Comparisons of Faulting-Based Pavement Performance Prediction Models
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 predictio...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2017-01-01
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| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2017/6845215 |
| _version_ | 1798044148572356608 |
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| author | Weina Wang Yu Qin Xiaofei Li Di Wang Huiqiang Chen |
| author_facet | Weina Wang Yu Qin Xiaofei Li Di Wang Huiqiang Chen |
| author_sort | Weina Wang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T22:58:54Z |
| format | Article |
| id | doaj.art-06089b55014241ca97fe176b3214b5c6 |
| institution | Directory Open Access Journal |
| issn | 1687-8434 1687-8442 |
| language | English |
| last_indexed | 2024-04-11T22:58:54Z |
| publishDate | 2017-01-01 |
| publisher | Hindawi Limited |
| record_format | Article |
| series | Advances in Materials Science and Engineering |
| spelling | doaj.art-06089b55014241ca97fe176b3214b5c62022-12-22T03:58:15ZengHindawi LimitedAdvances in Materials Science and Engineering1687-84341687-84422017-01-01201710.1155/2017/68452156845215Comparisons of Faulting-Based Pavement Performance Prediction ModelsWeina Wang0Yu Qin1Xiaofei Li2Di Wang3Huiqiang Chen4State and Local Engineering Laboratory for Civil Engineering Material, School of Civil Engineering, Chongqing Jiaotong University, Xuefu Avenue No. 66, Nan’an District, Chongqing, ChinaCREEC (Chongqing) Survey, Design and Research Co. Ltd., Kunlun Avenue No. 46, Liangjiang New Area, Chongqing, ChinaSchool of Civil Engineering, Chongqing Jiaotong University, Xuefu Avenue No. 66, Nan’an District, Chongqing, ChinaPavement Engineering Centre, Technical University of Braunschweig, Raum 104, Beethovenstraße 51 b, Braunschweig, GermanySchool of Civil Engineering, Chongqing Jiaotong University, Xuefu Avenue No. 66, Nan’an District, Chongqing, ChinaFaulting 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.http://dx.doi.org/10.1155/2017/6845215 |
| spellingShingle | Weina Wang Yu Qin Xiaofei Li Di Wang Huiqiang Chen Comparisons of Faulting-Based Pavement Performance Prediction Models Advances in Materials Science and Engineering |
| title | Comparisons of Faulting-Based Pavement Performance Prediction Models |
| title_full | Comparisons of Faulting-Based Pavement Performance Prediction Models |
| title_fullStr | Comparisons of Faulting-Based Pavement Performance Prediction Models |
| title_full_unstemmed | Comparisons of Faulting-Based Pavement Performance Prediction Models |
| title_short | Comparisons of Faulting-Based Pavement Performance Prediction Models |
| title_sort | comparisons of faulting based pavement performance prediction models |
| url | http://dx.doi.org/10.1155/2017/6845215 |
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