LM-BP神经网络在震后建筑损失评估模型中的应用
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摘要
当前震后建筑经济损失评估模型得到的震后建筑经济损失评估精确度、效率低,针对单一神经网络易产生局部极值等问题,对神经网络方法进行了改进,提出LM-BP神经网络在震后建筑损失评估模型中的应用。输入样本要素为影响震后建筑经济损失的5项因素,输出样本是震后建筑经济损失评估结果,在此基础上采用LM-BP神经网络将训练转化成最小二乘问题,结合LM算法重新定义隐含层节点数量,构建基于LM-BP的神经网络震后经济损失评估模型,采用该模型获取最优震后建筑经济损失评估结果。仿真实验结果表明,所设计的评估模型最小评估误差为0.1%,相比同类模型具有高精确度的优势,是一种可靠的震后建筑经济损失评估模型。
The current model for the post-earthquake economic loss assessment of buildings has low evaluation accuracy and efficiency. In this work, the neural network method is improved, and the application of the LM-BP neural network in the post-earthquake loss assessment model of buildings is proposed to resolve the problem of the easy production of the local extremum by the single neural network. The input sample elements are five factors that affect the post-earthquake economic loss of buildings, and the output samples are the evaluation results of the post-earthquake economic loss of buildings. The LM-BP neural network is used to transform the training set into the least-squares problem, and the number of hidden layer nodes is redefined through combination with the LM algorithm. The model based on the LM-BP neural network for post-earthquake economic loss evaluation is constructed and used to obtain the optimal evaluation results for the post-earthquake economic loss of buildings. Simulation results show that the minimum evaluation error of the proposed model is 0.1%. Given its high accuracy, the proposed model is a more reliable model for the assessment of the post-earthquake economic loss of buildings than other models.
The current model for the post-earthquake economic loss assessment of buildings has low evaluation accuracy and efficiency. In this work, the neural network method is improved, and the application of the LM-BP neural network in the post-earthquake loss assessment model of buildings is proposed to resolve the problem of the easy production of the local extremum by the single neural network. The input sample elements are five factors that affect the post-earthquake economic loss of buildings, and the output samples are the evaluation results of the post-earthquake economic loss of buildings. The LM-BP neural network is used to transform the training set into the least-squares problem, and the number of hidden layer nodes is redefined through combination with the LM algorithm. The model based on the LM-BP neural network for post-earthquake economic loss evaluation is constructed and used to obtain the optimal evaluation results for the post-earthquake economic loss of buildings. Simulation results show that the minimum evaluation error of the proposed model is 0.1%. Given its high accuracy, the proposed model is a more reliable model for the assessment of the post-earthquake economic loss of buildings than other models.
引文
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[15] CIOCCA G,CAROSA E,STORNELLI M,et al.Post-Traumatic Stress Disorder,Coping Strategies and Type 2 Diabetes:Psychometric Assessment after L'Aquila Earthquake[J].Acta Diabetologica,2015,52(3):513-521.
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[17] 李鑫,郭安宁,焦娇.地震灾害经济易损性评估研究:以甘肃省为例[J].西北地震学报,2012,34(3):284-288.LI Xin,GUO Anning,JIAO Jiao.Assessment of Economic Vulnerability Caused by Earthquake Hazard:Taking Gansu Province as an Example[J].Northwestern Seismological Journal,2012,34(3):284-288.
[18] 张晓东,朱丽霞.地震灾害经济损失理论估计及初步讨论[J].西北地震学报,1998,20(2):58-61.ZHANG Xiaodong,ZHU Lixia.Theoretical Evaluation and Preliminary Discussion on Economic Damage of Seismic Hazard[J].Northwestern Seismological Journal,1998,20(2):58-61.
[2] 温留汉·黑沙,张永山,汪大洋.工程结构地震易损性与经济损失评估研究现状[J].建筑科学与工程学报,2015,32(6):17-29.WENLIU Han Heisha,ZHANG Yongshan,WANG Dayang.Review on Seismic Vulnerability and Economic Loss Assessment of Engineering Structures[J].Journal of Architecture and Civil Engineering,2015,32(6):17-29.
[3] 魏本勇,苏桂武.基于投入产出分析的汶川地震灾害间接经济损失评估[J].地震地质,2016,38(4):1082-1094.WEI Benyong,SU Guiwu.Assessment on Indirect Economic Loss of Wenchuan Earthquake Disaster Based on Input-Output Analysis[J].Seismology and Geology,2016,38(4):1082-1094.
[4] 张加庆.基于大数据的地震损失价值评估模型设计[J].地震工程学报,2018,40(2):356-362.ZHANG Jiaqing.Design of Earthquake Loss Value Evaluation Model Based on Big Data[J].China Earthquake Engineering Journal,2018,40(2):356-362.
