{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:02:16Z","timestamp":1760238136319,"version":"build-2065373602"},"reference-count":60,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2020,7,13]],"date-time":"2020-07-13T00:00:00Z","timestamp":1594598400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Key Program of International (Regional) Cooperation and Exchange of National Natural Science Foundation of China","award":["41820104001"],"award-info":[{"award-number":["41820104001"]}]},{"name":"the State Key Program of the National Natural Science Foundation of China","award":["41430642"],"award-info":[{"award-number":["41430642"]}]},{"name":"the Special Fund for Major Scientific Instruments of the National Natural Science Foundation of China","award":["41627801"],"award-info":[{"award-number":["41627801"]}]},{"name":"Sponsored by Shanghai Sailing Program","award":["19YF1415500"],"award-info":[{"award-number":["19YF1415500"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>With the increase in transportation emissions, road diseases in the saline soil area of Jilin Province have become a problem that requires serious attention. In order to improve the subgrade performance, the structural yield strength (SYS) of remolded soil and its factor sensitivity are investigated in this study. Saline soils in Western Jilin are structural in the sense that the bonding strength of soil skeleton is mainly provided by the solidification bond formed by a physicochemical interaction between particles. Its SYS is influenced by its cementation type, genetic characteristics, original rock structure, and environment. Because of the high clay content in Zhenlai saline soil, the specific surface area of soil particles is large, and the surface adsorption capacity of soil particles is strong. In addition, the main cation is Na+. The cementation strength of bound water film between soil particles is thus easily affected by water content and salt content, and compaction is also an important factor affecting the strength of soil. Therefore, in this study, the back-propagation neural network (BPNN) model and a support vector machine (SVM) are used to explore the relationship of saline soil\u2019s SYS with its compactness, water content, and salt content. In total, 120 data points collected by a high-pressure consolidation experiment are applied to building BPNN and SVM model. For eliminate redundant features, Pearson correlation coefficient (rPCC) is used as an evaluation standard of feature selection. The K-fold cross-validation method was used to avoid over fitting. To compare the performance of the BPNN and SVM models, three statistical parameters were used: the determination coefficient (R2), root mean square error (RMSE), and mean absolute percentage deviation (MAPD). The result shows that the average values of R2, RMSE, and MAPD of the BPNN model are superior to the values of the SVM. We conclude that the BPNN model is slightly better than the SVM for predicting the SYS of saline soil. Thus, the BPNN model is used to analyze the factor sensitivity of SYS. The results indicate that the influence degrees of the three parameters are as follows: water content &gt; compactness &gt; salt content. This study can provide a basis for estimating the structural yield pressure of soil from its basic properties, and can provide a new way to obtain parameters for geotechnical engineering, ensuring safety while maintaining symmetry in engineering costs.<\/jats:p>","DOI":"10.3390\/sym12071163","type":"journal-article","created":{"date-parts":[[2020,7,22]],"date-time":"2020-07-22T05:10:30Z","timestamp":1595394630000},"page":"1163","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Prediction of the Structural Yield Strength of Saline Soil in Western Jilin Province, China: A Comparison of the Back-Propagation Neural Network and Support Vector Machine Models"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6635-8035","authenticated-orcid":false,"given":"Wei","family":"Peng","sequence":"first","affiliation":[{"name":"College of Construction Engineering, Jilin University, Changchun 130026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qing","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Construction Engineering, Jilin University, Changchun 130026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xudong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Shanghai University, 99 Shangda Road, Shanghai 200444, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaohui","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Construction Engineering, Jilin University, Changchun 130026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongchao","family":"Li","sequence":"additional","affiliation":[{"name":"College of Construction Engineering, Jilin University, Changchun 130026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yufeng","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Construction Engineering, Jilin University, Changchun 130026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanyuan","family":"Kong","sequence":"additional","affiliation":[{"name":"School of Highway, Chang\u2019an University, Xi\u2019an South Second Ring Road in the Middle, Xi\u2019an 710064, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.coldregions.2009.10.008","article-title":"Experimental study of a pseudo-preconsolidation pressure in frozen soils","volume":"60","author":"Qi","year":"2010","journal-title":"Cold Reg. Sci. Technol."