Development of a rate-dependent cumulative work and instantaneous power-based asphalt cracking performance index

Nemati, Rasool; Haslett, Katie E.; Dave, Eshan V.; Sias, Jo E.

doi:10.1080/14680629.2019.1586753

Cited by 24 publications

(4 citation statements)

References 24 publications

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“…To evaluate the field distress data, the IRI was selected as a reliable standardized parameter that can reflect both functional and structural pavement performance. There are regression-based equations (such as Equation 6) that can correlate the IRI to PSI (18). This can be used to calculate the initial and terminal serviceability (P i and P t ) values and subsequently the loss of serviceability (DPSI) parameter using IRI field performance data.…”

Section: Field Distress Data Analysismentioning

confidence: 99%

“…This parameter is based on the cumulative work under the load-load line displacement curve which is normalized by the instantaneous power of the fracture process at the peak load as indicated in Equation 5. The results from this parameter have been able to more reliably differentiate the mixtures fracture properties at different aging levels compared with the Flexibility Index parameter (18). The results from the evaluation are shown in Figure 1b.…”

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confidence: 96%

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Generalized Methodology to Develop Mechanistically Informed Asphalt Mixture Layer Coefficients for AASHTO 1993 Pavement Design Approach

Nemati

Dave

Sias

2021

Transportation Research Record: Journal of the Transportation R

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This paper presents a generalized framework for determining mechanistically informed layer coefficients (a-values) for asphalt mixtures in the AASHTO empirical pavement design approach. The layer coefficients influence the layer thicknesses and consequently the structural capacity of pavements. Therefore, it is critical to determine reliable mechanistically informed a-values. A set of 18 commonly used asphalt mixtures in New Hampshire was selected for investigation including different types of hot mix and cold central plant recycled mixtures that are used as wearing, binder, and base course layers. Laboratory characterization was conducted using the complex modulus, semi-circular bend, and direct tension cyclic fatigue testing methods. The mixtures were evaluated using three performance index parameters: complex modulus rutting index parameter, rate-dependent cracking index parameter, and a new continuum damage parameter ([Formula: see text]). The measured field performance of wearing course mixtures in terms of International Roughness Index was used to back-calculate the in situ performance-based layer coefficients (aIRI-values). Using a normal distribution function, the results from performance testing were incorporated with the aIRI-values to develop mechanistically informed mix-specific layer coefficients. In addition, a typical layer coefficient at specific reliability levels for each mix category including hot mix wearing course, hot mix binder and base course as well as cold central plant recycled mix course are proposed for New Hampshire. The recommended a-values are 0.48 for hot mix wearing, 0.41 for hot mixed binder and base, and 0.28 for cold recycled base mixtures; these are approximately 25% higher than the currently used a-values in New Hampshire.

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Section: Field Distress Data Analysismentioning

confidence: 99%

mentioning

confidence: 96%

Generalized Methodology to Develop Mechanistically Informed Asphalt Mixture Layer Coefficients for AASHTO 1993 Pavement Design Approach

Nemati

Dave

Sias

2021

Transportation Research Record: Journal of the Transportation R

Self Cite

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“…So, research on the mechanism of cracking growth will help monitor the cracks' behaviors from the beginning of their development and offer feasible ways of manufacturing and maintenance. Traditional fatigue methods explain the initiation and growth of cracks [1], but this only provides a rough approach that performs poorly while calculating longitudinal cracks. To solve this kind of problem, fracture mechanism theories were established.…”

Section: Introductionmentioning

confidence: 99%

Numerical Simulation on Reflective Cracking Behavior of Asphalt Pavement

Wang

Yang

et al. 2021

Applied Sciences

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Cracks are one of the main problems that plague road workers. A correct understanding of the internal crack propagation mechanism of asphalt pavement will help road workers evaluate the road’s working status more comprehensively and make more reasonable decisions in design, construction, and maintenance work. This paper established a three-dimensional asphalt pavement layered model using the software ABAQUS and fracture mechanics theory and the extended finite element method were used to explore the mechanical response of the pavement base layer’s preset reflective cracks. This paper investigated the influence of the modulus of each layer, vehicle load on the principal stress, shear stress, J-integral, and two stress intensity factors (K1, K2) during the predetermined crack propagation process of the pavement base layer, and the entropy method was used to analyze the above-mentioned mechanical response. The results show that the main factor affecting the propagation of reflective cracks on asphalt pavements is the modulus of the bottom surface layer. However, from a modeling perspective, the effect of increasing load on crack growth is obvious. Therefore, in terms of technical feasibility, the prevention of reflective cracks should still be achieved by controlling the driving load and prohibiting overloading.

