damage. The research indicates that the developedmodel can be used for the fast assessment of damage of RC columns under blast loading with reasonable accuracy,and the generated partition figure of damage can be effectively and intuitively used for the blasting risk assessmentof RC columns.
Keywords:blast loading  RC column  damage assessment  risk assessment  BP neural networks
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