Wireless Underground Sensor Networks (WUSNs), an important part of Internet of things (IoT), have many promising applications in various scenarios. Signal transmission in natural soil undergoes path loss due to absorption, radiation, reflection and scattering. The variability and dynamic of soil conditions and complexity of signal attenuation behavior make the accurate estimation of signal path loss challenging. Two existing propagation models for predicting path loss are reviewed and compared. Friis model does not consider the reflection loss and is only applicable in the far field region. The Fresnel model, only applicable in the near field region, has not considered the radiating loss and wavelength change loss. A new two stage model is proposed based on the field characteristics of antenna and considers four sources of path loss. The two stage model has a different coefficient m in the near field and far field regions. The far field distance of small size antenna is determined by three criteria: 2 D²/λ, 5 D, 1.6 λ in the proposed model. The proposed two stage model has a better agreement with the field experiment data compared to Friis and Fresnel models. The coefficient m is dependent on the soil types for the proposed model in near field region. It is observed from experiment data that the m value is in the range of 0~0.20 for sandy soils and 0.433~0.837 for clayey silt.

Accidental surcharges caused by unauthorized damped waste are the most serious hazard threatening the serviceability and structural safety of shield tunnels in Shanghai, China. It is highly desirable to assess the effect of such accidental surcharges on the performance of shield tunnels. Nevertheless, the occurrence and magnitude of the accidental surcharges are both highly uncertain. In this paper, a probabilistic method is suggested to evaluate the performance of shield tunnels subjected to accidental surcharges, which can systematically consider the uncertainty in the soil properties, the uncertainty in the magnitude of the accidental surcharges, and the uncertainty of the occurrence of the accidental surcharges. The suggested method is used to analyse the potential effect of accidental surcharges on the safety of tunnels in Shanghai. It is found that the maximum height of the surcharges and the mean rate of the surcharges can significantly affect the performance of shield tunnels. Considering the past performance of shield tunnels can increase our confidence in the future performance of the tunnels. The suggested method provides a practical means to quantify the risk posed by the accidental surcharges to the performance of the shield tunnels.

The main objective of this study is to propose an artificial neural network (ANN)-based tool for predicting the cantilever wall deflection in undrained clay. The excavation width, the excavation depth, the wall thickness, the at-rest lateral earth pressure coefficient, the soil shear strength ratio at mid-depth of the wall, and the soil stiffness ratio at mid-depth of the wall were selected as the input parameters, whereas the cantilever wall deflection was selected as an output parameter. A set of verified numerical data were utilized to train, test, and validate the ANN models. Two commonly used performance indicators, namely, root mean square error (RMSE) and mean absolute error (MAE), were selected to evaluate the performance of the proposed model. The results indicated that the proposed model can reliably predict the cantilever wall deflection in undrained clay. Moreover, the sensitivity analysis showed that the excavation depth is the most important parameter. Finally, a graphical user interface (GUI) tool was developed based on the proposed ANN model, which is much easier and less expensive to be used in practice. The results of this study can help engineers to better understand and predict the cantilever wall deflection in undrained clay.

Water conveyance tunnels usually experience high internal water pressures and complex soil conditions. Therefore, shield tunnels with double-lining structu