Tunnel water burst disaster management engineering based on artificial intelligence technology – taking Yonglian Tunnel in Jiangxi Province as the object in China

Abstract

Due to the influence of the groundwater system, mountain rock layers, climate rainfall, and tunnel length and depth, underground tunnels (UT) are prone to water inrush (WI) disasters, thus leading to delays and obstacles in construction projects. This paper takes the Yonglian Tunnel as the research objective and explores the water and mud inrush disasters that occurred from July to August 2012. The Yonglian Tunnel is a control project of the Jilian Expressway in Jiangxi Province. This paper aims to study and analyze the WI disaster management of the UT using artificial intelligence technology, and to deepen the understanding of its causes. It will affect the factors, hazards, and related disaster management engineering methods of the UT WI disaster. By establishing a back-propagation neural network model and a radial basis function neural network model, the risk of WI disasters in tunnels, the degree of harm caused by WI, and the ability to control them were predicted and analyzed, and the stability and error values of the models were compared. HIGHLIGHTS This paper aims to use artificial intelligence technology to study and analyze the water inrush disaster management of underground tunnels, and to deepen the understanding of its causes.; Through the establishment of a back-propagation neural network model and radial basis function neural network model, this paper predicts and analyzes the risk of tunnel flood disaster, the degree of damage caused by flood and the control ability.;

Keywords

• artificial intelligence technology
• disaster management engineering
• tunnel engineering information
• underground tunnel
• water inrush