No abstract

Conventional damage detection techniques are gradually being replaced by state-of-the-art smart monitoring and decision-making solutions. Near real-time and online damage assessment in structural health monitoring (SHM) systems is a promising transition toward bridging the gaps between the past’s applicative inefficiencies and the emerging technologies of the future. In the age of the smart city, Internet of Things (IoT), and big data analytics, the complex nature of data-driven civil infrastructures monitoring frameworks has not been fully matured. Machine learning (ML) algorithms are thus providing the necessary tools to augment the capabilities of SHM systems and provide intelligent solutions for the challenges of the past. This article aims to clarify and review the ML frontiers involved in modern SHM systems. A detailed analysis of the ML pipelines is provided, and the in-demand methods and algorithms are summarized in augmentative tables and figures. Connecting the ubiquitous sensing and big data processing of critical information in infrastructures through the IoT paradigm is the future of SHM systems. In line with these digital advancements, considering the next-generation SHM and ML combinations, recent breakthroughs in (1) mobile device-assisted, (2) unmanned aerial vehicles, (3) virtual/augmented reality, and (4) digital twins are discussed at length. Finally, the current and future challenges and open research issues in SHM-ML conjunction are examined. The roadmap of utilizing emerging technologies within ML-engaged SHM is still in its infancy; thus, the article offers an outlook on the future of monitoring systems in assessing civil infrastructure integrity.

No abstract

Bridge infrastructures are essential nodes in the transportation network. In earthquake-prone areas, seismic performance assessment of infrastructure is vital to identify, retrofit, reconstruct, or, if necessary, demolish the structural systems based on optimal decision-making processes. This research proposes the combined use of advanced tools used in the management and monitoring of bridges such as Geographical Information Systems (GIS) and Structural Health Monitoring (SHM) in a synergistic manner that can enable observation of bridges to construct an earthquake damage model. Post-earthquake disaster data can enhance and update this model to mitigate further damages both to the structure and transportation network in the future. Implications of new technologies such as drones and mobile devices in this scheme constitute the next step toward the future of the Cyber-Physical SHM systems. The proposed intelligent and sustainable cloud-based framework of SHM-GIS in this paper lays the core behind more robust impending systems. The synergistic behavior of the offered framework reduces the overall cost in large scale implementation and increases the accuracy of the results leading to a decision-making platform easing the management of bridges.

No abstract