},{"key":"ref_2","first-page":"59","article-title":"The New Konwlege on Theory of Preconsolidation Pressure","volume":"26","author":"Wang","year":"1996","journal-title":"J. Changchun Univ. Earth Sci."},{"key":"ref_3","first-page":"987","article-title":"Mechanical Effect of Pre-consolidation Pressure of Structural Behavior Soil","volume":"51","author":"Wang","year":"2016","journal-title":"J. Southwest Jiaotong Univ."},{"key":"ref_4","first-page":"416","article-title":"Study on the Shear Structural Yield of Soft Soil Affected by Drainage Condition","volume":"256\u2013259","author":"Yang","year":"2012","journal-title":"Appl. Mech. Mater."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1680\/geot.1979.29.4.469","article-title":"A Natural Compression Law for Soils","volume":"29","author":"Butterfield","year":"1979","journal-title":"G\u00e9otechnique"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1680\/geot.1991.41.2.263","article-title":"Improved technique for estimation of preconsolidation pressure","volume":"41","author":"Jose","year":"1991","journal-title":"G\u00e9otechnique"},{"key":"ref_7","first-page":"211","article-title":"A Method of Correcting Yield Stress and Compression Index of Ariaka Clays for Sample Disturbance","volume":"38","author":"Hong","year":"1998","journal-title":"Jpn. Geotech. Soc."},{"key":"ref_8","first-page":"1096","article-title":"Study on dispersive influencing factors of dispersive soil in western Jilin based on grey correlation degree method","volume":"291\u2013294","author":"Bao","year":"2013","journal-title":"Adv. Energy Sci. Technol."},{"key":"ref_9","unstructured":"Zhang, J. (2010). Research on the Dispersion Mechanism of the Dispersive Seasonal Frozen Soil in the Western of Jilin Province. [Ph. D. Thesis, Jilin University]."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1080\/21655979.2016.1226664","article-title":"Biological treatment of saline-alkali soil by Sulfur-oxidizing bacteria","volume":"7","author":"Bao","year":"2016","journal-title":"Bioengineered"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1080\/19648189.2014.926295","article-title":"On the intrinsic compressibility of common clayey soils","volume":"19","author":"Polidori","year":"2014","journal-title":"Eur. J. Environ. Civ. Eng."},{"key":"ref_12","unstructured":"Terzaghi, K., Peck, R., and Mesri, G. (1996). Soil Mechanics in Engineering Practice, John Wiley & Sons."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1016\/j.procs.2017.12.066","article-title":"Prediction of Geotechnical Parameters Using Machine Learning Techniques","volume":"125","author":"Puri","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Drnevich, V.P., Nagaraj, T., and Murthy, B.R. (1985). Prediction of the Preconsolidation Pressure and Recompression Index of Soils. Geotech. Test. J., 8.","DOI":"10.1520\/GTJ10538J"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.compgeo.2017.02.006","article-title":"Numerical prediction of the creep behaviour of an unstabilised and a chemically stabilised soft soil","volume":"87","author":"Oliveira","year":"2017","journal-title":"Comput. Geotech."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1016\/j.aei.2018.09.005","article-title":"Prediction of Soil Compression Coefficient for Urban Housing Project Using Novel Integration Machine Learning Approach of Swarm Intelligence and Multi-Layer Perceptron Neural Network","volume":"38","author":"Nhu","year":"2018","journal-title":"Adv. Eng. Inform."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.advengsoft.2018.10.002","article-title":"Prediction of dynamic properties of ultra-high performance concrete by an artificial intelligence approach","volume":"127","author":"Khosravani","year":"2019","journal-title":"Adv. Eng. Softw."},{"key":"ref_18","first-page":"449","article-title":"Machine learning techniques applied to prediction of residual strength of clay","volume":"3","author":"Das","year":"2011","journal-title":"Cent. Eur. J. Geosci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1016\/j.compgeo.2008.07.002","article-title":"ANN-based model for predicting the bearing capacity of strip footing on multi-layered cohesive soil","volume":"36","author":"Kuo","year":"2008","journal-title":"Comput. Geotech."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1495","DOI":"10.15244\/pjoes\/85952","article-title":"Experimental Investigation of Water Migration Characteristics for Saline Soil","volume":"28","author":"Zhang","year":"2019","journal-title":"Pol. J. Environ. Stud."