Neural Networks Modeling of Stress Growth in Asphalt Overlays due to Load and Thermal Effects during Reflection Cracking

Abstract

Although several techniques have been introduced to reduce reflective cracking, one of the primary forms of distress in hot-mix asphalt (HMA) overlays of flexible and rigid pavements, the underlying mechanism and causes of reflective cracking are not yet well understood. Fracture mechanics is used to understand the stable and progressive crack growth that often occurs in engineering components under varying applied stress. The stress intensity factor (SIF) is its basis and describes the stress state at the crack tip. This can be used with the appropriate material properties to calculate the rate at which the crack will propagate in a linear elastic manner. Unfortunately, the SIF is difficult to compute or measure, particularly if the crack is situated in a complex three-dimensional (3D) geometry or subjected to a non-simple stress state. In this study, the neural networks (NN) methodology is successfully used to model the SIF as cracks grow upward through a HMA overlay as a result of both load and thermal effects with and without reinforcing interlayers. Nearly 100,000 runs of a finite-element program were conducted to calculate the SIFs at the tip of the reflection crack for a wide variety of crack lengths and pavement structures. The coefficient of determination (R2) of all the developed NN models except one was above 0.99. Owing to the rapid prediction of SIFs using developed NN models, the overall computer run time for a 20-year reflection cracking prediction of a typical overlay was significantly reduced. Reference to this paper should be made as follows: Ceylan, H., Gopalakrishnan, K., and Lytton, R. (2011 Neural Networks Modeling of Stress Growth in Asphalt Overlays due to Load and Thermal Effects during Reflection CrackingHalil Ceylan 1 , Kasthurirangan Gopalakrishnan 2 , and Robert L. Lytton 3 Abstract: Although, several techniques have been introduced to reduce reflective cracking, one of the primary forms of distress in hot-mix asphalt (HMA) overlays of flexible and rigid pavements, the underlying mechanism and causes of reflective cracking is not yet well understood. Fracture mechanics is used to understand the stable and progressive crack growth that often occurs in engineering components under varying applied stress. The stress intensity factor (SIF) is its basis and describes the stress state at the crack tip. This can be used with the appropriate material properties to calculate the rate at which the crack will propagate in a linear elastic manner. Unfortunately, the SIF is difficult to compute or measure, particularly if the crack is situated in a complex three-dimensional geometry or subjected to a non-simple stress state. In this study, the Neural Networks (NN) methodology is successfully used to model the SIF as cracks grow upward through a HMA overlay due to both load and thermal effects with and without reinforcing interlayers. Nearly 100,000 runs of a finite element program were conducted to calculate the SIFs at the tip of the reflection crack for a wide variety of crack lengths ...

Effects of loading, geometry and material properties on fracture parameters of a pavement containing top-down and bottom-up cracks

“…As stated earlier in most of the previous papers the influence of only one wheel has been considered for analyzing the fracture behavior of cracked overlays [8,17]. However, it is shown here that considering both front and rear wheels may result in completely different fracture parameters than the case of considering just one wheel.…”

“…Positive and negative T-stress values can result in reduction or enhancement of the fracture resistance in cracked pavements. However in most of previous research studies [4][5][6][7][8][9][10][11][12][13][14][15][16][17], the influence of such parameter has not been investigated for pavements with different geometrical and material characteristics subjected to traffic loading. Fig.…”

“…Roesler and Khazanovich [16] modeled a partial -depth crack in a Portland cement concrete pavement and computed the stress intensity factors by means of finite element method. By employing neural networks method, Ceylan and his coworkers [17] modeled the stress intensity factor of a growing crack in a hot mix asphalt overlay subjected to both mechanical and thermal loads. Ganchang and Gansheng [18] proposed a method for calculating thermal stress intensity factors in an asphalt concrete pavement resting on semi-rigid base.…”

Application of ANN in Pavement Engineering: State-of-Art

“…The final results indicate that the PNN produced the best result, with a 95.2% accuracy on real validation images, despite its simpler structure with the lesser computing requirement (INN and HNN corresponding performances were 70.2% and 75%, respectively). Ceylan et al (2011) successfully used ANNs to model the stress intensity factor (SIF)…”

Prediction of stress intensity factors in pavement cracking with neural networks based on semi-analytical FEA

“…The results illustrated that a trained NN can predict the SCFs for the various load cases with a high level of accuracy. Ceylan, Gopalakrishnan, and Lytton (2011) had applied the NN methodology to model the SIF as cracks grow upward through a HMA overlay as a result of both load and thermal effects with and without reinforcing interlayers. Nearly 100,000 runs of a finite-element program were conducted to calculate the SIFs at the tip of the reflection crack.…”