Cement pavement performance evaluation based on the discrete Hopfield neural network

• Main Authors: Liu Huan, Liu Peng, Peng Qiuyu
• Format: Article
• Language: English
• Published: EDP Sciences 2019-01-01
• Series: E3S Web of Conferences
• Online Access: https://www.e3s-conferences.org/articles/e3sconf/pdf/2019/62/e3sconf_icbte2019_04069.pdf

Because of the deficiency of the index of cement pavement performance evaluation and the defect of the evaluation method in the specification, the performance of the pavement is comprehensively evaluated by seven optimized indexes and grading standards that reflect functional performance and structure of the pavement. Because the discrete Hopfield neural network is available with simple construction procedure, less training samples, and strong objectivity.The DHNN is constructed by MATLAB to evaluate the performance of test pavement. The ideal cement pavement performance grading evaluation index matrix and 6 places unclassified of test pavement performance evaluation index matrix are input to the neural network then the evaluation result is obtained after simulating and learning. Finally, comparing the result of the DHNN with the fuzzy complex matter element method and the nonlinear fuzzy method, it is proved that the discrete Hopfield neural network evaluation method is reliable.