Prediction Model of the Coring Asphalt Pavement Performance through Response Surface Methodology

Shaffie, Ekarizan; Jaya, Ramadhansyah Putra; Ahmad, J.; Arshad, Ahmad Kamil; Zihan, Mohd Afiq; Shiong, Fionna

doi:10.1155/2022/6723396

Cited by 9 publications

(3 citation statements)

References 27 publications

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“…To predict flexible pavements' durability over time, [20] puts forward integrating Non-Destructive Testing (NDT) and ground truth data. Shaffie proves RSM's statistical efficacy [21]. Cao simulates the thermodynamic, diffusion, and adhesion effects of asphalt cement aging using molecular dynamics [22].…”

Section: Methodsmentioning

confidence: 87%

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: 87%

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

“…To predict flexible pavements' durability over time, the study [22] puts forward integrating Non-Destructive Testing (NDT) and ground truth data. Shaffie [23] proves RSM's statistical efficacy. Cao simulates the thermodynamic, diffusion, and adhesion effects of asphalt cement aging using molecular dynamics [24].…”

Section: Different Types Of Testing and Mixture Effectsmentioning

confidence: 86%

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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“…Based on the analysis of variance, the influence of cement is more significant than other variables as the linear effect of cement is greater [22]. Response surface methodology is an active tool for providing a suitable empirical model for predicting the optimum performance of an asphaltic mixture to decrease flexible pavement failure [23]. Response surface models developed to evaluate the reliability index of steel towers have the desired accuracy in comparison with that installed inland [24].…”

Section: Introductionmentioning

confidence: 99%

A DOE (Response Surface Methodology) Approach to Predict the Strength Properties of Concrete Incorporated with Jute and Bamboo Fibres and Silica Fumes

Sridhar¹,

Jegatheeswaran²,

Gobinath³

2022

Advances in Civil Engineering

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Design of Experiment approach is adopted for deriving progression variables comprising jute fibres, bamboo fibres, and silica fumes. To obtain the optimal combination of progression variables, the effect of progression variable on the strength properties of concrete, Box–Behnken design of Response Surface Methodology was adopted. Totally four responses like compressive strength and split tensile strength at 14 days and 28 days were considered. Regression models for responses were tested using Analysis of Variance (ANOVA) and Pareto chart. The statistical importance of each progression variable was evaluated, and the attained models were articulated in second-order polynomial equation. The outcomes showed that addition of jute fibres, bamboo fibres, and silica fumes has enhanced the strength properties, but higher level of fibres incorporation exhibited reduction in strength. Surface plot, Pareto chart, and regression analysis outcomes show that the most substantial and influence factor at 14 days and 28 days for compressive strength is Jute fibres and for split tensile strength is both jute and bamboo fibres. The percentage of error of the validation tests is less than 4% for compressive strength and less than 3% for split tensile strength.

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Prediction Model of the Coring Asphalt Pavement Performance through Response Surface Methodology

Cited by 9 publications

References 27 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

A DOE (Response Surface Methodology) Approach to Predict the Strength Properties of Concrete Incorporated with Jute and Bamboo Fibres and Silica Fumes

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