[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121033-en":3,"doc-seo-121033-105":29,"detail-sidebar-cat-0-en-105":90},{"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},121033,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Forecasting the Properties of Concrete Employing Experimental Data Using Machine Learning Algorithms","Study develops data-driven predictions of concrete performance by forecasting compressive strength, flexural strength, and split tensile strength for M30 and M40 mixes. Experimental results from laboratory testing are used to train machine learning models, capturing time-dependent behavior influenced by curing duration and concrete age. The work compares regression approaches, including linear, ridge, and lasso, alongside ensemble methods such as Random Forest, and assesses algorithm suitability through prediction accuracy for durability and safety-oriented design.","Forecasting the Properties of Concrete Employing Experimental Data Using Machine Learning Algorithms  \n\n| Suggested Citation |\n| --- |\n| Jha, A.K., Parihar, R.S., Dongre, N., Misra, R. & Kumar, B. (2024) . Forecasting the Properties of Concrete Employing Experimental Data Using Machine Learning Algorithms. European Journal of Theoretical and Applied Sciences, 2(3), 259-266.\u003Cbr>DOI: 10.59324/ejtas.2024.2(3).22 |\n\nAbhay Kumar Jha 􀀍 Professor, LNCT, Bhopal, India  \nR.S. Parihar Professor, LNCT, Bhopal, India  \nNavneet Dongre [M.tech](M.tech) scholar, LNCT, Bhopal, India  \nRajesh Misra Assistant professor LNCT, Bhopal, India  \nBarun Kumar Assistant professor LNCT, Bhopal, India  \nAbstract:  \nThis study has been undertaken to investigate the compressive strength, Flexural strength and split tensile strength of concrete of grade M30 and M40 in present investigation by laboratory and predicting the strength through Machine learning technique. Flexural strength and split tensile strength which establishes the concrete class, is one of the most crucial characteristics of concrete. The primary characteristic of concrete's durability and safety is its predictable compressive strength, Flexural strength and split tensile strength which is necessary for the use of concrete structures. To explore the time-dependent behavior of concrete strength,  \nconsidering factors such as curing duration and age. Main aim is to compare the performance of different regression methods, such as linear regression, ridge regression, lasso regression, or machine learning approaches like Random Forest and evaluate their suitability for concrete strength prediction and to find the accuracy of algorithms and regression.  \nKeywords: machine learning, compressive strength, flexural strength, split tensile strength.  \nIntroduction  \nIn the realm of civil engineering and construction, predicting the fresh properties of concrete is a critical aspect that directly influences the structural integrity and performance of constructed infrastructure. Traditional methods of forecasting these properties often rely on empirical relationships  \nand extensive experimentation, which can be time-consuming and resource-intensive. However, with the advent of machine learning algorithms, there is a paradigm shift towards more efficient and accurate predictions based on experimental data. Machine learning algorithms, such as regression models, and neural networks, will be employed to analyse and interpret experimental data collected from various  \nconcrete mixes. These algorithms can identify intricate patterns and relationships within the data that may not be readily apparent through traditional analytical techniques. Through the utilization of machine learning, the study seeks to develop robust models that can accurately predict crucial fresh concrete properties. The integration of machine learning into concrete forecasting not only enhances accuracy but also provides a scalable and adaptable framework for handling diverse concrete mix designs. As we venture into this exciting intersection of concrete technology and machine learning, the outcomes of this study hold the potential to revolutionize how we approach the prediction of fresh concrete properties, ushering in a new era of efficiency and precision in the field of civil engineering and construction.  \nObjectives of the Present Study  \nThe main objectives of the present work:  \n1. To design a mix of M30 and M40 grade of concrete specimen in laboratory and predict the accuracy of present investigation.  \n2. To analyse the data through machine learning the mechanical property such as compressive Strength, Flexural strength, Split tensile strength of concrete for M30 and M40 grade.  \n3. Explore the time-dependent behaviour of concrete strength, considering factors such as curing duration and age.  \n4. Compare the performance of different regression methods, such as linear regression, ridge regression, lasso regression, or machine ","cbCaipRej0pYBi5v","https://ap.wps.com/l/cbCaipRej0pYBi5v","pdf",436065,1,"English","en",105,"# Abstract\n# Introduction\n# Objectives of the Present Study\n# Literature Review","[{\"question\":\"What concrete properties are predicted in this study?\",\"answer\":\"The study predicts compressive strength, flexural strength, and split tensile strength for M30 and M40 concrete grades.\"},{\"question\":\"Which machine learning methods are compared for forecasting strength?\",\"answer\":\"The study compares regression methods such as linear regression, ridge regression, and lasso regression, along with machine learning approaches like Random Forest.\"},{\"question\":\"How does the study account for the time-dependent behavior of concrete strength?\",\"answer\":\"It considers curing duration and concrete age to explore time-dependent strength behavior and to evaluate model performance accordingly.\"}]","Forecasting the Properties of Concrete Employing Experimental Data Using Machine Learning Algorithms | 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concrete properties are predicted in this study?","Question",{"text":74,"@type":75},"The study predicts compressive strength, flexural strength, and split tensile strength for M30 and M40 concrete grades.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which machine learning methods are compared for forecasting strength?",{"text":79,"@type":75},"The study compares regression methods such as linear regression, ridge regression, and lasso regression, along with machine learning approaches like Random Forest.",{"name":81,"@type":72,"acceptedAnswer":82},"How does the study account for the time-dependent behavior of concrete strength?",{"text":83,"@type":75},"It considers curing duration and concrete age to explore time-dependent strength behavior and to evaluate model performance 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