When a shield tunneling machine based on earth pressure balance (EPB) bores through the sandy pebble stratum, the conditioned sandy pebble inside the soil cabin of shield machine is an aggregation of numerous granules with pebble grains as skeleton. It is essential to construct a reasonable particle element model of the conditioned sandy pebble before carrying out discrete element simulation of the soil cabin system. Sandy pebble belongs to a kind of frictional material, the friction behavior of which is highly sensitive to the angularity of the grains. In order to take the shape effect into account, two particle element models—single sphere with rolling resistance and cluster of particles—were attempted in this paper. The undetermined contact parameters in two models were calibrated by virtue of least squares support vector regression machine (LS-SVR). With the purpose of making both the flow behavior and mechanical properties of the modeled soil consistent with reality, the calibration targets the result of laboratory test of slump test and large-scale triaxial test as goals. The presented comparative analysis indicates that the two established particle models both can well describe the strength property and fluidity of the actual soil due to properly calibrated parameters. So, the rolling resistance and cluster models are two effective ways to incorporate the shape effect. Besides, because of the angularity of the nonspherical grains, there exists strong interlocking between clusters. So, in the cluster model, relatively smaller rolling friction coefficient and surface energy are required. It is also concluded that the single sphere model is more computationally efficient than the cluster model.

The uncertainties in quality evaluations of rock mass are embedded in the underlying multi-source data composed by a variety of testing methods and some specialized sensors. To mitigate this issue, a proper method of data-driven computing for quality evaluation of rock mass based on the theory of multi-source data fusion is required. As the theory of multi-source data fusion, Dempster–Shafer (D-S) evidence theory is applied to the quality evaluation of rock mass. As the correlation between different rock mass indices is too large to be ignored, belief reinforcement and Murphy’s average belief theory are introduced to process the multi-source data of rock mass. The proposed method is designed based on RMR14, one of the most widely used quality-evaluating methods for rock mass in the world. To validate the proposed method, the data of rock mass is generated randomly to realize the data fusion based on the proposed method and the conventional D-S theory. The fusion results based on these two methods are compared. The result of the comparison shows the proposed method amplifies the distance between the possibilities at different ratings from 0.0666 to 0.5882, which makes the exact decision more accurate than the other. A case study is carried out in Daxiagu tunnel in China to prove the practical value of the proposed method. The result shows the rock mass rating of the studied section of the tunnel is in level III with the maximum possibility of 0.9838, which agrees with the geological survey report.

In recent years, offshore wind turbine technology has been widely developed, making a significant contribution to the advancement of renewable energy. Due to the predominant subsurface geological composition characterized by rocky formations in some marine areas, rock-socketed piles are commonly applied as offshore wind turbine foundations. Generally, rock-socketed piles need to be driven into rock layers that have not undergone significant weathering or erosion for optimal load-bearing capacity. This design is essential to ensure structural support for offshore wind turbines. However, during the long-term operation period of offshore wind turbines, the contact surface between the rock-socketed pile and the rock is prone to be detached under multiple dynamic loads. The generated channel makes seawater seep into the unweathered rock layer, resulting in the erosion of rock meso-structure and deterioration of mechanical properties. The reduced load-bearing capacity will adversely affect the operation of the offshore wind turbine. In this study, the meso-structural evolution of bedrock in pressurized seawater is investigated by X-ray CT imaging using tuff samples from the marine areas of an offshore wind farm in China. A cellular automata model is proposed to predict the long-term evolutionary process of tuff meso-structure. Results indicate that the porosity of the tuff sample in the pressurized seawater shows an upward trend over time. Based on the erosion rate of pores obtained from the CT scanning test, the proposed cellular automata model can predict the evolutionary process of tuff meso-structure and corresponding failure strength of the bedrock in the long term.

This paper presents a series of sandbox tests of large deformation in Toyoura sands. The image analysis software package GeoDPDM, which was developed by the authors, is used to investigate the shear patterns and the evolution of the local deformation in sands under different confining pressures. The results show that: (1) The deformation zone looks like fusiform, and can be divided into a number of large deformation bands and small deformation blocks between the bands, (2) The intense shear deformation zones migrate during shearing, (3) The shear strain continues to develop at high rate in the final shear bands, and (4) The soil deformation can be classified into three stages: initial stage of elastic deformation in the whole specimen, middle stage of elastic deformation in localized regions, and the final stage of plastic deformation in the final shear band.