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Back-Propagation Network Modeling for Concrete Pavement Faulting Using LTPP Data

並列摘要


A reliable pavement performance prediction model is essential for long-term pavement maintenance and rehabilitation planning. There are many factors affecting joint faulting such as heavy traffic, pavement structure, climatic conditions, pavement age, etc. Design features including dowel, base type, pavement thickness, joint spacing, drainage system, shoulder type are also important factors for faulting. So the factors selection has a big effect on the modeling. In this paper, the adaptability of the widely used Back-Propagation Network (BPN) pavement prediction method, using actual joint faulting data is studied. Prediction models with different factors, including 10-factor model, 8-factor model, 6-factor model and 4-factor model, are established and the prediction results are compared. Research outcomes show that the factors that choosing affect the prediction capability and 8-factor model is most effective. Then the proposed factor selection method can effectively support model development.

參考文獻


Ker, H. and Lee, Y.H. (2008). Development of Faulting Prediction Models for Rigid Pavements Using LTPP Database. International Conference on Industrial Technology (ICIT), Taiwan.
Mohammad, J.K. and Baladi, G.Y. (2008). Review of Louisiana's Pavement Management System Phase I, Transportation Research Record, No. 2084, pp. 10-18.
Khazanovich, L. and Darter, M.I. (2004). Mechanistic-Empirical Model to Predict Transverse Joint Faulting. Transportation Research Record, No. 1896, pp. 34–45.
Robinson, C. and Muhammad, A.B. (1996). Distress Prediction Models for Rigid Pavements for Texas Pavement Management Information System. Transportation Research Record, No. 1524, pp. 145-151.
Solminihac, H.D. and Salsilli, R. (1999). Rehabilitation Performance Prediction Models for Concrete Pavements. Transportation Research Record, No. 1684, pp. 137-146.

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