Mathematical Problems in Engineering

Volume 2019, Article ID 4952036, 10 pages

https://doi.org/10.1155/2019/4952036

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

^{1}School of Construction Machinery, Chang’an University, Xi’an 710064, China^{2}Xi’an University of Science and Technology, Xi’an 710054, China

Correspondence should be addressed to Haiying Wang; nc.ude.dhc@gniyhw

Received 4 September 2018; Revised 12 January 2019; Accepted 29 January 2019; Published 11 February 2019

Academic Editor: Alberto Campagnolo

Copyright © 2019 Haiying Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### 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, e_{1}, and the root mean square error, e_{2}, 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.

#### 1. Introduction

Expressway and railway construction projects have become more dependent on information technology in the past decade [1]; however, 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. Because concrete quality has far-reaching impacts on the overall project, strengthening compressive test process monitoring is vital to construction safety and project success. While test machine data is monitored over a long period of time, it is also 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. Compared with the traditional laboratory sampling of concrete cube specimen, conducting concrete quality assessments by applying time series estimation algorithms to the test data can more effectively utilize the massive information data and more accurately estimate the concrete compressive data.

There has been significant research conducted on estimation methods, with the most classical estimation algorithms being support vector machines (SVM), WNN, and decision trees. While SVM methods [3] use statistical theory to minimize structural risk, when there is a large data quantity, the algorithm is slow, takes up a great deal of computer memory, and is unable to resolve multiclassification problems [4]. Consequently, many alternative optimization methods have been proposed. In [5], for example, a cuckoo search (CS) algorithm was used to optimize the unknown parameters in a support vector machine model and in [6], a decision tree inductive algorithm was applied to classify specific data. However, when there is incomplete data, decision tree data performance degrades, which leads to overfitting and uneven distributions [7]. For that, Yang and Fong (2013) [8] developed an incremental optimization mechanism to optimize fast decision trees. The compression test data classifications were fixed and the data volume was large, which can be suitably predicted using artificial neural network (ANN). Filik (2016) developed a new hybrid approach for wind speed estimation using a fast block least mean square algorithm and an artificial neural network [9]; however, artificial neural networks have been found to have inherent faults such as weak error-tolerance and missing information [10].

WNN, which apply wavelet theory to neural networks, have been found to make up for the lack of a Fourier Transformation in time domains when predicting time series problems [11, 12]. For example, Zhang and Wu (2015) [13] proposed a GA-WNN model to achieve optimization of piezoresistive pressure sensors and corresponding measurement systems. Sharma and Yang (2016) [14] proposed a mixed WNN for short-term solar irradiance forecasting and compared three algorithms with the mixed WNN wavelet algorithm and proved that the wavelet algorithm significantly reduced estimation errors in solar radiation. Guan and Luh (2013) [15] used an improved WNN to solve a short-term load forecasting problem without an estimated forecast interval and Falamarzi and Palizdan (2016) [16] used a wavelet transformation to decompose input data and improve the estimation accuracy of a transpiration estimation model.

Therefore, WNN has been found to have good estimation performances and better practicability and application for compression data. On this basis, this paper applies wavelet technology to ensure effective estimations. While previous research has mainly focused on pressure transmitter research, solar radiation estimations, short-term load forecasting, and other fields, there has been no research to date that has examined concrete test data estimating methods for engineering construction nor the methods needed for the sample extraction and classification of mass test monitoring data.

In this paper, a time series estimation method based on WNN is therefore proposed, with the fracture load, F(KN), and the compressive strength, (MPa), being selected as the concrete cube specimen compressive data attributes. A FCM algorithm is optimized using a developed simulated annealing algorithm and genetic algorithm (SA+GA) applied to classify the test specimens from a YAW-2000 compression-testing machine in the TJ-01 contract section of the Shaanxi Xi'an-Hancheng Intercity Railway. Then, by using the improved WNN algorithm, the estimation model is used to train the clustering results, after which the trained estimation model is employed to estimate the test data. Finally, compare the estimation data with the actual measurements to verify the accuracy and the validity of the proposed method.

The remainder of this paper is organized as follows. In Section 2, a hybrid optimization FCM algorithm based on the GA+SA is proposed to cluster the sample data. In Section 3, an improved time series estimation model based on WNN is proposed. Section 4 uses the clustering method proposed in Section 2 to cluster the sample data, after which the sample data are trained to construct the compression test data estimation model, and the measured data compared with the estimation data from the estimation model. Finally, concluding comments are given in Section 5.

#### 2. Optimized FCM Clustering Based on the GA+SA

All the data in this paper comes from the information management system of the Shaanxi Xi'an-Hancheng Intercity Railway construction project. The concrete cube specimen compressive test data from the YAW-2000 compression-testing machine of the TJ-01 tender section are analyzed and estimated, the aim of which is to estimate the concrete quality in advance and to prevent unqualified concrete being used in the construction materials. The fracture load, F(KN), and the compressive strength, (MPa), are used to estimate the concrete compressive test data.

With the Lagrange multiplier as the objective function, the FCM algorithm calculates the clustering center by optimizing the objective function. However, because the initial FCM clustering center is random, it can easily fall into a local optimal solution if the clustering center is not properly chosen. Therefore, as the clustering results depend strongly on the clustering center, incorrect clustering boundary divisions and inaccurate clustering results could result [17]. To overcome this problem, following [18], a GA+SA is used to optimize the initial FCM algorithm clustering center.

##### 2.1. Genetic Algorithm (GA)

GA [19] are practical algorithms that imitate nature’s “selection and survival of the fittest” evolutionary process, in which the genes adapted to the environment remain. Individuals who fit the environment best should have a better chance to propagate their offspring. Similarly, GA transform data into corresponding binary numbers, corresponding to genes in genetic process, after which a natural selection process using an adaptive function takes place. The genetic process involves genetic inheritance, genetic variation, and gene selection to eventually produce a new population, in which the binary number crossovers are heredity; the jumps from 0 to 1 in the binary numbers are the genetic mutations, and the adaptive judgment is associated with natural gene selection.

The flowchart of GA is shown in Figure 1 and the overall structure of GA is described as follows.