Analysis of the Soil Loss and Settlement of Silty Clay with Different Subclassifications

Hou, Minglei; Ding, Wenqi; Wang, Xianggang; Liu, Ke-qi; Chen, Lei

doi:10.1007/s10706-020-01395-2

Cited by 2 publications

(2 citation statements)

References 32 publications

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“…After the installation is completed, excavation and ballasting are carried out, then concrete spraying for the first time, followed by excavation, ballasting (the operation of transporting the blasted ballast out of the tunnel and discarding it) and concrete spraying (construction method of spraying and pouring fine stone concrete with pressure spray gun), and so on repeatedly. During this period, it must continue to detect the terrain to prevent accidents [8], the process can be shown in Figure 3.…”

Section: Related Workmentioning

confidence: 99%

Forecast of Ground Deformation Caused by Tunnel Excavation Based on Intelligent Neural Network Model

Jia

Meng

et al. 2022

Mobile Information Systems

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With the development of science and technology, the demand for traffic has increased, and the requirements for tunnel excavation have become more and more stringent. Tunnel excavation is an important traffic construction engineering technology. Due to the influence of many factors in the excavation process, surface settlement or deformation will inevitably occur, so its deformation must be predicted in real time to prevent safety accidents and property losses. The previous numerical methods and neural network methods cannot accurately predict in real time, and the intelligent neural network model can more accurately predict the deformation of the ground because of the characteristics of adaptive organizational learning according to different situations and different environments. This article aims to study and design an intelligent neural network model to predict and calculate the amount of ground deformation caused by the tunnel excavation process. An intelligent neural network model with more accurate prediction is proposed, and simulation experiments are carried out on tunnel excavation of different terrains, and the accuracy of the model for predicting the deformation amount is calculated. The experimental results show that the prediction error range of the model is 10 times smaller than that of the traditional neural network. The prediction accuracy of this model is above 95%, and the volatility rate of prediction accuracy is lower than 11%, while the volatility rate of traditional prediction accuracy is even more than 365%. The intelligent neural network model can effectively predict the deformation of tunnel excavation.

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Section: Related Workmentioning

confidence: 99%

Forecast of Ground Deformation Caused by Tunnel Excavation Based on Intelligent Neural Network Model

Jia

Meng

et al. 2022

Mobile Information Systems

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“…The use of data mining techniques (including on the techniques used in this research) is being successfully used to recognize patterns and purpose models in geotechnical and geological study topics, as shown the published research of Debnath and Dey (2017), Santos et al (2018), Santos et al (2019), Hou et al (2020 and Do et al (2021).…”

Section: Introductionmentioning

confidence: 99%

K-means Based Soil Classification System Applicable to a Brazilian Mineral Province

Coelho

Nogueira

Sala

et al. 2021

Preprint

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This article presents a geotechnical soil classification system proposed for application on soils of a tropical mineral province, located in Minas Gerais state, Brazil. The system was constructed using data mining techniques, i.e., principal component analysis and k-means cluster analysis, which were applied to a dataset composed of 101 geotechnical characterization laboratory test results of soils from the Province of Quadrilátero Ferrífero. The main objective of the proposed soil classification method was to establish a regional soil classification system, which encompass the interpretability of the main geotechnical parameters of soils by means of the classification, given the little explanatory capacity of the Unified Soil Classification System classification system for the performance of such task. It was possible to establish a chart for soil classification capable of explaining 81.68% of the variability of the analyzed parameters, being established the soil classes A, B and C for the studied soils.

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Analysis of the Soil Loss and Settlement of Silty Clay with Different Subclassifications

Cited by 2 publications

References 32 publications

Forecast of Ground Deformation Caused by Tunnel Excavation Based on Intelligent Neural Network Model

Forecast of Ground Deformation Caused by Tunnel Excavation Based on Intelligent Neural Network Model

K-means Based Soil Classification System Applicable to a Brazilian Mineral Province

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