Performance-oriented road structure and material design method based on enhanced XGBoost algorithm

ABSTRACT

A well-designed road structure is critical for reducing road operational period diseases and saving construction management and maintenance funds throughout the life cycle of roads. Excessive design may cause waste in materials or pavement distresses. This study proposes an intelligent optimisation design method for road structure and materials based on pavement performance and cost. The Long-Term Pavement Performance (LTPP) database were used to training the prediction model using the particle swarm optimisation (PSO) algorithm and the extreme gradient boosting algorithm (XGBoost) algorithm. Subsequently, an automated design model encompassing pavement thickness, pavement material, and subgrade material was established based on the predictive model derived from the PSO algorithms. The results indicate that the R² between the actual values and the predicted values of the PSO-XGBoost model is above 0.935, and the Pareto optimal solution is obtained using the multi-objective Particle Swarm Optimisation algorithm. This investigation showcases the potential of data-driven methodologies in furnishing valuable guidance for the initial stages of road design and construction.

KEYWORDS:

• Optimal pavement design
• optimal subgrade design
• machine learning (ML)
• particle swarm optimisation (PSO)

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Natural Science Foundation of Hebei Province: [grant no E2022202007]; Natural Science Foundation of Tianjin City: [grant no 22JCQNJC00400].