Asphalt Pavement Performance Evaluation Based on SOM Neural Network

Abstract

In order to make the performance evaluation of asphalt pavement more scientific and reasonable, the author put forward an evaluation method based on SOM neural Network. This method takes comprehensive consideration of four affecting factors including pavement ride quality, pavement condition, pavement structure bearing capacity and pavement skid resistance. Then designs and simulates the SOMNN programming to comprehensive evaluate the pavement performance. Finally, the method was verified by an example, and the calculation has been compared with the traditional evaluation methods. Results showed that the method was reasonable and effective. Compared with others, this method was simpler, cheaper and easier to promote and has better superiority.

Comprehensive Evaluation of Asphalt Pavement Performance in Hot and Humid Areas Based on Self-Organizing Map and Random Forest Algorithms

“…Additionally, the application of the Support Vector Machine (SVM) algorithm and OTSU's method in asphalt pavement crack classification has been noted [16]. The effectiveness of Self-Organizing Maps (SOM) in uncovering deep data structures, thanks to its unique features as an unsupervised learning algorithm, has been demonstrated by studies [17][18]. Additionally, the proven efficacy of the Random Forest (RF) algorithm in handling large datasets, stemming from its characteristics as an ensemble learning algorithm, is evident in studies [19][20][21].…”