Total deformation prediction of the typical loess tunnels

Xue, Yiguo; Ma, Xiaoyan; Yang, Weimin; Ma, Lei; Qiu, Daohong; Li, Zhiqiang; Li, Xin; Zhou, Binghua

doi:10.1007/s10064-020-01782-2

Cited by 13 publications

(11 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zhou et al (2020) adopted the Dempster-Shaffer theory to assess the risks during the Xiamen Metro project. Xue et al (2020) found the back-propagation neural network model could effectively predict the total deformation of a typical loess tunnel. Li et al (2019) developed the Naive Bayes classifier to predict the stability of tunnel face.…”

Section: S D Strain Mean Vector V Ementioning

confidence: 97%

“…To overcome the drawbacks of low reliability and non-unified early-warning times when using single response indicators and comparison of same types of signals in predicting rock failure, multi-sensor data fusion methods have been applied to early warning provision in tunnel projects. These methods include clustering algorithm (Qin et al 2018), artificial neural network (ANN) (Xue et al 2020;Wang et al 2021a, b), Dempster-Shafer (D-S) evidence theory (Guo et al 2021), andBayesian reasoning (Li et al 2019). Zhou et al (2020) adopted the Dempster-Shaffer theory to assess the risks during the Xiamen Metro project.…”

Section: S D Strain Mean Vector V Ementioning

confidence: 99%

“…Data fusion allows users to make decisions by automatic analysis and comprehensive processing of multi-sensor data according to time sequence and a set of rules. Multi-sensor data fusion is introduced here to realize early warning for tunnel failure based on fusion of multi-sensor data, which overcomes the limitation of single factor analysis and subjectivity in comparison of multiple factors (Xue et al 2020;Zhang et al 2021a, b;Guo et al 2021;Li et al2019). The multi-sensor data are fused using D-S theory in the following process (Guo et al…”

Section: Early Warning Of Tunnel Failure Based On Multi-sensor Data F...mentioning

confidence: 99%

See 2 more Smart Citations

Crack propagation characteristics during progressive failure of circular tunnels and the early warning thereof based on multi-sensor data fusion

Zhang

Chao

Liu

et al. 2022

Geomech. Geophys. Geo-energ. Geo-resour.

View full text Add to dashboard Cite

increases in AE entropy and covariance of the strain field. The monitoring data of each physical field provide early warning asynchronously: the early-warning time of AE entropy is the earliest while that of the AE quiet period is the latest. Membership functions are constructed taking early-warning results of monitoring data in various physical cases as bodies of evidence, and their critical values are determined using normal distribution. Multi-sensor data are fused using the Dempster-Shafer (D-S) evidence theory to realize time-varying prediction and provision of early warning with graded probabilities for tunnel failure based on collaborative multi-sensor data fusion. Article Highlights• The development of cracks and their initiation mechanisms of the tunnel model are explored. • The monitoring data of each physical field provide early warning of tunnel asynchronously. • Multi-sensor data are fused to realize time-varying prediction and warning with graded probabilities of tunnel failure. Keywords Tunnel • Crack propagation • Acoustic emission • Failure warning • Multi-sensor data fusion List of symbols Ω Covariance matrix S dTotal number of strain components Abstract Biaxial compression tests were conducted on circular tunnels that constructed in sandstone to reveal the evolution of macro-cracks, the strain field, and acoustic emission (AE) parameters during deformation of tunnels. An early-warning method for tunnel failure based on multi-sensor data fusion was studied. The results indicate that tensile cracks first initiate at the arch haunch of the tunnel; then shear cracks occur on both sides of the opening, forming two asymmetric V-shaped grooves; finally, tensileshear cracks on both sides of the tunnel propagate to the vault, triggering brittle failure of the tunnel. The strain concentration zones appear earlier than cracks in the tunnel, and the location and range of strain concentration zones cover the crack-propagation paths. Before the failure of tunnels, a series of precursor events occur, including an AE quiet period, a significant, rapid drop in the AE-based b value, and abrupt

show abstract

Section: S D Strain Mean Vector V Ementioning

confidence: 97%

Section: S D Strain Mean Vector V Ementioning

confidence: 99%

Section: Early Warning Of Tunnel Failure Based On Multi-sensor Data F...mentioning

confidence: 99%

See 1 more Smart Citation

Crack propagation characteristics during progressive failure of circular tunnels and the early warning thereof based on multi-sensor data fusion

Zhang

Chao

Liu

et al. 2022

Geomech. Geophys. Geo-energ. Geo-resour.

View full text Add to dashboard Cite

show abstract

“…The fluid-structure coupling model for underwater shield tunnels was established by the finite difference method to study the evolution law of the excavation face instability in order to investigate the limit support pressure of the excavation face. The assumptions of the numerical model are as follows [26,[34][35][36].…”

Section: Numerical Simulation and Calculation (A) Boundary Conditions...mentioning

confidence: 99%

“…The BP neural network is one of the most widely used artificial neural network algorithms and has great generalization ability [50]. The network structure design mainly includes the following three steps [34][35][36]42,43].…”

Section: (B) Artificial Neural Network Evaluation Modelmentioning

confidence: 99%

Intelligent decision method for stability assessment of shield tunnel based on multi-objective data mining

Xue

et al. 2023

Phil. Trans. R. Soc. A.

Self Cite

View full text Add to dashboard Cite

Due to improper operation of the shield construction process and unknown geological surveys, shield construction faces many risks in passing through complex strata, among which the excavation face instability is the most serious, potentially leading to disastrous accidents. To address these issues, this research focuses on the limit support pressure and the excavation face stability in the soil when crossing the Yangtze River. First, an analytical formula for the limit support pressure of the excavation face is established through the wedge model. The support safety coefficient is used to assess the excavation face stability quantitatively. Then the rough set algorithm is used to analyse the sensitivity of each index to establish the reduced evaluation index system for the excavation face stability. The back propagation (BP) neural network is used to train the learning data, and a neural network evaluation model with a prediction error of 5.7675 × 10 −4 is established. The prediction performance of BP is verified by comparison with the TOPSIS prediction model and the cloud model. The evaluation method proposed in this paper provides an essential reference for evaluating the underwater shield tunnel excavation face stability. This article is part of the theme issue ‘Artificial intelligence in failure analysis of transportation infrastructure and materials’.

show abstract

Bridging the gap between engineering properties and grouting reinforcement mechanisms for loess in eastern China: taking Jinan loess as an example

Zhang

Xiao

et al. 2021

Bull Eng Geol Environ

View full text Add to dashboard Cite

Total deformation prediction of the typical loess tunnels

Cited by 13 publications

References 39 publications

Crack propagation characteristics during progressive failure of circular tunnels and the early warning thereof based on multi-sensor data fusion

Crack propagation characteristics during progressive failure of circular tunnels and the early warning thereof based on multi-sensor data fusion

Intelligent decision method for stability assessment of shield tunnel based on multi-objective data mining

Bridging the gap between engineering properties and grouting reinforcement mechanisms for loess in eastern China: taking Jinan loess as an example

Contact Info

Product

Resources

About