Bridge End Settlement Evaluation and Prediction
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Bridge End Settlement Evaluation and Prediction

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    A bridge approach is usually built to provide a smooth and safe transition for vehicles from the roadway pavement to the bridge structure. However, differential settlement between the roadway pavement that rests on embankment fill and the bridge abutment built on more rigid foundation often creates a bump in the roadway. Previous work examined this issue at a microscopic level and presented new methods for eliminating or minimizing the effects at specific locations. This research studies the problem at a macroscopic level by determining methods to predict settlement severity; this assists designers in developing remediation plans during project development to minimize the life cycle costs of bridge bump repairs. The study is based on historic bridge approach inspection data and maintenance history from a wide range of Kentucky roads and bridges. A macro method which considers a combination of maintenance times, maintenance measures, and observed settlement was used to classify the differential settlement scale as minimal, moderate, and severe. The scale corresponds to the approach performance status of good, fair, and poor. A series of project characteristics influencing differential settlement was identified and used as parameters to develop a model to accurately predict settlement severity during preliminary design. Eighty-seven bridges with different settlement severities were collected as the first sample by conducting a survey of local bridge engineers in 12 transportation districts. Sample 2 was created by randomly selecting 600 bridges in the inspection history of bridges in Kentucky. Ordinal and/or multinomial logistic regression analyses were implemented to identify the relationships between the levels of differential settlement and the input variables. Two predictive models were developed. Prediction of bridge approach settlement can play an important role in selecting proper design, construction, and maintenance techniques and measures. The models are contained within a Microsoft Excel tool that allows users to select one or two models to predict the approach settlement level for a new bridge or for an existing bridge with different purposes. The significance of this study lies in its identification of parameters that have the most influence on the settlement severity at bridge ends, and how those parameters interact in developing a prediction model. The important parameters include geographic regions, approach age, average daily traffic (ADT), the use of approach slabs, and the foundation soil depth. The regression results indicate that the use of approach slabs can improve the performance of approaches on mitigating the problem caused by differential settlement. In addition, current practices regarding differential settlement prediction and mitigation were summarized by surveying the bridge engineers in 5 transportation districts.
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