Effect of a filter cake on shear behavior of sand-concrete pile interface

A filter cake is often formed between soil and concrete during casting concrete in the ground,such as constructions of diaphragm walls and bored piles.The present study aims to investigate the effect of the filter cake on the shear behavior of the sand-concrete pile interface.A series of sand-concrete interface direct shear tests were performed with a large-direct shear apparatus while considering different roughness(I=0,10,20 and 30 mm)and filter cake thickness(Δh=0,5 and 10 mm).For a smooth interface without a filter cake,the shear stress-horizontal displacement curves showed a“softening”response.The peak shear strength and friction angle decreased exponentially with increasing theΔh.Whereas,for a rough interface withΔh=5 or 10 mm,the shear stress-horizontal displacement curves presented a“hardening”response.The peak strength,as well as friction angle,decreased linearly with increasing theΔh.Moreover,a critical roughness I_(cr)of 10 mm was observed in the tests without a filter cake.The interface shear strength initially increased with increasing I but gradually decreased when the I exceeded I_(cr).In addition,the filter cake could reduce the roughness sensitivity on shear strength.

Influence of the boundary effect on the mechanical response test of pavement cushion under the wetting effect of silt

Through a self-developed model test system,the mechanical properties of silt and the deformation characteristics of airport runways were investigated during the period of subgrade wetting.Based on the test results,the reliability of the numerical simulation results was verified.Numerical models with different sizes were established.Under the same cushion parameter and loading width ranges,the effects of the cushion parameters and loading conditions on the mechanical responses of the cushion before and after subgrade wetting were analyzed.The results show that the internal friction angles of silt with different wetting degrees are approximately 34°.The cohesion is from 8 to 44 kPa,and the elastic modulus is from 15 to 34 MPa.Before and after subgrade wetting,the variation rates of the cushion horizontal tensile stresses with the same cushion parameters and loading width ranges are different under the influence of boundary effects.After subgrade wetting,the difference in the variation rates of the cushion horizontal tensile stresses under the same cushion parameter range decreases compared with that before subgrade wetting;however,this difference increases under the same loading width range.Before and after subgrade wetting,the influence of the boundary effect on the mechanical response evaluation of the cushion is not beneficial for optimizing the pavement design parameters.When the cushion thickness is more than 0.25 m,the influence of the boundary effect can be disregarded.

Sensitivity analysis of regional rainfall-induced landslide based on UAV photogrammetry and LSTM neural network

Rainfall stands out as a critical trigger for landslides,particularly given the intense summer rainfall experienced in Zheduotang,a transitional zone from the southwest edge of Sichuan Basin to Qinghai Tibet Plateau.This area is characterized by adverse geological conditions such as rock piles,debris slopes and unstable slopes.Furthermore,due to the absence of historical rainfall records and landslide inventories,empirical methods are not applicable for the analysis of rainfall-induced landslides.Thus we employ a physically based landslide susceptibility analysis model by using highprecision unmanned aerial vehicle(UAV)photogrammetry,field boreholes and long short term memory(LSTM)neural network to obtain regional topography,soil properties,and rainfall parameters.We applied the Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability(TRIGRS)model to simulate the distribution of shallow landslides and variations in porewater pressure across the region under different rainfall intensities and three rainfall patterns(advanced,uniform,and delayed).The landslides caused by advanced rainfall pattern mostly occurred in the first 12 hours,but the landslides caused by delayed rainfall pattern mostly occurred in the last 12 hours.However,all the three rainfall patterns yielded landslide susceptibility zones categorized as high(1.16%),medium(8.06%),and low(90.78%).Furthermore,total precipitation with a rainfall intensity of 35 mm/h for 1 hour was less than that with a rainfall intensity of 1.775 mm/h for 24hours,but the areas with high and medium susceptibility increased by 3.1%.This study combines UAV photogrammetry and LSTM neural networks to obtain more accurate input data for the TRIGRS model,offering an effective approach for predicting rainfall-induced shallow landslides in regions lacking historical rainfall records and landslide inventories.

Automatic tunnel lining crack detection via deep learning with generative adversarial network-based data augmentation

Aiming at solving the challenges of insufficient data samples and low detection efficiency in tunnel lining crack detection methods based on deep learning,a novel detection approach for tunnel lining crack was proposed,which is based on pruned You Look Only Once v4(YOLOv4)and Wasserstein Generative Adversarial Network enhanced by Residual Block and Efficient Channel Attention Module(WGAN-RE).In this study,a data augmentation method named WGAN-RE was proposed,which can achieve the automatic generation of crack images to enrich data set.Furthermore,YOLOv4 was selected as the basic model for training,and a pruning algo-rithm was introduced to lighten the model size,thereby effectively improving the detection speed.Average Precision(AP),F1 Score(F1),model size,and Frames Per Second(FPS)were selected as evaluation indexes of the model performance.Results indicate that the storage space of the pruned YOLOv4 model is only 49.16 MB,which is 80%compressed compared with the model before pruning.In addition,the FPS of the model reaches 40.58f/s,which provides a basis for the real-time detection of tunnel lining cracks.Findings also demon-strate that the F1 score and AP of the pruned YOLOv4 are only 0.77%and 0.50%lower than that before pruning,respectively.Besides,the pruned YOLOv4 is superior in both model accuracy and efficiency compared with YOLOv3,SSD,and Faster RCNN,which indi-cated that the pruned YOLOv4 model can realize the accurate,fast and intelligent detection of tunnel lining cracks in practical tunnel engineering.