An eXtreme Gradient Boosting model for predicting dynamic modulus of asphalt concrete mixtures

Ali, Yasir; Hussain, Fizza; Irfan, Muhammad; Buller, Abdul Salam

doi:10.1016/j.conbuildmat.2021.123642

Cited by 32 publications

(6 citation statements)

References 42 publications

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“…The extreme gradient boosting (XGBoost) model is an ML algorithm that belongs to the decision-tree-based model category. It was specifically developed for high computational and prediction accuracy and efficiency by combining a wide range of gradient-boosted decision trees [53]. XGBoost adopts ensemble learning that utilizes the sequence of decision trees on which each decision tree depends and learns from the previous decision tree to develop a strong learning process that improves the performance of the developed models [53][54][55][56].…”

Section: Machine Learning Analysis and Modelingmentioning

confidence: 99%

“…It was specifically developed for high computational and prediction accuracy and efficiency by combining a wide range of gradient-boosted decision trees [53]. XGBoost adopts ensemble learning that utilizes the sequence of decision trees on which each decision tree depends and learns from the previous decision tree to develop a strong learning process that improves the performance of the developed models [53][54][55][56]. The independent variables x i and specifying dataset of n observations are typically used to develop XGBoost, where each of the independent variables has m unique features.…”

Section: Machine Learning Analysis and Modelingmentioning

confidence: 99%

“…There is a response (y i ) for each raw dependent variable. Based on the literature that used XGBoost for asphalt applications [53], the prediction model for XGBoost is shown in Eq. 2.…”

Section: Machine Learning Analysis and Modelingmentioning

confidence: 99%

See 2 more Smart Citations

Utilization of response surface methodology and machine learning for predicting and optimizing mixing and compaction temperatures of bio-modified asphalt

Al-Sabaeei

Alhussian

Abdulkadir

et al. 2023

Case Studies in Construction Materials

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Section: Machine Learning Analysis and Modelingmentioning

confidence: 99%

Section: Machine Learning Analysis and Modelingmentioning

confidence: 99%

See 1 more Smart Citation

Utilization of response surface methodology and machine learning for predicting and optimizing mixing and compaction temperatures of bio-modified asphalt

Al-Sabaeei

Alhussian

Abdulkadir

et al. 2023

Case Studies in Construction Materials

View full text Add to dashboard Cite

“…Some ML-based models used numerous input parameters, such as Ali [20]. Extreme gradient-boosting regression (EGBR) was used with a dataset of 1152 E* measurements to predict E* as a function of 24 variables representing testing conditions, mix volumetric properties, and gradation parameters with R 2 -value of 0.835.…”

Section: Literature Reviewmentioning

confidence: 99%

Boosting Hot Mix Asphalt Dynamic Modulus Prediction Using Statistical and Machine Learning Regression Modeling Techniques

Awed,

Awaad,

Kaloop

et al. 2023

Sustainability

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The prediction of asphalt mixture dynamic modulus (E*) was investigated based on 1128 E* measurements, using three regression and thirteen machine learning models. Asphalt binder properties and mixture volumetrics were characterized using the same feeding features in the NCHRP 1-37A Witczak model. However, three aggregate gradation characterization approaches were involved in both modelling techniques: the NCHRP 1-37A gradation parameters, Weibull distribution factors, and Bailey method parameters. This study evaluated the performance of these models based on various performance indicators, using both statistical and machine learning regression modeling techniques. K-fold cross-validation and learning curve analysis were conducted to assess the models’ generalization capabilities. The conclusions of this study demonstrate the superiority of the ML models, particularly the Catboost ensemble learning regression (CbR). Hyperparameter optimization and residual analysis were performed to fine-tune and confirm the heteroscedasticity of the CbR model. The Bailey-based CbR model showed the highest coefficient of determination (R2) of 0.998 and the lowest root mean square error (RMSE) of 220 MPa. Moreover, SHAP values interpreted the CbR model and showed the relative importance of its feeding features. Based on the findings of this study, the CbR model is suggested to accurately predict E* for a variety of asphalt mixtures. This information can be used to improve pavement design and construction, leading to more durable and long-lasting pavements.

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“…To characterize and simulate the LVE behavior of HMA in the temperature and frequency domains, researchers have developed various rheological models, e.g., Huet-Sayegh model, 2S2P1D model [6,7,20], Generalized Maxwell (GM) model [21], Generalized Kelvin (GK) model, Christensen-Anderson (CA) model, Sigmoidal model [22,23], Modified Christensen-Anderson-Marasteanu (CAM) model, Havriliak-Negami (HN) model [24], and machine learning models [25][26][27]. These models can be classified into three categories: physical models, mathematical shape functions, and machine learning models.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Dynamic Properties of the Polyurethane Mixture with Dense Gradation Using the 2S2P1D Model

Zhao,

Gao,

Cui

et al. 2023

Coatings

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The polyurethane (PU) mixture is a zero-emission and high-performance engineering mixture that could replace traditional asphalt mixtures for pavement paving. This paper investigated the feasibility of using the 2S2P1D model to characterize the linear viscoelastic (LVE) properties of the PU mixture in comparison to the styrene–butadiene–styrene (SBS)-modified asphalt mixture with identical aggregate gradation and binder content. The PU mixture showed higher prediction precision for dynamic modulus and lower prediction precision for phase angle compared to the SBS-modified asphalt mixture. The seven constants of the 2S2P1D model for the PU mixture differed significantly from those of the SBS-modified asphalt mixture. The PU mixture exhibited higher elastic properties and lower creep properties, but the viscous properties represented by the index of η were different from the loss dynamic modulus, which reflected the mixture’s viscous properties. The index η cannot accurately characterize the viscous properties of the PU mixture. The 2S2P1D model exhibited a higher prediction accuracy than the Sigmoidal Christensen Anderson and Marasteanu (SCM) model and could accurately simulate the LVE properties of the PU- and SBS-modified asphalt mixtures. However, the 2S2P1D model should enhance the creep and dashpot elements to provide a more accurate characterization of the properties of the PU mixture.

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An eXtreme Gradient Boosting model for predicting dynamic modulus of asphalt concrete mixtures

Cited by 32 publications

References 42 publications

Utilization of response surface methodology and machine learning for predicting and optimizing mixing and compaction temperatures of bio-modified asphalt

Utilization of response surface methodology and machine learning for predicting and optimizing mixing and compaction temperatures of bio-modified asphalt

Boosting Hot Mix Asphalt Dynamic Modulus Prediction Using Statistical and Machine Learning Regression Modeling Techniques

Modeling the Dynamic Properties of the Polyurethane Mixture with Dense Gradation Using the 2S2P1D Model

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