Predicting the master curves of bituminous mastics with micromechanical modelling

Fadil, Hassan; Jelagin, Denis; Partl, Manfred N.

doi:10.1080/14680629.2021.2011383

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…During the use of asphalt pavement, various factors come into play, including the inherent limitations of the material, the impact of traffic load, and the influence of environmental factors [1] . As a result, the microstructure of the pavement surface undergoes gradual polishing, while the macroscopic structure diminishes [2][3][4] , leading to a continuous attenuation in pavement skid resistance. This deterioration significantly compromises road safety for drivers.…”

Section: Introductionmentioning

confidence: 99%

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

Wang,

Xu,

Zhou

et al. 2024

International Conference on Smart Transportation and City Engineering (STCE 2023)

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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.

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Section: Introductionmentioning

confidence: 99%