[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125653-en":3,"doc-seo-125653-105":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},125653,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Machine Learning Methods to Predict and Analyse Unconfined Compressive Strength of Stabilised Soft Soil with Polypropylene Columns","This study applies multiple machine learning approaches to predict the unconfined compressive strength (UCS) of polypropylene-stabilised soft soil. The generated dataset contains 52 samples with five input features: column reinforcement type, column diameter, area replacement ratio, column penetration ratio, and max deviator stress, while the outputs are three UCS stress classes. Results indicate that Random Forest delivers strong predictive performance on unconfined compression testing, with an r2 of 0.8942 and accuracy of 0.9375. A sequential deep model also shows competitive training and validation accuracy, supporting the feasibility of practical, data-driven assessment to reduce reliance on extensive laboratory testing.","Cogent Engineering  \nISSN: (Print) (Online) Journal homepage: [https://www.tandfonline.com/loi/oaen20](https://www.tandfonline.com/loi/oaen20)  \nMachine Learning Methods to Predict and Analyse Unconfined Compressive Strength of Stabilised Soft Soil with Polypropylene Columns  \nMd. Ikramul Hoque, Muzamir Hasan, Md Shofiqul Islam, Moustafa Houda, Mirvat Abdallah & Md. Habibur Rahman Sobuz  \nTo cite this article: Md. Ikramul Hoque, Muzamir Hasan, Md Shofiqul Islam, Moustafa Houda, Mirvat Abdallah & Md. Habibur Rahman Sobuz (2023) Machine Learning Methods to Predict and Analyse Unconfined Compressive Strength of Stabilised Soft Soil with Polypropylene Columns, Cogent Engineering, 10:1, 2220492, DOI: 10.1080/23311916.2023.2220492  \nTo link to this article: [https://doi.org/10.1080/2331](https://doi.org/10.1080/2331) 1916.2023.2220492  \n© 2023 The Author(s) . Published by Informa UK Limited, trading as Taylor & Francis Group.  \n\n|  Published online: 14 Jun 2023. |\n| --- |\n|  Submit your article to this journal  |\n|  View related articles  |\n|  View Crossmark data |\n\nFull Terms & Conditions of access and use can be found at [https://www.tandfonline.com/action/journalInformation?journalCode=oaen20](https://www.tandfonline.com/action/journalInformation?journalCode=oaen20)  \nHoque et al., Cogent Engineering (2023), 10: 2220492  \n[https://doi.org/10.1080/23311916.2023.2220492](https://doi.org/10.1080/23311916.2023.2220492)  \nReceived: 25 January 2023  \nAccepted: 29 May 2023  \n*Corresponding author: Md. Ikramul Hoque, Faculty of Civil Engineering Technology, Universiti Malaysia Pahang, Gambang, Malaysia  \nE-mail: [ikramul3300@becm.kuet.ac.bd](ikramul3300@becm.kuet.ac.bd)  \n[Reviewing editor:](Reviewing editor:)  \nSanjay Kumar Shukla, School of Engineering, Edith Cowan University, AUSTRALIA  \nAdditional information is available atthe end of the article  \nCIVIL ENGINEERING | RESEARCH ARTICLE  \nMachine Learning Methods to Predict and Analyse Unconfined Compressive Strength of Stabilised Soft Soil with Polypropylene Columns  \nMd. Ikramul Hoque1*, Muzamir Hasan1, Md Shofiqul Islam2, Moustafa Houda3, Mirvat Abdallah3 and Md. Habibur Rahman Sobuz4  \nAbstract: In this study, several machine learning approaches are used for the prediction of the unconfined compressive strength (UCS) of polypropylene-stabilised soft soil. This research work generates new data and applies several machine learning algorithms for the analysis of UCS. Fifty-two samples are in our generated data. In our generated data, five input features are used: Column Reinforcement Type, Column Diameter, Area replacement ratio,Column Penetration Ratio and Max_Deviator Stress. On the other hand, the output consists of three target stress class. Our experimental result shows that Random Forest (RF) provides good prediction result of unconfined compressive test (UCT) and that is satisfied. RF model gets result of mean absolute error of 0.0625, mean square root error of 0.0625, root mean sqrt error of 0. 2500, r2 value of 0.8942 and accuracy of 0.9375. In addition, the sequential model got training loss of 0.2535, training accuracy of 0.9024, validation loss of 0.4056 and validation accuracy: 0.9091. The results showed that the suggested RF and sequential model performs excellently in predicting the UCSof stabilised soft soil with polypropylene. Our technique is more practical and time-  \nMd. Ikramul Hoque  \nABOUT THE AUTHOR  \nMd Ikramul Hoque is working as an Associate Professor at Khulna University of Engineering and Technology(KUET), Now he is studying as a Ph.D. student at Civil Engineering, at University Malaysia Pahang (UMP) . He is an active researcher in the field of Civil Engineering. He has Published 26 articles in different high-impact facto journals. His research focus is in Geotechnical Engineering, Construction engineering and management.  \nPUBLIC INTEREST STATEMENT  \nThis research work generates new data and applies several machine learning algorithms for the prediction o","cbCaiiREQvoI9bJb","https://ap.wps.com/l/cbCaiiREQvoI9bJb","pdf",7723087,1,21,"English","en",105,"# Introduction\n# Methodology and Data Setup\n## Input Features and Target Classes\n## Machine Learning Models\n# Results and Model Performance\n# Practical Implications and Future Work","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"To predict and analyze the unconfined compressive strength (UCS) of polypropylene-stabilised soft soil using machine learning methods.\"},{\"question\":\"What input features and output labels are used?\",\"answer\":\"The model uses five input features: column reinforcement type, column diameter, area replacement ratio, column penetration ratio, and max deviator stress, and the output is three UCS stress classes.\"},{\"question\":\"Which model performs best according to the reported metrics?\",\"answer\":\"The Random Forest model shows strong performance, achieving an r2 value of 0.8942 and accuracy of 0.9375.\"}]","Machine Learning Methods to Predict and Analyse Unconfined Compressive Strength of Stabilised Soft Soil with Polypropylene Columns | 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