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1680\/geot.2004.54.7.441","article-title":"Index properties of a highly weathered old alluvium","volume":"54","author":"Zhang","year":"2004","journal-title":"G\u00e9otechnique"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Saidi, R., Bouaguel, W., and Nadia, E. (2019). Hybrid Feature Selection Method Based on the Genetic Algorithm and Pearson Correlation Coefficient. Studies in Computational Intelligence, Springer.","DOI":"10.1007\/978-3-030-02357-7_1"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"105459","DOI":"10.1016\/j.porgcoat.2019.105459","article-title":"Correlation research of phase angle variation and coating performance by means of Pearson\u2019s correlation coefficient","volume":"139","author":"Fu","year":"2020","journal-title":"Prog. Org. Coat."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"680","DOI":"10.1007\/978-3-642-29216-3_74","article-title":"Evaluation of Classifier Models Using Stratified Tenfold Cross Validation Techniques","volume":"Volume 270","author":"Purushotham","year":"2011","journal-title":"Communications in Computer and Information Science"},{"key":"ref_25","unstructured":"Kohavi, R. (2001, January 10\u201314). A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. Proceedings of the 14th international joint conference on Artificial intelligence, Adelaide, Australia."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1016\/0045-7949(92)90132-J","article-title":"Use of neural network in detection of structural damage","volume":"42","author":"Wu","year":"1992","journal-title":"Comput. Struct."},{"key":"ref_27","first-page":"745","article-title":"Prediction of the unconfined compressive strength of soft rocks: A PSO-based ANN approach","volume":"74","author":"Tonnizam","year":"2014","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"873","DOI":"10.1007\/s10064-014-0657-x","article-title":"Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm","volume":"74","author":"Hajihassani","year":"2014","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1016\/S0266-352X(01)00011-8","article-title":"Neural network based prediction of ground surface settlements due to tunnelling","volume":"28","author":"Kim","year":"2001","journal-title":"Comput. Geotech."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.tust.2005.06.007","article-title":"Artificial neural networks for predicting the maximum surface settlement caused by EPB shield tunneling","volume":"21","author":"Suwansawat","year":"2006","journal-title":"Tunn. Undergr. Space Technol."},{"key":"ref_31","first-page":"927","article-title":"Comparative cost analysis of using high-performance concrete in tall building construction by artificial neural networks","volume":"96","author":"Tam","year":"1999","journal-title":"ACI Struct. J."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1451","DOI":"10.1002\/nme.549","article-title":"Life-cycle cost optimization of steel structures","volume":"55","author":"Sarma","year":"2002","journal-title":"Int. J. Numer. Methods Eng."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.conbuildmat.2015.03.059","article-title":"Prediction of expansion behavior of self-stressing concrete by artificial neural networks and fuzzy inference systems","volume":"84","author":"Wang","year":"2015","journal-title":"Constr. Build. Mater."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/S0266-352X(99)00002-6","article-title":"Triaxial compression behavior of sand and gravel using artificial neural networks (ANN)","volume":"24","author":"Penumadu","year":"1999","journal-title":"Comput. Geotech."},{"key":"ref_35","first-page":"1","article-title":"Prediction of Frost-Heaving Behavior of Saline Soil in Western Jilin Province, China, by Neural Network Methods","volume":"2017","author":"Zhang","year":"2017","journal-title":"Math. Probl. Eng."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.clay.2015.07.035","article-title":"Analyzing the effect of various soil properties on the estimation of soil specific surface area by different methods","volume":"116\u2013117","author":"Bayat","year":"2015","journal-title":"Appl. Clay Sci."},{"key":"ref_37","unstructured":"Vapnik, V.N. (1998). Statistical Learning Theory, Wiley Interscience."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Cristianini, N., and Shawe-Taylor, J. (2000). An Introduction of Support Vector Machines and Other Kernel-Based Learning Methods, Cambridge University Press.","DOI":"10.1017\/CBO9780511801389"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1023\/A:1009715923555","article-title":"A tutorial on support vector machines for pattern recognition","volume":"2","author":"Burges","year":"1998","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_40","unstructured":"Mukherjee, S., Osuna, E., and Girosi, F. (1997, January 24\u201326). Nonlinear prediction of chaotic time Series using support vector machines. Neural Networks for Signal Processing [1997] VII, Proceedings of the 1997 IEEE Workshop 1997, Amelia Island, FL, USA."