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Bridge Condition Assessment and Load Rating Using Dynamic Response
This report describes a method for the overall condition assessment and load rating of prestressed box beam (PSBB) bridges based on their dynamic response collected through wireless sensor networks (WSNs). Due to a large inventory of deficient and aging bridges in the United States, the health monitoring of bridges can be very expensive; therefore, new tools for quick, efficient and response-based condition assessment and load rating of bridges will be helpful. The hypothesis is based on the assumption that the health of a bridge is associated with its vibration signatures under vehicular loads. Two WSNs were deployed on a 25-year old PSBB bridge under trucks with variable loads and speeds, and its dynamic response was collected at the current condition. The acceleration response of the bridge at its newest condition was collected from dynamic simulations of its full-scale finite element analysis (FEA) models mimicking field conditions. The FEA bridge model was validated by the field testing and numerical analysis. The acceleration data in time domain were transformed into frequency domain using Fast Fourier Transform to determine peak amplitudes and corresponding fundamental frequencies for the newest and current conditions. The analyses and comparisons of the bridge dynamic response between the newest and the current bridge interestingly indicate a 37% reduction in its fundamental frequency over 25 years of service life. This frequency reduction is linked to the reduction in condition rating of the current bridge. The analysis data, bridge structural dynamics and bridge geometric parameters have been used to calculate the in-service stiffness of the bridge to estimate its load bearing capacity. Using the results and algorithms from this research, application software is developed to instantly estimate the overall condition rating and load bearing capacity of a PSBB bridge under vehicular loads.
Bridge Condition Assessment and Load Rating Using Dynamic Response
This report describes a method for the overall condition assessment and load rating of prestressed box beam (PSBB) bridges based on their dynamic response collected through wireless sensor networks (WSNs). Due to a large inventory of deficient and aging bridges in the United States, the health monitoring of bridges can be very expensive; therefore, new tools for quick, efficient and response-based condition assessment and load rating of bridges will be helpful. The hypothesis is based on the assumption that the health of a bridge is associated with its vibration signatures under vehicular loads. Two WSNs were deployed on a 25-year old PSBB bridge under trucks with variable loads and speeds, and its dynamic response was collected at the current condition. The acceleration response of the bridge at its newest condition was collected from dynamic simulations of its full-scale finite element analysis (FEA) models mimicking field conditions. The FEA bridge model was validated by the field testing and numerical analysis. The acceleration data in time domain were transformed into frequency domain using Fast Fourier Transform to determine peak amplitudes and corresponding fundamental frequencies for the newest and current conditions. The analyses and comparisons of the bridge dynamic response between the newest and the current bridge interestingly indicate a 37% reduction in its fundamental frequency over 25 years of service life. This frequency reduction is linked to the reduction in condition rating of the current bridge. The analysis data, bridge structural dynamics and bridge geometric parameters have been used to calculate the in-service stiffness of the bridge to estimate its load bearing capacity. Using the results and algorithms from this research, application software is developed to instantly estimate the overall condition rating and load bearing capacity of a PSBB bridge under vehicular loads.
Bridge Condition Assessment and Load Rating Using Dynamic Response
A. A. Islam (author) / F. Li (author) / H. Hamid (author) / A. Jaroo (author)
2014
128 pages
Report
No indication
English
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