Prediction of dynamic modulus of asphalt concrete using hybrid machine learning technique

Eleyedath, Abhary; Swamy, Aravind Krishna

doi:10.1080/10298436.2020.1841191

Cited by 19 publications

(2 citation statements)

References 55 publications

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“…In addition, machine learning methods have been utilized to predict the long-term performance of asphalt pavement in terms of rutting, cracks, and roughness [15][16][17]. An important application of machine learning is predicting the properties of asphalt mixtures, including the dynamic modulus and indirect tensile strength [18][19][20].…”

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

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“…The use of GEP over the last two decades has received significant research attention. GEP has been used in diverse subfields of pavement engineering such as the modelling of pavement performance (Hosseini, Faheem, Titi, & Schwandt, 2022), predicting parameters for pavement design (Tenpe & Patel, 2020), modelling pavement distresses (Hosseini et al, 2022;Leon & Gay, 2019), quantifying pavement strength (Yao, Leng, Jiang, Ni, & Zhao, 2021) for pavement maintenance decision, predicting flow numbers (Amir Hossein Gandomi, Alavi, Mirzahosseini, & Nejad, 2011), dynamic modulus (Eleyedath & Swamy, 2022;Liu, Yan, You, Liu, & Yan, 2017), fracture energy (Majidifard, Jahangiri, Buttlar, & Alavi, 2019) and rutting depth (Majidifard et al, 2021) of asphalt mixtures, predicting characteristics of foamed bitumen (Eleyedath, Kar, & Swamy, 2021) etc.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence-based gene expression programming (GEP) model for assessing sprayed seal performance

Tamanna

Shamsaei

Urquhart

et al. 2022

Road Materials and Pavement Design

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This research predicts residual solvent (α), which is a key component of the performance assessment for a sprayed/chip seal. Solvent evaporation from the bituminous binder film of a sprayed seal is too complex and poorly understood to develop a model in a traditional way. In this study, conventional equations for α were assessed that showed prediction inefficiency (R 2 value as low as 0.82) under different experimental conditions. Accordingly, gene expression programming (GEP), an emerging branch in artificial intelligence, was utilised to resolve these difficulties by developing empirical models for α. The data required for model development was obtained from extensive laboratory tests conducted on bitumen-solvent binder films in this research. Model evaluation results showed an excellent degree of correspondence between predictions and experimental results (R 2 = 0.94). This is the first study to model a key component of sprayed seal performance using GEP. The model is recommended for pre-design purposes or as a tool to determine residual solvent in a sprayed seal when laboratory testing is not feasible, thereby saving time and expenditure.

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Research Trends in Materials and Design of Asphalt Pavements

Saboo

Das

2022

Springer Transactions in Civil and Environmental Engineering

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Prediction of dynamic modulus of asphalt concrete using hybrid machine learning technique

Cited by 19 publications

References 55 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

Artificial intelligence-based gene expression programming (GEP) model for assessing sprayed seal performance

Research Trends in Materials and Design of Asphalt Pavements

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