Concrete Compression Test Data Estimation Based on a Wavelet Neural Network Model

Haiying Wang,Chao Wang,Xinping Wang,Jian Xu

doi:10.1155/2019/4952036

Abstract

Firstly, a genetic algorithm (GA) and simulated annealing (SA) optimized fuzzy c-means clustering algorithm (FCM) was proposed in this paper, which was developed to allow for a clustering analysis of the massive concrete cube specimen compression test data. Then, using an optimized error correction time series estimation method based on the wavelet neural network (WNN), a concrete cube specimen compressive strength test data estimation model was constructed. Taking the results of cluster analysis as data samples, the short-term accurate estimation of concrete quality was carried out. It was found that the mean absolute percentage error, e1, and the root mean square error, e2, for the samples were 6.03385% and 3.3682KN, indicating that the proposed method had higher estimation accuracy and was suitable for concrete compressive test data short-term quality estimations.

Highlights

Expressway and railway construction projects have become more dependent on information technology in the past decade [1]; much of the collected information is not being extracted or effectively utilized [2]
Much of this construction data is related to laboratory based concrete cube specimen compression tests, the quality of which directly affects the quality of the whole project and is relevant to the project operations and maintenance stages
While test machine data is monitored over a long period of time, it is necessary to make accurate short-term estimations based on previous test data to identify any possible problems before any abnormalities occur and to make corrections to ensure the concrete quality being used for the project

Summary

Introduction

Expressway and railway construction projects have become more dependent on information technology in the past decade [1]; much of the collected information is not being extracted or effectively utilized [2]. The simulated annealing algorithm takes the initial temperature, T, as the starting point, sets the objective function and acceptance probability, and continuously reduces the temperature to determine the optimal solution [21]. It has strong local search ability and short running time advantages; to overcome its poor global searching abilities, so it is combined with GA [22] to get better. N, genetic number of iterations, G, crossover probability, Pc, mutation probability, Pm, starting temperature, T0, cooling coefficient, q, and end temperature, Td. Step 2 (fitness evaluation).

Wavelet Neural Network Time Series Estimation Algorithm

Simulation Analysis

Conclusions