[5] 谢全敏,宋啸,杨文东.基于功效系数法的滑坡灾害风险评估研究[J].水利水电技术,2017,48(6):120-123.XIE Quanmin,SONG Xiao,YANG Wendong.Efficacy Coefficient Method-based Study on Landslide Disaster Risk Assessment[J].Water Resources and Hydropower Engineering,2017,48(6):120-123.
[6] 王子珺,赵伯明.高速铁路地震预警线路场地地震动快速准确预测方法[J].中国铁道科学,2016,37(6):128-134.WANG Zijun,ZHAO Boming.Fast and Accurate Prediction Method for Track Ground Motion in Earthquake Early Warning for High Speed Railway[J].China Railway Science,2016,37(6):128-134.
[7] 梁喜,刘雨.基于模糊神经网络的建筑工程造价预测模型[J].技术经济,2017,36(3):109-113.LIANG Xi,LIU Yu.Predicting Model for Construction Engineering Cost Based on Fuzzy Neural Network[J].Technology Economics,2017,36(3):109-113.
[8] 李扬,孙泽烨,胡镜清.改进的神经网络综合评价模型研究[J].数学的实践与认识,2015,45(10):59-68.LI Yang,SUN Zeye,HU Jingqing.Study on the Novel Neural Network Comprehensive Evaluation Model with Expert and Fuzzy System[J].Mathematics in Practice and Theory,2015,45(10):59-68.
[9] MICHEL C,GUéGUEN P,CAUSSE M.Seismic Vulnerability Assessment to Slight Damage Based on Experimental Modal Parameters[J].Earthquake Engineering & Structural Dynamics,2012,41(1):81-98.
[10] 刘勇,方伍宝,李振春,等.基于叠前地震的脆性预测方法及应用研究[J].石油物探,2016,55(3):425-432.LIU Yong,FANG Wubao,LI Zhenchun,et al.Brittleness Prediction and Application Based on Pre-stack Seismic Inversion[J].Geophysical Prospecting for Petroleum,2016,55(3):425-432.
[11] 熊岩,赵成勇,许建中.模块化多电平换流器电容电压均衡排序算法综述[J].电力工程技术,2017,36(2):1-8.XIONG Yan,ZHAO Chengyong,XU Jianzhong.A Review of Ranking Algorithms for MMC Capacitor Voltages Balancing[J].Jiangsu Electrical Engineering,2017,36(2):1-8.
[12] 张加庆.基于大数据的地震损失价值评估模型设计[J].地震工程学报,2018,40(2):356-362.ZHANG Jiaqing.Design of Earthquake Loss Value Evaluation Model Based on Big Data[J].China Earthquake Engineering Journal,2018,40(2):356-362.
[13] HWANG S H,LIGNOS D G.Nonmodel-based Framework for Rapid Seismic Risk and Loss Assessment of Instrumented Steel Buildings[J].Engineering Structures,2018,156:417-432.
[14] 朱汉波,梁兴文,党英杰.框架-剪力墙结构基于地震强度的新一代抗震性能评估方法研究[J].振动与冲击,2017,36(10):140-148.ZHU Hanbo,LIANG Xingwen,DANG Yingjie.New Generation of Seismic Performance Evaluation Method for Frame-Shear Wall Structures Based on Seismic Intensity[J].Journal of Vibration and Shock,2017,36(10):140-148.
[15] CIOCCA G,CAROSA E,STORNELLI M,et al.Post-Traumatic Stress Disorder,Coping Strategies and Type 2 Diabetes:Psychometric Assessment after L'Aquila Earthquake[J].Acta Diabetologica,2015,52(3):513-521.
[16] 郭继东,杨月巧.地震应急物资需求预测的模糊案例推理技术[J].中国安全生产科学技术,2017,13(2):176-180.GUO Jidong,YANGYueqiao.Study on Fuzzy Case-based Reasoning (FCBR) for Demand Forecast of Emergency Material in Earthquake[J].Journal of Safety Science and Technology,2017,13(2):176-180.
[17] 李鑫,郭安宁,焦娇.地震灾害经济易损性评估研究:以甘肃省为例[J].西北地震学报,2012,34(3):284-288.LI Xin,GUO Anning,JIAO Jiao.Assessment of Economic Vulnerability Caused by Earthquake Hazard:Taking Gansu Province as an Example[J].Northwestern Seismological Journal,2012,34(3):284-288.
[18] 张晓东,朱丽霞.地震灾害经济损失理论估计及初步讨论[J].西北地震学报,1998,20(2):58-61.ZHANG Xiaodong,ZHU Lixia.Theoretical Evaluation and Preliminary Discussion on Economic Damage of Seismic Hazard[J].Northwestern Seismological Journal,1998,20(2):58-61.