Estimation of compressive strength of earth block masonry prisms based on artificial neural networks

被引：0
作者：
Lan G. [1 ]
Wang Y. [1 ]
Zhang J. [1 ]
Dong F. [1 ]
机构：
[1] School of Civil Engineering, Chang'an University, Xi'an
来源：
Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition) | 2019年 / 47卷 / 08期
关键词：
Artificial neural networks; Compressive strength; Computational methods; Earth materials; Masonry;
D O I：
10.13245/j.hust.190810
中图分类号：
学科分类号：
摘要：
This study used back propagation (BP) artificial neural networks to predict the compressive strength of masonry prisms formed by earth blocks and cement mortar or mud, by using three parameters: the height-to-thickness ratio of prisms and the compressive strength of the mortar and that of the blocks. A simplified formula for calculating the compressive strength of the earth masonry prism was proposed. The predicted results were compared with the experimental values and the calculated values of the existing formulas. The results show that the BP neural network model with 10 hidden layer neurons has a good predictive performance for the compressive strength of earth masonry. The accuracy and stability of the simplified formula are superior with the mean value of the ratio between the calculated value and the experimental value is 0.92, and the standard deviation is 0.28, which can be used to determine the compressive strength of earth masonry prisms. © 2019, Editorial Board of Journal of Huazhong University of Science and Technology. All right reserved.
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页码：50 / 54
页数：4
相关论文
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