Combination of GIS and SHM in Prognosis and Diagnosis of Bridges in Earthquake-Prone Locations

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.

Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights

“…355 Figure 13. The system architecture of a ML-enhanced cloud-based SHM-GIS decision-making system for bridge monitoring applications, modified after Malekloo et al 294 .…”

“…293 Given their versatility, low cost, and ease of deployment elements of a flying piece of technology, they are becoming increasingly accretive. 294,295 UAVs can easily be integrated into the design phase workflow of civil structures for simple imaging or scene reconnaissance, monitor the work-in-progress and document phases of the construction, and lastly, it can be used for monitoring and inspection. UAVs enable investors to visit hard-to-access areas of many structures, such as tall buildings or bridges with a river flowing underneath.…”

“…Although this topic has been studied for stationary WSNs in SHM systems, with the introduction of next-generation SHM modules such as smartphones and UAVs, time synchronization becomes critical due to lack of centralized data acquisition. Transmission delays due to communication overhead and the differences between the target and achieved sample rate introduced in the SHM system are exacerbated with multisensory systems.

Figure 13.The system architecture of a ML-enhanced cloud-based SHM-GIS decision-making system for bridge monitoring applications, modified after Malekloo et al 294 .

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Artificial Intelligence and Structural Health Monitoring of Bridges: A Review of the State-of-the-Art

“…As shown in Figure 3, artificial intelligence solutions and machine learning algorithms can play a significant role in most of these eight steps based on the process of each of these steps. Pattern recognition is one of these eight steps, and machine learning algorithms play a big role in making this step more efficient [57]. Many machine learning approaches are put to use in pattern recognition, which is an extensively used subject.…”

Design of Internet of Things and big data analytics-based disaster risk management

“…For a proper disaster management approach, rescue events and connected through IoT. For this purpose, systems for the emergency warning, evacuation methods, surveillance, and regulatory strategies may be merged (Malekloo et al 2020). IoT technologies cannot avoid disasters and help prepare disasters, including prediction systems and early warning systems (Al-Turjman 2019a).…”