Intelligence at the Interface: Data-Centric Methods for Sensing, Modeling, and Decision Support
暫譯: 介面智慧：以數據為中心的感知、建模與決策支持方法

本書展示了計算智能應用中的創新方法。最初的章節討論了人工智慧在分析物理系統中的應用，特別是當代神經網絡在關鍵基礎設施的快速且準確的地震分析中的應用。它還討論了先進的元啟發式和混合人工智慧方法，這些方法提供了對地質和岩石特性的穩健、非侵入性評估，取代了昂貴且耗時的傳統方法。隨後的章節擴展了範疇，包括戰略和操作智能的維度，其中生成式人工智慧的使用旨在增強物聯網生態系統的安全性和功能性，而自然語言處理與物聯網技術的無縫互動使系統的控制更加以人為中心。它還描述了邊緣人工智慧，詳細說明了使快速、安全、實時決策成為可能的設備端處理能力。

Dr. Aydin Azizi holds a PhD in Mechanical Engineering-Mechatronics, an MSc in Mechatronics, and a BSc in Mechanical Engineering. Certified as a Fellow of the Higher Education Academy, official instructor for the Siemens Mechatronic Certification Program (SMSCP), and Editor-in-Chief of the book series Emerging Trends in Mechatronics published by Springer Nature Group, he currently serves as a Senior Lecturer and the Academic Partnership Liaison Manager at Oxford Brookes University. His current research focuses on investigating and developing novel techniques to model, control, and optimize complex systems, with expertise in Control & Automation, AI, and Simulation Techniques. Dr. Azizi is the recipient of the National Research Award of Oman for his AI-based controllers research, DELL EMC's "Envision the Future" award for the "Automated Irrigation System," and 'Exceptional Talent' recognition by the British Royal Academy of Engineering. He has also been recognized for three consecutive years (2023-2025) among the World's Top 2% Scientists by Stanford University & Elsevier for his impactful research contributions.

Dr Danial Jahed Armaghani is an internationally recognised researcher and one of the most highly cited scientists globally in tunnelling, geomechanics, and AI-driven predictive modelling. He has authored 400 peer-reviewed publications, more than 83% in Q1 journals, and has an h-index of 93 (Scopus) / 104 (Google Scholar), with more than 29,000 citations in Google Scholar. He has been consistently ranked among the top 2% of researchers worldwide (Stanford University Global Citation Ranking) from 2020 to 2025. He is also ranked among the top 0.05% of all scholars worldwide, according to ScholarGPS Highly Ranked Scholars in Engineering and Computer Science. His research has advanced theory-guided machine learning and real-time TBM performance forecasting, establishing him as a leading expert driving innovation in mechanised tunnelling and intelligent underground construction.

Dr. Mirrashid applies computational intelligence methods to problems in structural and earthquake engineering, with an emphasis on reducing the environmental footprint of built infrastructure. In her capacity as Research Consultant at Abu Dhabi University, she has devised machine-learning approaches that advance predictive modelling of structural response, guide optimisation of low-carbon construction materials, and inform rigorous assessments of infrastructure safety. Her scholarship appears in leading peer-reviewed outlets and has been funded by both international and national grants. Her professional service includes editorial appointments at several international journals, participation on technical committees for more than twenty international conferences, and completion of in excess of 950 peer reviews for over 80 Scopus-indexed journals. Principal research contributions comprise data-driven models for seismic vulnerability assessment and algorithms for evaluating structural resilience to seismic sequences. Her work on sustainable materials includes predictive systems for recycled-aggregate concrete, carbon-nanotube-modified cementitious composites, and FRP-strengthened elements, and she has proposed revised damage-state definitions for RC buildings that address ambiguities in seismic codes and support retrofitting strategies. Beyond peer-reviewed publications, she has produced applied resources, most notably the book Soft Computing in Civil Engineering and professional training series (neuro-fuzzy methods and optimisation) available on online learning platforms.

艾丁·阿齊茲博士（Dr. Aydin Azizi）擁有機械工程-機電一體化的博士學位、機電一體化的碩士學位以及機械工程的學士學位。他是高等教育學院的研究員，西門子機電認證計畫（SMSCP）的官方講師，以及施普林格自然集團出版的《機電一體化新興趨勢》系列書籍的主編。目前，他在牛津布魯克斯大學擔任高級講師及學術夥伴聯絡經理。他目前的研究重點是調查和開發新技術，以建模、控制和優化複雜系統，專長於控制與自動化、人工智慧及模擬技術。阿齊茲博士因其基於人工智慧的控制器研究獲得阿曼國家研究獎，因「自動灌溉系統」獲得DELL EMC的「展望未來」獎，並因其卓越才能獲得英國皇家工程院的認可。他在2023年至2025年間，連續三年被史丹佛大學與Elsevier評選為全球前2%的科學家，以表彰其對研究的重大貢獻。

丹尼爾·賈赫德·阿馬甘尼博士（Dr. Danial Jahed Armaghani）是一位國際知名的研究者，也是全球在隧道工程、地質力學和基於人工智慧的預測建模領域中被引用最多的科學家之一。他已發表400篇同行評審的論文，其中超過83%發表在Q1期刊，並擁有93（Scopus）/ 104（Google Scholar）的h指數，在Google Scholar上有超過29,000次引用。他自2020年至2025年持續被評為全球前2%的研究者（史丹佛大學全球引用排名）。根據ScholarGPS的工程與計算機科學高排名學者，他也被評為全球所有學者中的前0.05%。他的研究推進了理論指導的機器學習和實時隧道掘進機（TBM）性能預測，確立了他在機械隧道和智能地下建設創新方面的領導地位。

米拉希德博士（Dr. Mirrashid）將計算智能方法應用於結構和地震工程的問題，重點在於減少建設基礎設施的環境足跡。作為阿布達比大學的研究顧問，她設計了機器學習方法，以推進結構反應的預測建模，指導低碳建材的優化，並提供基礎設施安全的嚴謹評估。她的學術成果發表在多個領先的同行評審期刊上，並獲得國際和國內的資助。她的專業服務包括在多個國際期刊的編輯任命、參與超過二十個國際會議的技術委員會，以及為超過80個Scopus索引期刊完成超過950篇的同行評審。她的主要研究貢獻包括用於地震脆弱性評估的數據驅動模型和評估結構對地震序列的韌性的算法。她在可持續材料方面的工作包括對回收骨料混凝土、碳納米管改性水泥複合材料和FRP加固元件的預測系統，並提出了針對RC建築的修訂損傷狀態定義，以解決地震法規中的模糊性並支持加固策略。除了同行評審的出版物外，她還製作了應用資源，最著名的是《土木工程中的軟計算》一書及在線學習平台上提供的專業培訓系列（神經模糊方法和優化）。