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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.

Show MeSH
Predicted KESALs values by AASHTO equation, NNET model, and the relations of them to the observed values.
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pone-0113226-g003: Predicted KESALs values by AASHTO equation, NNET model, and the relations of them to the observed values.

Mentions: Figure 2 provides a plot of predicted KESALs versus those estimated by the SHA's throughout 1989 and extrapolated through 1991. As can be seen, the traffic predicted by the AASHTO equation is consistently much higher than the estimates of historical traffic provided by the SHAs. Only nine of the 244 predictions were lower than the estimates of the state highway agencies. Almost half of the estimates (in 112 sections) predicted traffic levels 100-fold greater than the SHA estimates (Figure 3). Note that the average ratio (8770) and standard deviation (51,800) are distorted by several sections where this ratio exceeded 100,000. This extreme lack of fit of the design equation to the in-service data is not entirely due to the shortcomings of the equation itself. Limitation of the input data are also believed to have contributed to the apparent differences between predicted and estimated ESALs. The future availability of ESALs estimates that would include some years of measured data, plus higher [resent serviceability index values should allow a somewhat more accurate evaluation of the deficiencies in the equation itself.


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

Tiğdemir M - PLoS ONE (2014)

Predicted KESALs values by AASHTO equation, NNET model, and the relations of them to the observed values.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0113226-g003: Predicted KESALs values by AASHTO equation, NNET model, and the relations of them to the observed values.
Mentions: Figure 2 provides a plot of predicted KESALs versus those estimated by the SHA's throughout 1989 and extrapolated through 1991. As can be seen, the traffic predicted by the AASHTO equation is consistently much higher than the estimates of historical traffic provided by the SHAs. Only nine of the 244 predictions were lower than the estimates of the state highway agencies. Almost half of the estimates (in 112 sections) predicted traffic levels 100-fold greater than the SHA estimates (Figure 3). Note that the average ratio (8770) and standard deviation (51,800) are distorted by several sections where this ratio exceeded 100,000. This extreme lack of fit of the design equation to the in-service data is not entirely due to the shortcomings of the equation itself. Limitation of the input data are also believed to have contributed to the apparent differences between predicted and estimated ESALs. The future availability of ESALs estimates that would include some years of measured data, plus higher [resent serviceability index values should allow a somewhat more accurate evaluation of the deficiencies in the equation itself.

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