Investigation of fatigue and thermal cracking behavior of rejuvenated reclaimed asphalt pavement binders and mixtures

Elkashef, Mohamed; Williams, R. Christopher; Cochran, Eric W.

doi:10.1016/j.ijfatigue.2017.11.013

Cited by 90 publications

(17 citation statements)

References 14 publications

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“…The results clearly show that the addition of rejuvenators decreases both high and low temperatures of VBR. This observation confirms that the rejuvenators soften the aged asphalt binder, as many other studies (e.g., 23 , 27 – 32 ) have shown. Figure 2 a demonstrates that the high-temperature PG controls the maximum allowable dosage of rejuvenator, and the low-temperature PG limits the minimum required dosage (Figure 2 b ).…”

Section: Laboratory Tests and Resultssupporting

confidence: 91%

Mechanical-Chemical Characterization of the Effects of Type, Dosage, and Treatment Methods of Rejuvenators in Aged Bituminous Materials

Nsengiyumva

Haghshenas

Kim

et al. 2020

Transportation Research Record: Journal of the Transportation R

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Proper use of rejuvenators can improve the properties and performance of aged asphalt mixtures; however, there is currently a lack of understanding on how rejuvenator treatment details, such as type-dosage of rejuvenators and blending-curing of rejuvenation, affect properties and performance characteristics. This study aims to investigate the effects of type, dosage, and treatment methods of rejuvenators when added into aged bituminous materials. To meet the goal, three rejuvenators, categorized by their production technology, were selected to improve a high-recycled asphalt pavement mixture by conducting various binder-level and mixture-level tests. For the binder-level testing, the performance grading (PG) method was used to primarily determine proper dosages targeting desired binder grades, and two chemical tests (Fourier-transform infrared spectroscopy and Saturate, Aromatic, Resin and Asphaltene) were conducted to examine chemical characteristics altered by rejuvenation and the further aging process. The selected dosage levels from the binder testing were then applied to mixture-level performance evaluation by conducting two tests: flow number for rutting and semicircular bending fracture for cracking. Test-analysis results indicated that although it can successfully determine the proper dosage range of rejuvenators, PG binder testing is limited to only assessing the effects of rejuvenators on mechanical properties; this can be better aided by integrating chemical characterization that provides a more in-depth material-specific rejuvenation process. Moreover, it appears that rejuvenation methods can alter the performance of aged mixtures. Therefore, the selection of rejuvenators and their implementation into practice should be carried out considering multiple aspects, not only its PG recovery.

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Section: Laboratory Tests and Resultssupporting

confidence: 91%

Mechanical-Chemical Characterization of the Effects of Type, Dosage, and Treatment Methods of Rejuvenators in Aged Bituminous Materials

Nsengiyumva

Haghshenas

Kim

et al. 2020

Transportation Research Record: Journal of the Transportation R

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“…Further investigation of SAS proved its efficiency as a rejuvenator. Mixture with 100% RAP that contained SAS showed better fracture resistance than mixtures fabricated by PG 58-28 at a temperature of −6 • C [179].…”

Section: Bio-binder Modifiers (Bbm)mentioning

confidence: 94%

Sustainability Assessment of Green Asphalt Mixtures: A Review

Pouranian

Shishehbor

2019

Environments

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During recent decade, the pavement sustainability has received much attention by road agencies, companies, governments and research institutes. The aim of this paper is to introduce and evaluate the sustainability of the technologies developed or underdeveloped to address environmental issues of asphalt mixtures, and the waste materials and by-products that can be recycled and reused in asphalt production. Warm Mix Asphalt (WMA) technology, the most popular waste materials to substitute neat binder (crumb rubber modifier (CRM), recycled plastic (RP), vacuum tower bottoms (VTB)) and/or virgin aggregates (reclaimed asphalt pavement (RAP), reclaimed asphalt shingles (RAs), construction and demolition (C and D) wastes, steel and copper slags), and bio-binders were evaluated with respect to their environmental and economic benefits and engineering performance as the main components of pavement sustainability. The performance evaluation was carried out by examining rutting, moisture susceptibility, thermal and fatigue cracking resistance. Two main environmental impacts, greenhouse gas (GHG) emission, and energy consumption were considered to study the environmental effects of these technologies and materials. Additionally, the economic effect was investigated considering initial cost and long-term benefit. Results of investigation illustrated that although each individual technology and waste material is valuable in terms of performance and/or the environment, specific combinations such as WMA + RAP, WMA + CRM, RAP + CRM, and WMA + CRM + RAP lead to further benefits. Notably, these combinations suffer from a lack of comprehensive economic analysis, thus, their sustainability cannot be fully assessed and is prone to future studies.

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“…Asphalt pavement cracking is a common distress, which can be caused from repeated traffic loading (Elkashef, Williams, & Cochran, 2018), rapid temperature change © 2020 Computer-Aided Civil and Infrastructure Engineering (Teltayev & Radovskiy, 2018), and reflection from base layers (Golestani, Nam, Noori, An, & Tatari, 2018). Pavement cracking can affect driving safety and comfort (Zaloshnja & Miller, 2009).…”

Section: Introductionmentioning

confidence: 99%

Automated pavement crack detection and segmentation based on two‐step convolutional neural network

Liu

Yang

Lau

et al. 2020

Computer aided Civil Eng

252

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Cracking is a common pavement distress that would cause further severe problems if not repaired timely, which means that it is important to accurately extract the information of pavement cracks through detection and segmentation. Automated pavement crack detection and segmentation using deep learning are more efficient and accurate than conventional methods, which could be further improved. While many existing studies have utilized deep learning in pavement crack segmentation, which segments cracks from non-crack regions, few studies have taken the exact pavement crack detection into account, which identifies cracks in the images from other objects. A two-step pavement crack detection and segmentation method based on convolutional neural network was proposed in this paper. An automated pavement crack detection algorithm was developed using the modified You Only Look Once 3rd version in the first step. The proposed crack segmentation method in the second step was based on the modified U-Net, whose encoder was replaced with a pre-trained ResNet-34 and the upsample part was added with spatial and channel squeeze and excitation (SCSE) modules. Proposed method combines pavement crack detection and segmentation together, so that the detected cracks from the first step are segmented in the second step to improve the accuracy. A dataset of pavement crack images in different circumstances were also built for the study. The F1 score of proposed crack detection and segmentation methods are 90.58% and 95.75%, respectively, which are higher than other state-of-the-art methods. Compared with existing one-step pavement crack detection or segmentation methods, proposed two-step method showed advantages of accuracy.

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Investigation of fatigue and thermal cracking behavior of rejuvenated reclaimed asphalt pavement binders and mixtures

Cited by 90 publications

References 14 publications

Mechanical-Chemical Characterization of the Effects of Type, Dosage, and Treatment Methods of Rejuvenators in Aged Bituminous Materials

Mechanical-Chemical Characterization of the Effects of Type, Dosage, and Treatment Methods of Rejuvenators in Aged Bituminous Materials

Sustainability Assessment of Green Asphalt Mixtures: A Review

Automated pavement crack detection and segmentation based on two‐step convolutional neural network

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