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1007\/s12665-012-2214-x","article-title":"Calculation of surface settlements caused by EPBM tunneling using artificial neural network, SVM, and Gaussian processes","volume":"70","author":"Ocak","year":"2013","journal-title":"Environ. Earth Sci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2016\/3989743","article-title":"Damage Detection of Structures for Ambient Loading Based on Cross Correlation Function Amplitude and SVM","volume":"2016","author":"Huo","year":"2016","journal-title":"Shock Vib."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1007\/s11069-015-1620-2","article-title":"The prediction model of earthquake casuailty based on robust wavelet v-SVM","volume":"77","author":"Huang","year":"2015","journal-title":"Nat. Hazards"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Nurmemet, I., Sagan, V., Ding, J.-L., Halik, \u00dc., Abliz, A., and Yakup, Z. (2018). A WFS-SVM Model for Soil Salinity Mapping in Keriya Oasis, Northwestern China Using Polarimetric Decomposition and Fully PolSAR Data. Remote Sens., 10.","DOI":"10.3390\/rs10040598"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Vapnik, V. (1995). The Nature of Statistical Learning Theory, Springer science & business media.","DOI":"10.1007\/978-1-4757-2440-0"},{"key":"ref_46","unstructured":"Vn, V. (1998). Statistical Learning Theory, Wiley."},{"key":"ref_47","first-page":"04018033","article-title":"Energy Dissipation Prediction for Stepped Spillway Based on Genetic Algorithm\u2013Support Vector Regression","volume":"144","author":"Lei","year":"2018","journal-title":"J. Irrig. Drain. Eng."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1228","DOI":"10.1016\/j.dss.2012.11.012","article-title":"A hybrid approach by integrating wavelet-based feature extraction with MARS and SVR for stock index forecasting","volume":"54","author":"Kao","year":"2013","journal-title":"Decis. Support Syst."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"117","DOI":"10.14311\/NNW.2013.23.009","article-title":"Prediction martensite fraction of microalloyed steel by artificial neural networks","volume":"23","author":"Khalaj","year":"2013","journal-title":"Neural Netw. World"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1111\/j.1467-8667.1994.tb00369.x","article-title":"Developing Practical Neural Network Applications Using Back\u2014Propagation","volume":"9","author":"Hegazy","year":"2008","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_51","unstructured":"Bishop, C., and Ligne, S. (2006). Pattern Recognition and Machine Learning, Springer."},{"key":"ref_52","unstructured":"Heaton, J. (2008). Introduction to Neural Networks for C#, Heaton Research Inc.. [2nd ed.]."},{"key":"ref_53","unstructured":"Heaton, J. (2015). Artificial Intelligence for Humans, Volume 3: Deep Learning and Neural Networks, Heaton Research Inc."},{"key":"ref_54","unstructured":"Heaton, J. (2011). Programming Neural Networks with Encog3 in C#, Heaton Research Inc.. [2nd ed.]."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"562","DOI":"10.1139\/t77-057","article-title":"Statistical forecasting of compressibility of peaty ground","volume":"14","author":"Keiji","year":"1977","journal-title":"Can. Geotech. J."},{"key":"ref_56","unstructured":"Stas, C., and Kulhawy, F. (1984). Critical Evaluation of Design Methods for Foundations under Axial Uplift and Compression Loading, Cornell University. Final report."},{"key":"ref_57","unstructured":"DeGroot, D.J., Knudsen, S., and Lunne, T. (1999, January 2\u20133). Correlations among pc\u2032, su and the index properties for offshore clays. Proceedings of the International Conference on Offshore and Nearshore Geotechnical Engineering, Panvel, India."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"04016049","DOI":"10.1061\/(ASCE)GT.1943-5606.0001519","article-title":"Index Test Method for Estimating the Effective Preconsolidation Stress in Clay Deposits","volume":"142","author":"Kootahi","year":"2016","journal-title":"J. Geotech. Geoenviron. Eng."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"791","DOI":"10.13031\/2013.32339","article-title":"Effect of Sample Cross-Sectional Area on Saturated Hydraulic Conductivity in Tow Structured Clay Soils","volume":"28","author":"Zobeck","year":"1985","journal-title":"Trans. Am. Soc. Agric. Eng."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.jhydrol.2015.06.024","article-title":"Sample dimensions effect on prediction of soil water retention curve and saturated hydraulic conductivity","volume":"528","author":"Ghanbarian","year":"2015","journal-title":"J. Hydrol."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/7\/1163\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:50:55Z","timestamp":1760176255000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/7\/1163"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,13]]},"references-count":60,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2020,7]]}},"alternative-id":["sym12071163"],"URL":"https:\/\/doi.org\/10.3390\/sym12071163","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2020,7,13]]}}}