Comparisons of Faulting-Based Pavement Performance Prediction Models
| Autor: | Di Wang, Weina Wang, Hui-qiang Chen, Xiao-fei Li, Yu Qin |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Multivariate statistics
Materials science Operations research Article Subject 0211 other engineering and technologies 02 engineering and technology Machine learning computer.software_genre Set (abstract data type) 021105 building & construction 0502 economics and business Performance prediction lcsh:TA401-492 General Materials Science 050210 logistics & transportation Markov chain Artificial neural network business.industry 05 social sciences General Engineering Pavement maintenance Survey data collection lcsh:Materials of engineering and construction. Mechanics of materials Artificial intelligence business Nonlinear regression computer |
| Zdroj: | Advances in Materials Science and Engineering, Vol 2017 (2017) |
| ISSN: | 1687-8434 |
| DOI: | 10.1155/2017/6845215 |
| Popis: | Faulting prediction is the core of concrete pavement maintenance and design. Highway agencies are always faced with the problem of lower accuracy for the prediction which causes costly maintenance. Although many researchers have developed some performance prediction models, the accuracy of prediction has remained a challenge. This paper reviews performance prediction models and JPCP faulting models that have been used in past research. Then three models including multivariate nonlinear regression (MNLR) model, artificial neural network (ANN) model, and Markov Chain (MC) model are tested and compared using a set of actual pavement survey data taken on interstate highway with varying design features, traffic, and climate data. It is found that MNLR model needs further recalibration, while the 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. |
| Databáze: | OpenAIRE |
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