Abstract
Today’s complex modern infrastructure requires robust and autonomous condition assessment as they continue to age with increasing operational loads and extreme climatic events. Structural Health Monitoring (SHM) has recently gained significant interests in inspection and maintenance of large-scale structures. However, large amount of raw data resulting from the data logger of these SHM systems require appropriate tools to systematically visualize and diagnose the data. Building Information Modelling (BIM) is a powerful data management tool that can be utilized as a base platform to analyze and visualize long-term SHM data. Current BIM-based approaches have the capabilities of facilitating design, production, and construction management of structures. BIM models in such approaches can serve as static information sources that contain as-built data. The objective of this paper is to take one-step forward from static towards dynamic BIM by representing and visualizing real-time SHM data. The web-based framework developed in this study features online visualization of data, real-time system identification and efficient decision-making. In this paper, a steel bridge located in London, Ontario is utilized as a case study where both BIM and SHM are integrated in a unified fashion. The proposed framework improves the visualization of SHM data and facilitates infrastructure owners in real-time tracking of critical infrastructure.
Highlights
Civil infrastructure such as bridges, buildings, dams, wind turbines, and pipelines are prone to deterioration as they age
This study investigated the potential of Building Information Modeling (BIM) in data management and maintenance of infrastructure using a web-based workflow
The use of different data formats can be omitted since the process is web-based and features real-time integration of sensor data with the BIM model
Summary
Civil infrastructure such as bridges, buildings, dams, wind turbines, and pipelines are prone to deterioration as they age. An SHM system, with the aid of long-term monitored data, can evaluate the structural integrity and perform accurate damage assessment (Aktan and Grimmelsman, 1999; Somwanshi and Gawalwad, 2016; Sadhu et al, 2017) Most of these techniques (Farrar and Worden, 2013) are data-driven in nature, where either modal (e.g., natural frequency, damping and mode shapes) or physical (e.g., stiffness and mass) parameters are estimated or tracked based on the measured data. System identification is performed using the TVF-EMD algorithm, which is integrated with REVIT through an online MATLAB portal linked via the “Properties” box of the virtual sensor in the BIM model. This model is used to define the real-time dynamic behavior of the bridge that can be used for visualization of long-term monitored SHM data. Sensors were placed at a distance of 10, 20, 50, and 100 feet on both sides from the centerline of the bridge shown in FIGURE 6 | Three-dimensional virtual model for bridge and sensor
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