Search Results (9)

In order to quantitatively assess the risk of deeply buried tunnel construction in the case of a hidden karst cave at the bottom of the tunnel, the first deeply buried tunnel construction failure model (PH−1) is firstly proposed based on the upper bound theorem of limit analysis and Protodyakonov’s theory. Thereafter, based on the upper bound theorem of limit analysis and the energy dissipation theory, the second deeply buried tunnel construction failure model (PH−2) is proposed. Furthermore, on the basis of the reliability theory, the dimensionless performance functions of each damage region under these two deeply buried tunnel construction damage modes are constructed. Finally, the instability probability of each damage region under the two damage modes is calculated using the Monte Carlo sampling method. This study suggests that the PH−1 failure model is not suitable for analysing the probability of instability in tunnel construction in hard plastic red clay strata, while the PH−2 failure model is appropriate for analysing the probability of instability in tunnel construction in hard plastic red clay strata. The worse the geological conditions, the larger the diameter of the cavern, the smaller the clearance between the cavern and the tunnel, and the greater the threat to safe tunnel construction. Full article

The micro-nanoparticles found in geothermal fluids exhibit distinct characteristics that hold great potential for detecting deeply concealed geothermal resources. Utilizing a nanoparticle tracking analyzer (NTA), we conducted observations on karst geothermal fluids collected from the central region of Shandong Province, specifically Jinan and Zibo. Our investigation revealed the presence of a significant quantity of naturally occurring micro-nanoparticles within these geothermal fluids, with particle sizes typically falling in the range of 100 nm to 5 μm. To gain a comprehensive understanding of these micro-nanoparticles, we subjected them to a detailed analysis, encompassing their type, shape, crystal structure, and chemical composition. This in-depth examination was carried out using transmission electron microscopy (TEM). Our findings, supported by TEM images and energy dispersive spectroscopy, indicated that these micro-nanoparticles in the geothermal fluid samples predominantly exhibit amorphous characteristics and possess irregular or nearly spherical shapes, often accompanied by rough edges. Furthermore, it was evident that the composition of these micro-nanoparticles primarily consists of carbonates, sulfates, and chlorides, which contain elements such as Fe, Ca, and Na. The distinctive features of these micro-nanoparticles provide valuable insights into the properties of the high-temperature reservoirs and aquifers from which they originate. As a result, we firmly assert that natural micro-nanoparticles can significantly contribute to the detection and comprehensive study of concealed geothermal resources within the Earth. This novel approach offers a promising method for exploring and gaining a deeper understanding of these hidden geothermal resources. Full article

Based on published anatomical-morphological and ecological characteristics and phylogenetic evidence, six species of the Ganoderma genus are known to occur in Europe, namely, G. applanatum (Pers.) Pat., G. adspersum (Schulzer) Donk, G. pfeifferi Bres., G. resinaceum Boud., G. carnosum Pat., and G. lucidum (Curtis) P. Karst. Molecular markers (DNA sequences of selected genes or intergenic spacers) revolutionized our view of fungal variability. Every one of the four most frequently used molecular markers (ITS (internal transcribed spacer) and partial sequences of LSU (rRNA large subunit), tef1-α (translation elongation factor 1-alpha), and Rpb2 (RNA polymerase II second largest subunit)) provides a different view on the variability of European species of the Ganoderma genus. Both the lowest intraspecies variability and the best species differentiation (interspecies divergence) were observed for the LSU marker, and based on our data, this marker could be recommended for identification and species delineation in European Ganoderma spp. The sequences of the most frequently used ITS marker were unable to discriminate between G. lucidum and G. carnosum, and in general, this species pair showed the lowest interspecies divergence using all markers tested. Surprisingly, up to now, hidden variability has been detected in several Ganoderma spp., indicating the existence of possible cryptic taxa within the European Ganoderma morphospecies. Full article

Based on the hidden karst exposed in Jurong Pumped Storage Power Station, combined with the field exploration data, the temporal and spatial development characteristics of hidden karst in the power station area are analyzed using the methods of specific solubility and specific corrosion, water chemical composition analysis, borehole television imaging, tracer test, and water pressure test. The results show that the karst development in the study area can be divided into three periods: pre-Cretaceous, Pleistocene, and modern karst. Karst development is controlled by soluble rock, non-soluble rock, and their combination, and the development direction is basically consistent with the fault strike. Karst caves are mainly distributed below the elevation of 100 m, with different shapes and scales and randomness. Non-soluble rocks and impure carbonate rocks are widely distributed in the study area, and surface karst is not developed. The underground karst caves are filled with red clay, and the rate of groundwater circulation is slow. The existence of geological bodies such as rock veins causes the groundwater levels to have obvious double-layer characteristics, which results in weak connectivity between karst caves. Although the karst in the power station area has a certain hydraulic connection with the surface water outside the area, the hydraulic connection of karst in the power station area is generally weak. The research results provide a scientific basis for the anti-seepage measures of underground powerhouses. Full article

