Characterization of Rutting Damage by the Change of Air-Void Characteristics in the Asphalt Mixture Based on Two-Dimensional Image Analysis

Zhao, Kang; Yang, Hailu; Wang, Wentao; Wang, Linbing

doi:10.3390/ma15207190

Cited by 4 publications

(3 citation statements)

References 38 publications

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“…Wu intends to conduct a comprehensive investigation to determine the law of skid resistance attenuation of SMA pavement [18]. Langa examines how highdensity polyethylene (HDPE) modified asphalt binder changes in terms of its physical, rheological, and thermal properties after soybean oil is added [19].…”

Section: Methodsmentioning

confidence: 99%

Unlocking Road Stability: Machine Learning-powered Prediction of Bitumen Distortion under Traffic Flow Influence

Vrtagic¹,

Dordevic²,

Doğan³

et al. 2023

Preprint

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Road intersections are made of asphalt pavement, a popular road surface material used worldwide. The pavement may suffer deformities and deterioration, resulting in higher maintenance expenses and an elevated likelihood of road accidents, due to factors such as heavy vehicles and environmental variables like temperature and rainfall. To tackle these obstacles, researchers have devised several machine-learning algorithms and optimization techniques. These tools aim to forecast and scrutinize pavement deformation, with the goal of refining pavement design and maintenance approaches, as well as obtaining a more comprehensive comprehension of the factors that impact pavement effectiveness. This paper shows that heavy vehicles contribute significantly more to road erosion, and the retention and braking of vehicles greatly impact roadways. We also emphasize the statistical errors computed on the actual data range and demonstrate the results of the multilayer perceptron (MLP) model. The MLP model used the lath erosion standard to simulate future impact. Even though the model given is based on a small sample of data from one intersection, its estimates for road erosion in a year were found to be accurate when contrasted to real data. Controlling traffic flow can significantly improve road conditions, reducing erosion decay by reducing the time spent at intersections and other parameters. We conclude that machine learning can help control traffic flow, which can significantly improve road conditions, reducing vehicle time stretches at intersections.

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Section: Methodsmentioning

confidence: 99%

Unlocking Road Stability: Machine Learning-powered Prediction of Bitumen Distortion under Traffic Flow Influence

Vrtagic¹,

Dordevic²,

Doğan³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Wu intends to conduct a comprehensive investigation to determine the law of skid resistance attenuation of SMA pavement [20]. Langa examines how high-density polyethylene (HDPE) modified asphalt binder changes in terms of its physical, rheological, and thermal properties after soybean oil is added [21].…”

Section: Different Types Of Testing and Mixture Effectsmentioning

confidence: 99%

AI-Enabled Assessment of Roadway Integrity: Forecasting Bitumen Deformation and Road Stability Throughout the Lifecycle Under Traffic Impact

Vrtagic,

Dordevic,

Dogan

et al. 2023

IJTDI

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Asphalt-paved Road junctions frequently encounter deformation and degradation challenges due to heavy vehicular traffic and varying climatic conditions, such as temperature fluctuations and precipitation. This study employs a multifaceted approach, incorporating a Multilayer Perceptron (MLP) model, ancillary machine learning techniques, and optimization methodologies, to address these challenges effectively. The primary objectives are the prediction and analysis of pavement deformation, the optimization of maintenance strategies, and the evaluation of road effectiveness. Our findings underscore the substantial contribution of heavy vehicles to road erosion and the profound impact of vehicular retention and braking at intersections. A Multilayer Perceptron (MLP) model is utilized to simulate future pavement degradation accurately at a specific intersection, leveraging real-time traffic flow data. This approach showcases the advantages of using real-world traffic data to model the lifecycle of asphalt dependencies dynamically at the intersection level. Mitigation of road deterioration is proposed via controlled traffic flow and optimization of relevant parameters, such as minimization of intersection wait times. The integration of machine learning substantially enhances road conditions and reduces vehicular waiting times at intersections. The implementation of this study's findings in pavement design and preservation practices could enable transportation authorities to improve road safety, reduce maintenance costs, and decrease the incidence of road accidents. Overall, this paper presents a comprehensive approach towards sustainable and efficient road infrastructure management, highlighting the potential of AI in tackling pressing infrastructure challenges.

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“…Three papers utilize digital image processing (DIP) technology for either the performance test or meso-structure reconstruction of asphalt mixtures. The relationship between the rutting damage and the air void change was investigated via a 2D image technology [9]. An adaptive image processing method for CT images of asphalt mixtures was proposed to improve the accuracy of the meso-structure reconstruction of asphalt mixtures [10].…”

mentioning

confidence: 99%

Experimental Testing and Constitutive Modelling of Pavement Materials

et al. 2023

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Characterization of Rutting Damage by the Change of Air-Void Characteristics in the Asphalt Mixture Based on Two-Dimensional Image Analysis

Cited by 4 publications

References 38 publications

Unlocking Road Stability: Machine Learning-powered Prediction of Bitumen Distortion under Traffic Flow Influence

Unlocking Road Stability: Machine Learning-powered Prediction of Bitumen Distortion under Traffic Flow Influence

AI-Enabled Assessment of Roadway Integrity: Forecasting Bitumen Deformation and Road Stability Throughout the Lifecycle Under Traffic Impact

Experimental Testing and Constitutive Modelling of Pavement Materials

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