Fragility Analysis of Instream Bridges Subjected to Flood and Scour

Bridges are one of the critical civil infrastructures -- due to its significance to a nation's economy and the safety of the traveling public. However, according to the ASCE Infrastructure Report Card 2017, the United States' overall bridge infrastructure received a score 'C+' for its structural condition. Most of the bridges in the United States collapse due to floods, and there are many scour critical old bridges in the inventory. During floods, scours around piers weaken the capacity of foundations, and stream pressure leads to bridges' failure. Along with the scouring and stream pressure, other two important flood associated factors are debris accumulation and change in flow direction, which are found to have a significant role in bridges' failures. Flood vulnerability analysis is essential for making repair, maintenance, and replacement decision in a rational way to maximize the utilities of limited resources. The current flood vulnerability assessment method of bridges is based on visual inspection hence subjective in nature, which does not consider associated uncertainties. Numerical flood fragility analysis, in the literature, still in the development stage and does not comprehensively account for essential components of a bridge-river system. Moreover, the current scour monitoring techniques lack a reliable, real-time scour monitoring technology. This study proposes a comprehensive fragility analysis framework that can effectively incorporate both flow hydraulics and geotechnical uncertainties, in addition to commonly considered components in flood-fragility analysis of bridges. The significance of flow hydraulics and geotechnical uncertainties has been demonstrated through real-bridge case studies. Conventional fragility curves with maximum scour depth may not represent actual vulnerability during floods, as the scour may not reach maximum in many cases. Therefore, fragility surface with two intensity measures, i.e., flow discharges and scour depths, is introduced for real-time vulnerability assessment during floods. A reliable, real-time scour monitoring using sonar sensors is proposed to use the results of fragility surface. Sonar signals' characteristics under turbid water condition are studied in the laboratory setup and machine learning based algorithms are proposed for the processing of sonars' signal. The proposed methods identify outliers, replace the outlying values and missing values, and fuse sensors' data to achieve significant improvement in the data quality. Evaluation of performance showed the promise and robustness of the proposed method over traditional statistical methods