Prediction of concrete strength using fuzzy neural networks

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

CORC > 兰州理工大学 > 兰州理工大学 > 土木工程学院

	Prediction of concrete strength using fuzzy neural networks
	Xu, Jing 1,2; Wang, Xiuli 1
	2011
会议日期	June 18, 2011 - June 20, 2011
会议地点	Haikou, China
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
卷号	243-249
DOI	10.4028/www.scientific.net/AMR.243-249.6121
页码	6121-6126
英文摘要	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.
会议录	Advanced Materials Research
会议录出版者	Trans Tech Publications, P.O. Box 1254, Clausthal-Zellerfeld, D-38670, Germany
语种	英语
ISSN号	10226680
内容类型	会议论文
源URL	[http://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
推荐引用方式 GB/T 7714	Xu, Jing,Wang, Xiuli. Prediction of concrete strength using fuzzy neural networks[C]. 见:. Haikou, China. June 18, 2011 - June 20, 2011.