The field monitoring experiment of the high-level key stratum movement in coal mining based on collaborative DOFS and MPBX

The deformation and movement characteristics of high-level key stratums in overlying strata are important for estimating ground subsidence and understanding failure characteristics of ultrathick strata during mining. In this study, a distributed optical fiber sensor (DOFS) and multipoint borehole extensometers (MPBXs) were collaboratively employed to monitor the deformation of high-level key stratums in situ during the mining process at working face 130,604 of the Maiduoshan Coal Mine. DOFS monitoring results showed that the distance from advance influence of mining on the ground surface is 219.2 m. The deformation of the shallow stratums were greater and was affected earlier than that of the deep stratums. The deformation in the strata did not occur continuously and the boundary curve of the impact from advance mining was not a straight line with the advancement of the working face. By the MPBX technology, we measured the strata movement and obtained four-stage characteristics of high-level key stratum movement. The subsidence of the primary key stratum and the sub key stratum were monitored to reach 1389 and 1437 mm; their final relative displacement differed by 48 mm. No bed separation was observed in between the strata, and the key stratums tended to sink as a whole with the advancement of the working face. This research guides the analysis the movement of thick high-level key stratums.

Evolution Assessment of Mining Subsidence Characteristics Using SBAS and PS Interferometry in Sanshandao Gold Mine, China

Ground subsidence is a common geological phenomenon occurring in mining areas. As an important Chinese gold mine, Sanshandao Gold Mine has a mining history of 25 years, with remarkable ground subsidence deformation. Mining development, life security, property security and ecological protection all require comprehension of the ground subsidence characteristics and evolution in the mining area. In this study, the mining subsidence phenomenon of the Sanshandao Gold Mine was investigated and analyzed based on Persistent Scatterer Interferometry (PSI) and small baseline subset (SBAS). The SAR (synthetic aperture radar) images covering the study area were acquired by the Sentinel-1A satellite between 2018 and 2021; 54 images (between 22 February 2018 and 25 May 2021) were processed using the PSI technique and 24 images (between 11 April 2018 and 12 July 2021) were processed using the SBAS technique. In addition, GACOS (generic atmospheric correction online service) data were adopted to eliminate the atmospheric error in both kinds of data processing. The interferometric synthetic aperture radar (InSAR) results showed a basically consistent subsidence area and a similar subsidence pattern. Both InSAR results indicated that the maximum LOS (line of sight) subsidence velocity is about 49 mm/year. The main subsidence zone is situated in the main mining area, extending in the northwest and southeast directions. According to the subsidence displacement of several representative sites in the mining area, we found that the PSI result has a higher subsidence displacement value compared to the SBAS result. Mining activities were accompanied by ground subsidence in the mining area: the ground subsidence phenomenon is exacerbated by the increasing mining quantity. Temporally, the mining subsidence lags behind the increase in mining quantity by about three months. In summary, the mining area has varying degrees of ground subsidence, monitored by two reliable time-series InSAR techniques. Further study of the subsidence mechanism is necessary to forecast ground subsidence and instruct mining activities.

Ground Subsidence Response Conditional On Large-Scale Geothermal Exploitation In A Karst Reservoir Area of North China

Carbonate karst geothermal resources are widely distributed and have large reserves in North China. Nowadays, the scale of exploitation and utilization of the carbonate karst geothermal resources is gradually increasing. In this work, a geothermal exploitation area where the karst geothermal reservoirs are exploited on a large scale, is selected as the study area, and methods including experiment and numerical simulation are used to study the exploitation-induced ground subsidence problems based on the long-term water level monitoring data of the geothermal reservoir. Through analyses of ground subsidence caused by water level change of the geothermal reservoir, the following conclusions were obtained. The water level drawdown of different types of geothermal reservoirs had different effects on ground subsidence. The maximum ground subsidence of the study area caused by the water level decline of the Jx w carbonate geothermal reservoir was only 0.29 mm/a from 1983 to 2019, which is generally insignificant. In contrast, the same water level change of the N m sandstone geothermal reservoir was predicted to cause 8.9 mm/a ground subsidence. To slow down or even prevent the ground subsidence, balanced production and reinjection are required. From the result of this work, the decline of the water level of the Jx w carbonate geothermal reservoir caused by current large-scale geothermal exploitation will not cause serious ground subsidence. However, attention should be paid to the N m sandstone type geothermal reservoirs as their structures are much more sensitive to the water pressure change.

