Xiaoyun Wan
Research Subjects
Xiaoyun Wan has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Haixin Ye
Jiangxi Ganyue Expressway Co., Ltd., Nanchang 330025, China
1 shared publication - Maomao Chen
Jiangxi Ganyue Expressway Co., Ltd., Nanchang 330025, China
1 shared publication - Qifeng Yu
College of Transport and Communications, Shanghai Maritime University, Shanghai 201306, China
1 shared publication - Xiaoqing Yu
Jiangxi Ganyue Expressway Co., Ltd., Nanchang 330025, China
1 shared publication - Zhanghong Liu
Jiangxi Ganyue Expressway Co., Ltd., Nanchang 330025, China
1 shared publication
Latest Publications
- Open Access
Prediction of Skid Resistance of Asphalt Pavements on Highways Based on Machine Learning: The Impact of Activation Functions and Optimizer Selection
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.
Open AccessPrediction of Skid Resistance of Asphalt Pavements on Highways Based on Machine Learning: The Impact of Activation Functions and Optimizer Selection
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.