Prediction of Reflection Cracking in Hot-Mix Asphalt Overlays

Tsai, F.C.; Lytton, Robert L.; Lee, Jun Young

doi:10.3141/2155-05

Cited by 12 publications

(9 citation statements)

References 18 publications

(36 reference statements)

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“…Gopalakrishnan and Manik 53 used the falling weight deflectometer data to determine the in situ mechanical properties (elastic moduli) of the pavement layers through inverse analysis, a process commonly referred to as backcalculation. Tsai et al 54 calibrated mechanistic-based models to field data to produce a design process for predicting reflection cracks. Sakhaeifar et al 55 presented a set of dynamic modulus (|E*|) predictive models to estimate the |E*| of hot-mix asphalt layers in long-term pavement performance (LTPP) test sections.…”

Section: Published Literature About Artificial Neural Network Applicamentioning

confidence: 99%

Utilising neural networks and closed form solutions to determine static creep behaviour and optimal polypropylene amount in bituminous mixtures

2012

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The testing procedure in order to determine the precise mechanical testing results in Marshall design is very time consuming. Also, the physical properties of the asphalt samples are obtained by further calculations. Therefore if the researchers can obtain the stability and flow values of a standard mixture with the help of mechanical testing, the rest of the calculations will just be mathematical manipulations. Determination of mechanical testing parameters such as strain accumulation, creep stiffness, stability, flow and Marshall Quotient of dense bituminous mixtures by utilising artificial neural networks is important in the sense that, cumbersome testing procedures can be avoided with the help of the closed form solutions provided in this study. Marshall specimens, prepared by utilising polypropylene fibers, were tested by universal testing machine carrying out static creep tests to investigate the rutting potential of these mixtures. On the very well trained data basis, artificial neural network analyses were carried out to propose five separate models for mechanical testing properties. The explicit formulation of these five main mechanical testing properties by closed form solutions are presented for further use for researches.

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Section: Published Literature About Artificial Neural Network Applicamentioning

confidence: 99%

Utilising neural networks and closed form solutions to determine static creep behaviour and optimal polypropylene amount in bituminous mixtures

2012

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“…It is well known that there are many approaches for estimating stress intensity factors by using finite element methods: (a) extrapolation of displacements near crack tip; (b) extrapolation of stresses near crack tip; (c) domain integral; (d) contour integral [7][8][9][10][11]. However, approaches (a) and (b) require finer meshes and (a) is better than (b).…”

Section: Finite Element Modeling Of 4-layered Flexible Pavementsmentioning

confidence: 99%

“…It is well known that analytical methods, computational schemes and experimental skills based on linear elastic fracture mechanics (LEFM) have been applied for stress analysis near crack tip for homogeneous and isotropic materials [7]. The crack analyses for multi-layered flexible pavements are limited to the numerical analyses [8,9].…”

Section: Introductionmentioning

confidence: 99%

Finite Element Analysis of Cracked Flexible Pavements Embedded with Controlled Low-Strength Material Bases

Tu¹,

Wang²,

Li-min³

2017

Proceedings of the Second International Conference on Mechanics, Materials and Structural Engineering (ICMMSE 2017)

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Abstract. Cracks resulting from shrinkage due to temperature or moisture usually exist in the flexible pavements in highways and runways of airport. This paper presents at finite element analysis of 4-layered flexible pavements with a penetrated longitudinal crack on surface layer embedded with controlled low-strength materials (CLSM) bases which are well-known recycling sustainable materials. Two-dimensional deformation assumption is employed. Emphasis is put on the effect of using CLSM bases on the reduction surface settlement, horizontal and vertical stresses as well as crack face displacements. Two kinds of CLSMs, i.e., CLSM-B80/30% and CLSM-B130/30% are compared with graded crushed stones and AC used for base materials. Numerical study shows that vertical settlements, vertical and horizontal stresses by using CLSM-B130/30% base is smaller than that by using graded crushed stone and is shown to be good material employed as base substitute for graded crushed stone in flexible pavement design.

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“…The study and the results reported in this paper are related to a National Cooperative Highway Research Program (NCHRP) project (1-41: models for predicting reflection cracking of HMA overlays) whose objective is to identify or develop mechanistic-based models for predicting reflection cracking in HMA overlays of flexible and rigid pavements and associated computational software for use in mechanistic-empirical procedures for overlay design and analysis (Tsai et al, 2010). In this study, the Neural Networks (NN) methodology is successfully used to model the stress intensity factor (SIF) as cracks grow upward through a HMA overlay due to both load and thermal effects with and without reinforcing interlayers.…”

Section: Objective and Scopementioning

confidence: 99%

“…For the thermal load SIF calculation, a 2-D finite element program specifically for pavement thermal SIF analysis was employed. For the traffic load SIF calculation, the finite element model used a Fourier Series to represent the effects of loads acting at some lateral distance from the 2-D plane where the calculations were made (Tsai et al, 2010).…”

Section: Nn Architecturementioning

confidence: 99%

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

Ceylan

Gopalakrishnan

Lytton

2011

J. Mater. Civ. Eng.

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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 ...

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Prediction of Reflection Cracking in Hot-Mix Asphalt Overlays

Cited by 12 publications

References 18 publications

Utilising neural networks and closed form solutions to determine static creep behaviour and optimal polypropylene amount in bituminous mixtures

Utilising neural networks and closed form solutions to determine static creep behaviour and optimal polypropylene amount in bituminous mixtures

Finite Element Analysis of Cracked Flexible Pavements Embedded with Controlled Low-Strength Material Bases

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

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