Artificial Neural Network Models for the Estimation of the Optimum Asphalt Content of Asphalt Mixtures

Othman, Kareem

doi:10.1007/s42947-022-00179-6

Cited by 9 publications

(3 citation statements)

References 38 publications

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“…The constraints of the genetic algorithm were formulated according to the requirements of the Marshall test. Othman [23], based on the Marshall mix design, utilized an ANN model to directly predict the asphalt content by inputting aggregate gradation information into the model. However, the gradation of the mixture was the only input variable.…”

Section: Introductionmentioning

confidence: 99%

Optimizing asphalt mix design using machine learning methods based on RIOCHTrack data

Cheng,

Liu,

Yaohui

et al. 2023

Preprint

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Traditional mix design is a time-consuming and labor-intensive process ,requiring extensive testing and relying heavily on engineering experience. In order to enhance the speed and efficiency of asphalt concrete mix design process,this study investigated the use of machine learning techniques to predict key parameters of concrete mixture design,such as voids in the mineral aggregate (VMA), voids in the coarse aggregate(VCA), and dry density of the mixture(pd). Four machine learning methods, namely support vector regression, artificial neural network, random forest, and AdaBoost models were trained using data from RIOHTRack. Metircs releatde to asphalt mix design such as gradation, asphalt content, asphalt properties, compaction method, and compaction temperature were used as input variables. Various encoding methods were employed to encode classification variables, with the ordinal encoding method yielding the most favorable results. Through the calculation of different performance scoring metrics, such as coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), and by plotting the development curve of volume parameters and asphalt content, the most suitable prediction model was selected for each target variable. The analysis revealed that the random forest model (R2 = 0.8595 for pd, R2 = 0.9488 for VMA) demonstrated the best performance in predicting pd and VMA, while the Adaboost model (R2 = 0.9716) was chosen for predicting VCA. By calculating different performance scoring metrics, such as coefficient of determination (R2), root means square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) and plotting the development curve of volume parameters and asphalt content, the final prediction model was selected for each target variable. The analysis revealed that the random forest model (R2 = 0.8595 for pd, R2 = 0.9488 for VMA) demonstrated the best performance in predicting pd and VMA, while the Adaboost model (R2 = 0.9716) was chosen for predicting VCA.

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Section: Introductionmentioning

confidence: 99%

Optimizing asphalt mix design using machine learning methods based on RIOCHTrack data

Cheng,

Liu,

Yaohui

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Recently, the machine learning approaches have been utilized in several studies to predict models for different aspects of construction materials and civil engineering applications (Murad et al, 2021;Al Bodour et al, 2022;Iftikhar et al, 2022;Momani et al, 2022). The artificial neural networks (ANNs), which is a recent learning machine tool, was also utilized by researchers in asphalt mix design applications (Tapkın et al, 2010;Ozgan, 2011;Singh et al, 2013;Ozturk and Kutay, 2014;Shafabakhsh et al, 2015;Ozturk et al, 2016;Zavrtanik et al, 2016;Pasetto et al, 2019;Fadhil et al, 2022;Othman, 2022). An ANN-based model to predict the Marshall mix volumetric properties has been proposed by Ozturk et al (Ozturk et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…They used five parameters including asphalt penetration, kinematic viscosity, aggregate surface area, abrasion, and binder content as inputs in the models to predict five parameters of HMA design including the bulk density, air voids, Marshall stability, Marshall flow, and Marshall stiffness. Othman (Othman, 2022) has predicted the optimum asphalt content (OAC) based on different aggregate gradations using ANN approaches of different activation functions, number of hidden layers, and number of neurons per layer. The volumetric properties of Superpave asphalt mixes were also modeled utilizing the ANN approach at different gyration levels (Ozturk and Kutay, 2014).…”

Section: Introductionmentioning

confidence: 99%

Prediction of Marshall Test Results for Dense Glasphalt Mixtures Using Artificial Neural Networks

Jweihan

Alawadi

Momani

et al. 2022

Front. Built Environ.

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Asphalt mixture comprising waste glass as an aggregate is referred to as “glasphalt”. Limited studies have been oriented to investigate the Marshall test results of dense-graded glasphalt mixes considering a wide range of variables. This study aims to utilize the artificial neural networks (ANNs) to develop predictive models for Marshall stability and Marshall flow of dense glasphalt mixes based on a large experimental database collected from the literature. Eight independent variables covering the material and mix properties were utilized as inputs in the models. The proposed models resulted in an experimental-to-predicted ratio of 1.00 and 1.00, coefficient of variation of 8.6% and 8.7%, RMSE of 1.63 kN and 0.54 mm, and R-squared of 93.6% and 85.7% for the glasphalt stability and flow models, respectively. Comprehensive parametric analyses have been conducted to further validate the models by investigating the sensitivity of their parameters to the predicted stability and flow values. The analyses revealed some desirable design values that could be considered for a better performance of dense glasphalt mixes. The results indicate that 4% is the desired design air void content of glasphalt mixes. High stability value can be achieved for glasphalt mixes containing a crushed aggregate of 12.5 mm maximum size and 50% glass cullet of 4.75 mm maximum size. Lower viscosity asphalt binder would provide uniformly compacted mixes. Furthermore, glasphalt flow increases as the maximum size of ingredient particles, the penetration grade of asphalt cement, asphalt cement content, and VMA% increase.

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Utilising machine learning algorithms to predict the Marshall characteristics of asphalt pavement layers

Tangga,

Mufargi,

Milad

et al. 2024

Innov. Infrastruct. Solut.

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Artificial Neural Network Models for the Estimation of the Optimum Asphalt Content of Asphalt Mixtures

Cited by 9 publications

References 38 publications

Optimizing asphalt mix design using machine learning methods based on RIOCHTrack data

Optimizing asphalt mix design using machine learning methods based on RIOCHTrack data

Prediction of Marshall Test Results for Dense Glasphalt Mixtures Using Artificial Neural Networks

Utilising machine learning algorithms to predict the Marshall characteristics of asphalt pavement layers

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