Prediction of concrete strength using fuzzy neural networks

doi:10.4028/www.scientific.net/AMR.243-249.6121

IR > 土木工程学院

	Prediction of concrete strength using fuzzy neural networks
	Xu, Jing 1,2; Wang, Xiuli1
	2011
会议名称	1st International Conference on Civil Engineering, Architecture and Building Materials, CEABM 2011
会议录名称	Advanced Materials Research
卷号	243-249
页码	6121-6126
会议日期	June 18, 2011 - June 20, 2011
会议地点	Haikou, China
出版者	Trans Tech Publications, P.O. Box 1254, Clausthal-Zellerfeld, D-38670, Germany
摘要	The purpose of this paper is to develop the I-PreConS (Intelligent PREdiction system of CONcrete Strength) that predicts the compressive strength of concrete to improve the accuracy of concrete undamaged inspection. For this purpose, the system is developed with adaptive neuro-fuzzy inference system (ANFIS) that can learn cube test results as training patterns. ANFIS does not need a specific equation form differ from traditional prediction models. Instead of that, it needs enough input-output data. Also, it can continuously re-train the new data, so that it can conveniently adapt to new data. In the study, adaptive neuro-fuzzy inference system (ANFIS) based on Takagi-Sugeno rules is built up to prediction concrete strength. According to the expert experience, the relationship between the rebound value and concrete strength tends to power function. So the common logarithms of rebound value and strength value are used as the inputs and outputs of the ANFIS. System parameter sets are iteratively adjusted according to input and output data samples by a hybrid-learning algorithm. In the system, in order to improve of the ANFIS, condition parameter sets can be determined by the back propagation gradient descent method and conclusion parameter sets can be determined by the least squares method. As a result, the concrete strength can be inferred by the fuzzy inference. The method takes full advantage of the characteristics of the abilities of Fuzzy Neural Networks (FNN) including automatic learning, generation and fuzzy logic inference. The experiment shows that the average relative error of the predicted results is 10.316% and relative standard error is 12.895% over all the 508 samples, which are satisfied with the requirements of practical engineering. The ANFIS-based model is very efficient for prediction the compressive strength of in-service concrete. © (2011) Trans Tech Publications.
关键词	Algebra Building materials Civil engineering Compressive strength Construction equipment Forecasting Fuzzy inference Fuzzy logic Fuzzy systems Learning algorithms Least squares approximations Mathematical models Network architecture Adaptive neuro-fuzzy inference system Automatic-learning Average relative error Compressive strength of concrete Concrete strength Concrete strength prediction Condition parameters Expert experience Fuzzy logic inference Fuzzy Neural Networks (FNN) Gradient Descent method Hybrid-learning algorithm Input and outputs Input-output data Intelligent prediction Least squares methods Parameter set Power functions Practical engineering Prediction model Rebound value Relative standard error Specific equations Strength values Takagi-sugeno Test results Training patterns
DOI	10.4028/www.scientific.net/AMR.243-249.6121
收录类别	EI
语种	英语
EI入藏号	20112314045943
EI主题词	Fuzzy neural networks
ISSN	10226680
来源库	Compendex
分类代码	723.4 Artificial Intelligence - 723 Computer Software, Data Handling and Applications - 721.1 Computer Theory, Includes Formal Logic, Automata Theory, Switching Theory, Programming Theory - 421 Strength of Building Materials; Mechanical Properties - 415 Metals, Plastics, Wood and Other Structural Materials - 921 Mathematics - 414 Masonry Materials - 412 Concrete - 411 Bituminous Materials - 409 Civil Engineering, General - 405.1 Construction Equipment - 413 Insulating Materials
引用统计	无
文献类型	会议论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/116707
专题	土木工程学院
作者单位	1.College of Civil Engineering, Lanzhou University of Technology, Lan gong ping road 287, Lanzhou, 730050, China; 2.College of Civil Engineering, Qingdao Technological University, Shandong, Qingdao, 266033, China
第一作者单位	土木工程学院
推荐引用方式 GB/T 7714	Xu, Jing,Wang, Xiuli. Prediction of concrete strength using fuzzy neural networks[C]:Trans Tech Publications, P.O. Box 1254, Clausthal-Zellerfeld, D-38670, Germany,2011:6121-6126.