Prediction of Marshall design parameters of asphalt mixtures via machine learning algorithms based on literature data

Atakan, Mert; Yıldız, Kürşat

doi:10.1080/14680629.2023.2213774

Road Materials and Pavement Design

2023

DOI: 10.1080/14680629.2023.2213774

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Prediction of Marshall design parameters of asphalt mixtures via machine learning algorithms based on literature data

Mert Atakan

Kürşat Yıldız

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2024

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Cited by 2 publications

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Predicting Marshall stability and flow parameters in asphalt pavements using explainable machine-learning models

Asi,

Alhadidi,

Alhadidi

2024

Transportation Engineering

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Predicting Marshall stability and flow parameters in asphalt pavements using explainable machine-learning models

Asi,

Alhadidi,

Alhadidi

2024

Transportation Engineering

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A Deep Neural Network Approach towards Performance Prediction of Bituminous Mixtures Produced Using Secondary Raw Materials

Rondinella,

Oreto,

Abbondati

et al. 2024

Coatings

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With the progressive reduction in virgin material availability and the growing global concern for sustainability, civil engineering researchers worldwide are shifting their attention toward exploring alternative and mechanically sound technological solutions. The feasibility of preparing both cold and hot asphalt mixtures (AMs) for road pavement binder layers with construction and demolition wastes (C&DWs) and reclaimed asphalt pavement (RAP) partially replacing virgin materials like limestone aggregates and filler has already been proven. The technical suitability and compliance with technical specifications for road paving materials involved the evaluation of mechanical and volumetric aspects by means of indirect tensile strength tests and saturated surface dry voids, respectively. Thus, the main goal of the present study is to train, validate, and test selected machine learning algorithms based on data obtained from the previous experimental campaign with the aim of predicting the volumetric properties and the mechanical performance of the investigated mixtures. A comparison between the predictions made by ridge and lasso regression techniques and both shallow (SNN) and deep neural network (DNN) models showed that the latter achieved better predictive capabilities, highlighted by fully satisfactory performance metrics. DNN performance can be summarized by R2 values equal to 0.8990 in terms of saturated surface dry void predictions, as well as 0.9954 in terms of indirect tensile strength predictions. Predicted observations can be thus implemented within the traditional mix design software. This would reduce the need to carry out additional expensive and time-consuming experimental campaigns.

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Prediction of Marshall design parameters of asphalt mixtures via machine learning algorithms based on literature data

Cited by 2 publications

References 25 publications

Predicting Marshall stability and flow parameters in asphalt pavements using explainable machine-learning models

Predicting Marshall stability and flow parameters in asphalt pavements using explainable machine-learning models

A Deep Neural Network Approach towards Performance Prediction of Bituminous Mixtures Produced Using Secondary Raw Materials

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