Quantifying the Influence of Geosynthetics on Pavement Performance (2017) / Chapter Skim
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O-1 APPENDIX O VALIDATION OF ARTIFICIAL NEURAL NETWORK APPROACH FOR PREDICTING GEOSYNTHETIC-REINFORCED PAVEMENT PERFORMANCE The performance of geosynthetic-reinforced flexible pavements includes fatigue cracking, permanent deformation, and international roughness index (IRI)
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O-2 Figure O-1. Flow Chart of the Process of Validating the Proposed ANN Approach After a thorough review of the in-service pavement sections in the LTPP database and PMIS, a total of 74 pavement sections containing geosynthetics were found in the LTPP database, and a total of 51 pavement sections containing geosynthetics were found in the PMIS.
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O-3 of these identified pavement sections had the stabilized soil over the untreated subgrade, as illustrated in Figure O-2. Figure O-2.
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O-4 has beneficial effects for reducing the rutting and international roughness index of flexible pavements. Figure O-3.
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O-5 Figure O-5. Comparison of IRI between ANN Approach Prediction and Field Measurement for Pavement Section 16-9032 LTPP Section 48-0167 The pavement section 48-0167 consists of a 5.6-inch hot-mixed and dense-graded asphalt concrete, a 13-inch crushed gravel unbound base, a 12-inch lime-treated soil, and a semi-infinite subgrade sandy soil.
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O-6 Figure O-6. Comparison of Rutting Depth between ANN Approach Prediction and Field Measurement for Pavement Section 48-0167 Figure O-7.
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O-7 between the predictions by the proposed ANN approach and the field measurements are presented in Figures O-8–O-9. Figure O-8.
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O-8 were constructed in January 2005. The average daily traffic is 800.
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O-9 Figure O-12. Comparison of Rutting Depth between ANN Approach Prediction and Field Measurement for Pavement Section FM 02-3 Figure O-13.
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O-10 Figure O-14. Comparison of Rutting Depth between ANN Approach Prediction and Field Measurement for Pavement Section FM 02-4 Figure O-15.
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