A Molecular Dynamics Approach to the Impacts of Oxidative Aging on the Engineering Characteristics of Asphalt

Cao, Wei; Fini, Elham H.

doi:10.3390/polym14142916

Cited by 13 publications

(3 citation statements)

References 49 publications

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“…Shaffie proves RSM's statistical efficacy [21]. Cao simulates the thermodynamic, diffusion, and adhesion effects of asphalt cement aging using molecular dynamics [22]. [23] carried out a modelbased farm-scale exploratory study using two farms as case studies.…”

Section: Methodsmentioning

confidence: 98%

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: 98%

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

“…Shaffie [23] proves RSM's statistical efficacy. Cao simulates the thermodynamic, diffusion, and adhesion effects of asphalt cement aging using molecular dynamics [24]. The study [25] carried out a model-based farm-scale exploratory study using two farms as case studies.…”

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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“…Molecular dynamics simulation (MD) software "Materials Studio 2020" can accurately simulate interactions between the atoms and molecules within materials and has been extensively applied in asphalt binder simulations [32][33][34][35]. Previous research has validated its precision in modeling the aging and rejuvenation processes of asphalt binders [36][37][38]. Yan et al [39] conducted a systematic study on the rejuvenating behavior of aged asphalt binder with 14 common rejuvenators.…”

Section: Introductionmentioning

confidence: 99%

Molecular Dynamics Simulation and the Regeneration and Diffusion Effects of Waste Engine Oil in Aged Asphalt Binder

Sun,

Cannone Falchetto,

Zhang

et al. 2024

Materials

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In recent years, the potential of waste engine oil (WEO) as a rejuvenator for aged asphalt binders has gained significant attention. Despite this interest, understanding WEO’s regeneration mechanism within aged asphalt binders, particularly its diffusion behavior when mixed with both aged and virgin asphalt binders, remains limited. This study adopts a molecular dynamics approach to constructing models of virgin, aged, and rejuvenated asphalt binders with different WEO contents (3%, 6%, 9%, and 12%). Key properties such as the density, glass transition temperature, cohesive energy density, solubility parameter, viscosity, surface free energy, fractional free volume, and diffusion coefficient are simulated. Additionally, models of rejuvenated asphalt binder are combined with those of aged asphalt binder to investigate mutual diffusion, focusing on the impact of WEO on the relative concentration and binding energy. The findings reveal that WEO notably decreased the density, viscosity, and glass transition temperature of aged asphalt binders. It also improved the molecular binding within the asphalt binder, enhancing crack resistance. Specifically, a 9% WEO content can restore the diffusion coefficient to 93.17% of that found in virgin asphalt binder. Increasing the WEO content facilitates diffusion toward virgin asphalt binders, strengthens molecular attraction, and promotes the blending of virgin and aged asphalt binders.

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A Molecular Dynamics Approach to the Impacts of Oxidative Aging on the Engineering Characteristics of Asphalt

Cited by 13 publications

References 49 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

Molecular Dynamics Simulation and the Regeneration and Diffusion Effects of Waste Engine Oil in Aged Asphalt Binder

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