The tunnel seismic ahead-prospecting method is used to estimate adverse geology ahead of the tunnel face. Accurately realizing this geology helps to guarantee safety during construction using a tunnel boring machine (TBM). The paper proposes a three-dimensional (3D) seismic ahead-prospecting method by optimizing a filtering method and an imaging algorithm. In this paper, a united filtering method based on $f–k$ and $\tau –p$ filtering is introduced. First, the waves from the interfaces behind the tunnel face are removed and P- and S-waves are separated. Then the interference waves are further eliminated by $\tau –p$ filtering. Mirrored artifacts resulting from the equitravel time plane algorithm are suppressed by the directional weighting integrated into the algorithm. Imaging results indicate that the new method suppresses mirrored artifacts better and produces accurate results. Moreover, the improved algorithm produces reflections that are more consistent with the interfaces than those produced by the original algorithm without directional weighting. Field studies using the 3D seismic ahead-prospecting method have accurately identified and located the main fractured zones, proving the reliability and superiority of the method.

Substrate clogging seriously affects the lifetime and treatment performance of subsurface flow constructed wetlands (SSF CWs), and the quantitative detection of clogging is the key challenge in the management of substrate clogging. This paper explores the feasibility of the resistivity method to detect the clogging degree of an SSF CW. The clogged substrate was found to have a high water-holding capacity, which led to low apparent resistivity in the draining phase. On the basis of the resistivity characteristics, clogging quantification was performed with a standard laboratory procedure, i.e., the Wenner method used in a Miller Soil Box. The apparent resistivity to sediment fraction (v/v) (ARSF) model was established to evaluate the degree of clogging from the apparent resistivity. The results showed that the ARSF model fit well with the actual values (linear slope = 0.986; R-squared = 0.98). The methods for in situ resistivity detection were applied in a lab-scale horizontal subsurface flow constructed wetland (HSSF CW). Combined with the ARSF model, the two-probe method demonstrated high accuracy for clogging quantification (relative error less than 9%). These results suggest that the resistivity method is a reliable and feasible technique for in situ detection of clogging in SSF CWs.

Cutter life is an important economical index for tunnel boring machines (TBM) excavation, and its prediction is widely concerned. This paper introduces a method for predicting cutter life on the basis of statistics and regression. Traditional researches only evaluate the average mileage or time interval of each cutter change. Differently, the proposed method can accurately predict the mileage position of each cutter relatively, on the premise of knowing the installment radius of each cutter and the rock properties along the tunnel. In this procedure, the influence of the rock classification and rock properties of each cutter passing area on their passing distance is obtained by linear regression. For proposing and verifying the prediction method, totally 1,200 records of cutter changing caused by normal cutter wear are collected from the 4th Section of Water Supply Project from Songhua River. Among them, randomly selected 920 samples are used to determine the regression coefficients involved in the method. The method is verified by the rest 280 samples, and a reliable predicted result is obtained (MAPE = 27%, and R² = 0.69).