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Predicting resilient modulus of flexible pavement foundation using extreme gradient boosting based optimised models

Sarkhani Benemaran, Reza / Esmaeili-Falak, Mahzad / Javadi, Akbar

Resilient modulus ( $M_R$ $M_{R}$ ) plays the most critical role in the evaluation and design of flexible pavement foundations. $M_R$ $M_{R}$ is utilised as the principal parameter for representing stiffness and behaviour of flexible pavement foundation in experimental and semi-empirical approaches. To determine $M_R$ $M_{R}$ , cyclic triaxial compressive experiments under different confining pressures and deviatoric stresses are needed. However, such experiments are costly and time-consuming. In the present study, an extreme gradient boosting-based ( $XGB$ $X G B$ ) model is presented for predicting the resilient modulus of flexible pavement foundations. The model is optimised using four different optimisation methods (particle swarm optimisation ( $PSO$ $P S O$ ), social spider optimisation ( $SSO$ $S S O$ ), sine cosine algorithm ( $SCA$ $S C A$ ), and multi-verse optimisation ( $MVO$ $M V O$ )) and a database collected from previously published technical literature. The outcomes present that all developed designs have good workability in estimating the $M_R$ $M_{R}$ of flexible pavement foundation, but the $PSO-XGB$ $P S O - X G B$ models have the best prediction accuracy considering both training and testing datasets.

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Predicting resilient modulus of flexible pavement foundation using extreme gradient boosting based optimised models

Sarkhani Benemaran, Reza / Esmaeili-Falak, Mahzad / Javadi, Akbar

Resilient modulus ( $M_R$ $M_{R}$ ) plays the most critical role in the evaluation and design of flexible pavement foundations. $M_R$ $M_{R}$ is utilised as the principal parameter for representing stiffness and behaviour of flexible pavement foundation in experimental and semi-empirical approaches. To determine $M_R$ $M_{R}$ , cyclic triaxial compressive experiments under different confining pressures and deviatoric stresses are needed. However, such experiments are costly and time-consuming. In the present study, an extreme gradient boosting-based ( $XGB$ $X G B$ ) model is presented for predicting the resilient modulus of flexible pavement foundations. The model is optimised using four different optimisation methods (particle swarm optimisation ( $PSO$ $P S O$ ), social spider optimisation ( $SSO$ $S S O$ ), sine cosine algorithm ( $SCA$ $S C A$ ), and multi-verse optimisation ( $MVO$ $M V O$ )) and a database collected from previously published technical literature. The outcomes present that all developed designs have good workability in estimating the $M_R$ $M_{R}$ of flexible pavement foundation, but the $PSO-XGB$ $P S O - X G B$ models have the best prediction accuracy considering both training and testing datasets.

Predicting resilient modulus of flexible pavement foundation using extreme gradient boosting based optimised models

Sarkhani Benemaran, Reza / Esmaeili-Falak, Mahzad / Javadi, Akbar

Resilient modulus ( $M_R$ $M_{R}$ ) plays the most critical role in the evaluation and design of flexible pavement foundations. $M_R$ $M_{R}$ is utilised as the principal parameter for representing stiffness and behaviour of flexible pavement foundation in experimental and semi-empirical approaches. To determine $M_R$ $M_{R}$ , cyclic triaxial compressive experiments under different confining pressures and deviatoric stresses are needed. However, such experiments are costly and time-consuming. In the present study, an extreme gradient boosting-based ( $XGB$ $X G B$ ) model is presented for predicting the resilient modulus of flexible pavement foundations. The model is optimised using four different optimisation methods (particle swarm optimisation ( $PSO$ $P S O$ ), social spider optimisation ( $SSO$ $S S O$ ), sine cosine algorithm ( $SCA$ $S C A$ ), and multi-verse optimisation ( $MVO$ $M V O$ )) and a database collected from previously published technical literature. The outcomes present that all developed designs have good workability in estimating the $M_R$ $M_{R}$ of flexible pavement foundation, but the $PSO-XGB$ $P S O - X G B$ models have the best prediction accuracy considering both training and testing datasets.

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Titel:

Predicting resilient modulus of flexible pavement foundation using extreme gradient boosting based optimised models

Beteiligte:

Sarkhani Benemaran, Reza (Autor:in) / Esmaeili-Falak, Mahzad (Autor:in) / Javadi, Akbar (Autor:in)

Erschienen in:

International Journal of Pavement Engineering ; 24

Erscheinungsdatum:

28.01.2023

ISSN:

1029-8436 , 1477-268X

DOI:

https://doi.org/10.1080/10298436.2022.2095385

Medientyp:

Aufsatz (Zeitschrift)

Format:

Elektronische Ressource

Sprache:

Englisch

Schlagwörter:

Resilient modulus prediction , artificial neural networks , extreme gradient boosting , PSO , MVO , SSO , SCA

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