[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117360-en":3,"doc-seo-117360-105":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},117360,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Big data-driven global modeling of cohesive soil compaction across conceptual and arbitrary energies","This study develops an intelligent modeling framework to predict compaction characteristics of cohesive soils across multiple compaction energy (CE) levels. A dataset of 1001 observations within theoretical bounds is assembled from experimental work and literature, using sieve analysis, hydrometer analysis, liquid limit (wL), plastic limit (wP), specific gravity (Gs), and compaction tests. Multiple machine learning models, including XGBoost, Random Forest, GEP, MEP, ANN, and MLR, are trained and validated. XGBoost yields the best accuracy for γdmax and wopt while identifying CE as most influential and soil gradation as a key secondary factor.","31 Abstract: This study aims to develop an intelligent modeling approach for accurately  \n32 predicting compaction characteristics of cohesive soils across compaction energy (CE) levels.  \n33 A comprehensive database of 1001 observations falling within the theoretical bounds was  \n34 created through experimental investigation encompassing sieve analysis, hydrometer analysis, 35 liquid limit (wL), plastic limit (wP), specific gravity (Gs), and compaction tests on natural soil  \n36 samples and literature review, encompassing diverse cohesive soils, CE levels, and compaction  \n37 characteristics. Multiple machine learning techniques, including Extreme Gradient Boosting  \n38 (XGBoost), Random Forest (RF), Gene Expression Programming (GEP), Multi Expression  \n39 Programming (MEP), Artificial Neural Networks (ANN), and Multiple Linear Regression 40 (MLR), were applied to develop predictive models. XGBoost demonstrated superior  \n41 performance in predicting maximum dry density (γdmax) and optimum moisture content (wopt)  \n42 as evaluated by statistical indicators and external validation and compared with existing models  \n43 in the literature. The models effectively captured the influence of key parameters, highlighting  \n44 the primary role of CE and wL, the secondary role of plastic limit (wP), the tertiary role of  \n45 plasticity index (IP) and fines activity (AF), and the quaternary role of soil gradation in  \n46 predicting and influencing the compaction characteristics of cohesive soils. This approach  \n47 enables accurate global modeling of cohesive soil compaction across varying CE levels, 48 providing a valuable tool for geotechnical engineers and researchers to determine compaction  \n49 characteristics for a known CE level using basic soil properties used for soil classification.  \n50 Keywords: Machine learning; XGBoost; Compaction; Cohesive soils; Big geotechnical data  \n51  \n52  \n53  \n54  \n55  \n56  \n57  \n58  \n59 1. Introduction  \n60 Compaction control is a crucial factor that governs the engineering properties of fine-grained  \n61 soils also referred to as cohesive soils, across various earthwork projects, including the  \n62 construction of foundation structures, roads, compacted clay liners, earthen dams, nuclear  \n63 repositories, and embankments, among others (Wang and Yin, 2020; Zhu et al., 2024) . To  \n64 attain desired engineering characteristics through optimal densification in a project, the soil  \n65 density and moisture content are benchmarked against the maximum dry density (γdmax) and  \n66 optimum water content (wopt) obtained through a compaction test (Horpibulsuk et al., 2009) .  \n67 The field compaction parameters are benchmarked against the parameters obtained at specified  \n68 CE levels linked with the standard and modified proctor tests to obtain the degree of  \n69 compaction (Dc) (Ran et al., 2024) . Thus, compaction parameters are crucial in designing and  \n70 commencing the compaction process in earthwork, while simultaneously establishing a  \n71 standard for quality control and assurance (QA/QC) during field operations (J. Li et al., 2024;  \n72 Ma et al., 2024; Tarantino and De Col, 2008) . However, the testing methodologies required to  \n73 obtain these parameters are characterized by labor-intensive and tedious procedures, 74 necessitating a substantial quantity of representative material for the examination, factors  \n75 linked to escalating project time and cost (Teramoto et al., 2024; Wang et al., 2020) .  \n76 Furthermore, this challenge is exacerbated by the adoption of test protocols standardized across  \n77 various conceptual compaction energy (CE) levels, i.e., standard or modified, which may or  \n78 may not align with actual field conditions (Alzubaidi et al., 2024; Jia et al., 2024) . In practical  \n79 applications, compaction energy (CE) levels can vary significantly, either adhering to  \n80 established conceptual levels or being set at project-specific levels. This variability underscore","cbCaiqPB1TLHd045","https://ap.wps.com/l/cbCaiqPB1TLHd045","pdf",5452590,1,74,"English","en",105,"# Introduction\n## Compaction control and QC/QA needs\n## Challenges with conceptual vs. field compaction energy\n## Existing estimation methods and their limitations","[{\"question\":\"What does the study aim to achieve for cohesive soils?\",\"answer\":\"It develops an intelligent modeling approach to accurately predict cohesive soil compaction characteristics across different compaction energy (CE) levels.\"},{\"question\":\"What dataset and inputs are used to build the models?\",\"answer\":\"The work compiles 1001 observations, including sieve and hydrometer analyses, liquid limit (wL), plastic limit (wP), specific gravity (Gs), and results from compaction tests, supported by literature review.\"},{\"question\":\"Which model performs best and what does it predict?\",\"answer\":\"XGBoost shows superior performance for predicting maximum dry density (γdmax) and optimum moisture content (wopt) based on statistical evaluation and external validation.\"}]","Big data-driven global modeling of cohesive soil compaction across conceptual and arbitrary energies | 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