Prediction Model of Deck Condition for Steel Bridges | IConEST

Paper Detail

Title

Prediction Model of Deck Condition for Steel Bridges

Authors

Assist. Prof. Dr. Sahar Hasan, Construction and Project Management Institute, Housing and Building National Research Center (Hbrc), Egypt
Assoc. Prof. Dr. Emad Elwakil, Purdue University, United States of America

Abstract

Steel bridges represent about 11% of California's bridges network, and according to ASCE (2018), there is about 9% of total bridges classified as deficient bridges and estimated to cost about $123 billion for rehabilitation. The deterioration rate of steel bridges is considerably higher than other different types of bridges, so deploying the prediction models is valuable to determine the condition rating objectively instead of subjectively that may vary from inspector to another. Deterioration modeling is one of the essential supporting tools of Bridge Management Systems (BMS) that provide decision-makers with accurate decisions. Therefore, this paper proceeds with a proactive approach based on mathematical, statistical analysis that leads to prolonging the bridges' service life, and minimize the maintenance cost substantially. The paper methodology has applied the Regression technique as an empirical approach, and modeling to help the highway agencies to make a more reliable decision for future maintenance based on predicted conditions. The developed Regression models have been built using the National Bridge Inventory (NBI) database for California State steel bridges. The developed models have been validated using Average Validity Percentage (85.6 %) and coefficient of determination (91.5 %).

 Keywords

steel bridges, deck condition, deterioration models, regression technique, predictive models  

Citation

Hasan, S. & Elwakil, E. (2020). Prediction Model of Deck Condition for Steel Bridges. In M. Shelley & I. Sahin (Eds.), Proceedings of IConEST 2020--International Conference on Engineering, Science and Technology (pp. 1-8). Monument, CO, USA: ISTES Organization. Retrieved 15 January 2021 from www.2020.iconest.net/proceedings/6/.

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