Re-evaluation of the AASHTO-flexible pavement design equation with neural network modeling.
Here we establish that equivalent single-axle loads values can be estimated using artificial neural networks without the complex design equality of American Association of State Highway and Transportation Officials (AASHTO). More importantly, we find that the neural network model gives the coefficie...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2014-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC4232605?pdf=render |
| _version_ | 1818312365661224960 |
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| author | Mesut Tiğdemir |
| author_facet | Mesut Tiğdemir |
| author_sort | Mesut Tiğdemir |
| collection | DOAJ |
| description | Here we establish that equivalent single-axle loads values can be estimated using artificial neural networks without the complex design equality of American Association of State Highway and Transportation Officials (AASHTO). More importantly, we find that the neural network model gives the coefficients to be able to obtain the actual load values using the AASHTO design values. Thus, those design traffic values that might result in deterioration can be better calculated using the neural networks model than with the AASHTO design equation. The artificial neural network method is used for this purpose. The existing AASHTO flexible pavement design equation does not currently predict the pavement performance of the strategic highway research program (Long Term Pavement Performance studies) test sections very accurately, and typically over-estimates the number of equivalent single axle loads needed to cause a measured loss of the present serviceability index. Here we aimed to demonstrate that the proposed neural network model can more accurately represent the loads values data, compared against the performance of the AASHTO formula. It is concluded that the neural network may be an appropriate tool for the development of databased-nonparametric models of pavement performance. |
| first_indexed | 2024-12-13T08:16:42Z |
| format | Article |
| id | doaj.art-cf098979676b4926bf8fefaf362b0f7e |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-13T08:16:42Z |
| publishDate | 2014-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-cf098979676b4926bf8fefaf362b0f7e2022-12-21T23:54:05ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-01911e11322610.1371/journal.pone.0113226Re-evaluation of the AASHTO-flexible pavement design equation with neural network modeling.Mesut TiğdemirHere we establish that equivalent single-axle loads values can be estimated using artificial neural networks without the complex design equality of American Association of State Highway and Transportation Officials (AASHTO). More importantly, we find that the neural network model gives the coefficients to be able to obtain the actual load values using the AASHTO design values. Thus, those design traffic values that might result in deterioration can be better calculated using the neural networks model than with the AASHTO design equation. The artificial neural network method is used for this purpose. The existing AASHTO flexible pavement design equation does not currently predict the pavement performance of the strategic highway research program (Long Term Pavement Performance studies) test sections very accurately, and typically over-estimates the number of equivalent single axle loads needed to cause a measured loss of the present serviceability index. Here we aimed to demonstrate that the proposed neural network model can more accurately represent the loads values data, compared against the performance of the AASHTO formula. It is concluded that the neural network may be an appropriate tool for the development of databased-nonparametric models of pavement performance.http://europepmc.org/articles/PMC4232605?pdf=render |
| spellingShingle | Mesut Tiğdemir Re-evaluation of the AASHTO-flexible pavement design equation with neural network modeling. PLoS ONE |
| title | Re-evaluation of the AASHTO-flexible pavement design equation with neural network modeling. |
| title_full | Re-evaluation of the AASHTO-flexible pavement design equation with neural network modeling. |
| title_fullStr | Re-evaluation of the AASHTO-flexible pavement design equation with neural network modeling. |
| title_full_unstemmed | Re-evaluation of the AASHTO-flexible pavement design equation with neural network modeling. |
| title_short | Re-evaluation of the AASHTO-flexible pavement design equation with neural network modeling. |
| title_sort | re evaluation of the aashto flexible pavement design equation with neural network modeling |
| url | http://europepmc.org/articles/PMC4232605?pdf=render |
| work_keys_str_mv | AT mesuttigdemir reevaluationoftheaashtoflexiblepavementdesignequationwithneuralnetworkmodeling |