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引用本文:王凤来,朱飞,周强.基于BP神经网络的灌孔砌块砌体抗压强度预测[J].建筑材料学报,2015,18(6):1010-1017
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基于BP神经网络的灌孔砌块砌体抗压强度预测
王凤来1, 朱飞2, 周强2
1.哈尔滨工业大学结构工程灾变与控制教育部重点实验室,黑龙江哈尔滨150090;2.哈尔滨工业大学土木工程学院,黑龙江哈尔滨150090
摘要:
完成了36件灌孔砌块砌体的抗压强度试验,统计了既有研究530件灌孔砌块砌体的抗压强度试验数据,建立了输入层为4个参数(砌块抗压强度、砂浆抗压强度、灌孔混凝土抗压强度和灌孔混凝土面积与砌体毛截面面积比值)的BP神经网络,推导出简化的灌孔砌块砌体抗压强度计算公式,分析了灌孔砌块砌体抗压强度试验值与计算值的比值(平均值).结果表明:在统计样本空间内,简化的灌孔砌块砌体抗压强度计算公式预测结果良好.BP神经网络方法可以作为灌孔砌块砌体抗压强度计算的一种新方法使用.
关键词:  灌孔砌块砌体  抗压强度  BP神经网络  公式对比
DOI:10.3969/j.issn.1007 9629.2015.06.017
分类号:
基金项目:“十二五”国家科技支撑计划项目(2013BAJ12B03)
Estimation of Compressive Strength of Grouted Block Masonry Based on BP Neural Network
WANG Fenglai1, ZHU Fei2, ZHOU Qiang2
1.Key Lab of Structures Dynamic Behavior and Control, Ministry of Education, Harbin Institute of Technology,Harbin 150090, China;2.School of Civil Engineering, Harbin Institute of Technology, Harbin 150090, China
Abstract:
36 grouted block masonry specimens were applied to test their compressive strength, and compressive strength test results of 530 specimens in existing researches were counted. A BP neural networks model with four parameters(block compressive strength, mortar compressive strength, grouted concrete compressive strength and the ratio of grouted concrete area to gross section area of masonry) in input layer was established to predict the compressive strength of grouted block masonry.Then, based on the BP model, a simplified formula for compressive strength of grouted block masonry was deduced. In addition, the ratio(average value) of test data and calculated data of compressive strength of grouted block masonry was also analyzed.The results show that, in the statistical sample space, the compressive strength of grouted block masonry predicted by the simplified formula is suitable. The BP neural networks method is feasible in calculating the compressive strength of grouted block masonry.
Key words:  grouted block masonry  compressive strength  BP neural network  formula comparison