[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128576-en":3,"doc-seo-128576-105":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},128576,549768064778,"Finn","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Developing predictive models for the load-displacement response of laterally loaded reinforced concrete piles in stiff unsaturated clay using machine learning algorithms","The design of pile foundations expected to develop significant lateral loading relies on objective and accurate formulations rather than semi-empirical know-how. This research develops predictive models to compute the mechanical response of reinforced concrete (RC) piles embedded in unsaturated clay. Experimental data and advanced nonlinear 3D detailed finite element modelling create datasets covering ultimate capacity and horizontal deformation to failure. Multiple machine learning algorithms train and test models, followed by out-of-sample validation, and the best model predicts a laterally tested RC pile response.","Structures 64 (2024) 106532  \nContents lists available at ScienceDirect  \nStructures  \njournal [homepage:](homepage: www.elsevier.com/locate/structures)[ www.elsevier.com/locate/structures](homepage: www.elsevier.com/locate/structures)  \n| Developing predictive models for the load-displacement response of laterally loaded reinforced concrete piles in stiff unsaturated clay using machine learning algorithms |  |  |  |\n| --- | --- | --- | --- |\n| K.T. Braun a, *, G. Markoua, b, S.W. Jacobsza, D. Calitz c\u003Cbr>a Civil Engineering Department Hatfield Campus, University of Pretoria, South Africa b Department of Civil Engineering, Neapolis University Pafos, 2 Danais Avenue, Pafos 8042, Cyprus c SRK Consulting, 265 Oxford Rd, Illovo, Johannesburg, South Africa |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Soil-structure interaction Machine learning algorithms Predictive models Reinforced concrete pile\u003Cbr>Horizontal load-displacement response |  | The design of pile foundations that are expected to develop significant lateral loading is a complex procedure that requires the development of objective and accurate design formulae that will not be based on semi-empirical know-how. For this reason, the main objective of this research work is to develop predictive models that will be able to compute the overall mechanical response of reinforced concrete (RC) piles embedded in unsaturated clay. To achieve this goal, experimental data, and advanced nonlinear 3D detailed finite element (FE) modelling were used to construct datasets comprising multiple results related to the ultimate capacity and corresponding horizontal deformation of RC piles that are loaded horizontally until failure. In total, three datasets were developed and then used to train and test predictive models through the use of various machine learning (ML) algorithms. After successfully developing various predictive models, an out-of-sample dataset was developed and used to further validate the accuracy and extendibility of the predictive models. Finally, the most accurate MLgenerated predictive model was used to predict the mechanical response of a RC pile embedded in unsaturated clay that was experimentally tested. The ability of the proposed predictive model is demonstrated through this pilot research work. |  |\n\n1. Introduction  \nWhen engineers design the foundation systems of any type of structure, design codes that are based on semi-empirical formulae are used. In cases where the design of the foundation involves reinforced concrete (RC) piles that support structures that are expected to develop significant horizontal loads, the current design procedures do not offer the required accuracy. This is due to the lack of accurate design formulae developed based on the real mechanical response of RC piles. For this reason, the development of improved and more accurate design formulae that will be able to predict the capacity of RC piles embedded in soil is of significant importance.  \nPiled foundations are frequently used as a common type of deep foundation for providing support to structures situated in loose or soft soils. In contrast, shallow foundations are recognized for their incapacity to prevent substantial settlements and shear failure in such soil  \nconditions [36]. Piles can effectively resist vertical loads, lateral loads, or a combination of both. Analysing piles under combined loading is a complex task, as highlighted by Karthigeyan et al. [18], therefore, it is recommended to model the load cases independently [5,33].  \nIt is well known that prior to the development of advanced 3D finite element (FE) models, the soil-structure interaction (SSI) phenomenon proved to be somewhat difficult to define and analyze, as highlighted by Kausel (2010). This difficulty arose from the complex dynamic interactions resulting from the amplification of seismic waves within soil layers, where the nonlinear modelling of RC piles was also challenging, es","cbCair7fMZ88eLvr","https://ap.wps.com/l/cbCair7fMZ88eLvr","pdf",10052770,1,15,"English","en",105,"# Introduction\n## Foundation design challenges for laterally loaded RC piles\n## Soil-structure interaction (SSI) and pile-soil interaction effects\n## Motivation for improved predictive formulations using ML","[{\"question\":\"What is the main objective of the study?\",\"answer\":\"To develop predictive models that compute the overall mechanical response of reinforced concrete (RC) piles embedded in unsaturated clay under horizontal loading until failure.\"},{\"question\":\"How were the datasets for training and testing created?\",\"answer\":\"The study used experimental data combined with advanced nonlinear 3D detailed finite element (FE) modelling to build datasets including ultimate capacity and corresponding horizontal deformation.\"},{\"question\":\"How were the predictive models validated?\",\"answer\":\"Models were trained and tested with multiple machine learning algorithms, then an out-of-sample dataset was used to further validate accuracy and extendibility.\"}]","Developing predictive models for the load-displacement response of laterally loaded reinforced concrete piles in stiff unsaturated clay using machine learning algorithms | 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is the main objective of the study?","Question",{"text":76,"@type":77},"To develop predictive models that compute the overall mechanical response of reinforced concrete (RC) piles embedded in unsaturated clay under horizontal loading until failure.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were the datasets for training and testing created?",{"text":81,"@type":77},"The study used experimental data combined with advanced nonlinear 3D detailed finite element (FE) modelling to build datasets including ultimate capacity and corresponding horizontal deformation.",{"name":83,"@type":74,"acceptedAnswer":84},"How were the predictive models validated?",{"text":85,"@type":77},"Models were trained and tested with multiple machine learning algorithms, then an out-of-sample dataset was used to further validate accuracy and 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