[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117550-en":3,"doc-seo-117550-105":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},117550,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Applying advanced data analytics and machine learning to enhance the safety control of dams - Chapter 1","Protection of critical engineering infrastructures is essential for maintaining vital services such as water supply, energy production, and transport while preventing large-scale disasters. Dam safety relies on measuring structural quantities like displacements, strains, and stresses, and evaluating monitoring data against physical and mathematical models. Traditional multiple linear regression models are contrasted with modern machine learning approaches, including deep learning and recurrent neural networks. The chapter covers the full data lifecycle, from acquisition and storage to quality, outlier detection, and analysis, using monitoring data from concrete dams in Portugal.","Repositório ISCTE-IUL  \nDeposited in Repositório ISCTE-IUL: 2023-01-17  \nDeposited version:  \nAccepted Version  \nPeer-review status of attached file:  \nPeer-reviewed  \nCitation for published item:  \nRico, J., Barateiro, J., Mata, J., Antunes, A. & Cardoso, E. (2019) . Applying advanced data analyticsand machine learning to enhance the safety control of dams. In George A. Tsihrintzis, Maria Virvou, Evangelos Sakkopoulos, Lakhmi C. Jain (Ed.), Machine learning paradigms: Applications of learning  \nand analytics in intelligent systems. Cham: Springer.  \nFurther information on publisher's website:  \n10. 1007/978-3-030-15628-2_ 10  \nPublisher's copyright statement:  \nThis is the peer reviewed version of the following article: Rico, J., Barateiro, J., Mata, J., Antunes, A.& Cardoso, E. (2019) . Applying advanced data analytics and machine learning to enhance the safety control of dams. In George A. Tsihrintzis, Maria Virvou, Evangelos Sakkopoulos, Lakhmi C. Jain (Ed.), Machine learning paradigms: Applications of learning and analytics in intelligent systems. Cham: Springer., which has been published in final form at [https://dx.doi.org/10.1007/978-3-030-15628-](https://dx.doi.org/10.1007/978-3-030-15628-)[ ](https://dx.doi.org/10.1007/978-3-030-15628-)[2_10. This article may be used for non-commercial purposes in accordance with the Publisher](2_10. This article may be used for non-commercial purposes in accordance with the Publisher)'[s](s) Terms and Conditions for self-archiving.  \nUse policy  \nCreative Commons CC BY 4.0  \nThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-profit purposes provided that:  \n• a full bibliographic reference is made to the original source  \n• a link is made to the metadata record in the Repository  \n• the full-text is not changed in any way  \nThe full-text must not be sold in any format or medium without the formal permission of the copyright holders.  \nServiços de Informação e Documentação, Instituto Universitário de Lisboa (ISCTE-IUL) Av. das Forças Armadas, Edifício II, 1649-026 Lisboa Portugal Phone: +(351) 217 903 024 | e-mail: [administrador.repositorio@iscte-iul.pt](administrador.repositorio@iscte-iul.pt)[ ](administrador.repositorio@iscte-iul.pt)[https://repositorio.iscte-iul.pt](https://repositorio.iscte-iul.pt)  \nChapter 1  \nApplying advanced data analytics and machine learning to enhance the safety control of dams  \nJoo Rico, Jos Barateiro, Juan Mata, Antnio Antunes and Elsa Cardoso  \nJoo Rico  \nLaboratrio Nacional de Engenharia Civil, Av. Brasil, 101, Lisbon, e-mail: [jmrico@lnec.pt](jmrico@lnec.pt)[ ](jmrico@lnec.pt)Jos Barateiro  \nLaboratrio Nacional de Engenharia Civil, Av. Brasil, 101, Lisbon, e-mail: [jbarateiro@lnec.pt](jbarateiro@lnec.pt)[ ](jbarateiro@lnec.pt)INESC-ID, R. Alves Redol, 9, Lisbon  \nJuan Mata  \nLaboratrio Nacional de Engenharia Civil, Av. Brasil, 101, Lisbon, e-mail: [jmata@lnec.pt](jmata@lnec.pt)[ ](jmata@lnec.pt)Antnio Lorvo Antunes  \nLaboratrio Nacional de Engenharia Civil, Av. Brasil, 101, Lisbon, e-mail: [aantunes@lnec.pt](aantunes@lnec.pt)  \nElsa Cardoso  \nISCTE-Instituto Universitrio de Lisboa, Av. Forc¸as Armadas, Lisbon, e-mail: [earc@iscte-iul.pt](earc@iscte-iul.pt)[ ](earc@iscte-iul.pt)INESC-ID, R. Alves Redol, 9, Lisbon  \n2 Joo Rico, Jos Barateiro, Juan Mata, Ant´onio Antunes and Elsa Cardoso  \nAbstract The protection of critical engineering infrastructures is vital to today's society, not only to ensure the maintenance of their services (e.g., water supply, energy production, transport), but also to avoid large-scale disasters. Therefore, technical and financial efforts are being continuously made to improve the safety control of large civil engineering structures like dams, bridges and nuclear facilities. This control is based on the measurement of physical quantities that characterize the structural behavior, such as d","cbCaiaR2jEaXrQeA","https://ap.wps.com/l/cbCaiaR2jEaXrQeA","pdf",3526830,1,39,"English","en",105,"# Abstract\n## Dam safety and monitoring data\n## Data lifecycle: acquisition, processing, storage\n## Data quality and outlier detection\n## Machine learning for safety control\n## Case scenarios with Portuguese concrete dams","[{\"question\":\"Why is dam safety control considered vital?\",\"answer\":\"Dam safety helps ensure continued critical services and reduces the risk of environmental, social, and economic disasters.\"},{\"question\":\"What traditional modeling approach is commonly used for dam response analysis?\",\"answer\":\"Dam specialists commonly use multiple linear regression models, a well-known approach among dam engineers since the 1950s.\"},{\"question\":\"What topics does the chapter cover regarding monitoring data?\",\"answer\":\"It presents the full data lifecycle: acquisition, processing, storage, data quality, outlier detection, and subsequent data analysis using machine learning.\"}]","Applying advanced data analytics and machine learning to enhance the safety control of dams - 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