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Comparison of pavement performance prediction models based on fault action

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Document pages: 9 pages

Abstract: Fault prediction is the core of concrete pavement maintenance design. The highway department has been facing the problem of low prediction accuracy, which will lead to expensive maintenance costs. Although many researchers have developed some performance prediction models, the accuracy of prediction is still a challenge. This paper reviews the performance prediction model and jpcp fault model used in previous studies. Then, using a set of actual pavement survey data collected on interstate highways with different design characteristics, traffic and climate data, three models including multiple nonlinear regression (mnlr) model, artificial neural network (ANN) model and Markov chain (MC) model are tested and compared. It is found that the mnlr model needs further recalibration, while ANN model needs more data for training the network. MC model seems a good tool for pavement performance prediction when the data is limited, but it is based on visual inspections and not explicitly related to quantitative physical parameters. This paper then suggests that the further direction for developing the performance prediction model is incorporating the advantages and disadvantages of different models to obtain better accuracy.

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