- #Flexible pavement design aashto example verification#
- #Flexible pavement design aashto example plus#
#Flexible pavement design aashto example verification#
Results of calibration and verification processes indicate that for new flexible pavements, the AASHTO-Ontario model is in good agreement with the observed results. The end result was a new grouping of Ontario soils for pavement design, recommended values for the resilient modulus of below grade soils, recommendations for structural layer coefficients for Ontario pavement materials and recommendations for the initial pavement serviceability based on Ontario smoothness specifications. Other data was also collected from research and development studies, laboratory experiments, and from a survey of experienced pavement design engineers. Data collected on Ontario highway pavements and materials was used for assessing the design inputs. The AASHTO Guide uses two parameters to deal with design reliability: design reliability level and overall standard deviation.
#Flexible pavement design aashto example plus#
The paper focused only on the design of flexible pavements in terms of load characterization using equivalent single axle loads along with axle load spectra, below grade and material characterization, plus initial and terminal serviceability and reliability. A summary was also provided of the additional work done to prepare for the transition to the proposed mechanistically-based 2002 AASHTO Guide. The factors are summarized to yield the number of Equivalent Single Axle Loads (ESALs) a pavement is expected to sustain during its life. The size of LEF is related to the damage that is expected to occur from a standard load of 80 kN carried by a single axle with dual tires. LEF is regarded as a pavement damage factor assigned to each specific load and axle configuration. The guide expresses the effect of traffic loads on pavement performance using the concept of axle Load Equivalency Factors (LEF). The methodology used to adapt the 1993 AASHTO Guide for Design of Pavement Structures to Ontario conditions was described.
![flexible pavement design aashto example flexible pavement design aashto example](https://aashtojournal.org/wp-content/uploads/2020/04/040320Pubs.jpg)
[Applied Research Associates Inc., Toronto, ON (Canada) It is concluded that the neural network may be an appropriate tool for the development of databased-nonparametric models of pavement performance.Īdaptation of AASHTO Pavement Design Guide for Local ConditionsĮnergy Technology Data Exchange (ETDEWEB) 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.
![flexible pavement design aashto example flexible pavement design aashto example](https://r9e3k2m7.stackpathcdn.com/wp-content/uploads/2018/07/flexible-pavement-design-spreadsheet.jpg)
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. The artificial neural network method is used for this purpose. 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. 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.
![flexible pavement design aashto example flexible pavement design aashto example](http://image.slideserve.com/25851/calculation-of-aashto-load-equivalency-factors-flexible-pavement-l.jpg)
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). Re-evaluation of the AASHTO-flexible pavement design equation with neural network modeling.