Structural Health Monitoring is gaining popularity in recent times because of advancements in technology and the increasing need for repair and rehabilitation. The shift from conventional wired technologies to advanced wireless technologies is also gradually increasing in the past decade. These sensor networks are economical when used for monitoring huge structures with high design life and safety requirements like highway and roadway bridges, multi-story buildings, chimneys, offshore platforms, and nuclear reactors. Smart sensors when paired along with Artificial Intelligence tools like Artificial Neural Networks, Machine Learning, Deep Learning, and its derivatives Convolutional Neural Networks, Hybrid Intelligence, Cloud Computing make the monitoring system completely automated. This paper is a comprehensive review of advances in data acquisition, processing, diagnosis, and retrieval stages of Structural Health Monitoring both academically and commercially. The review primarily focuses on the recently used wireless data acquisition system and execution of AI resources for data prediction and data diagnosis in RCC buildings and bridges. The review also indicates the lag in real-world execution of structural health monitoring technologies despite advances in academia and insists on the development of standards to gel the gap.

由于技术的进步以及对修复和康复的需求不断增加，结构健康监测最近越来越受欢迎。在过去十年中，从传统有线技术向先进无线技术的转变也在逐渐增加。这些传感器网络在用于监测具有高设计寿命和安全要求的大型结构（如公路和公路桥梁、多层建筑、烟囱、海上平台和核反应堆）时是经济的。智能传感器与人工智能工具（如人工神经网络、机器学习、深度学习及其衍生产品卷积神经网络、混合智能、云计算）配合使用时，可以使监控系统完全自动化。本文对结构健康监测的数据采集、处理、诊断和检索阶段的学术和商业进展进行了全面回顾。审查主要集中在最近使用的无线数据采集系统和人工智能资源的执行，用于 RCC 建筑物和桥梁的数据预测和数据诊断。该审查还表明，尽管学术界取得了进步，但结构健康监测技术在现实世界中的执行仍存在滞后，并坚持制定标准以缩小差距。