基于LM-BD-ESN模型的路面抗滑性能预测

s the main index of highway performance evaluation, pavement skid resistance performance analysis and trend prediction are important items to improve the construction quality and ensure service safety of highway. In order to solve the problem of single index and poor prediction accuracy, an improved Echo State Network (LM-BD-ESN) model optimized by Logical Mapping (LM) and Bias Dropout (BD) was proposed on the basis of the traditional Echo State Network (ESN) model, which was further applied to the prediction task of pavement anti-sliding performance. Compared with traditional ESN model, LM-BD-ESN model has stronger generalization ability and better network model structure. Among them, the logic mapping module of LM can optimize the input weight matrix, thus producing a higher degree of fitting with the multivariate time series data. The deviation loss module of BD can delete redundant storage cells independently, thus reducing the complexity of the model. In view of the nonlinear relationship between pavement materials and anti-sliding performance, multiple sets of three-dimensional topography data of pavement was collected based on three-dimensional measuring instrument. Based on this, the anti-sliding data of pavement using support vector machine (SVM), relevance vector machine (RVM), extreme learning machine (ELM), ESN and LM-BD-ESN were analyzed and verified, respectively. The results show that the root mean square error and average absolute percentage error of the LM-BD-ESN algorithm proposed in this paper are 0.085 8 and 0.066 4, respectively in the prediction task, which has higher efficiency and accuracy compared with other algorithms.