Special Issue "Land Subsidence: Monitoring, Prediction and Modeling"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences and Geography".

Deadline for manuscript submissions: 30 May 2020.

Special Issue Editors

Prof. Ryszard Hejmanowski
E-Mail Website
Guest Editor
AGH University of Science and Technology, Krakow, Poland
Interests: prediction (modeling) of land subsidence for the hard coal, copper ore, salt, gas and oil deposits; risk assessment and mitigation on transformed terrains; planning of the surveying systems to subsidence monitoring; IT systems developing (GIS-based) for building damage risk assessment and management on human transformed areas; mining seismicity and its influence on the terrain movements
Prof. Pietro Teatini
E-Mail Website
Guest Editor
Department of Civil, Environmental and Architectural Engineering (DICEA), University of Padova, Padova, Italy
Interests: Modelling geomechanical issues related to fluid withdrawal/injection from/into the subsurface. Specifically, land subsidence due to aquifer overexploitation and production of hydrocarbon reservoirs, land uplift caused by fluid injection (aquifer recharge, underground gas storage, CO2 geologic sequestration), stress / strain analyses for safety evaluations (induced seismicity, aseismic earth fissuring accompanying land subsidence). Modelling land subsidence due to peat oxidation and natural consolidation in deltas and wetlands. Use of land subsidence measurements (integrating levelling, GPS, SAR interferometry), together with deformation at depth (borehole extensometers, well-logs) to characterize the geomechanical properties and calibrate the numerical models

Special Issue Information

Dear Colleagues,

Recently, land subsidence has become one of the important risk factors. Taking into consideration global warming and sea-level rise, many regions of the world, large cities, and land users will be affected by the changes. In many of those areas, the land subsides because of water pumping, gas, and oil extraction, soft soils or peat compaction and additional building load. On the other hand, there are terrains where the mining of raw materials is or was lately active. Mining is one the most important factors of subsidence, sinkholes, and other related damage. It can affect buildings and infrastructure, threatening and decreasing quality of life. In any area transformed by human activity, the ground movements should also be considered. New ideas in modeling approach development, rock mechanics, and civil engineering have emerged in many countries. Novel measurement technics, sensors, and expanding availability of remote sensing data pushes the monitoring of land subsidence towards new possibilities.

This Special Issue of Applied Sciences is intended for specialists and an interdisciplinary audience and covers recent advances in the following topics:

  • Land subsidence innovative monitoring technologies and untypical case studies
  • Prediction of land subsidence: case studies for different kind of raw materials
  • Modeling: new and improved approaches, parametrization, accuracy, and reliability

Prof. Ryszard Hejmanowski
Prof. Pietro Teatini
Guest Editors

Manuscript Submission Information

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Keywords

  • land subsidence
  • modeling
  • uplift
  • sea-level rise
  • rock mechanics
  • monitoring
  • geodesy
  • remote sensing
  • GIS
  • risk assessment
  • prediction

Published Papers (2 papers)

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Research

Open AccessArticle
Application of Artificial Neural Networks in Assessing Mining Subsidence Risk
Appl. Sci. 2020, 10(4), 1302; https://doi.org/10.3390/app10041302 - 14 Feb 2020
Abstract
Subsidence at abandoned mines sometimes causes destruction of local areas and casualties. This paper proposes a mine subsidence risk index and establishes a subsidence risk grade based on two separate analyses of A and B to predict the occurrence of subsidence at an [...] Read more.
Subsidence at abandoned mines sometimes causes destruction of local areas and casualties. This paper proposes a mine subsidence risk index and establishes a subsidence risk grade based on two separate analyses of A and B to predict the occurrence of subsidence at an abandoned mine. For the analyses, 227 locations were ultimately selected at 15 abandoned coal mines and 22 abandoned mines of other types (i.e., gold, silver, and metal mines). Analysis A predicts whether subsidence is likely using an artificial neural network. Analysis B assesses a mine subsidence risk index that indicates the extent of risk of subsidence. Results of both analyses are utilized to assign a subsidence risk grade to each ground location investigated. To check the model’s reliability, a new dataset of 22 locations was selected from five other abandoned mines; the subsidence risk grade results were compared with those of the actual ground conditions. The resulting correct prediction percentage for 13 subsidence locations of the abandoned mines was 83–86%. To improve reliability of the subsidence risk, much more subsidence data with greater variations in ground conditions is required, and various types of analyses by numerical and empirical approaches, etc. need to be combined. Full article
(This article belongs to the Special Issue Land Subsidence: Monitoring, Prediction and Modeling)
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Open AccessArticle
Numerical Parametric Study of Countermeasures to Alleviate the Tunnel Excavation Effects on an Existing Tunnel in a Shallow-Buried Environment near a Slope
Appl. Sci. 2020, 10(2), 608; https://doi.org/10.3390/app10020608 - 15 Jan 2020
Abstract
This paper studies the influence law of existing tunnels on the construction of intersecting new tunnels in a shallow slope burial context through 3D numerical analysis. The emphasis is on exploring the effect of new tunnels constructed in 54 conditions, including three ratios [...] Read more.
This paper studies the influence law of existing tunnels on the construction of intersecting new tunnels in a shallow slope burial context through 3D numerical analysis. The emphasis is on exploring the effect of new tunnels constructed in 54 conditions, including three ratios of overburden to tunnel height (C/H), three ratios of slope distance to tunnel span (D/W), two backfilling conditions of the existing tunnel (“hty” and “htn” conditions), and three magnitudes of surface loads (10 kPa, 20 kPa, and 30 kPa), on the deformation of lateral slopes and the overlying road. As the results show, the rigidly separated area between the existing and newly built tunnels in parallel to the excavation direction was precisely the sensitive area affected by the existing tunnel backfilling condition. The road settlement simulations perpendicular to the excavation direction revealed that various C/H and D/W ratio combinations controlled the shape and size differences of the settlement trough curve. This was because the C/H ratio primarily controlled the effective span and height transition of the newly built tunnel, whereas the D/W ratio mainly controlled the intersection position of the tunnels. Next, model A-A (“hty” condition) was identified as the only feasible construction model among all models in accordance with the engineering safety control criteria. Lastly, comparison of monitoring data with simulations found a slight difference in the distribution pattern between the two. Nevertheless, the final maximum settlement fully satisfied the construction control requirements overall. Aside from proving the correctness of simulation results, the present study also sets an excellent referential example for similar projects. Full article
(This article belongs to the Special Issue Land Subsidence: Monitoring, Prediction and Modeling)
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