Performance Research of Materials and Engineering Application of Overburden Strata Separation-Zone Grouting Technology

To solve the ground subsidence problem associated with thick coal seam mining under the railway in the Tangshan Mine, the technology of overburden strata separation-zone grouting (OSSG) was proposed. Based on the analysis of the full height overlying strata structure in the range of the six working face areas of the second mining district, the spatial distribution characteristics of the separation zone within the overlying strata are obtained after fully mining the six working faces. Then, emphasis was placed on the selection ratio of grouting materials and the hydrodynamic properties of different grout types, and grouting grout with a high concentration, slow precipitation rate, and good stability was obtained by taking fly ash and local clay as aggregates. The designed grout concentration was approximately 40%; the bulk density was approximately 1.20; and the clay content in the aggregates was approximately 40–50%. The separation-zone grouting plan was designed for the six working faces, and continuous grouting technology with the characteristics of multiple separation zones within the full-height section with a large flow and a high concentration was proposed to form a complete grouting system and reasonable grouting process. After engineering verification, the technology has an ash injection ratio of 24.2%, a grouting ratio of 100.3%, and a reduction in the ground subsidence ratio of 51.5%, effectively reducing mining damage to the ground surface and ensuring the safe operation of ground surface railways. Simultaneously, this advancement improves the resource recovery rate of coal mines and provides greater benefits for mining enterprises.

Prediction of ground subsidence due to underground mining through time using multilayer feed-forward artificial neural networks and back-propagation algorithm – case study at Mong Duong underground coal mine (Vietnam)

The paper is devoted to studying the possibility of using artificial neural networks (ANN) to estimate ground subsidence caused by underground mining. The experiments showed that the most suitable network structure is a network with three layers of perceptrons and four neurons in the hidden layer with the back propagation algorithm (BP) as a training algorithm. The subsidence observation data in the Mong Duong underground coal mine and other parameters, including: (1) the distance from the centre of the stope to the ground monitoring points; (2) the volume of mined-out space; (3) the positions of the ground points in the direction of the main cross-section of the trough; and (4) the time (presented by cycle number), were used as the input data for the ANN. The findings showed that the selected model was suitable for predicting subsidence along the main profile within the subsidence trough. The prediction accuracy depended on the number of cycles used for the network training as well as the time interval between the predicted cycle and the last cycle in the training dataset. When the number of monitoring cycles used for the network training was greater than eight, the largest values of RMS and MAE were less than 10 % compared to the actual maximum subsidence value for each cycle. If the network training was less than eight cycles, the results of prediction did not meet the accuracy requirements.

Flood Susceptibility Modeling in a Subtropical Humid Low-Relief Alluvial Plain Environment: Application of Novel Ensemble Machine Learning Approach

This study has developed a new ensemble model and tested another ensemble model for flood susceptibility mapping in the Middle Ganga Plain (MGP). The results of these two models have been quantitatively compared for performance analysis in zoning flood susceptible areas of low altitudinal range, humid subtropical fluvial floodplain environment of the Middle Ganga Plain (MGP). This part of the MGP, which is in the central Ganga River Basin (GRB), is experiencing worse floods in the changing climatic scenario causing an increased level of loss of life and property. The MGP experiencing monsoonal subtropical humid climate, active tectonics induced ground subsidence, increasing population, and shifting landuse/landcover trends and pattern, is the best natural laboratory to test all the susceptibility prediction genre of models to achieve the choice of best performing model with the constant number of input parameters for this type of topoclimatic environmental setting. This will help in achieving the goal of model universality, i.e., finding out the best performing susceptibility prediction model for this type of topoclimatic setting with the similar number and type of input variables. Based on the highly accurate flood inventory and using 12 flood predictors (FPs) (selected using field experience of the study area and literature survey), two machine learning (ML) ensemble models developed by bagging frequency ratio (FR) and evidential belief function (EBF) with classification and regression tree (CART), CART-FR and CART-EBF, were applied for flood susceptibility zonation mapping. Flood and non-flood points randomly generated using flood inventory have been apportioned in 70:30 ratio for training and validation of the ensembles. Based on the evaluation performance using threshold-independent evaluation statistic, area under receiver operating characteristic (AUROC) curve, 14 threshold-dependent evaluation metrices, and seed cell area index (SCAI) meant for assessing different aspects of ensembles, the study suggests that CART-EBF (AUCSR = 0.843; AUCPR = 0.819) was a better performant than CART-FR (AUCSR = 0.828; AUCPR = 0.802). The variability in performances of these novel-advanced ensembles and their comparison with results of other published models espouse the need of testing these as well as other genres of susceptibility models in other topoclimatic environments also. Results of this study are important for natural hazard managers and can be used to compute the damages through risk analysis.

