Plasticity solutions for ground deformation prediction of shallow tunnels in undrained clay

2022 ◽  
Vol 120 ◽  
pp. 104277
Author(s):  
Pei-Zhi Zhuang ◽  
He Yang ◽  
Hong-Ya Yue ◽  
Raul Fuentes ◽  
Hai-Sui Yu
Keyword(s):  
Ground Deformation ◽  
Deformation Prediction ◽  
Shallow Tunnels

The Research of Ground Deformation Prediction Neural Network Model on City Underground Construction

2014 ◽  
Vol 926-930 ◽  
pp. 708-711
Author(s):  
Wen Xin Zhu
Keyword(s):  
Neural Network ◽  
System Analysis ◽  
Influence Factor ◽  
Ground Deformation ◽  
Influence Factors ◽  
Shield Tunnel ◽  
Construction Technology ◽  
Underground Construction ◽  
Deformation Prediction ◽  
Main Influence

This paper sees the underground construction shield tunnel and foundation trench as study object. It has analyzed disturbance mechanism and ground deformation mechanism, which were caused by shield tunnel and building pit construction. Through the system analysis of ground deformation influence factor, it has confirmed main influence factors, like geological environment condition of overall consideration, physical parameter and construction technology. And it has established ground deformation prediction model based on neural network. Then it has made sensitivity analysis of affecting ground deformation factor by neural network hierarchical analytical approach.

Failure Analysis Of Buried Gas Pipelines Crossing Seismic Faults

2020 ◽  
Vol 3 (2) ◽  
pp. 781-790
Author(s):  
M. Rizwan Akram ◽  
Ali Yesilyurt ◽  
A.Can. Zulfikar ◽  
F. Göktepe
Keyword(s):  
Ground Deformation ◽  
Warning System ◽  
Strike Slip ◽  
Gas Pipelines ◽  
Steel Pipes ◽  
Seismic Fault ◽  
Fault Parameters ◽  
Dip Angle ◽  
Permanent Ground Deformation ◽  
Recent Earthquakes

Research on buried gas pipelines (BGPs) has taken an important consideration due to their failures in recent earthquakes. In permanent ground deformation (PGD) hazards, seismic faults are considered as one of the major causes of BGPs failure due to accumulation of impermissible tensile strains. In current research, four steel pipes such as X-42, X-52, X-60, and X-70 grades crossing through strike-slip, normal and reverse seismic faults have been investigated. Firstly, failure of BGPs due to change in soil-pipe parameters have been analyzed. Later, effects of seismic fault parameters such as change in dip angle and angle between pipe and fault plane are evaluated. Additionally, effects due to changing pipe class levels are also examined. The results of current study reveal that BGPs can resist until earthquake moment magnitude of 7.0 but fails above this limit under the assumed geotechnical properties of current study. In addition, strike-slip fault can trigger early damage in BGPs than normal and reverse faults. In the last stage, an early warning system is proposed based on the current procedure. 

scholarly journals Dam Deformation Interpretation and Prediction Based on a Long Short-Term Memory Model Coupled with an Attention Mechanism

2021 ◽  
Vol 11 (14) ◽  
pp. 6625
Author(s):  
Yan Su ◽  
Kailiang Weng ◽  
Chuan Lin ◽  
Zeqin Chen
Keyword(s):  
Short Term Memory ◽  
Attention Mechanism ◽  
Impact Factors ◽  
Nonlinear Prediction ◽  
Time Dimension ◽  
Short Term ◽  
Deformation Prediction ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Dam Deformation

An accurate dam deformation prediction model is vital to a dam safety monitoring system, as it helps assess and manage dam risks. Most traditional dam deformation prediction algorithms ignore the interpretation and evaluation of variables and lack qualitative measures. This paper proposes a data processing framework that uses a long short-term memory (LSTM) model coupled with an attention mechanism to predict the deformation response of a dam structure. First, the random forest (RF) model is introduced to assess the relative importance of impact factors and screen input variables. Secondly, the density-based spatial clustering of applications with noise (DBSCAN) method is used to identify and filter the equipment based abnormal values to reduce the random error in the measurements. Finally, the coupled model is used to focus on important factors in the time dimension in order to obtain more accurate nonlinear prediction results. The results of the case study show that, of all tested methods, the proposed coupled method performed best. In addition, it was found that temperature and water level both have significant impacts on dam deformation and can serve as reliable metrics for dam management.

