Prediction of geological hazardous zones in front of a tunnel face using TSP-203 and artificial neural networks

Comprehensive geophysical prediction and treatment measures of karst caves in deep buried tunnel

“…TSP203plus is based on propagation and reflection of seismic waves through geotechnical layers (karst caves, karst collapse columns, fault fracture zones and other unfavorable geological conditions) in front of an excavation face (Alimoradi et al, 2008;Gong et al, 2010;Chen et al, 2011).…”

“…Another typical example was the Qiyueshan tunnel, even though half of the tunnel had been excavated until May 2014, 50 karst caves were discovered (Guo, 2014). Therefore, it is a crucial task to accurately predict the hazardous geological conditions in front of a tunnel face during excavation (Alimoradi et al, 2008). Geological prediction technique is commonly used in practice as it can provide related information regarding rock blocks in the project region, and make accurate predictions regarding geological conditions ahead and the possibility of disasters during tunnel construction (Chen et al, 2011).…”

“…The destructive methods are time-consuming and costly. On the contrary, the non-destructive geophysical methods are developed fast and utilized commonly for prediction in China, especially the GPR and TSP methods (Osanloo, 1996;Madani, 1998;Locatelli et al, 2001;Delatte et al, 2002;Alimoradi et al, 2008;Chen et al, 2011).…”

Microtunneling decision support system (MDS) using Neural-Autoregressive Hidden Markov Model

“…Hu and Huang (2007) used 2D conditional Markov process to predict the soil transition and get the probabilistic risk index by Monte Carlo simulation. Neural Network approaches is also used by several researchers to predict geological condition in the tunnel construction, such as: geological hazards at the tunnel face (Alimoradi, Ali, Reza, Mojtaba, & Fshin, 2008) and prediction of tunnel settlement (Santos & Celestino, 2008 because it can analyze non-linear patterns and trends common to geology. The geological prediction model which is proposed in this paper was designed using Hybrid Neural-Autoregression Hidden Markov Model (Neural-ARHMM).…”

Recent Advances and Future Challenges for Artificial Neural Systems in Geotechnical Engineering Applications

“…Other applications of ANNs in geotechnical engineering include earth retaining structures [56], dams [57,58], blasting [59], mining [60], environmental geotechnics [61], rock mechanics [62][63][64][65][66][67], site characterization [68], tunnels and underground openings [69][70][71][72][73][74], slope stability and landslides [71,[75][76][77][78][79], and deep excavation [80].…”