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Re-evaluation of the AASHTO-flexible pavement design equation with neural network modeling.

Tiğdemir M - PLoS ONE (2014)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Civil Engineering, Süleyman Demirel University, Engineering Faculty, Isparta, Turkey.

ABSTRACT
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.

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The sensitivity analysis of relative contributions of each input parameters to the outputs.
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pone-0113226-g009: The sensitivity analysis of relative contributions of each input parameters to the outputs.

Mentions: A sensitivity analysis was performed to determine the relative contribution of each input parameters to the estimation of the output. The outputs are KESALs and the ratio of estimated KESALs to observed KESALs. This analysis have been performed for the change of +10% of all inputs' individually for the twenty sections. The inputs of the delta-PSI, the MR, and the SN give the most relative contribution to the estimation of outputs (Figure 9).


Re-evaluation of the AASHTO-flexible pavement design equation with neural network modeling.

Tiğdemir M - PLoS ONE (2014)

The sensitivity analysis of relative contributions of each input parameters to the outputs.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4232605&req=5

pone-0113226-g009: The sensitivity analysis of relative contributions of each input parameters to the outputs.
Mentions: A sensitivity analysis was performed to determine the relative contribution of each input parameters to the estimation of the output. The outputs are KESALs and the ratio of estimated KESALs to observed KESALs. This analysis have been performed for the change of +10% of all inputs' individually for the twenty sections. The inputs of the delta-PSI, the MR, and the SN give the most relative contribution to the estimation of outputs (Figure 9).

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Civil Engineering, Süleyman Demirel University, Engineering Faculty, Isparta, Turkey.

ABSTRACT
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.

Show MeSH