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

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]  C. Suresh Kumar,et al.  Failure strength prediction of glass/epoxy composite laminates from acoustic emission parameters using artificial neural network , 2017 .

[2]  Hilmi Berk Celikoglu,et al.  Extension of Traffic Flow Pattern Dynamic Classification by a Macroscopic Model Using Multivariate Clustering , 2016, Transp. Sci..

[3]  Cencen Niu,et al.  Experimental study on grain size and soluble salt of saline soil in western Jilin Province, China , 2018 .

[4]  D. Singh,et al.  Analyzing the effect of various soil properties on the estimation of soil specific surface area by different methods , 2015 .

[5]  Boxin Wang,et al.  Prediction of expansion behavior of self-stressing concrete by artificial neural networks and fuzzy inference systems , 2015 .

[6]  Zhang Xu-don.  Research on Soil Dispersion of Qian'an Soil Forest , 2015 .

[7]  Aminaton Marto,et al.  Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm , 2015, Bulletin of Engineering Geology and the Environment.

[8]  Edy Tonnizam Mohamad,et al.  Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach , 2015, Bulletin of Engineering Geology and the Environment.

[9]  Qing Wang,et al.  Study on Dispersive Influencing Factors of Dispersive Soil in Western Jilin Based on Grey Correlation Degree Method , 2013 .

[10]  Oguz Kaynar,et al.  Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils , 2010, Expert Syst. Appl..

[11]  Okan Karahan,et al.  Comparison of artificial neural network and fuzzy logic models for prediction of long-term compressive strength of silica fume concrete , 2009, Adv. Eng. Softw..

[12]  Mahmud Güngör,et al.  Generalized Regression Neural Networks and Feed Forward Neural Networks for prediction of scour depth around bridge piers , 2009, Adv. Eng. Softw..

[13]  İlker Bekir Topçu,et al.  Prediction of long-term effects of GGBFS on compressive strength of concrete by artificial neural networks and fuzzy logic , 2009 .

[14]  Hilmi Berk Celikoglu.  A Dynamic Network Loading Model for Traffic Dynamics Modeling , 2007, IEEE Transactions on Intelligent Transportation Systems.

[15]  Mohammad Hassan Baziar,et al.  Assessment of liquefaction triggering using strain energy concept and ANN model: Capacity Energy , 2007 .

[16]  Hilmi Berk Celikoglu,et al.  A dynamic network loading process with explicit delay modelling , 2007 .

[17]  Hilmi Berk Celikoglu,et al.  Public transportation trip flow modeling with generalized regression neural networks , 2007, Adv. Eng. Softw..

[18]  Özer Çinar,et al.  Comparison of artificial neural network and regression pedotransfer functions for prediction of soil water retention and saturated hydraulic conductivity , 2006 .

[19]  Hilmi Berk Celikoglu,et al.  Application of radial basis function and generalized regression neural networks in non-linear utility function specification for travel mode choice modelling , 2006, Math. Comput. Model..

[20]  S. Suwansawat,et al.  Artificial neural networks for predicting the maximum surface settlement caused by EPB shield tunneling , 2006 .

[21]  Hikmet Kerem Cigizoglu,et al.  Generalized regression neural network in modelling river sediment yield , 2006, Adv. Eng. Softw..

[22]  Peter Keith Woodward,et al.  Artificial neural network for stress-strain behavior of sandy soils: Knowledge based verification , 2005 .

[23]  Seongjin Choi,et al.  Prediction of plasma etching using a randomized generalized regression neural network , 2004 .

[24]  Hojjat Adeli,et al.  Life‐cycle cost optimization of steel structures , 2002 .

[25]  S. W. Hong,et al.  Neural network based prediction of ground surface settlements due to tunnelling , 2001 .

[26]  Hojjat Adeli,et al.  Neural Networks in Civil Engineering: 1989–2000 , 2001 .

[27]  C. M. Tam,et al.  Comparative Cost Analysis of Using High-Performance Concrete in Tall Building Construction by Artificial Neural Networks , 1999 .

[28]  Dayakar Penumadu,et al.  Triaxial compression behavior of sand and gravel using artificial neural networks (ANN) , 1999 .

[29]  James H. Garrett,et al.  Use of neural networks in detection of structural damage , 1992 .

[30]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.