Improvement to the calculating model of the pressure arch’s height considering the confining pressure in the excavation of shallow tunnels

2021 ◽  
Vol 14 (12) ◽  
Author(s):  
Hongyun Fan ◽  
Liping Li ◽  
Hongliang Liu ◽  
Jie Hu ◽  
Mingguang Zhang ◽  
...  
Keyword(s):  
Confining Pressure ◽  
Shallow Tunnels ◽  
Calculating Model

scholarly journals Subsidence Monitoring of Fill Area in Yan’an New District Based on Sentinel-1A Time Series Imagery

2021 ◽  
Vol 13 (15) ◽  
pp. 3044
Author(s):  
Mingjie Liao ◽  
Rui Zhang ◽  
Jichao Lv ◽  
Bin Yu ◽  
Jiatai Pang ◽  
...  
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Ground Deformation ◽  
Dynamic Change ◽  
Deformation Monitoring ◽  
Chinese Loess Plateau ◽  
Ground Subsidence ◽  
Shanxi Province ◽  
Environment Change ◽  
Loess Area

In recent years, many cities in the Chinese loess plateau (especially in Shanxi province) have encountered ground subsidence problems due to the construction of underground projects and the exploitation of underground resources. With the completion of the world’s largest geotechnical project, called “mountain excavation and city construction,” in a collapsible loess area, the Yan’an city also appeared to have uneven ground subsidence. To obtain the spatial distribution characteristics and the time-series evolution trend of the subsidence, we selected Yan’an New District (YAND) as the specific study area and presented an improved time-series InSAR (TS-InSAR) method for experimental research. Based on 89 Sentinel-1A images collected between December 2017 to December 2020, we conducted comprehensive research and analysis on the spatial and temporal evolution of surface subsidence in YAND. The monitoring results showed that the YAND is relatively stable in general, with deformation rates mainly in the range of −10 to 10 mm/yr. However, three significant subsidence funnels existed in the fill area, with a maximum subsidence rate of 100 mm/yr. From 2017 to 2020, the subsidence funnels enlarged, and their subsidence rates accelerated. Further analysis proved that the main factors induced the severe ground subsidence in the study area, including the compressibility and collapsibility of loess, rapid urban construction, geological environment change, traffic circulation load, and dynamic change of groundwater. The experimental results indicated that the improved TS-InSAR method is adaptive to monitoring uneven subsidence of deep loess area. Moreover, related data and information would provide reference to the large-scale ground deformation monitoring and in similar loess areas.

Three-dimensional face stability analysis of shallow tunnels using numerical limit analysis and material point method

2021 ◽  
Vol 112 ◽  
pp. 103904
Author(s):  
Fabricio Fernández ◽  
Jhonatan E.G. Rojas ◽  
Eurípedes A. Vargas ◽  
Raquel Q. Velloso ◽  
Daniel Dias
Keyword(s):  
Stability Analysis ◽  
Limit Analysis ◽  
Three Dimensional ◽  
Material Point Method ◽  
Material Point ◽  
Point Method ◽  
Face Stability ◽  
Shallow Tunnels ◽  
Dimensional Face

scholarly journals Anatomy of a Paroxysmal Lava Fountain at Etna Volcano: The Case of the 12 March 2021, Episode

2021 ◽  
Vol 13 (15) ◽  
pp. 3052
Author(s):  
Sonia Calvari ◽  
Alessandro Bonaccorso ◽  
Gaetana Ganci
Keyword(s):  
Lava Flow ◽  
Ground Deformation ◽  
Monitoring Network ◽  
Lava Flows ◽  
Etna Volcano ◽  
Lava Fountain ◽  
Eruptive Episode ◽  
Lava Fountains ◽  
Borehole Strainmeter ◽  
Ash Plume

On 13 December 2020, Etna volcano entered a new eruptive phase, giving rise to a number of paroxysmal episodes involving increased Strombolian activity from the summit craters, lava fountains feeding several-km high eruptive columns and ash plumes, as well as lava flows. As of 2 August 2021, 57 such episodes have occurred in 2021, all of them from the New Southeast Crater (NSEC). Each paroxysmal episode lasted a few hours and was sometimes preceded (but more often followed) by lava flow output from the crater rim lasting a few hours. In this paper, we use remote sensing data from the ground and satellite, integrated with ground deformation data recorded by a high precision borehole strainmeter to characterize the 12 March 2021 eruptive episode, which was one of the most powerful (and best recorded) among that occurred since 13 December 2020. We describe the formation and growth of the lava fountains, and the way they feed the eruptive column and the ash plume, using data gathered from the INGV visible and thermal camera monitoring network, compared with satellite images. We show the growth of the lava flow field associated with the explosive phase obtained from a fixed thermal monitoring camera. We estimate the erupted volume of pyroclasts from the heights of the lava fountains measured by the cameras, and the erupted lava flow volume from the satellite-derived radiant heat flux. We compare all erupted volumes (pyroclasts plus lava flows) with the total erupted volume inferred from the volcano deflation recorded by the borehole strainmeter, obtaining a total erupted volume of ~3 × 106 m3 of magma constrained by the strainmeter. This volume comprises ~1.6 × 106 m3 of pyroclasts erupted during the lava fountain and 2.4 × 106 m3 of lava flow, with ~30% of the erupted pyroclasts being remobilized as rootless lava to feed the lava flows. The episode lasted 130 min and resulted in an eruption rate of ~385 m3 s−1 and caused the formation of an ash plume rising from the margins of the lava fountain that rose up to 12.6 km a.s.l. in ~1 h. The maximum elevation of the ash plume was well constrained by an empirical formula that can be used for prompt hazard assessment.

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