Work

Prediction of Skid Resistance of Asphalt Pavements on Highways Based on Machine Learning: The Impact of Activation Functions and Optimizer Selection

Year: 2025

Type: article

Abstract: Skid resistance is a key factor in road safety, directly affecting vehicle stability and braking efficiency. To enhance predictive accuracy, this study develops a multilayer perceptron (MLP) model for forecasting the Sideway Force Coefficient (SFC) of asphalt pavements and systematically examines the role of activation functions and optimizers. Seven activation functions (Sigmoid, Tanh, ReLU, Leaky ReLU, ELU, Mish, Swish) and three optimizers (SGD, RMSprop, Adam) are evaluated using regression metrics (MSE, RMSE, MAE, R2) and loss-curve analysis. Results show that ReLU and Mish provide notable improvements over Sigmoid, with ReLU increasing goodness of fit and accuracy by 13–15%, and Mish further enhancing nonlinear modeling by 12–14%. For optimizers, Adam achieves approximately 18% better performance than SGD, offering faster convergence, higher accuracy, and stronger stability, while RMSprop shows moderate performance. The findings suggest that combining ReLU or Mish with Adam yields highly precise and robust predictions under multi-source heterogeneous inputs. This study offers a reliable methodological reference for intelligent pavement condition monitoring and supports safety management in highway transportation systems.

Source: Symmetry

DOI: 10.3390/sym17101708

Authors: Xiaoyun Wan, Xiaoqing Yu, Maomao Chen, Haixin Ye, Zhanghong Liu, Qifeng Yu

Institution: Shanghai Maritime University

Language: English

Topic: Asphalt Pavement Performance Evaluation

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $2,896