Prediction of Frost-Heaving Behavior of Saline Soil in Western Jilin Province, China, by Neural Network Methods

Mathematical Problems in Engineering, Jul 2017

In this study, backpropagation neural network (BPNN) and generalized regression neural network (GRNN) approaches are used to predict the frost-heaving ratio (FR) of the saline soil specimen collected from Nong’an, Western Jilin, China. Four variables, namely, water content (WC), compactness, temperature, and content of soluble salts (CSS), are considered in predicting FR. A total of 360 pieces of data, collected from the experimental results, in which 30 pieces of data were selected randomly as the testing set data and the rest of the data were treated as the training set data, are applied to develop the prediction models. The predicted data from the models are compared with the experimental data. Then, the results of the two approaches are compared to obtain a relatively reliable model. Results indicate that the prediction model for the FR of saline soil in Nong’an can be successfully established using the GRNN method. Four new GRNN models are constructed for sensitivity analysis to assess the influence degree of the influencing factors, and the results indicate that water content is the most influential variable in the FR of the saline soil specimen, whereas content of soluble salts is the least influential variable.

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Prediction of Frost-Heaving Behavior of Saline Soil in Western Jilin Province, China, by Neural Network Methods

Prediction of Frost-Heaving Behavior of Saline Soil in Western Jilin Province, China, by Neural Network Methods Xudong Zhang,1 Qing Wang,1 Zhensheng Huo,1 Tianwen Yu,1 Gang Wang,1 Tianbao Liu,1 and Wenhua Wang2 1College of Construction Engineering, Jilin University, Changchun 130026, China 2College of Civil Engineering of Changchun Institute of Technology, Changchun 130012, China Correspondence should be addressed to Qing Wang; nc.ude.ulj@gniqgnaw Received 10 November 2016; Accepted 13 June 2017; Published 24 July 2017 Academic Editor: Francesco Pesavento Copyright © 2017 Xudong Zhang 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 In this study, backpropagation neural network (BPNN) and generalized regression neural network (GRNN) approaches are used to predict the frost-heaving ratio (FR) of the saline soil specimen collected from Nong’an, Western Jilin, China. Four variables, namely, water content (WC), compactness, temperature, and content of soluble salts (CSS), are considered in predicting FR. A total of 360 pieces of data, collected from the experimental results, in which 30 pieces of data were selected randomly as the testing set data and the rest of the data were treated as the training set data, are applied to develop the prediction models. The predicted data from the models are compared with the experimental data. Then, the results of the two approaches are compared to obtain a relatively reliable model. Results indicate that the prediction model for the FR of saline soil in Nong’an can be successfully established using the GRNN method. Four new GRNN models are constructed for sensitivity analysis to assess the influence degree of the influencing factors, and the results indicate that water content is the most influential variable in the FR of the saline soil specimen, whereas content of soluble salts is the least influential variable. 1. Introduction Seasonally frozen ground is widely distributed around the world. However, the seasonally frozen ground in Western Jilin is saline soil, which contains some soluble salts. Meanwhile, some of the soil in Western Jilin is dispersive soil [1], which is prone to many engineering problems, such as salinization, corrosion of the building foundation, frost heave, and thaw settlement. Western Jilin Province is a typical seasonal frost region, which is also one of the most severe salinization areas of China [2]. The ecosystem in this area is fragile, and the saline soil area increases year by year. Considerable research has been conducted to analyze the soil properties in Western Jilin. Zhang [1] collected soil specimens in Western Jilin and investigated its dispersing mechanism. Bao et al. [2] investigated the influencing factors of dispersive soil in Western Jilin using the gray correlation degree method. In previous research, the basic physicochemical properties [3], dispersion of soils [4], and hydraulic heat multiphases coupled geological environmental system of saline soil in Western Jilin Province, China were investigated [5]. However, there is few literatures that studied the mechanism of salinization and frost heave; thus research on the engineering problems of saline soil in Western Jilin is insufficient. This study focused on studying the frost-heaving behavior of saline soil and the influence degree of the influencing factors. Soil samples were collected from Nong’an, Western Jilin, China, to assess the frost-heaving behavior of seasonally frozen ground. In contrast to other kinds of seasonally frozen grounds, the soil collected from Nong’an, Western Jilin, is a typical saline soil. According to the rock soil engineering reconnaissance specification (GB50021-2001), the content of soluble salts (CSS) of saline soil is more than 0.3%. In addition, according to previous research, the CSS of most soil samples was more than 0.3% in the study area, some of which were even approximately 1.5%. Considering subgrade engineering and previous research [6], the frost-heaving ratio (FR) was measured and four parameters were selected for analysis, namely, compactness (), temperature (), water content (WC), and CSS. Modeling these factors using mathematical modeling and traditional process is complex. Thus, the neural network is assumed to be a feasible method to predict the FR from the design parameters. The neural network method is inspired by the biological nervous system, which is widely used in engineering, image recognition, and voice recognition. The neural network was first applied in civil engineering in 1989 [7]. Then, the neural network method was used in many research subjects in engineering geology and civil engineering. The neural network has been applied in structural damage inspection [8], soft rocks strength prediction [9], ground vibration prediction [10], (...truncated)


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Xudong Zhang, Qing Wang, Zhensheng Huo, Tianwen Yu, Gang Wang, Tianbao Liu, Wenhua Wang. Prediction of Frost-Heaving Behavior of Saline Soil in Western Jilin Province, China, by Neural Network Methods, Mathematical Problems in Engineering, 2017, 2017, DOI: 10.1155/2017/7689415