Modeling the Salinity Effect on the Water Retention Curve of Geosynthetic Clay Liner (GCL) on the Drying Path.

The water retention curve (WRC) of a geosynthetic clay liner (GCL) is influenced by the presence of exchangeable cations in the leachate during changes in water content in a landfill construction. This research aims to investigate the impact of salinity on the WRC of GCL. To measure the WRC of GCL under different sodium chloride (NaCl) concentrations on the drying path, a chilled-mirror dew-point device capable of controlling the GCL's volume was employed. Additionally, the dry state microstructure of the GCL was examined using electron microscopy. The test outcomes indicate that GCL hydrated with higher salinity has greater suction at the same water content during drying. This influence can be attributed to changes in salinity and the precipitation of NaCl crystals within the bentonite when water evaporates, which in turn affects the bentonite's microstructure and leads to increased matric suction. By introducing the Fredlund and Xing model and parameter relationship, it is possible to predict the WRC of GCL under salinity effects after measuring the WRC under different salinity conditions on the drying path.

Preliminary Mechanical Evaluation of Grouting Concrete as a Protective Layer for Tunnelling.

The aim of this study is to introduce a protective layer to safeguard tunnel structures. In practice, one viable approach to create this protective layer between the tunnel structure and surrounding rocks is to pump the material during tunnel construction. The primary components of the proposed material are porous sand, rubber, and cement. Static and dynamic experiments were conducted to assess the unconfined compressive strength (UCS), flexural stiffness, and compaction resistance at various mixing ratios. The results indicate that the addition of porous sand decreases the UCS compared to the solid sand under similar mixing conditions. The addition of rubber offers the elasticity, thereby enhancing the compaction resistance. However, increasing the rubber content compromises UCS. Furthermore, this study presents a linear equation to predict the 7-day UCS, which can be used as a rapid estimation for UCS, flexural stiffness, and compaction resistance of the proposed material. It is important to note that this study only investigates the fundamental mechanical properties of the proposed material, and further comprehensive research is necessary to fully understand its workability, durability, and other behaviour before practical application.

Airborne LiDAR point cloud classification using PointNet++ network with full neighborhood features.

Compared with other point clouds, the airborne LiDAR point cloud has its own characteristics. The deep learning network PointNet++ ignores the inherent properties of airborne LiDAR point, and the classification precision is low. Therefore, we propose a framework based on the PointNet++ network. In this work, we proposed an interpolation method that uses adaptive elevation weight to make full use of the objects in the airborne LiDAR point, which exhibits discrepancies in elevation distributions. The class-balanced loss function is used for the uneven density distribution of point cloud data. Moreover, the relationship between a point and its neighbours is captured, densely connecting point pairs in multiscale regions and adding centroid features to learn contextual information. Experiments are conducted on the Vaihingen 3D semantic labelling benchmark dataset and GML(B) benchmark dataset. The experiments show that the proposed method, which has additional contextual information and makes full use of the airborne LiDAR point cloud properties to support classification, achieves high accuracy and can be widely used in airborne LiDAR point classification.