Evaluation of Structural Integrity of Asphalt Pavement System From FWD Test Data Considering Modeling Errors
This study examines the structural integrity assessment technique used for the asphalt pavement system that considers the modeling errors introduced by material uncertainties. To this end, the artificial neural network is utilized to estimate the elastic modulus of soil layers by using the measured...
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| Format: | Article |
| Language: | English |
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Riga Technical University Press
2010-03-01
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| Series: | The Baltic Journal of Road and Bridge Engineering |
| Subjects: | |
| Online Access: | https://bjrbe-journals.rtu.lv/article/view/3768 |
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| _version_ | 1846094928588832768 |
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| author | Jin Hak Yi Young Sang Kim Sung Ho Mun Jae Min Kim |
| author_facet | Jin Hak Yi Young Sang Kim Sung Ho Mun Jae Min Kim |
| author_sort | Jin Hak Yi |
| collection | DOAJ |
| description | This study examines the structural integrity assessment technique used for the asphalt pavement system that considers the modeling errors introduced by material uncertainties. To this end, the artificial neural network is utilized to estimate the elastic modulus of soil layers by using the measured deflection data from the Falling Weight Deflectometer test. A wave analysis program for a multi-layered pavement system is developed based on the spectral element method for more accurate and faster calculation. The developed program is applied for the numerical simulation of the Falling Weight Deflectometer tests, specifically for the reliability analysis and the generation of training and testing patterns for the neural network. The effects of uncertainties in the material properties for modeling a given pavement system such as Poisson ratio and layer thickness are intensively investigated using the Monte Carlo Simulation. Results reveal that the amplitude of impact loads is most significant, followed by the layer thickness and the Poisson ratio, which are more significant on the max deflections than other parameters. The evaluation capability of the neural network is also investigated when the input data is corrupted by the modeling errors. It is found that the estimation results can be significantly deviated due to the modeling errors. To reduce the effect of the modeling error, (to improve the robustness of the algorithm), we proposed an alternative scheme in order to generate the training patterns taking into consideration any modeling errors. The study then concludes that the estimation results can be improved by using the proposed training patterns from an extensive numerical simulation study. |
| format | Article |
| id | doaj-art-5d4d4650e6d34b77ab98ba95779f80b9 |
| institution | Kabale University |
| issn | 1822-427X 1822-4288 |
| language | English |
| publishDate | 2010-03-01 |
| publisher | Riga Technical University Press |
| record_format | Article |
| series | The Baltic Journal of Road and Bridge Engineering |
| spelling | doaj-art-5d4d4650e6d34b77ab98ba95779f80b92025-01-02T12:01:01ZengRiga Technical University PressThe Baltic Journal of Road and Bridge Engineering1822-427X1822-42882010-03-0151101810.3846/bjrbe.2010.022098Evaluation of Structural Integrity of Asphalt Pavement System From FWD Test Data Considering Modeling ErrorsJin Hak Yi0Young Sang Kim1Sung Ho Mun2Jae Min Kim3Korea Ocean Research and Development Institute, Sa-2-dong, Sangnok-gu, Ansan-si, Gyeonggi-do, 426-744, KoreaDept of Civil and Environmental Engineering, Chonnam National University, Dundeok-dong, Yeosusi, Jeonnam, 550-749, KoreaKorea Expressway Corporation, Dongtan-myeon, Hwaseong-si, Gyeonggi-do, 445-812, KoreaDept of Civil and Environmental Engineering, Chonnam National University, Dundeok-dong, Yeosusi, Jeonnam, 550-749, KoreaThis study examines the structural integrity assessment technique used for the asphalt pavement system that considers the modeling errors introduced by material uncertainties. To this end, the artificial neural network is utilized to estimate the elastic modulus of soil layers by using the measured deflection data from the Falling Weight Deflectometer test. A wave analysis program for a multi-layered pavement system is developed based on the spectral element method for more accurate and faster calculation. The developed program is applied for the numerical simulation of the Falling Weight Deflectometer tests, specifically for the reliability analysis and the generation of training and testing patterns for the neural network. The effects of uncertainties in the material properties for modeling a given pavement system such as Poisson ratio and layer thickness are intensively investigated using the Monte Carlo Simulation. Results reveal that the amplitude of impact loads is most significant, followed by the layer thickness and the Poisson ratio, which are more significant on the max deflections than other parameters. The evaluation capability of the neural network is also investigated when the input data is corrupted by the modeling errors. It is found that the estimation results can be significantly deviated due to the modeling errors. To reduce the effect of the modeling error, (to improve the robustness of the algorithm), we proposed an alternative scheme in order to generate the training patterns taking into consideration any modeling errors. The study then concludes that the estimation results can be improved by using the proposed training patterns from an extensive numerical simulation study.https://bjrbe-journals.rtu.lv/article/view/3768fwd (falling weight deflectometer)asphalt concrete (ac) pavementneural network (nn)noise injection trainingnondestructive structural integrity |
| spellingShingle | Jin Hak Yi Young Sang Kim Sung Ho Mun Jae Min Kim Evaluation of Structural Integrity of Asphalt Pavement System From FWD Test Data Considering Modeling Errors The Baltic Journal of Road and Bridge Engineering fwd (falling weight deflectometer) asphalt concrete (ac) pavement neural network (nn) noise injection training nondestructive structural integrity |
| title | Evaluation of Structural Integrity of Asphalt Pavement System From FWD Test Data Considering Modeling Errors |
| title_full | Evaluation of Structural Integrity of Asphalt Pavement System From FWD Test Data Considering Modeling Errors |
| title_fullStr | Evaluation of Structural Integrity of Asphalt Pavement System From FWD Test Data Considering Modeling Errors |
| title_full_unstemmed | Evaluation of Structural Integrity of Asphalt Pavement System From FWD Test Data Considering Modeling Errors |
| title_short | Evaluation of Structural Integrity of Asphalt Pavement System From FWD Test Data Considering Modeling Errors |
| title_sort | evaluation of structural integrity of asphalt pavement system from fwd test data considering modeling errors |
| topic | fwd (falling weight deflectometer) asphalt concrete (ac) pavement neural network (nn) noise injection training nondestructive structural integrity |
| url | https://bjrbe-journals.rtu.lv/article/view/3768 |
| work_keys_str_mv | AT jinhakyi evaluationofstructuralintegrityofasphaltpavementsystemfromfwdtestdataconsideringmodelingerrors AT youngsangkim evaluationofstructuralintegrityofasphaltpavementsystemfromfwdtestdataconsideringmodelingerrors AT sunghomun evaluationofstructuralintegrityofasphaltpavementsystemfromfwdtestdataconsideringmodelingerrors AT jaeminkim evaluationofstructuralintegrityofasphaltpavementsystemfromfwdtestdataconsideringmodelingerrors |