KEYWORDS: Diseases and disorders, Asphalt pavements, Random forests, Data modeling, Roads, Machine learning, Dimension reduction, Decision trees, Principal component analysis, Education and training
To construct an automatic discrimination method for the causes of pavement diseases, the typical characteristics of different types of asphalt pavement diseases of the Inner Ring Expressway in Chongqing, which was taken as an engineering example, were analyzed, and the feasibility of data dimension reduction analysis was determined based on the correlation characteristics of different types of damage. Then, numerous state information data were subjected to dimension reduction through the principal component analysis (PCA), followed by the automatic cause analysis of pavement diseases using the random forest algorithm. The results show that the cause conclusions acquired through machine learning model training basically accord with the actual field survey conclusions. Thus, it can be deemed that the intelligent discrimination method based on machine learning is reliable, to some extent, for the cause analysis of pavement diseases and can serve as an automatic discrimination method for the follow-up development of an intelligent maintenance decision system.
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.
In this study, we used the SEM electron microscope to observe the morphological characteristics of each phase interface in the mixture, focusing on the effects of gradation, cement content, curing age, and mixing method on the unconfined compressive strength and splitting strength of the cold recycled mixture, to optimize the material composition design of cement composite recycled mixture. The results showed that dry shrinkage cracks and cement aggregates appeared on the surface of the three aggregates formed by conventional mixing, while the old cement that was not closely connected on the RBP surface of the mixture fell off using the vibration mixing method. The cement on the surface of all kinds of aggregates was evenly coated, with a reduced interface strength variability. The strength of the mixture increased with age was in line with the law of cement strength growth; The increase in cement content could increase the unconfined compressive strength and splitting strength of the mixture; The strength of the mixture increased and then decreased with the increase of RAP content. The unconfined compressive strength and splitting strength of the mixture were increased by 32.4% and 44.3%, and the coefficient of variation was reduced by 27.1% and 31.0%, respectively, by using vibration mixing compared with conventional mixing.
KEYWORDS: Shape memory alloys, Resistance, Roads, Minerals, Particles, Raw materials, Kinematics, Interfaces, Information and communication technologies, Absorption
For the mix proportion design of bituminous mixture, gap graded SMA-13 is selected and 4‰ of basalt fiber is added to it. Moreover, its pavement performance is compared with that of the ordinary SMA-13 bituminous mixture with the same amount of lignin fiber, so as to study the effect of the basalt fiber on the pavement performance of the SMA-13 bituminous mixture. The test results show that compared with the ordinary SMA-13 asphalt mixture, the bitumen aggregate ratio of the SMA-13 bituminous mixture with basalt fiber is reduced, and the high-temperature stability and low-temperature crack resistance are improved, while the water stability is improved to a limited extent.
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