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

• Published in: The international journal of pavement engineering Vol. 25; no. 1
• Main Authors: Li, Jiale, Zhang, Zhishuai, Wang, Xuefei
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 31.12.2024; Taylor & Francis LLC
• Subjects: Algorithms; Construction management; Design optimization; Design techniques; machine learning (ML); Optimal pavement design; optimal subgrade design; Pareto optimum; particle swarm optimisation (PSO); Particle swarm optimization; Pavement construction; Pavement materials; Pavements; Prediction models; Predictions; Road design; Road maintenance; Roads & highways; Subgrades
• ISSN: 1029-8436; 1477-268X
• DOI: 10.1080/10298436.2023.2295899

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 R 2 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.