Development of prediction models for interlayer shear strength in asphalt pavement using machine learning and SHAP techniques

AL-Jarazi, Rabea; Rahman, Ali; Ai, Changfa; Al-Huda, Zaid; Ariouat, Hamza

doi:10.1080/14680629.2023.2276412

Road Materials and Pavement Design

2023

DOI: 10.1080/14680629.2023.2276412

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Development of prediction models for interlayer shear strength in asphalt pavement using machine learning and SHAP techniques

Rabea AL-Jarazi,

Ali Rahman,

Changfa Ai

et al.

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2024

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Cited by 3 publications

References 43 publications

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Development of a Novel Prediction Model for Interface Shear Strength in Asphalt Pavement Using the CART Model

Al-Jarazi,

Rahman,

et al. 2024

KSCE J Civ Eng

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Development of a Novel Prediction Model for Interface Shear Strength in Asphalt Pavement Using the CART Model

Al-Jarazi,

Rahman,

et al. 2024

KSCE J Civ Eng

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Evaluation and prediction of interface fatigue performance between asphalt pavement layers: application of supervised machine learning techniques

AL-Jarazi,

Rahman,

et al. 2024

International Journal of Pavement Engineering

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A Robust Statistical Analysis of Factors Affecting Interface Bonding between Asphalt Pavement Layers

Al-Jarazi,

Rahman,

2024

KEM

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Interlayer bonding within a multilayered pavement system plays an essential role in the overall performance of the pavement structure. Therefore, studying the interlayer shear strength (ISS) as a major index of the bonding strength and its accurate evaluation is of great importance. The main objective of this research is to assess the ISS of pavement using an experimental and statistically rigorous approach. The results showed that the ISS is highly temperature-dependent, experienced a rapid decline with increasing temperature. As tack coat rate increased, the ISS initially increased to reach a pick at 0.8kg/m2 rate and then started to decline. The ISS demonstrated an almost linear correlation with vertical pressure at all temperature levels. Two-way factorial analysis of variance (FAV) underscored the significant impact of any two, namely temperature (T), tack coat rate (TC), and vertical pressure (VP), on ISS results. However, three-way FAV results indicated that the combined effect of T, TC and VP did not hold statistically significant influence on ISS. Moreover, all ISS models developed in this study were statistically significant at a 0.05 significance level, with a good coefficient of determination (R2 =0.73) for multiple linear regression (MLR) and an excellent R2 of 0.976 for polynomial regression (PR).

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Development of prediction models for interlayer shear strength in asphalt pavement using machine learning and SHAP techniques

Cited by 3 publications

References 43 publications

Development of a Novel Prediction Model for Interface Shear Strength in Asphalt Pavement Using the CART Model

Development of a Novel Prediction Model for Interface Shear Strength in Asphalt Pavement Using the CART Model

Evaluation and prediction of interface fatigue performance between asphalt pavement layers: application of supervised machine learning techniques

A Robust Statistical Analysis of Factors Affecting Interface Bonding between Asphalt Pavement Layers

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