Titlebook: Engineering Geology for a Habitable Earth: IAEG XIV Congress 2023 Proceedings, Chengdu, China; Volume 4: Technologi Sijing Wang,Runqiu Huan

記憶法

Evaluation of Soil Properties at Pile Location Based on CPT Data Using Spectral Clustering and Hiddta to evaluate the soil distribution and property at pile location is an important goal for the design of pile structure. This paper aims to evaluate the needed soil properties for design at pile location with the limited CPT data at several boreholes. In this paper, a new soil classification method

Commodious

1863-5520 nsportation, urban and ecological environment protection, engineering geology and resilience engineering construction, intelligent engineering geology, and new theories, methods, and techniques in engineering geology..978-981-99-9071-9978-981-99-9069-6Series ISSN 1863-5520 Series E-ISSN 1863-5539

Aboveboard

Nomadic

Slope Displacement Prediction Based on Cross Distillation for Small Samples,e accumulated estimation errors and training parameters are reduced. To verify the validity of the proposed prediction model, an experiment was carried out with the slope displacement data collected by Jinyu Zenith Cement plant. Experimental results show that when there are only a few samples, the p

GUILT

A Novel Remote Sensing Landslide Semantic Segmentation Method: Using CycleGAN-Based Change Detectio we applied the cycleGAN approach to produce a non-landslide image from the landslide remote sensing image. Finally, using change detection method, landslide regions are segregated. In addition, we evaluated the suggested technique on the Bijie remote sensing landslide dataset, and got 0.845 accurac

潛移默化

Assessing the Impact of Borehole Coupling Materials on Shallow Downhole Fiber-Optic Distributed Acoher than unbackfilled and 3?dB higher than unsaturated gravel. Analysis of background noise spectra also revealed that gravel backfilling progressively eliminated high (95–115?Hz) and low (5–65?Hz) frequency noise bands as compaction increased. In conclusion, this study demonstrates that backfill ma

Corporeal

Ancestor

NEEDY

Prediction of Maximum Reinforcement Load of Reinforced Soil Retaining Walls Based on Machine Learnie models. It was found that there is a good agreement between the predicted values and the measured values of the validation sets, especially the XGBoost model with stable and reliable performance. Therefore, machine learning can be regarded as a powerful tool to quickly evaluate the performance of

難理解