Artificial Intelligence and Data Analytics for Structural Health Monitoring: A Review of Recent Developments

IF 12.1 2区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Archives of Computational Methods in Engineering Pub Date : 2025-04-07 DOI:10.1007/s11831-025-10276-x

Shrikant M. Harle, Amol Bhagat, Ruchita Ingole, Nilesh Zanjad

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引用次数: 0

Abstract

Structural health monitoring (SHM) has witnessed a transformative evolution with the integration of Artificial Intelligence (AI) and data analytics. This review synthesizes recent developments in the realm of AI-powered SHM, elucidating key findings and emphasizing the pivotal role of these technologies in shaping the future of infrastructure monitoring. The review highlights the efficacy of AI in processing and analyzing vast structural datasets, leading to improved detection, diagnosis, and prediction of structural issues. Machine learning algorithms contribute to a proactive approach, enabling the identification of subtle patterns indicative of deterioration. The symbiosis of AI and SHM not only enhances accuracy in anomaly detection but also holds promise in revolutionizing maintenance strategies. This abstract encapsulates the significance of AI and data analytics in SHM, concluding with insights into future research directions to address challenges and unlock untapped potentials in this dynamic field.

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结构健康监测中的人工智能和数据分析：最新发展综述

随着人工智能（AI）和数据分析的融合，结构健康监测（SHM）经历了革命性的发展。本综述综合了人工智能驱动的SHM领域的最新发展，阐明了关键发现，并强调了这些技术在塑造基础设施监测未来方面的关键作用。该综述强调了人工智能在处理和分析大量结构数据集方面的功效，从而改进了对结构问题的检测、诊断和预测。机器学习算法有助于积极主动的方法，能够识别指示恶化的细微模式。人工智能和SHM的共生不仅提高了异常检测的准确性，而且有望彻底改变维护策略。该摘要概括了人工智能和数据分析在SHM中的重要性，并总结了未来研究方向的见解，以应对这一动态领域的挑战并释放未开发的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Archives of Computational Methods in Engineering 工程技术-工程：综合

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.