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The fully coupled methane hydrate model developed in Cambridge was adopted in this numerical study on gas production trial at the Eastern Nankai Trough, Japan 2013. Based on the provided experimental data of hydrate soil core samples, the soil parameters at Eastern Nankai Trough were successfully calibrated. With calibrated soil parameters and site geometry, a 50 days gas production trail was numerically simulated using the fully coupled simulator CMHGS (Cambridge Methane Hydrate Geomechanics Simulator). The geomechanical behavior of hydrate bearing sediments and production history results under 3 different depressurization strategies were explored and discussed. With the latest gas production site data, several input parameters for the numerical study were calibrated and an updated numerical simulation of the gas production test was carried out. The comparison of gas production history and vertical displacement along the wellbore between the updated simulation and the previous simulation results suggest large discrepancy, which highlights the importance of parametric study for numerical simulation of the gas hydrate production test. Therefore, parameter sensitivity of production history and vertical displacement were investigated and concluded the relative permeability curve; temperature profile, sea water salinity and permeability anisotropy all influence production results and mechanical responses.

On-site manual inspection of metro tunnel leakages has been faced with the problems of low efficiency and poor accuracy. An automated, high-precision, and robust water leakage inspection method is vital to improve the manual approach. Existing approaches cannot provide the leakage location due to the lack of spatial information. Therefore, an integrated deep learning method of water leakage inspection using tunnel lining point cloud data from mobile laser scanning is presented in this paper. It is composed of three parts as follows: (1) establishment of the water leakage dataset using the acquired point clouds of tunnel linings; (2) automated leakage detection via a mask-region-based convolutional neural network; and (3) visualization and quantitative evaluation of the water leakage in 3D space via a novel triangle mesh method. The testing result reveals that the proposed method achieves automated detection and evaluation of tunnel lining water leakages in 3D space, which provides the inspectors with an intuitive overall 3D view of the detected water leakages and the leakage information (area, location, lining segments, etc.).

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The detection of concrete spalling is critical for tunnel inspectors to assess structural risks and guarantee the daily operation of the railway tunnel. However, traditional spalling detection methods mostly rely on visual inspection or camera images taken manually, which are inefficient and unreliable. In this study, an integrated approach based on laser intensity and depth features is proposed for the automated detection and quantification of concrete spalling. The Railway Tunnel Spalling Defects (RTSD) database, containing intensity images and depth images of the tunnel linings, is established via mobile laser scanning (MLS), and the Spalling Intensity Depurator Network (SIDNet) model is proposed for automatic extraction of the concrete spalling features. The proposed model is trained, validated and tested on the established RSTD dataset with impressive results. Comparison with several other spalling detection models shows that the proposed model performs better in terms of various indicators such as MPA (0.985) and MIoU (0.925). The extra depth information obtained from MLS allows for the accurate evaluation of the volume of detected spalling defects, which is beyond the reach of traditional methods. In addition, a triangulation mesh method is implemented to reconstruct the 3D tunnel lining model and visualize the 3D inspection results. As a result, a 3D inspection report can be outputted automatically containing quantified spalling defect information along with relevant spatial coordinates. The proposed approach has been conducted on several railway tunnels in Yunnan province, China and the experimental results have proved its validity and feasibility.

Turbidite formation is a common feature of natural hydrate‐bearing sediments that has been observed and reported at several hydrate exploration sites. It is therefore important to incorporate this anisotropic geological feature into the constitutive modeling when evaluating the geomechanical risks involved during hydrate‐based gas production. To date, a number of constitutive models have been proposed to capture the isotropic geomechanical behavior of homogeneous hydrate‐bearing sediments. Since the turbidite formation contains soil layers at a scale much smaller than the size of the numerical element used for reservoir scale simulations, it is necessary to upscale the geomechanical behavior of a layered system to an equivalent anisotropic continuum model by adopting some homogenization techniques. In this study, an anisotropic methane hydrate critical state model is developed by modifying the original isotropic version of Uchida et al. (2012, https://doi.org/10.1029/2011JB008661). The calibration methodology of the anisotropic model parameters for a given set of hydrate heterogeneity and the turbidite formation at the Eastern Nankai Trough is proposed and demonstrated. The upscaled parameters are calibrated by curve fitting the numerically simulated stress‐strain curves of the layered system with the original isotropic constitutive model at the layered scale. Forty‐two sets of model parameters are calibrated from different site element models of this site. They are used to develop empirical correlations between the model parameters and the site input properties within the turbidite formation. This paper presents the details of the new anisotropic constitutive model and the performance of the proposed upscaling procedure for the Eastern Nankai Trough case.