Work

Enhanced Short-Term Temperature Prediction of Seasonally Frozen Soil Subgrades Using the NARX Neural Network

Year: 2024

Type: article

Abstract: Accurate prediction of subgrade temperatures in seasonally frozen regions is crucial for understanding thermal states, frost heave phenomena, stability, and other critical characteristics. This study employs a nonlinear autoregressive with exogenous input (NARX) network to predict short-term subgrade temperatures in the Golmud-Nagqu section of China’s National Highway 109. The methodology involves preprocessing subgrade monitoring data, including temperature, water content, and frost heave, followed by developing a temperature prediction model. This tailored NARX neural network, compared to the traditional BP neural network, integrates feedback and delay mechanisms for monitoring data, offering superior memory and dynamic response capabilities. The precision of the NARX model is assessed with the backpropagation (BP) network, indicating that the NARX neural network significantly outperforms the BP model in both precision and stability for temperature prediction in seasonally frozen subgrades. These findings suggest that the NARX model is a valuable tool for accurately predicting subgrade temperatures in seasonally frozen regions, offering significant insights for practical engineering applications.

Source: Applied Sciences

DOI: 10.3390/app142210257

Authors: Chao Zeng, Xiao Liu, Liyue Chen, Xianzhi He, Zeyu Kang

Institutions: China University of Geosciences, China Communications Construction Company (China)

Language: English

Topic: Climate change and permafrost

Subfield: Atmospheric Science

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,728