Study on prediction of skid resistance of asphalt pavement based on genetic neural network

Quanlei Wang; Zhoucong Xu; Haonan Zhou; Jie Wu; You Zhou; Tianlong Chen

doi:10.1117/12.3024354

14 February 2024 Study on prediction of skid resistance of asphalt pavement based on genetic neural network

Quanlei Wang, Zhoucong Xu, Haonan Zhou, Jie Wu, You Zhou, Tianlong Chen

Proceedings Volume 13018, International Conference on Smart Transportation and City Engineering (STCE 2023); 130182V (2024) https://doi.org/10.1117/12.3024354
Event: International Conference on Smart Transportation and City Engineering (STCE 2023), 2023, Chongqing, China

Abstract

The prediction of skid resistance of asphalt pavement plays a pivotal role in formulating maintenance plans and determining maintenance schemes. At present, the typical intelligent algorithms such as the neural network and the genetic algorithm have seen extensive applications in the evaluation and prediction of pavement performance. The combination forecasting model can leverage the complementary advantages of the two, thereby enhancing the reliability of prediction. As a case study, this paper focuses on the prediction of pavement skid resistance for an expressway in Chongqing. The research establishes a pavement skid resistance forecasting model using a genetic neural network and compares it with the single neural network, genetic algorithm, and regression models. Through this comparative analysis, the study validates the applicability and reliability of the genetic neural network approach for predicting asphalt pavement skid resistance. The results demonstrate that the regression model exhibits a limited fitting degree for highly nonlinear problems, leading to noticeably lower prediction accuracy than the genetic algorithm or neural network algorithm. In contrast, the combination forecasting model significantly enhances prediction accuracy in comparison to a single neural network model or genetic algorithm. Notwithstanding, it is worth noting that the operational efficiency of the combination forecasting model is inferior to that of a single neural network model. Consequently, the genetic neural network combination forecasting model proves more suitable for pavement skid resistance prediction, and nevertheless, there is room for further improvement in the operational efficiency of the model.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Quanlei Wang, Zhoucong Xu, Haonan Zhou, Jie Wu, You Zhou, and Tianlong Chen "Study on prediction of skid resistance of asphalt pavement based on genetic neural network", Proc. SPIE 13018, International Conference on Smart Transportation and City Engineering (STCE 2023), 130182V (14 February 2024); https://doi.org/10.1117/12.3024354

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KEYWORDS

Neural networks

Genetic algorithms

Asphalt pavements

Statistical modeling

Data modeling

Evolutionary algorithms

Mathematical optimization

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