[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118069-en":3,"doc-seo-118069-105":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},118069,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Concrete’s Strength Prediction using Machine Learning Method","Concrete’s Strength Prediction using Machine Learning Method investigates reliable prediction of concrete strength while addressing key obstacles in construction analytics. The study proposes the Ensemble-Based Outlier Detection (EBOD) algorithm to reduce bias from noisy data by combining multiple outlier detectors. It applies Gaussian Process Regression (GPR) to forecast strength and quantify uncertainty, improving interpretability for engineering decisions. To further enhance transparency, symbolic regression generates explainable models, supported by data augmentation using SMOTE. Finally, it analyzes how dataset size affects performance and practical limitations.","UCLA  \nUCLA Electronic Theses and Dissertations  \nTitle  \nConcrete’s Strength Prediction using Machine Learning Method  \nPermalink  \n[https://escholarship.org/uc/item/1xk4r5m2](https://escholarship.org/uc/item/1xk4r5m2)  \nAuthor  \nOuyang, Boya  \nPublication Date  \n2024  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA Los Angeles  \nConcrete’s Strength Prediction using Machine Learning Method  \nA dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Materials Science and Engineering  \nby  \nBoya Ouyang  \n2024  \n© Copyright by Boya Ouyang 2024  \nABSTRACT OF THE DISSERTATION  \nConcrete’s Strength Prediction using Machine Learning Method  \nby  \nBoya Ouyang  \nDoctor of Philosophy in Materials Science and Engineering University of California, Los Angeles, 2024  \nProfessor Gaurav Sant, Chair  \nIn this study, I present a comprehensive study that addresses the complex challenge of predicting concrete strength, leveraging the power of advanced machine learning techniques. Recognizing the limitations of traditional prediction models, I have introduced innovative methodologies to enhance accuracy and interpretability in this crucial aspect of construction.  \nCentral to my approach is the development of the Ensemble-Based Outlier Detection (EBOD) algorithm. Recognizing the detrimental impact of noisy data on model performance, I designed EBOD to integrate multiple detection algorithms, thereby significantly reducing the bias associated with single-algorithm methods. This innovation ensures that the datasets used for model training and analysis are of the highest quality, laying a solid foundation for more accurate predictive modeling.  \nMoving forward, I explored the capabilities of Gaussian Process Regression (GPR) in predicting concrete strength. My work with GPR is not just about prediction; it’s about understanding the intricacies of the data. I optimized the GPR model to not only forecast concrete strength with remarkable accuracy but also to quantify the uncertainties associated with these predictions. This dual capability of the GPR model enriches the interpretability  \nof the results, providing deeper insights that are invaluable for material engineering and construction management.  \nIn my pursuit of transparency and interpretability in predictive modeling, I introduced symbolic regression into the study. I recognized the need for models that not only predict but also explain. Symbolic regression offered a solution, enabling me to construct interpretable models that shed light on the underlying physical phenomena governing concrete strength. To enhance the predictive power of these models, I incorporated advanced data augmentation techniques, such as the Synthetic Minority Over-sampling Technique (SMOTE), pushing the boundaries of prediction and understanding in unexplored domains.  \nA pivotal aspect of my study involved a meticulous analysis of the balance between data volume and the precision of machine learning models. I undertook a comprehensive evaluation of a vast dataset, assessing the performance of various algorithms in predicting concrete strength. This rigorous analysis highlights my commitment to not only advancing the accuracy of predictive models but also to understanding the practical challenges and limitations of employing machine learning in the field of concrete strength prediction.  \nThrough the development of innovative algorithms, the application of advanced machine learning techniques, and a thorough analysis of extensive datasets, I aim to revolutionize the way we predict, understand, and apply concrete strength models in industrial applications, setting new benchmarks for accuracy and interpretability.  \nThe dissertation of Boya Ouyang is approved.  \nAli Mosleh  \nJaime Marian Amartya Sankar Banerjee Gaurav Sant, Committee Chair  \nUniversi","cbCaieJQnvWVAE3D","https://ap.wps.com/l/cbCaieJQnvWVAE3D","pdf",5827743,1,138,"English","en",105,"# 1 Introduction\n# 2 Research Challenges and Related Work\n## 2.1 Noisy Datasets and Outlier Handling in Concrete Data\n## 2.2 Limited Availability of Reliable Concrete Strength Data\n## 2.3 Mapping Data Uncertainty to Concrete Strength Prediction\n## 2.4 Extrapolating Concrete Strength with Unknown Feature Domain\n# 3 An Ensemble-Based Outlier Detection Algorithm for Noisy Concrete datasets\n## 3.1 Overview and realted works\n## 3.2 Feature selection\n## 3.3 Artificial neural network model\n## 3.4 Outlier detection algorithms\n## 3.5 Alternative ensemble-based detectors\n## 3.6 Optimal outlier removal based on the union of detectors\n## 3.7 Influence of data cleansing on model complexity\n## 3.8 Influence of data cleansing on learning efficiency\n## 3.9 Influence of data cleansing on model accuracy\n## 3.10 Alternative ensemble-based detectors\n## 3.11 Non-parametric statistical tests\n## 3.12 Conclusions\n# 4 Using Machine Learning To Predict Concrete’s Strength: Learning From Small Datasets\n## 4.1 INTRODUCTION\n## 4.2 Machine learning algorithms\n## 4.3 Model training\n## 4.4 Accuracy evaluation\n## 4.5 Accuracy of the machine learning models","[{\"question\":\"What problem does the dissertation address in concrete strength prediction?\",\"answer\":\"It focuses on predicting concrete strength accurately despite noisy data and limited availability of reliable concrete strength measurements, improving both accuracy and interpretability.\"},{\"question\":\"How does the EBOD algorithm improve model performance?\",\"answer\":\"EBOD integrates multiple outlier detection algorithms to reduce bias caused by noisy data, ensuring higher-quality datasets for model training and analysis.\"},{\"question\":\"What role does Gaussian Process Regression play in the study?\",\"answer\":\"GPR is used not only to forecast concrete strength but also to quantify predictive uncertainty, providing deeper interpretability for engineering and construction decisions.\"}]","Concrete’s Strength Prediction using Machine Learning Method | 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problem does the dissertation address in concrete strength prediction?","Question",{"text":75,"@type":76},"It focuses on predicting concrete strength accurately despite noisy data and limited availability of reliable concrete strength measurements, improving both accuracy and interpretability.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the EBOD algorithm improve model performance?",{"text":80,"@type":76},"EBOD integrates multiple outlier detection algorithms to reduce bias caused by noisy data, ensuring higher-quality datasets for model training and analysis.",{"name":82,"@type":73,"acceptedAnswer":83},"What role does Gaussian Process Regression play in the study?",{"text":84,"@type":76},"GPR is used not only to forecast concrete strength but also to quantify predictive uncertainty, providing deeper interpretability for engineering and construction 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