[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119719-en":3,"doc-seo-119719-105":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":20,"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},119719,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Assessment of compressive strength of jet grouting by machine learning","Jet grouting is a widely used soil improvement technique, yet its design carries significant uncertainties that can cause construction cost overruns. High variability in the treated material often pushes designers toward conservative, arbitrary strength assumptions, sometimes misaligned with test-field outcomes. This study predicts the uniaxial compressive strength (UCS) of jet grouting columns by analyzing multiple machine learning algorithms using a dataset of 854 results and using extremely randomized trees for soil type and process parameters to deliver improved predictive performance.","Journal Pre-proof  \nAssessment of compressive strength of jet grouting by machine learning Esteban Díaz, Edgar Leonardo Salamanca-Medina, Roberto Tomás  \nPII: S1674-7755(23)00100-2  \nDOI: [https://doi.org/10.1016/j.jrmge.2023.03.008](https://doi.org/10.1016/j.jrmge.2023.03.008)  \nReference: JRMGE 1177  \nTo appear in: Journal of Rock Mechanics and Geotechnical Engineering  \nReceived Date: 23 December 2022  \nRevised Date: 15 February 2023  \nAccepted Date: 12 March 2023  \nPlease cite this article as: Díaz E, Salamanca-Medina EL, Tomás R, Assessment of compressive strength of jet grouting by machine learning, Journal of Rock Mechanics and Geotechnical Engineering (2023), doi: [https://doi.org/10.1016/j.jrmge.2023.03.008](https://doi.org/10.1016/j.jrmge.2023.03.008) .  \nThis is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.  \n© 2023 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting by Elsevier B.V. All rights reserved.  \nAssessment of compressive strength of jet grouting by machine learning Esteban Díaz*, Edgar Leonardo Salamanca-Medina, Roberto Tomás  \nDepartamento de Ingeniería Civil, Escuela Politécnica Superior, Universidad de Alicante, P.O. Box 99, E-03080 Alicante, Spain  \n\n| A R T I C L E I N F O |  | A B S T R A C T |\n| --- | --- | --- |\n| Article history:\u003Cbr>Received 23 December 2022\u003Cbr>Received in revised form\u003Cbr>15 February 2023\u003Cbr>Accepted 12 March 2023\u003Cbr>Available online |  | Jet grouting is one of the most popular soil improvement techniques, but its design usually involves great uncertainties that can lead to economic cost overruns in construction projects. The high dispersion in the properties of the improved material leads to designers assuming a conservative, arbitrary and unjustified strength, which is even sometimes subjected to the results of the test fields. The present paper presents an approach for prediction of the uniaxial compressive strength (UCS) of jet grouting columns based on the analysis of several machine learning algorithms on a database of 854 results mainly collected from different research papers. The selected machine learning model (extremely randomized trees) relates the soil type and various parameters of the technique to the value of the compressive strength. Despite the complex mechanism that surrounds the jet grouting process, evidenced by the high dispersion and low correlation of the variables studied, the trained model allows to optimally predict the values of compressive strength with a significant improvement with respect to the existing works. Consequently, this work proposes for the first time a reliable and easily applicable approach for estimation of the compressive strength of jet grouting columns.\u003Cbr>©2023 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ([http://creativecommons. org/](http://creativecommons. org/)[ ](http://creativecommons. org/)licenses/by-nc-nd/4.0/) . |\n| Keywords: Jet grouting Ground improvement\u003Cbr>Compressive strength Machine learning |  |  |\n\n1. Introduction  \nNowadays, the land for construction is increasingly scarce, resulting in a greater use of underground space, reclaimed land, or inadequate soils. Under these conditions, it is necessary to improve the soil properties to meet engineering requirements with the appropriate safety conditions. Among the existing ground improvement techn","cbCaidcbuNiKFbuZ","https://ap.wps.com/l/cbCaidcbuNiKFbuZ","pdf",1781196,1,14,"English","en",105,"# Introduction\n## Jet grouting as a ground improvement method\n## Classification of jet grouting systems (EN 12716)\n## Role of uniaxial compressive strength (UCS)\n## Motivation: variability and prediction needs","[{\"question\":\"Why is predicting the uniaxial compressive strength (UCS) of jet grouting columns important?\",\"answer\":\"The paper notes that jet grouting design has substantial uncertainty, and the high dispersion in properties leads to conservative strength assumptions that may not match test results.\"},{\"question\":\"What machine learning approach is used in the study?\",\"answer\":\"The selected model is extremely randomized trees, trained to relate soil type and multiple jet grouting parameters to the UCS value.\"},{\"question\":\"What data is the prediction model based on?\",\"answer\":\"The model is built using a database of 854 results, mainly collected from different research papers.\"}]","Assessment of compressive strength of jet grouting by machine learning | 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