Long-Term Structural Health Monitoring by Remote Sensing and Advanced Machine Learning: A Practical Strategy Via Structural Displacements from Synthet
Entezami, Alireza, Behkamal, Bahareh, De Michele, Carlo
- 出版商: Springer
- 出版日期: 2024-02-22
- 售價: $2,110
- 貴賓價: 9.5 折 $2,005
- 語言: 英文
- 頁數: 110
- 裝訂: Quality Paper - also called trade paper
- ISBN: 303153994X
- ISBN-13: 9783031539947
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相關分類:
Machine Learning
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商品描述
This book offers an in-depth investigation into the complexities of long-term structural health monitoring (SHM) in civil structures, specifically focusing on the challenges posed by small data and environmental and operational changes (EOCs). Traditional contact-based sensor networks in SHM produce large amounts of data, complicating big data management. In contrast, synthetic aperture radar (SAR)-aided SHM often faces challenges with small datasets and limited displacement data. Additionally, EOCs can mimic the structural damage, resulting in false errors that can critically affect economic and safety issues. Addressing these challenges, this book introduces seven advanced unsupervised learning methods for SHM, combining AI, data sampling, and statistical analysis. These include techniques for managing datasets and addressing EOCs. Methods range from nearest neighbor searching and Hamiltonian Monte Carlo sampling to innovative offline and online learning frameworks, focusing on data augmentation and normalization. Key approaches involve deep autoencoders for data processing and novel algorithms for damage detection. Validated using simulated data from the I-40 Bridge, USA, and real-world data from the Tadcaster Bridge, UK, these methods show promise in addressing SAR-aided SHM challenges, offering practical tools for real-world applications. The book, thereby, presents a comprehensive suite of innovative strategies to advance the field of SHM.
商品描述(中文翻譯)
本書深入探討了土木結構長期結構健康監測（SHM）的複雜性，特別關注小數據和環境和操作變化（EOCs）所帶來的挑戰。傳統的基於接觸的SHM傳感器網絡產生大量數據，使大數據管理變得複雜。相比之下，合成孔徑雷達（SAR）輔助的SHM常常面臨小數據集和有限位移數據的挑戰。此外，EOCs可能模擬結構損壞，導致虛假錯誤，可能對經濟和安全問題產生重大影響。為應對這些挑戰，本書介紹了七種先進的無監督學習方法，結合了人工智能、數據抽樣和統計分析。這些方法包括管理數據集和解決EOCs的技術。方法範圍從最近鄰搜索和Hamiltonian Monte Carlo抽樣到創新的離線和在線學習框架，重點放在數據擴充和歸一化上。關鍵方法包括用於數據處理的深度自編碼器和用於損壞檢測的新算法。通過使用來自美國I-40橋的模擬數據和英國Tadcaster橋的實際數據進行驗證，這些方法在應對SAR輔助的SHM挑戰方面表現出潛力，為實際應用提供了實用工具。因此，本書提供了一套全面的創新策略，以推進SHM領域的發展。
作者簡介
Prof. Alireza Entezami has been an assistant professor in the Department of Civil and Environmental Engineering (DICA) at Politecnico di Milano, Italy, since November 2022. His current role also includes co-supervision of a research project granted by the European Space Agency (ESA), which employs data mining and machine learning techniques for monitoring the structural integrity of large infrastructures using earth observation. Prior to joining the DICA department as a faculty member, he was a post-doctoral research fellowship selected by ESA, working in the DICA at Politecnico di Milano since May 2021. In April 2020, he received a Ph.D. in Structural, Seismic, and Geotechnical Engineering from Politecnico di Milano with Cum Laude degree His research interests span from model-driven structural damage detection to data-driven structural health monitoring, with the focus on large civil infrastructures.
Dr. Bahareh Behkamal, a dynamic researcher in the realm of computer science, has been contributing to the fields of artificial intelligence, machine learning, deep learning, and health monitoring of structures through her expertise. Prior to her current engagement, from August 2018 to December 2021, she was a researcher, collaborating with the Department of Applied Science and Technology at Politecnico di Torino, Turin, Italy. Since January 2022, she has been serving as a post-doctoral researcher in the Department of Civil and Environmental Engineering (DICA) at Politecnico di Milano, contributing to a project focused on the application of artificial intelligence and machine learning in addressing natural hazards and hydrological challenges. Additionally, since April 2023, she has been a post-doctoral research fellowship of the European Space Agency (ESA), continuing her work at DICA, Politecnico di Milano.
Prof. Carlo De Michele has been a professor in the Department of Civil and Environmental Engineering (DICA) at Politecnico di Milano since June2019. He served as an associate professor at Politecnico di Milano from 2008 to 2019, following his tenure as an assistant professor in the same department since 1999. In his current role, he also supervises research sponsored by the European Space Agency (ESA). This project leverages advanced data mining and machine learning methodologies to monitor large-scale infrastructures, utilizing data gathered from earth observation and remote sensing. His research interests are broad and impactful, encompassing statistics, stochastic and multivariate modeling, and climate and environmental variability effects. Prof. De Michele has also made significant contributions to understanding precipitation dynamics, hydrological safety of dams, the water-energy nexus, and compound climate-related extremes. Mentoring has been a crucial part of his career.
作者簡介(中文翻譯)
Prof. Alireza Entezami自2022年11月起擔任意大利米蘭理工大學土木與環境工程系（DICA）的助理教授。他目前的職責還包括共同指導一項由歐洲太空總署（ESA）資助的研究項目，該項目利用數據挖掘和機器學習技術，通過地球觀測來監測大型基礎設施的結構完整性。在加入DICA系作為教職成員之前，他是ESA選定的博士後研究員，自2021年5月起在米蘭理工大學DICA系工作。2020年4月，他以優異成績獲得了米蘭理工大學結構、地震和岩土工程博士學位。他的研究興趣涵蓋了基於模型的結構損傷檢測和基於數據的結構健康監測，尤其關注大型土木基礎設施。
Dr. Bahareh Behkamal是計算機科學領域的一位活躍研究者，通過她的專業知識在人工智能、機器學習、深度學習和結構健康監測等領域做出了貢獻。在目前的工作之前，她從2018年8月到2021年12月在意大利都靈理工大學應用科學與技術系進行研究。自2022年1月起，她擔任意大利米蘭理工大學土木與環境工程系（DICA）的博士後研究員，為一個專注於應用人工智能和機器學習解決自然災害和水文挑戰的項目做出貢獻。此外，自2023年4月起，她成為歐洲太空總署（ESA）的博士後研究員，繼續在米蘭理工大學DICA系工作。
Prof. Carlo De Michele自2019年6月起擔任意大利米蘭理工大學土木與環境工程系（DICA）的教授。他曾在2008年至2019年期間擔任米蘭理工大學副教授，並在1999年以助理教授的身份加入該系。在他目前的職位中，他還負責指導歐洲太空總署（ESA）資助的研究項目。該項目利用先進的數據挖掘和機器學習方法監測大型基礎設施，利用從地球觀測和遙感獲取的數據。他的研究興趣廣泛而有影響力，包括統計學、隨機和多變量建模，以及氣候和環境變異效應。De Michele教授還在了解降水動力學、水文安全性、水能聯網和氣候相關極端事件方面做出了重要貢獻。指導學生是他職業生涯中的重要組成部分。