Prediction of Ground Subsidence During Underground Construction of Metro Line 2, Section 1, Ben Thanh - Tham Luong

Urban metro line No. 2 from An Suong station to Thu Thiem is one of the six metro lines thatis planned to be built in Ho Chi Minh City (HCMC). The metro line goes through the area in which thestratigraphy consists of many units, distributed from 20-80 m. The hydrogeology mainly has 2 aquifers,namely Holocene, and Pleistocene which affecting the deep excavation. During construction, there willbe some problems that will affect the work on the surface such as settlement, cracking, and damage. Byfinite element method on Plaxis software, the article forecasts the surface settlement during this metroline No.2. The results show that the ground settlement is relatively large in areas with soft groundstructures. The settlement results depend on the geological structure characteristics, hydrogeologicalcharacteristics, and the shape and size of the tunnels.

Karst Collapse Risk Zonation and Evaluation in Wuhan, China Based on Analytic Hierarchy Process, Logistic Regression, and InSAR Angular Distortion Approaches

The current study presents a detailed assessment of risk zones related to karst collapse in Wuhan by analytical hierarchy process (AHP) and logistic regression (LR) models. The results showed that the LR model was more accurate with an area under the receiver operating characteristic (ROC) curve of 0.911 compared to 0.812 derived from the AHP model. Both models performed well in identifying high-risk zones with only a 3% discrepancy in area. However, for the medium- and low-risk classes, although the spatial distribution of risk zoning results were similar between two approaches, the spatial extent of the risk areas varied between final models. The reliability of both methods were reduced significantly by excluding the InSAR-based ground subsidence map from the analysis, with the karst collapse presence falling into the high-risk zone being reduced by approximately 14%, and karst collapse absence falling into the karst area being increased by approximately 6.5% on the training samples. To evaluate the practicality of using only results from ground subsidence maps for the risk zonation, the results of AHP and LR are compared with a weighted angular distortion (WAD) method for karst risk zoning in Wuhan. We find that the areas with relatively large subsidence horizontal gradient values within the karst belts are generally spatially consistent with high-risk class areas identified by the AHP- and LR-based approaches. However, the WAD-based approach cannot be used alone as an ideal karst collapse risk assessment model as it does not include geological and natural factors into the risk zonation.

Preparation and Finite Element Analysis of Fly Ash/HDPE Composites for Large Diameter Bellows

In recent years, buried bellows have often had safety accidents such as pipeline bursts and ground subsidence due to the lack of adequate mechanical properties and other quality problems. In order to improve the mechanical properties of bellows, fly ash (FA) was used as a reinforced filler in high density polyethylene (HDPE) to develop composites. The FA was surface treated with a silane coupling agent and HDPE-g-maleic anhydride was used as compatibilizer. Dumbbell-shaped samples were prepared via extrusion blending and injection molding. The cross-section morphology, thermal stability and mechanical properties of the composites were studied. It was observed that when 10% modified FA and 5% compatibilizer were added to HDPE, the tensile yield strength and tensile breaking strength of the composites were nearly 30.2% and 40.4% higher than those of pure HDPE, respectively, and the Young’s modulus could reach 1451.07 MPa. In addition, the ring stiffness of the bellows was analyzed using finite element analysis. Compared with a same-diameter bellows fabricated from common commercially available materials, the ring stiffness increased by nearly 23%. The preparation method of FA/HDPE is simple, efficient, and low-cost. It is of great significance for the popularization of high-performance bellows and the high value-added utilization of FA.