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Paper lc3-2017-060:
Predictive Risk Modeling of Differential Bridge Settlement

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Jiwen Zhang, Tim Taylor, Roy Sturgill, Gabe Dadi and Nikiforos Stamatiadis

Predictive Risk Modeling of Differential Bridge Settlement

Abstract: Differential settlement between the roadway pavement resting on embankment fill and the bridge abutment built on more rigid foundation often creates a bump when driving from roadway to bridge, and vice versa. This paper studies the problem at a macroscopic level by determining a method to predict the levels of approach settlement to assist designers in developing remediation plans during project development to minimize the lifecycle costs of bridge bump repairs. A macro method considering a combination of maintenance times, maintenance measures, and observed settlement was used to classify the differential settlement scale as minimal, moderate, and severe. A set of project characteristics including approach, abutment type, embankment, foundation, and traffic volume that may influence the formation of differential settlement were identified and used as parameters to develop a model to predict the settlement severity for a given approach. Logistic regression analyses were implemented to identify the relationships between the levels of differential settlement and the input variables for a sample of 600 randomly selected bridges in Kentucky. Geographic region, approach age, average daily traffic, and the use of approach slabs are identified as the four most predominant factors that can significantly affect the formation of differential settlement. Based on the performance of bridge approaches in Kentucky, how those parameters interacted in the prediction model is illustrated in the logistic regressions.

Keywords: Differential Settlement, Logistic Regression, Prediction Model

DOI: https://doi.org/10.24928/JC3-2017/0060

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Series: jc3:2017 (browse)
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