Date of Award
8-2012
Degree Name
Master of Science in Engineering
Department
Civil and Construction Engineering
First Advisor
Dr. Upul Attanayake
Second Advisor
Osama Abudayyeh
Third Advisor
Haluk Aktan
Keywords
Structural health monitoring, statistical models, finite element, neural network, deterioration detection
Access Setting
Masters Thesis-Open Access
Abstract
Bridges are the substantial part of the transportation infrastructure. Most recent report shows that of the 605,086 bridges in the United States, 67,526 (11%) are deemed structurally deficient, and 76,363 (13%) are declared functionally obsolete (FHWA, 2011). Deck is the shelter of a bridge that is subjected to severe loads due to exposure and traffic. Importance of detecting deck deterioration is further highlighted with the introduction of accelerated bridge construction (ABC) where prefabricated components are brought to the site, assembled, and connected using field cast joints. However, durability performance of field cast connections is not encouraging. Hence, continuous monitoring of structural integrity of bridges built using prefabricated components is vital to detect onset of deterioration. The thesis focuses on developing a tool based on statistical model(s) to present the structural health monitoring data in a meaningful and easily understood format and combining the statistical model(s) and detailed numerical model for damage detection is examined to simulate possible joint failure.
Recommended Citation
Mansiz, Cem, "Statistical and Numerical Integrated Approach for Detecting Onset of Prefabricated Bridge Component Connection Deterioration" (2012). Masters Theses. 29.
https://scholarworks.wmich.edu/masters_theses/29