Gypsum collapse hazards and importance of hazard mapping

Yilmaz, Işık; Marschalko, Marián; Bednařík, Martin

doi:10.1007/s13146-011-0055-4

Cited by 27 publications

(10 citation statements)

References 59 publications

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“…Significant progress has been made during the last years with the growing use of radar interferometry for sinkhole detection [e.g., Castañeda et al ., ; Gutiérrez et al ., ] and prediction [ Nof et al ., ; Jones and Blom , ] in salt (halite) environments. These capabilities may be extended to other sinkhole environments, such as gypsum [e.g., Klimchouk and Andrejchuk , ; Yilmaz et al ., ] and possibly also carbonate karst terrains [ Gutiérrez et al . [], and references therein], and to additional occurrences of subsurface cavities, such as tunnels, mines, and caves.…”

Section: Discussionmentioning

confidence: 99%

“…Geophysical Research Letters 10.1002/2015GL066053 Jones and Blom, 2014] in salt (halite) environments. These capabilities may be extended to other sinkhole environments, such as gypsum [e.g., Klimchouk and Andrejchuk, 2005;Yilmaz et al, 2011] and possibly also carbonate karst terrains [Gutiérrez et al [2014], and references therein], and to additional occurrences of subsurface cavities, such as tunnels, mines, and caves. Previous theoretical sinkhole modeling considered different overburden rheology and pore water pressure conditions.…”

Section: Induced Stresses Above a Deflating Cavitymentioning

confidence: 99%

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InSAR-based modeling and analysis of sinkholes along the Dead Sea coastline

Atzori

Baer

Antonioli

et al. 2015

Geophys. Res. Lett.

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Sinkholes commonly form by subsurface dissolution cavities that collapse after the overlying layers become mechanically unsupported. Sinkholes along the Dead Sea shorelines are preceded by, associated with, and followed by gradual surface subsidence that accompanies the cavities' growth. We exploit satellite radar interferometry (interferometric synthetic aperture radar) to resolve temporal and spatial relationships between gradual subsidence and sinkhole collapse. The geometry of the deflating cavity roof is determined by elastic inverse modeling of the surface displacements. A Coulomb failure stress criterion is applied to calculate the stress field induced by the deflating cavity at the ground surface. We find that the induced stress field favors generation of sinkholes at the perimeters of the subsiding areas rather than at their centers, in agreement with field observations, providing important information for sinkhole hazard assessment. Further, our analysis suggests that short‐term deformation in consolidated gravel layers at shallow depths could be approximated by simple elastic modeling.

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Section: Discussionmentioning

confidence: 99%

Section: Induced Stresses Above a Deflating Cavitymentioning

confidence: 99%

InSAR-based modeling and analysis of sinkholes along the Dead Sea coastline

Atzori

Baer

Antonioli

et al. 2015

Geophys. Res. Lett.

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“…Collapse is a type of geological phenomenon in which a soil-rock mass on a steep slope suddenly falls from a parent body under the effect of gravity and then rolls and accumulates at the foot of the slope (Yilmaz et al 2011). Collapses in the southeastern hill and mountainous region of China are serious geological disasters that have damaged buildings, roads, natural lakes and other engineering structures.…”

Section: Introductionmentioning

confidence: 99%

Collapse susceptibility assessment using a support vector machine compared with back-propagation and radial basis function neural networks

Sheng

Chai

et al. 2020

Geomatics, Natural Hazards and Risk

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Machine learning models are regarded as efficient and popular models for natural disaster susceptibility prediction. However, few studies have focussed on the applications of the latest popular machine learning models in collapse susceptibility assessment (CSA). This paper proposes a 3S (RS, GPS and GIS) technologybased support vector machine (SVM) to map collapse susceptibility in the Nantian area of China. The 3S technology-based backpropagation neural network (BPNN) and radial basis function neural network (RBFNN) models are also proposed for comparison. First, 44 recorded collapses are identified through field investigation, and fourteen collapse-related causal factors are acquired using 3S technology. Second, among these recorded collapses and randomly selected 'non-collapses', 70% of the collapse and non-collapse grid cells are used to train the three models, while the remaining 30% are used to test the models. Third, the collapse susceptibility maps of the Nantian area are produced using the three models. Finally, the prediction accuracies of these models are evaluated. The results indicate that the SVM model has the highest prediction accuracy, while the RBFNN model has the lowest prediction accuracy for CSA. In addition, the distribution characteristics of collapse susceptibility in the Nantian area are produced well by all three models. ARTICLE HISTORY

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“…In fact, the construction of the sinkhole inventory is considered to be the most important step in sinkhole hazard assessment [11,17,22]. The predictions derived from these models assume that sinkhole phenomena in the future will form in similar conditions as sinkholes in the past [12,23]. Quantitative risk assessment then involves the statistical analysis of sinkhole events in relation to their controlling conditions.…”

Section: Introduction To Sinkhole Hazardsmentioning

confidence: 99%

The Role of Earth Observation, with a Focus on SAR Interferometry, for Sinkhole Hazard Assessment

Theron

Engelbrecht

2018

Remote Sensing

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Sinkholes are global phenomena with significant consequences on the natural-and built environment. Significant efforts have been devoted to the assessment of sinkhole hazards to predict the spatial and temporal occurrence of future sinkholes as well as to detect small-scale deformation prior to collapse. Sinkhole hazard maps are created by considering the distribution of past sinkholes in conjunction with their geomorphic features, controlling conditions and triggering mechanisms. Quantitative risk assessment then involves the statistical analysis of sinkhole events in relation to these conditions with the aim of identifying high risk areas. Remote sensing techniques contribute to the field of sinkhole hazard assessment by providing tools for the population of sinkhole inventories and lend themselves to the monitoring of precursory deformation prior to sinkhole development. In this paper, we outline the background to sinkhole formation and sinkhole hazard assessment. We provide a review of earth observation techniques, both for the compilation of sinkhole inventories as well as the monitoring of precursors to sinkhole development. We discuss the advantages and limitations of these approaches and conclude by highlighting the potential role of radar interferometry in the early detection of sinkhole-induced instability resulting in a potential decrease in the risk to human lives and infrastructure by enabling proactive remediation.

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Gypsum collapse hazards and importance of hazard mapping

Cited by 27 publications

References 59 publications

InSAR-based modeling and analysis of sinkholes along the Dead Sea coastline

InSAR-based modeling and analysis of sinkholes along the Dead Sea coastline

Collapse susceptibility assessment using a support vector machine compared with back-propagation and radial basis function neural networks

The Role of Earth Observation, with a Focus on SAR Interferometry, for Sinkhole Hazard Assessment

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