Laboratory Characterization of Low–Rolling Resistance Danish Stone-Matrix Asphalt

Espinoza-Luque, Arturo F.; Al‐Qadi, Imad L.; Özer, Hasan; Pettinari, Matteo

doi:10.1061/jpeodx.0000094

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…There are many causes of rutting on asphalt pavements, and differences in structural combinations or material properties can lead to differences in rutting changes and levels. The research mainly focuses on the above three semi-rigid base asphalt pavements with a certain representative of asphalt and asphalt mixture grading (Espinoza-Luque et al, 2019). The laser road detection vehicle and 3 m ruler are used for on-site inspection of the rutting, comparing and analyzing the results of laboratory rutting tests and on-site test results to determine the preferred During the research, the CICS laser road inspection vehicle carried out long-term tracking observation on the ruts of the above three semi-rigid base asphalt pavements in Beijing fullscale pavement test track.…”

Section: On-site Inspection and Evaluation Of Laboratory Test Methodsmentioning

confidence: 99%

Rutting Performance of Semi-Rigid Base Pavement in RIOHTrack and Laboratory Evaluation

Fan

et al. 2021

Front. Mater.

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Through a simple performance dynamic modulus test (SPT), standard rutting test, Hamburg rutting test, French rutting test and asphalt pavement analyzer rutting test, the rutting resistance of asphalt mixtures in the middle and lower courses of three semi-rigid base asphalt pavement of Beijing full-scale test track road in China was evaluated. The test results show that the rutting resistance of asphalt mixtures of the middle and lower courses can be greatly improved by using low-grade asphalt, especially 30# asphalt. The rutting resistance of SBS modified asphalt mixtures is also better. The SPT dynamic modulus test can indirectly characterize the rutting performance of the asphalt mixture. The rutting test results of a laser road detection vehicle and 3 m ruler show that the asphalt grade has a significant impact on the rutting performance of semi-rigid base asphalt pavement. Compared with 70# asphalt used in the middle and lower courses, the rutting resistance of the pavement structure can be improved by more than 20%. The result also show that the APA rutting test results can closely characterize the full-scale test track results, which is an optimal test method for evaluating the rutting performance of semi-rigid base asphalt pavement. The research results can provide a theoretical basis and reference for the rational design and rutting evaluation of semi-rigid base asphalt pavement.

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Section: On-site Inspection and Evaluation Of Laboratory Test Methodsmentioning

confidence: 99%

Rutting Performance of Semi-Rigid Base Pavement in RIOHTrack and Laboratory Evaluation

Fan

et al. 2021

Front. Mater.

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“…Initiatives to quantify the road infrastructure impact on energy consumption have focused on measuring/modeling rolling resistance [9][10][11][12][13]. This has led to the development of energy-saving products such as asphalt courses with low-rolling resistance properties [14,15]. However, from a road operator's perspective, there is currently no standard method for monitoring or labeling the energy efficiency of road pavements.…”

Section: Introductionmentioning

confidence: 99%

Internet-of-Things (IoT) Platform for Road Energy Efficiency Monitoring

Skar

Vestergaard

Pour

et al. 2023

Sensors

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The road transportation sector is a dominant and growing energy consumer. Although investigations to quantify the road infrastructure’s impact on energy consumption have been carried out, there are currently no standard methods to measure or label the energy efficiency of road networks. Consequently, road agencies and operators are limited to restricted types of data when managing the road network. Moreover, initiatives meant to reduce energy consumption cannot be measured and quantified. This work is, therefore, motivated by the desire to provide road agencies with a road energy efficiency monitoring concept that can provide frequent measurements over large areas across all weather conditions. The proposed system is based on measurements from in-vehicle sensors. The measurements are collected onboard with an Internet-of-Things (IoT) device, then transmitted periodically before being processed, normalized, and saved in a database. The normalization procedure involves modeling the vehicle’s primary driving resistances in the driving direction. It is hypothesized that the energy remaining after normalization holds information about wind conditions, vehicle-related inefficiencies, and the physical condition of the road. The new method was first validated utilizing a limited dataset of vehicles driving at a constant speed on a short highway section. Next, the method was applied to data obtained from ten nominally identical electric cars driven over highways and urban roads. The normalized energy was compared with road roughness measurements collected by a standard road profilometer. The average measured energy consumption was 1.55 Wh per 10 m. The average normalized energy consumption was 0.13 and 0.37 Wh per 10 m for highways and urban roads, respectively. A correlation analysis showed that normalized energy consumption was positively correlated to road roughness. The average Pearson correlation coefficient was 0.88 for aggregated data and 0.32 and 0.39 for 1000-m road sections on highways and urban roads, respectively. An increase in IRI of 1 m/km resulted in a 3.4% increase in normalized energy consumption. The results show that the normalized energy holds information about the road roughness. Thus, considering the emergence of connected vehicle technologies, the method seems promising and can potentially be used as a platform for future large-scale road energy efficiency monitoring.

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