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Most civil infrastructure worldwide is currently past its design life. This situation has precipitated the need for more systematic inspections and continual monitoring of infrastructure to assure structural integrity. With the advancement of sensing and computing technologies, rapid, remote, real-time, and robust structural health monitoring (SHM) techniques have been significantly developed. These smart SHM technologies often result in big data that require advanced data management, visualization, diagnostic, and prognostic techniques. Over the past few decades, researchers have developed numerous machine learning and artificial intelligence (AI)–based damage diagnostic and prognosis methods, which have been systematically reviewed in the recent state-of-the-art papers. In parallel, various data management and visualization techniques have been explored in SHM using building information modeling (BIM), virtual reality (VR), and augmented reality (AR). Both BIM and AR/VR present a unique opportunity to document, systematically interpret, and visualize SHM data in a three-dimensional (3D) environment and have shown significant promise in a wide range of infrastructure monitoring applications. Unlike diagnostics and prognostics methods of SHM, there has been a very limited systematic review of the latest visualization and data management techniques, which are both the objective and novelty of this review paper. Each subtopic reviews The recent data management and visualization technologies, their relevance to SHM, and their implementation challenges in broader structural engineering applications. Finally, this state-of-the-art review summarizes potential future research directions for BIM and VR/AR associated with SHM.
Sadhu et al. (Wed,) studied this question.