The objective of this work was to modify the HEC-HMS flood prediction for the karstic watershed of the Lijiang River, South China, through the quantitative inclusion into the model of the available reservoir capacity of karst (ARCK) as a case study. Due to the complexities caused by hidden drainage networks in karst hydrology, as a new approach, soil moisture accounting loss was used to reflect the ARCK in flood forecasting. The soil moisture loss was analyzed against daily rainfall runoff data across 1.5 years by using an artificial neural network via phyton programming. Through the correlations found among the amounts of soil moisture and river flow fluctuations in response to precipitation and its intervals, coefficients were introduced to the model for output modifications. ARCK analysis revealed that while heavy rainfalls with longer intervals (i.e., 174 mm/2d after 112 days of the dry season) may not cause considerable changes in the river flow magnitude (0.1–0.64 higher owing to high ARCK), relatively small rainfalls with higher frequency (i.e., 83 mm/4d during the wet season) can cause drastic raise of river flow (10–20 times greater at different stations) due to lower ARCK. Soil moisture accounting loss coefficients did enhance the model’s simulated hydrographs accuracy (NSE) up to 16% on average as compared to the initial forecasting via real data. However, the modifications were valid for flood events within a few years from the soil moisture observation period. Our result suggested that the inclusion of ARCK in modeling through soil moisture accounting loss can lead to increased prediction accuracy through consistent monitoring. Full article

In order to improve land-use efficiency and solve traffic congestion, in recent years, many cities in China have focused on developing urban underground space resources and urban rail transit projects. However, there are various hidden risks for the sustainable development of the ecological environment and water resources. In this paper, a comprehensive investigation and analysis of spring water resources are carried out using the example of the karst area of Jinan, which is known as ‘spring city’. The engineering geological and hydrogeological conditions in Jinan are introduced in detail, and the geological causes of springs are analyzed. In addition, the causes of spring flow attenuation are revealed based on the investigation of the flow dynamics of spring water. Based on the current situation of traffic congestion in Jinan, the necessity and development statuses of rail transit construction are analyzed. Then, according to the different stratigraphic structure, limestone roof depth and karst water head depth, the Jinan spring area is divided into three research regions including the shallow limestone area, concentrated spring water area and deep limestone area. The spring protection problems faced by each region during the construction of urban railways are systematically described. In addition, the countermeasures and suggestions for spring protection are presented. This study aims to reduce the impact of urban rail transit construction on Jinan spring water so as to protect the Jinan spring. It also provides the water resources protection experience for urban rail transit construction in similar karst areas. Full article

The crystalline blockage of tunnel drainage pipes in a karst area seriously affects the normal operation of drainage system and buries hidden dangers for the normal operation of the tunnel. In order to obtain the influencing factors and laws of tunnel drainage pipe crystallization in a karst area, based on the field investigation of crystallization pipe plugging, the effects of groundwater velocity, drainage pipe diameter, drainage pipe material, and structure on the crystallization law of tunnel drainage pipe in karst area are studied by indoor model test. The results show that: (1) With the increase of drainage pipe diameter (20–32 mm), the crystallinity of drainage pipes first increases and then decreases. (2) With the increase of water velocity in the drainage pipe (22.0–63.5 cm·s⁻¹), the crystallinity of the drainage pipes gradually decreases from 1.20 g to 0.70 g. (3) The crystallinity of existing material drainage pipe is: M3 (poly tetra fluoroethylene) > M2 (pentatricopeptide repeats) > M4 (high density polyethylene) > M1 (polyvinyl chloride); M8 (polyvinyl chloride + coil magnetic field) is used to change the crystallinity of drain pipe wall material. (4) When the groundwater flow rate is 34.5 cm·s⁻¹, M1 (polyvinyl chloride) and M8 (polyvinyl chloride + coil magnetic field) can be selected for the tunnel drainage pipe. The research on the influencing factors of tunnel drainage pipe crystallization plugging fills a gap in the research of tunnel drainage pipe crystallization plugging. The research results can provide a basis for the prevention and treatment technology of tunnel drainage pipe crystallization plugging. Full article

The deformation process of landslide displacement has complex nonlinear characteristics. In view of the problems of large error, slow convergence and poor stability of the traditional neural network prediction model, in order to better realize the accurate and effective prediction of landslide displacement, this research proposes a landslide displacement prediction model based on Genetic Algorithm (GA) optimized Elman neural network. This model combines the GA with the Elman neural network to optimize the weights, thresholds and the number of hidden neurons of the Elman neural network. It gives full play to the dynamic memory function of the Elman neural network, overcomes the problems that a single Elman neural network can easily fall into local minimums and the neuron data is difficult to determine, thereby effectively improving the prediction performance of the neural network prediction model. The displacement monitoring data of a slow-varying landslide in the Guizhou karst mountainous area are selected to predict and verify the landslide displacement, and the results are compared with the traditional Elman neural network prediction results. The results show that the prediction results of GA-Elman model are in good agreement with the actual monitoring data of landslide. The average error of the model is low and the prediction accuracy is high, which proves that the GA-Elman model can play a role in the prediction of landslide displacement and can provide reference for the early warning of landslide displacement deformation. Full article

Karst collapse columns (KCCs) are naturally formed geological structures that are widely observed in North China. Given their influence on normal mining operations and the progress of mining work, collapse columns pose a hidden danger in coal mining under the influence of manual mining. By communicating often with the aquifer, the water inrush from KCCs poses a serious threat to construction projects. This paper adopts three flow field models, namely, Darcy aquifer laminar flow, Forchheimer flow, and Navier–Stokes turbulent flow, based on the changes in the water inrush flow pattern in the aquifer and laneway, and uses COMSOL Multiphysics software to produce the numerical solutions of these models. As the water inrush flow velocity increases, the Forchheimer flow shows the effect of additional force (inertial resistance) on flow in KCCs, in addition to the effect of viscous resistance. After the joint action of viscous resistance and inertial resistance, the inertial resistance ultimately dominates and gradually changes the water inrush from the KCCs to fluid seepage. Forchheimer flow can comprehensively reflect the nonlinear flow process in the broken rock mass of KCCs, demonstrate the dynamic process from the Darcy aquifer to the final tunnel turbulence layer, and quantitatively show the changes in the flow patterns of the water inrush from KCCs. Full article