[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120581-en":3,"doc-seo-120581-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120581,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Enhancing Swelling Pressure Prediction Through Integrated Machine Learning Methods","Swelling soils, driven by variations in water content, create major civil engineering risks including structural deformation, cracking, and foundation instability that elevate costs and jeopardize safety. This study presents an integrated machine learning framework for predicting swelling pressure (SP, kPa) using seven geotechnical variables: LL, PI, WS, particle size fractions, and methylene blue index (VBS). Four feature selection methods (Lasso, Random Forest, Gradient Boosting, and F-test) are evaluated to select the five most influential inputs for an ANN. Random Forest provides the strongest predictive accuracy (MSE 785.31, R² 0.9706, MAE 21.39), outperforming alternatives, with results revealing nonlinear parameter–SP relationships and offering a faster, cost-effective alternative to oedometer testing.","ISSN ONLINE: 2447-0228  \nITEGAM-JETIA  \nManaus, v.12 n.57, p. 323-331. January/February, 2026. DOI: [https://doi.org/10.5935/jetia. v12i57.2927](https://doi.org/10.5935/jetia. v12i57.2927)  \nRESEARCH ARTICLE OPEN ACCESS  \n\n| ENHANCING SWELLING PRESSURE PREDICTION THROUGH INTEGRATED MACHINE LEARNING METHODS\u003Cbr>Laid Lekouara1, Fatima Zohra Tebbi2, Ouassila Bahloul3 and Rachid Rabehi*4\u003Cbr>1Faculty of Science and Technology, Department of Civil Engineering, University Abbes Laghrour, Khenchela, Algeria. 2LRNAT Laboratory, Earth Sciences and Universe Institute. University of Mostepha Ben Boulaid, Batna 2. Algeria. 3Civil Engineering Laboratory – Risks and Structures in Interactions,Department of Civil Engineering,Faculty of Technology, University of Batna 2\u003Cbr>– Mostefa Ben Boulaïd Batna 05000, Algeria.\u003Cbr>4Faculty of Civil Engineering, University of Science and Technology Houari Boumediene (USTHB), Bab Ezzouar, 16111, Algeria.\u003Cbr>1[https://orcid.org/0009-0006-6700-1245](https://orcid.org/0009-0006-6700-1245), 2https://orcid.org/0000-0003-3321-5609\u003Cbr>3[https://orcid.org/0000-0001-8554-7563](https://orcid.org/0000-0001-8554-7563), 4https://orcid.org/0000-0002-9398-7291\u003Cbr>Email: [lekouara_laid@univ-khenchela.dz](lekouara_laid@univ-khenchela.dz), [f.tebbi@univ-batna2.dz](f.tebbi@univ-batna2.dz), [o.bahloul@univ-batna2.dz](o.bahloul@univ-batna2.dz), [rachid.rabehi@usthb.edu.dz](rachid.rabehi@usthb.edu.dz) |  |  |\n| --- | --- | --- |\n| ARTICLE INFO |  | ABSTRACT\u003Cbr>Swelling soils, characterized by their high capacity to change volume due to variations in water content, pose significant challenges in civil engineering, including structural deformations, cracks in infrastructure, and foundation instabilities. These issues lead to high maintenance costs and safety risks for structures. To address these challenges, this study proposes an integrated approach combining machine learning and feature selection techniques to predict the swelling pressure (SP, kPa) of soils. Using a dataset comprising seven explanatory variables (liquid limit (LL,%), plasticity index (PI,%), shrinkage limit (WS,%), particle size fractions (Ff2mm, Ff80µm, Ff2µm), and methylene blue index (VBS, g/100 g)), four feature selection methods were evaluated: Least Absolute Shrinkage and Selection Operator (Lasso), Random Forest, Gradient Boosting, and the F-statistic test. These methods identified the five most influential variables for training an artificial neural network (ANN) . The results show that Random Forest achieved the best predictive performance (MSE = 785.31, R² = 0.9706, MAE = 21.39), followed by the F-test (MSE = 1784.99, R² = 0.9332, MAE = 30.66), Gradient Boosting (MSE = 2497.38, R² = 0.9066, MAE = 40.20), and Lasso CV (MSE = 2605.78, R² = 0.9035, MAE = 40.29) . Analysis of the variable distributions revealed complex nonlinear relationships between geotechnical parameters and swelling pressure. This multi-method approach demonstrates the effectiveness of machine learning techniques for accurate and cost-effective prediction of geotechnical properties, offering a promising alternative to traditional, time-consuming, and costly oedometer tests. |\n| Article History\u003Cbr>Received: November 11, 2025\u003Cbr>Revised: December 10, 2025\u003Cbr>Accepted: January 1, 2026\u003Cbr>Published: January 31, 2026 |  |  |\n| Keywords:\u003Cbr>Swelling pressure, Machine learning, Feature selection, Random forest, Neural network. |  |  |\n|  | Copyright ©2026 by authors and Galileo Institute of Technology and Education of the Amazon (ITEGAM) . This work is licensed under the Creative Commons Attribution International License (CC BY 4.0) . |  |\n\nI. INTRODUCTION  \nExpansive soils, characterized by their significant volumetric changes in response to moisture content variations, represent one of the most challenging geotechnical phenomena in civil engineering practice [1],[2]. These soils, predominantly composed of clay minerals with high swelling potential, pose substantial threats to infrastructure","cbCaiobtRuOhyZDs","https://ap.wps.com/l/cbCaiobtRuOhyZDs","pdf",939732,1,9,"English","en",105,"# Introduction\n## Problem and engineering impact of expansive soils\n## Limitations of traditional oedometer testing\n## Motivation for machine learning and ANN\n## Study approach and selected input variables","[{\"question\":\"Why is predicting swelling pressure important in civil engineering?\",\"answer\":\"Swelling pressure from expansive soils can produce uplift and lateral forces that lead to deformations, foundation failures, and infrastructure cracking, affecting safety and design cost.\"},{\"question\":\"Which soil parameters are used as explanatory variables in the prediction model?\",\"answer\":\"The model uses seven inputs: liquid limit (LL), plasticity index (PI), shrinkage limit (WS), particle size fractions (Ff2mm, Ff80µm, Ff2µm), and methylene blue index (VBS).\"},{\"question\":\"How do the feature selection methods affect model performance?\",\"answer\":\"Four feature selection approaches—Lasso, Random Forest, Gradient Boosting, and the F-statistic test—identify the most influential variables for ANN training; Random Forest achieves the best results with MSE 785.31 and R² 0.9706.\"}]","Enhancing Swelling Pressure Prediction Through Integrated Machine Learning Methods | PDF",1785730749,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"enhancing-swelling-pressure-prediction-through-integrated-machine-learning-methods","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/enhancing-swelling-pressure-prediction-through-integrated-machine-learning-methods/120581/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is predicting swelling pressure important in civil engineering?","Question",{"text":75,"@type":76},"Swelling pressure from expansive soils can produce uplift and lateral forces that lead to deformations, foundation failures, and infrastructure cracking, affecting safety and design cost.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which soil parameters are used as explanatory variables in the prediction model?",{"text":80,"@type":76},"The model uses seven inputs: liquid limit (LL), plasticity index (PI), shrinkage limit (WS), particle size fractions (Ff2mm, Ff80µm, Ff2µm), and methylene blue index (VBS).",{"name":82,"@type":73,"acceptedAnswer":83},"How do the feature selection methods affect model performance?",{"text":84,"@type":76},"Four feature selection approaches—Lasso, Random Forest, Gradient Boosting, and the F-statistic test—identify the most influential variables for ANN training; 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