The influence of distant coal seam edges on seismic hazard during longwall mining

Wojtecki, Łukasz; Gołda, Iwona; Mendecki, Maciej J.

doi:10.1007/s10950-020-09959-8

Cited by 12 publications

(7 citation statements)

References 23 publications

(34 reference statements)

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“…Mining remnants are responsible for the stress increase in the rock mass. Even with the greater vertical distance of the remainders, the occurrence of high-energy tremors in the area cannot be ruled out [31]. The impact of remnants should decrease with time; however, even remnants from several decades earlier can affect the stress level in the rock mass [2].…”

Section: Characteristics Of the Input Datasetmentioning

confidence: 99%

An Attempt to Use Machine Learning Algorithms to Estimate the Rockburst Hazard in Underground Excavations of Hard Coal Mine

et al. 2021

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Rockburst is a dynamic rock mass failure occurring during underground mining under unfavorable stress conditions. The rockburst phenomenon concerns openings in different rocks and is generally correlated with high stress in the rock mass. As a result of rockburst, underground excavations lose their functionality, the infrastructure is damaged, and the working conditions become unsafe. Assessing rockburst hazards in underground excavations becomes particularly important with the increasing mining depth and the mining-induced stresses. Nowadays, rockburst risk prediction is based mainly on various indicators. However, some attempts have been made to apply machine learning algorithms for this purpose. For this article, we employed an extensive range of machine learning algorithms, e.g., an artificial neural network, decision tree, random forest, and gradient boosting, to estimate the rockburst risk in galleries in one of the deep hard coal mines in the Upper Silesian Coal Basin, Poland. With the use of these algorithms, we proposed rockburst risk prediction models. Neural network and decision tree models were most effective in assessing whether a rockburst occurred in an analyzed case, taking into account the average value of the recall parameter. In three randomly selected datasets, the artificial neural network models were able to identify all of the rockbursts.

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Section: Characteristics Of the Input Datasetmentioning

confidence: 99%

An Attempt to Use Machine Learning Algorithms to Estimate the Rockburst Hazard in Underground Excavations of Hard Coal Mine

et al. 2021

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“…(2) Other scholars focus on the stress distribution and the support technology of the roadway under a failure zone. Suchowerska 16 , Wojtecki 17 and Lv 18 clari ed that the design of the roadway under the mined-out space should avoid the stressed area of residual pillars and the roadway should be away from the upper residual pillar as far as possible. Bi 19 concluded that the failures of shallow and deep surrounding rocks of the roadway under the mined-out space are determined by vertical stress and horizontal stress, respectively.…”

Section: Introductionmentioning

confidence: 99%

Technique and practice of surrounding rock control for the roadway driven under the goafs of small collieries and heading for adjacent advancing face

Niu,

Wang,

Wang

et al. 2024

Preprint

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Aiming at the problems of complex stress and large deformations in the surrounding rocks of the roadway driven under the goafs of small collieries and heading for adjacent advancing coal face, by numerical modeling and field practice, the failure characteristics of the overlying coal and rocks were investigated, and the stopping and resuming times of the roadway excavation were identified. A zoning-based reinforcement technique was put forward and applied in engineering practice. The results showed that (1) The roadway roof was divided into four zones: “solid coal zone”, “residual pillar zone”, “roof caved zone”, and “roof un-caved zone”. (2) It was determined that the roadway excavation was stopped when the unmined distance between the return airway face and the 32101 working face was 70 m. After the 32101 working face passed the return airway face by 90 m, the roadway driving was restarted. (3) I.e. cable reinforcement for the “residual pillar zone” and bolt-wire mesh-cable-shotcreting-groutingreinforcement for the “roof failure zone” (“roof caved zone” + “roof un-caved zone”). The field observation results indicated that the maximum amount of the roof-to-floor convergence and the wall-to-wall convergence was 97 mm and 69 mm, respectively, which ensured the safety of roadway excavating.

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“…If the old goaf in the suburbs can be “reused” (Chen et al, 2022; Dudek et al, 2022; Modeste et al, 2021; Srivastava et al, 2020; Wojtecki et al, 2021), the problem of construction land shortage will be effectively alleviated. In the past, suburban coal mines mostly used strip mining to control surface subsidence and protect surface buildings (structures).…”

Section: Introductionmentioning

confidence: 99%

Prediction Method of Residual Subsidence of Strip Goaf Located in Suburb for Safely Constructing Viaduct

Tang,

Li,

Guo

et al. 2023

Energy Exploration & Exploitation

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Resource-based cities often face land scarcity during their developmental and expansionary phases. However, repurposing the goaf sites of suburban coal mines has been recognized as an effective strategy for alleviating this issue, particularly pertinent for suburban coal mines using strip mining techniques to protect ground-level structures. Old strip mining goaf is easily influenced by disturbance and leads to secondary surface deformation, which will threaten the construction above old strip mining goaf. Therefore, it is essential to provide a basis and reference for decision-makers regarding surface residual subsidence for strip mining. In the paper, based on the morphology and structure of the old strip mining goaf before the surface residual subsidence, the reason and mechanism of the residual subsidence of old strip mining goaf are studied. The results show that the main reasons for the “activation” of old strip mining goaf are the re-compaction of the under-compacted part of the fractured rock mass in the caving zone, the stripping and yielding of coal pillar under the action of groundwater erosion, weathering, and the collapse of residual voids. Based on the above analysis, a prediction model of the surface residual subsidence of the old strip mining goaf is proposed, which takes into account the compaction of the under-compacted area, the collapse of the residual voids, and the stripping and yielding of the coal pillar. The proposed method is implemented to evaluate the stability of a city viaduct goaf. Our research outcomes bear valuable theoretical and practical implications for site stability assessments of old strip mining goafs and the sustainable repurposing of land resources in suburban resource-based cities.

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The influence of distant coal seam edges on seismic hazard during longwall mining

Cited by 12 publications

References 23 publications

An Attempt to Use Machine Learning Algorithms to Estimate the Rockburst Hazard in Underground Excavations of Hard Coal Mine

An Attempt to Use Machine Learning Algorithms to Estimate the Rockburst Hazard in Underground Excavations of Hard Coal Mine

Technique and practice of surrounding rock control for the roadway driven under the goafs of small collieries and heading for adjacent advancing face

Prediction Method of Residual Subsidence of Strip Goaf Located in Suburb for Safely Constructing Viaduct

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