Building information modeling-enhanced visualization tool for structural health monitoring
Master of Science
DisciplineEngineering : Civil
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With growing number of modern complex infrastructure, robust and autonomous condition assessment of large-scale structures under operational loads and extreme climatic events has garnered significant attention. Data-driven structural health mon- itoring (SHM) techniques offer valuable information of existing health of the struc- tures, maintain the safety and their uninterrupted use under varied operational condi- tions by undertaking risk and hazard mitigation promptly. However, just data-driven approaches are not enough to monitor a large amount of SHM data and conduct systematic decision making for future maintenance. Recently, Building Information Modeling (BIM) has become a valuable tool for design, production, construction, facility management and life-cycle analysis of buildings and bridges. Such a hybrid information modeling platform integrates the architectural, engineering and construc- tion systems of a structure into one place allowing all users to incorporate various features effectively and accurately. In this thesis, a BIM-enabled system is utilized as a promising computing environment and integrated digital representation plat- form of SHM that can visualize a considerable amount of sensor data and subsequent structural health conditions over a prolonged period. In this research, three-dimensional Autodesk Revit® models of a large-span bridge and a pedestrian bridge in Thunder Bay, Ontario are developed to enable automated sensor data inventory into the BIM environment. Such automated tool facilitates achieving systematic maintenance and risk management, while avoiding manual errors resulting from visual inspections of the structures. The proposed integrated tool allows the practicing engineers in organizing, processing, and visualizing the sensor data from the monitoring system, updating relevant finite element (FE) models, and providing valuable feedback for structural retrofitting in a single platform. The data acquisition was performed on various seasons of the year to check the performance of the structure under various temperatures and traffic loading conditions. The results reveal that the proposed method can be considered as a user-friendly and economic framework for condition assessment of large-scale structures in ease.