[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117831-en":3,"doc-seo-117831-105":30,"detail-sidebar-cat-0-en-105":92},{"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},117831,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Automatic determination of the Atterberg limits with machine learning","The study develops machine-learning and statistical models to determine the liquid limit, plasticity index, and plastic limit of natural fine-grained soil samples. With a single measurement from pressure-membrane extractors, the method positions each soil type on the Casagrande plasticity chart and supports practical design-oriented adjustments. Multiple Linear Regression and Support Vector Regression provide explainable plasticity models, improving liquid limit and plasticity index estimation versus standardized approaches. Plastic limit results are suited for control works, and an automatic multi-sample static protocol is proposed.","Automatic determination ofthe Atterberg limits with machine learning  \nDavid Antonio Rosas a, Daniel Burgos a, b, John Willian Branch b & Alberto Corbi a  \na Research Institute for Innovation & Technology in Education (UNIR iTED), Universidad Internacional de La Rioja (UNIR), Logroño, La Rioja, Spain.  \n[davidantonio.rosas@unir.net](davidantonio.rosas@unir.net), [daniel.burgos@unir.net](daniel.burgos@unir.net), [alberto.corbi@unir.net](alberto.corbi@unir.net)  \nb Universidad Nacional de Colombia, Sede Medellín, Facultad de Minas, Departamento de Ciencias de la Computacióny de la Decisión, Medellín,  \n[Colombia. jwbranch@unal.edu.co](Colombia. jwbranch@unal.edu.co)  \nReceived: May 12th, 2022. Received in revised form: September 9th, 2022. Accepted: September 15th, 2022.  \nAbstract  \nIn this study, we determine the liquid limit (􀜹􀯟), plasticity index (PI), and plastic limit (􀜹􀯣) of several natural fine-grained soil samples with the help of machine-learning and statistical methods. This enables us to locate each soil type analysed in the Casagrande plasticity chart with a single measure in pressure-membrane extractors. These machine-learning models showed adjustments in the determination of the liquid limit for design purposes when compared with standardised methods. Similar adjustments were achieved in the determination of the plasticity index, whereas the plastic limit determinations were applicable for control works. Because the best techniques were based in Multiple Linear Regression and Support Vector Machines Regression, they provide explainable plasticity models. In this sense, 􀜹􀯟 =(9 . 94 ± 4 . 2) + (2 .25 ± 0 . 3) ∙ 􀝌􀜨4.2 , PI = (−20 .47 ± 5. 6) + (1 .48 ± 0 . 3) ∙ 􀝌􀜨4.2 + (0 . 21 ± 0 . 1) ∙ 􀜨 , and 􀜹􀯣 = (23 . 32 ± 3 . 5) + (0 .60 ± 0 . 2) ∙ 􀝌􀜨4.2 −(0 . 13 ± 0 . 04) ∙ 􀜨 . So that, we propose an alternative, automatic, multi-sample, and static method to address current issues on Atterberg limits determination with standardised tests.  \nKeywords: machine learning; Atterberg limits; pressure-membrane extractor; determination; soils  \nDeterminación automática de los límites de Atterberg con machine  \nlearning  \nResumen  \nEn este estudio, determinamos el límite líquido (􀜹􀯟 ), el índice de plasticidad (PI) y el límite plástico (􀜹􀯣) de suelos naturales finos con ayuda de machine-learning y métodos estadísticos. Ello permite localizarlos en la Carta de Plasticidad de Casagrande con una sola medida en extractores de presión-membrana. Los modelos de machine-learning mostraron ajustes en la determinación de 􀜹􀯟 apropiados para propósitos de diseño, comparados con métodos estandarizados. Ajustes similares se alcanzaron en la determinación de PI, mientras que las determinaciones de 􀜹􀯣 permiten ajustes apropiados para trabajos de control. Debido a que las técnicas más apropiadas se basaron en Regresión Lineal Múltiple y Máquinas de Soporte de Vectores, aportaron modelos de plasticidad explicables. En este sentido, 􀜹􀯟 =(9 . 94 ± 4 . 2) + (2 .25 ± 0 . 3) ∙ 􀝌􀜨4.2 , 􀜲􀜫 = (−20 .47 ± 5. 6) + (1 .48 ± 0 . 3) ∙ 􀝌􀜨4.2 + (0 . 21 ± 0 . 1) ∙ 􀜨 y 􀜹􀯣 = (23 .32 ± 3. 5) + (0 .60 ± 0 . 2) ∙ 􀝌􀜨4.2 −(0 . 13 ± 0 . 04) ∙ 􀜨 . Por consiguiente, proponemos un método alternativo, automático, estático y multimuestra para enfrentar problemas frecuentes en la determinación de los Límites de Atterberg con ensayos normalizados.  \nPalabras clave: machine learning; límites de Atterberg; extractor de presión membrana; determinación; suelo  \n1 Introduction  \nWe know since the early works of Albert Atterberg (1846– 1916) and Arthur Casagrande (1902–1981) that the plasticity of fine-grained soils resembles a fundamental characteristic of them [1,2]. In this sense, the consistency of such soils varies with increasing moisture content from solid, semi-solid, plastic, and  \nliquid states, coined the arbitrary borders between them as the shrinkage limit, the plastic limit (􀜹􀯣), and the liquid limit (􀜹􀯟), respectively [3]. Moreover, the difference between 􀜹􀯟 and 􀜹􀯣 is named P","cbCairca4ijEmkvF","https://ap.wps.com/l/cbCairca4ijEmkvF","pdf",572514,1,9,"English","en",105,"# Introduction\n# Materials and Methods\n## Data and Features\n## Machine-Learning Models\n## Statistical Evaluation\n# Results and Discussion\n## Liquid Limit Performance\n## Plasticity Index Performance\n## Plastic Limit Performance\n# Conclusions\n# References","[{\"question\":\"What soil properties are estimated using the proposed machine-learning approach?\",\"answer\":\"The approach estimates the liquid limit, plasticity index (PI), and plastic limit of natural fine-grained soil samples.\"},{\"question\":\"How does the method determine the Atterberg limits in practice?\",\"answer\":\"It uses a single measure obtained from pressure-membrane extractors to locate each soil type on the Casagrande plasticity chart.\"},{\"question\":\"Which machine-learning techniques performed best in the study?\",\"answer\":\"The best-performing techniques were Multiple Linear Regression and Support Vector Machines Regression, producing explainable plasticity models.\"}]","Automatic determination of the Atterberg limits with machine learning | 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soil properties are estimated using the proposed machine-learning approach?","Question",{"text":76,"@type":77},"The approach estimates the liquid limit, plasticity index (PI), and plastic limit of natural fine-grained soil samples.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the method determine the Atterberg limits in practice?",{"text":81,"@type":77},"It uses a single measure obtained from pressure-membrane extractors to locate each soil type on the Casagrande plasticity chart.",{"name":83,"@type":74,"acceptedAnswer":84},"Which machine-learning techniques performed best in the study?",{"text":85,"@type":77},"The best-performing techniques were Multiple Linear Regression and Support Vector Machines Regression, producing explainable plasticity 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