Fei Tong
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Fei Tong has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Haoqing Xu
Jiangsu Province Engineering Research Center of Geoenvironmental Disaster Prevention and Remediation, School of Architecture and Civil Engineering, Jiangsu University of Science and Technology, Zhenjiang 212100, China
1 shared publication - Jun Luo
China Institute of Water Resources and Hydropower Research, Beijing 100038, China
1 shared publication - Junlong Zhou
School of Civil Engineering, Southeast University, Nanjing 211189, China
1 shared publication - Lianfa Wang
State Key Laboratory of Safety, Durability and Healthy Operation of Long Span Bridges, Nanjing 210012, China
1 shared publication - Sanping Han
China Railway 17th Bureau Group Co., Taiyuan 030000, China
1 shared publication - Yaping Yang
Inner Mongolia Urban Planning and Municipal Design and Research Institute Limited, Hohhot 010000, China
1 shared publication
Latest Publications
- Open Access
Study on Cumulative Deformation of Silt Soil Under Traffic Loading Based on PSO-BP Neural Network
In order to investigate the cumulative deformation characteristics of silt soil in highway subgrade engineering, dynamic triaxial tests were carried out to examine the influences of dynamic stress, confining pressure, and moisture content on the cumulative plastic strain of silt soil under traffic loading conditions. Particle Swarm Optimization (PSO) is employed to enhance the prediction performance of cumulative plastic strain in silt soils. Specifically, the architecture of the traditional Backpropagation (BP) neural network is optimized for its learning process, and weight parameters are introduced to achieve more effective control over the development and analysis of prediction results. Furthermore, this optimized neural network also enables more accurate predictions regarding multiple influencing factors, which further improves the overall accuracy of the prediction outcomes. The results show that. The cumulative deformation of the silt soil decreases gradually with increasing confining pressure. The cumulative deformation decreases from 3.32 percent to 2.82 percent when the confining pressure increases from 60 kPa to 150 kPa. With the increase in dynamic stress and moisture content, the cumulative deformation gradually increases. The cumulative deformation rate was derived from the cumulative deformation and the number of loading cycles, and it was found that the cumulative deformation rate decreases gradually with the increase in the number of cycles. Specifically, when the moisture content is 17.4%, the cumulative deformation rate decreases from 0.3912 to 4.54 × 10−5 as the number of cycles increases from 1 to 10,000. Based on the cumulative deformation test data of silt soil, the Monismith model was applied to predict plastic deformation. Meanwhile, a cumulative plastic deformation prediction model was constructed by leveraging the learning capability of the PSO-BP neural network, which incorporates multiple influencing factors including moisture content, confining pressure, and dynamic stress magnitude. By comparing the three cumulative deformation prediction models (i.e., the Monismith model, the traditional BP neural network model, and the PSO-BP neural network model), it was found that the PSO-BP neural network model exhibits the optimal prediction performance, with its correlation coefficient (R2) all exceeding 0.99.
Open AccessStudy on Cumulative Deformation of Silt Soil Under Traffic Loading Based on PSO-BP Neural Network
In order to investigate the cumulative deformation characteristics of silt soil in highway subgrade engineering, dynamic triaxial tests were carried out to examine the influences of dynamic stress, confining pressure, and moisture content on the cumulative plastic strain of silt soil under traffic loading conditions. Particle Swarm Optimization (PSO) is employed to enhance the prediction performance of cumulative plastic strain in silt soils. Specifically, the architecture of the traditional Backpropagation (BP) neural network is optimized for its learning process, and weight parameters are introduced to achieve more effective control over the development and analysis of prediction results. Furthermore, this optimized neural network also enables more accurate predictions regarding multiple influencing factors, which further improves the overall accuracy of the prediction outcomes. The results show that. The cumulative deformation of the silt soil decreases gradually with increasing confining pressure. The cumulative deformation decreases from 3.32 percent to 2.82 percent when the confining pressure increases from 60 kPa to 150 kPa. With the increase in dynamic stress and moisture content, the cumulative deformation gradually increases. The cumulative deformation rate was derived from the cumulative deformation and the number of loading cycles, and it was found that the cumulative deformation rate decreases gradually with the increase in the number of cycles. Specifically, when the moisture content is 17.4%, the cumulative deformation rate decreases from 0.3912 to 4.54 × 10−5 as the number of cycles increases from 1 to 10,000. Based on the cumulative deformation test data of silt soil, the Monismith model was applied to predict plastic deformation. Meanwhile, a cumulative plastic deformation prediction model was constructed by leveraging the learning capability of the PSO-BP neural network, which incorporates multiple influencing factors including moisture content, confining pressure, and dynamic stress magnitude. By comparing the three cumulative deformation prediction models (i.e., the Monismith model, the traditional BP neural network model, and the PSO-BP neural network model), it was found that the PSO-BP neural network model exhibits the optimal prediction performance, with its correlation coefficient (R2) all exceeding 0.99.