Physics Informed Machine Learning (PIML) for Design, Management and Resilience-Development of Urban Infrastructures: A Review

IF 9.7 2区 工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Archives of Computational Methods in Engineering Pub Date : 2024-06-12 DOI:10.1007/s11831-024-10145-z
Alvin Wei Ze Chew, Renfei He, Limao Zhang
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Abstract

Building resilient and sustainable urban infrastructures is imperative to prepare future generations against new pandemics and climate change uncertainties. In general, modelling of urban infrastructures requires modelers to carefully consider their initial design phase, subsequent life-span management, and long-term resilience development. With the continual development of machine learning (ML) and artificial intelligence (AI) approaches, significant opportunities are available to civil engineers to improve the existing computing systems of urban infrastructures to contribute to their overall design, management, and resilience-development. Often, an important requirement for the successful adoption of ML/AI techniques is to ensure sufficient field data for training effective predictive models for the above objectives. However, this requirement may be difficult to achieve for all infrastructure engineering applications in the practical field context due to sensor constraints (e.g., limited sensor deployment), coupled with other computational challenges. To address the multiple challenges, this review paper evaluates the important and relevant physics informed machine learning (PIML) publications from 1992 to 2022 for various critical infrastructure engineering applications, namely: (1) PIML for Infrastructures Design and Analysis, (2) PIML for Infrastructure Built-Environment Modelling, (3) PIML for Infrastructures Health Monitoring, and (4) PIML for Infrastructures Resilience Management/Development. In each application, we discuss on the key modelling objectives involved for the specific infrastructure systems, and their associated advantages and/or likely limitations obtained from the PIML implementation. Finally, we then summarize the key research trends and their associated challenges to continue leveraging on PIML techniques to better benefit the overall design, management, and resilience-development of urban infrastructures.

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用于城市基础设施设计、管理和弹性开发的物理信息机器学习(PIML):概念、最新技术、挑战与机遇
建设有复原力和可持续的城市基础设施是使子孙后代做好应对新的流行病和气候变化不确定性的准备的必要条件。一般来说,城市基础设施的建模要求建模者仔细考虑其初始设计阶段、随后的生命周期管理和长期弹性发展。随着机器学习(ML)和人工智能(AI)方法的不断发展,土木工程师有机会改进现有的城市基础设施计算系统,为其整体设计、管理和弹性发展做出贡献。通常,成功采用ML/AI技术的一个重要要求是确保有足够的现场数据来训练有效的预测模型以实现上述目标。然而,由于传感器的限制(例如,有限的传感器部署),再加上其他计算方面的挑战,这一要求可能难以在实际领域的所有基础设施工程应用中实现。为了应对多重挑战,本文评估了1992年至2022年期间重要和相关的物理信息机器学习(PIML)出版物,用于各种关键基础设施工程应用,即:(1)基础设施设计和分析的PIML,(2)基础设施建筑环境建模的PIML,(3)基础设施健康监测的PIML,以及(4)基础设施弹性管理/发展的PIML。在每个应用程序中,我们讨论了特定基础设施系统所涉及的关键建模目标,以及从PIML实现中获得的相关优势和/或可能的限制。最后,我们总结了主要的研究趋势及其相关的挑战,以继续利用PIML技术更好地造福城市基础设施的整体设计、管理和弹性发展。
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来源期刊
CiteScore
19.80
自引率
4.10%
发文量
153
审稿时长
>12 weeks
期刊介绍: Archives of Computational Methods in Engineering Aim and Scope: Archives of Computational Methods in Engineering serves as an active forum for disseminating research and advanced practices in computational engineering, particularly focusing on mechanics and related fields. The journal emphasizes extended state-of-the-art reviews in selected areas, a unique feature of its publication. Review Format: Reviews published in the journal offer: A survey of current literature Critical exposition of topics in their full complexity By organizing the information in this manner, readers can quickly grasp the focus, coverage, and unique features of the Archives of Computational Methods in Engineering.
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