Road condition monitoring (RCM) has been a demanding strategic research area in maintaining a large network of transport infrastructures. With advancements in computer vision and data mining techniques along with high computing resources, several innovative pavement distress evaluation systems have been developed in recent years. The majority of these technologies employ next-generation distributed sensors and vision-based artificial intelligence (AI) methodologies to evaluate, classify and localize pavement distresses using the measured data. This paper presents an exhaustive and systematic literature review of these technologies in RCM that have been published from 2017–2022 by utilizing next-generation sensors, including contact and noncontact measurements. The various methodologies and innovative contributions of the existing literature reviewed in this paper, together with their limitations, promise a futuristic insight for researchers and transport infrastructure owners. The decisive role played by smart sensors and data acquisition platforms, such as smartphones, drones, vehicles integrated with non-intrusive sensors, such as RGB, and thermal cameras, lasers and GPR sensors in the performance of the system are also highlighted. In addition to sensing, a discussion on the prevalent challenges in the development of AI technologies as well as potential areas for further exploration paves the way for an all-inclusive and well-directed futuristic research on RCM.

As innovative technologies emerge, extensive research has been undertaken to develop new structural health monitoring procedures. The current methods, involving on-site visual inspections, have proven to be costly, time-consuming, labor-intensive, and highly subjective for assessing the safety and integrity of civil infrastructures. Mobile and stationary LiDAR (Light Detection and Ranging) devices have significant potential for damage detection, as the scans provide detailed geometric information about the structures being evaluated. This paper reviews the recent developments for LiDAR-based structural health monitoring, in particular, for detecting cracks, deformation, defects, or changes to structures over time. In this regard, mobile laser scanning (MLS) and terrestrial laser scanning (TLS), specific to structural health monitoring, were reviewed for a wide range of civil infrastructure systems, including bridges, roads and pavements, tunnels and arch structures, post-disaster reconnaissance, historical and heritage structures, roofs, and retaining walls. Finally, the existing limitations and future research directions of LiDAR technology for structural health monitoring are discussed in detail.

Structural health monitoring (SHM) is one of the most emerging approaches for early damage detection, which leads to improved safety and efficient maintenance of large-scale civil structures. Data-driven vibration-based SHM techniques rely on sophisticated signal processing methods to analyze and interpret the complex measured data collected from the instrumented structures. Empirical mode decomposition (EMD) is one of the robust time-frequency decomposition techniques that has been widely used in SHM. Numerous studies have used EMD and its variants in different applications specific to structural modal identification and damage detection, which have been presented in various academic journals, conference papers, and technical reports. This paper presents a comprehensive and systematic review and summary of applications of EMD and its variants that have been extensively implemented in SHM. A brief background and illustration of EMD and its variants are presented first to show their performance under various cases, followed by a detailed literature review of their recent applications specific to SHM.

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.