Collaborative Research: Fatigue Damage Prognosis for Slender Coastal Bridges

Arizona State University, Scottsdale AZ

Investigators

Abstract

Infrastructure of slender coastal bridges is critical for the economy and the quality of life for people living in that region. Coastal bridges are subjected to hazardous conditions including salt water and high winds. Corrosion of steel is one of the items that deteriorates the bridges and reduces their capacity to carry the load. Accurate prognosis and integrity evaluation of bridges will significantly enhance the nation's economic development and public safety. The fundamental understanding of the complex degradation process will enable reliable prediction of structural deterioration and enable the systematic decision-making for maintenance of bridges. This research will also benefit the safety evaluation of many other types of infrastructures, such as power plants, offshore structures, and pipelines. Outcome of this research has potential to reduce the economic and social losses due to structural failures in many aging infrastructure systems. In addition, this project can have impact on education, such as cultivating interests of graduate, undergraduate and K-12 students in structural and coastal engineering. A novel maximum entropy-based Bayesian network for multi-scale corrosion-fatigue damage prognosis for slender coastal bridges is planned. The framework fuses the information and knowledge from the material level, the structural level, and the system level for the probabilistic prognosis and reliability assessment. The inter-correlations among different levels of nodes in the network are developed by using coupled dynamic analysis and corrosion-fatigue damage analysis. Advanced experimental testing for fatigue and simulation methods will be combined together for the physics-based prediction of remaining useful life of costal bridges. Uncertainties will be propagated through the Bayesian network and the system level reliability will be updated and reassessed. The Bayesian network can update itself with information from experimental measurements, field observation, and historical experiences. In this methodology, coupled structure dynamics model will capture the realistic service and environmental loads during the lifetime span of the bridges.