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“…However, in the simple tests investigated herein, simple load-line displacement machine control is used, which tends to lower device cost and operational complexity. The erratic curve that results will have very few data points (perhaps only one or two), and is therefore poorly characterized, making it difficult to determine an "m" value and subsequently the FI index [50], [58] (Figure 2 Finally, the FI parameter has been shown to have a high Coefficient of Variation (COV), which brings into question whether the parameter has sufficient repeatability to enable reliable asphalt cracking performance evaluation [27], [40], [58], [63]. A wellknown statistical principle exists, which pertains to the use of replicate measurements to estimate the mean of a population with inherent material and measurement variability [64].…”

Section: Scb Flexibility Index Testmentioning

confidence: 99%

Developing a modern recycled mix design and automatic pavement monitoring system

Majidifard¹

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The main aim of agencies involved in the construction of asphalt roads is to improve the field performance of the asphalt mixtures. The rising use of recycled and novel materials in asphalt mixture has rendered the previous semi-empirical methods of mixture design partly incapable of accurately predicting the mixture field performance with high precision. Meeting this challenge calls for a shift towards an approach involving mixture performance tests. This project deals with investigating the performance of modern recycled asphalt mixes containing ground tire rubber, Recycled Asphalt Shingles (RAS), Recycled Asphalt Pavement (RAP) and rejuvenators. Various performance tests for various type of distresses were considered to evaluate the effect of using these components in asphalt mixtures. Combining these performance tests with prediction of field performance of mixtures should provide more robust and reliable design criteria for the modern recycled asphalt mixtures leading to better roads. To this end, the performance of eighteen different dense-graded asphalt mixtures paved in Missouri were investigated. The sections contain a wide range of reclaimed asphalt pavement (RAP) and recycled asphalt shingles (RAS), and different types of additives. The large number of sections investigated and the associated breadth of asphalt mixtures tested provided a robust data set to evaluate the range, repeatability, and relative values provided by modern mixture performance tests. As cracking is one of the most prevalent distresses in Missouri, performance tests such as the disk-shaped compact tension test (DC[T]) and Illinois flexibility index test (I-FIT) were used to evaluate the cracking potential of the sampled field cores. In addition, the Hamburg wheel tracking test (HWTT) was employed to assess rutting and stripping potential. Asphalt binder replacement (ABR) and binder grade bumping at low temperature were found to be critical factors in low-temperature cracking resistance as assessed by the DC(T) fracture energy test. Six sections were found to perform well in the DC(T) test, likely as a result of binder grade bumping (softer grade selection) or because of low recycling content. However, all of the sections were characterized as having brittle behavior as predicted by the I-FIT flexibility index. Service life and ABR were key factors in the I-FIT test. Finally, a performance-space diagram including DC(T) fracture energy and HWTT depth was used to identify mixtures with higher usable temperature interval (UTI mix), some of which contained significant amounts of recycled material. In the second phase of chapter 2, the poor performing mixtures were redesigned in order to improve their performance by changing the components of the mixtures including recycling content, rejuvenator type and amount, binder type, crumb rubber quantity, etc.. Finally, the optimum content of the components based on mixture performance and materials costs was determined. The testing results along with the field performance data was used to develop a specification for MoDOT to screen the mixtures and use it for quality control and quality assurance of plant-produced asphalt concrete. Field monitoring is a potential means to identify the most reliable cracking performance test. Also, a new cracking index was introduced based on SCB (I-FIT) test to improve the test reliability and correlation with field results. In the third chapter of this study a prediction tool was developed to predict the performance of asphalt mixture at high and low temperatures. This tool is based on two different prediction models for DC(T) fracture energy and Hamburg wheel track tests. For DC(T) fracture energy model, genetic programming was used to develop the prediction model, and Convolution Neural Network (CNN) was used to train the Hamburg wheel track model on 10,000 data points. A database containing a comprehensive collection of Hamburg and DC(T) tests results were used to develop the machine learning-based prediction models. This tool can be used for pre-design purposes to design an asphalt mixture with balanced performance in rutting and cracking. The models were formulated in terms of typical influencing mixture properties variables such as asphalt binder high-temperature performance grade (PG), mixture type, aggregate size, aggregate gradation, asphalt content, total asphalt binder recycling content and tests parameters like temperature and number of cycles. Models accuracy were assessed through a rigorous validation process and found to be quite acceptable, despite the relatively small size of the training set. Since performing performance tests might be cost-restrictive for some users, using the proposed ML-based models can save time and expense during the material screening phase. Pavement distress inspections are performed using sophisticated data collection vehicles and/or foot-on-ground surveys. In either approach, the process of distress detection is human-dependent, expensive, inefficient, and/or unsafe. Automated pavement distress detection via road images is still a challenging issue among pavement researchers and computer-vision community. In the forth chapter of dissertation, we extracted 7237 google street-view, manually annotated for classification (nine categories of distress classes). Afterward, the YOLO (you look only once) deep learning framework was implemented to train the model using the labeled dataset. Also, U-net based model is developed to quantify the severity of the distresses, and finally, a hybrid model is developed by integrating the YOLO and U-net model to classify the distresses and quantify their severity simultaneously. The output of the distress classification and segmentation models are used to develop a comprehensive pavement condition tool which rates each pavement image according to the type and severity of distress extracted. As a result, we are able to avoid over-dependence on human judgement throughout the pavement condition evaluation process. The outcome of this study could be conveniently employed to evaluate the pavement conditions during its service life and help to make valid decisions for rehabilitation of the roads at the right time.

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Development of a rate-dependent cumulative work and instantaneous power-based asphalt cracking performance index

Cited by 24 publications

References 24 publications

Generalized Methodology to Develop Mechanistically Informed Asphalt Mixture Layer Coefficients for AASHTO 1993 Pavement Design Approach

Generalized Methodology to Develop Mechanistically Informed Asphalt Mixture Layer Coefficients for AASHTO 1993 Pavement Design Approach

Numerical Simulation on Reflective Cracking Behavior of Asphalt Pavement

Developing a modern recycled mix design and automatic pavement monitoring system

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