Objective The soil solidification technique is widely used in soft foundation treatment. To exploit spatial plasticity of this technique, composite slab wall soil solidification foundations have gradually been applied in engineering projects. However, a reliable method for calculating the bearing capacity of composite slab wall soil solidification foundations is lack, and the mechanical parameters of both solidified and soft soil remain uncertain. These factors complicate the optimization of the composite slab wall soil solidification foundation designs. Therefore, it is crucial to propose a method for calculating the bearing capacity and optimizing the design of such foundations. Methods This study focuses on the 110 kV Jingwei coastal substation in Taizhou, Zhejiang Province. A numerical model is established based on the mechanical parameters of solidified and soft soil to calculate the bearing capacity of composite slab wall soil solidification foundations. The results of these calculations are used to train a neural network, enabling predictions of the bearing capacity for various design parameters, thus facilitating engineering applications. Uncertainties of the mechanical parameters are addressed through Monte Carlo simulations, and their impact on design is estimated using the robustness evaluation index standard deviation. The design cost is approximately estimated by the cross-sectional area of the foundation. Robust design theory is introduced to optimize the design while balancing cost-effectiveness and robustness. Results This method is implemented in an engineering project, resulting in an optimal design with solidified plate thickness P=2 m, solidified wall depth W=3 m, solidified wall thickness D=1.5 m, solidified wall net spacing S=1 m, and upper foundation width B=4 m, providing a reference for engineering designs. Conclusion The proposed methods for calculating bearing capacity and optimizing the design of composite slab wall soil solidification foundations offer new concepts and approaches for similar projects.
Objective In the southwest region of China, where canyons are deeply incised and water flows are turbulent, disasters frequently occur. The accumulated masses are widely distributed, and understanding the mechanisms and evolutionary processes of riverbank slopes composed of these masses under dynamic water scour, such as dam collapses and reservoir flood discharge, is of significant practical importance for hydropower, road construction, and urban development. Methods Building upon previous research, this study qualitatively analyzes the progressive deterioration of riverbank slopes under flood conditions. The development mechanisms of erosion grooves on both straight and concave riverbank slopes under dynamic water scour are theoretically derived. Furthermore, the multistage sliding process of the Ganhaizi landslide in Danba County, triggered by rising water levels, was simulated using Geo-studio software. Results A function describing the extent of erosion in straight riverbank slopes over time, considering factors such as water flow shear stress, slope shear strength, and initial shear stress was established. Both qualitative and quantitative analyses of the sliding process under dynamic water scour conditions show that erosion begins near the water surface and progresses inward, leading to traction landslides at the rear edge of the erosion groove. This is followed by erosion at the slope foot, resulting in continuously changes in slope morphology and multistage traction landslides. The Ganhaizi landslide experienced multiple traction stages due to a 15-meter rise in water levels and extended erosion time. Even currently stable bank slopes of accumulated masses remain vulnerable to large-scale sliding disasters under extreme hydraulic conditions. Conclusion This study offers a novel theoretical framework for analyzing riverbank collapse and provides guidance for preventing downstream disasters in water conservancy projects, such as reservoirs.
The water dynamics of barrier lakes in high mountain areas are crucial for risk assessment, disaster prediction, safety management, and decision-making. Objective and Methods To accurately and efficiently extract the water boundaries of mountainous barrier lakes, this paper focuses on the Attabad barrier lake along the Karakoram Highway, proposing a water extraction method based on decision tree classification. This method incorporates slope information into six conventional water extraction methods for decision tree classification. The effectiveness of these six methods was compared for extracting water from barrier lakes in the experimental area. The best-performing methods suitable for barrier lakes in high-altitude areas were applied to extract the water body range of the Attabad barrier lake.Accuracy was assessed using a confusion matrix, and classification post-processing was performed to refine the water boundary extraction. Results The research results indicate that (1) among the six models, the CWI model demonstrates the best performance, effectively distinguishing between slope, water, and shadow water, leading to a highly accurate outline of the barrier lake. However, a limitation of this model is the presence of a few mountain shadows in the middle of the slope. (2) The decision tree classification method based on slope achieved an overall accuracy of 89.31% and a kappa coefficient of 0.84. It effectively extracts the actual water range, excluding slope shoreline and mountain shadows, and provides a clearer lake boundary. Nevertheless, black fragments observed in the lower area of the barrier lake, likely due to landslides and mountain shadows, remained challenging to classify. Overall, the decision tree classification-based method proved effective in identifying water bodies, particularly in areas with rugged terrain and numerous shadows. Conclusion This paper proposes a method for extracting water bodies from barrier lakes in high mountain areas using decision tree classification. By incorporating slope information into conventional water body extraction methods, this approach accurately extracts the water boundary, effectively eliminates shadows from steep slopes, retain shadowed water on gentler slopes, and improves extraction efficiently. The simplicity and high extraction efficiency of this method make it a practical solution for widespread application.
[Objective]With the rapid development of China's transportation industry,the geological conditions encountered in highway construction are becoming increasingly complex.Tunnels,owing to their ability to traverse mountainous terrain,are widely used in highway projects through challenging geological environments.However,as the number of tunnel projects has increased,the frequency of rock collapses and landslides during highway tunnel construction has also risen,resulting in significant economic losses and casualties.Therefore,accurate risk assess-ments have become crucial in tunnel engineering.[Methods]To address this,40 cases of instability engineering were summarized and analyzed,refining 14 secondary indicators and establishing a comprehensive risk assessment index system.Risk was then assessed in terms of disaster probability and its consequences.The interpretive struc-tural modeling(ISM)method was employed to construct a hierarchical topology diagram,and a Bayesian network model was established and refined using a causality graph method.The model was trained on 80％of the case data and validated with the remaining 20％.Based on this,the highway tunnel instability risk assessment Bayesian net-work evaluation system(RIAS)was independently developed,offering both engineering applicability and user-friendly functionality,and enabling accurate and rapid assessments of surrounding rock instability during highway tunnel construction.[Results]The system was applied to sections such as ZK5+937～ZK5+917 of the Beigushan Tunnel,predicting an 18.2％probability of tunnel instability with a"None(no risk)"magnitude of instability and a risk level of"Low Ⅰ"-consistent with the actual excavation results.[Conclusion]This study introduces and in-novative approach by constructing a Bayesian network model tailored for highway tunnel risk assessments,overco-ming the limitations of single-risk-level models and the challenge of insufficient engineering datasets.The model is successfully tested in the Beigushan Tunnel and holds significant potential for application in other highway tunnel projects,enhancingsafety and risk prediction capabilities.
Objective To address current engineering challenges such as poor stability and accuracy, limited coverage, and high costs associated with soil landslide displacement monitoring, a novel method based on microelectromechanical system (MEMS) sensor technology is proposed. Methods By considering the kinematic characteristics of slope deformation, a time-domain acceleration integration algorithm is designed to correct random and systematic errors in the acceleration data collected by MEMS sensor. To verify the effectiveness and practicality of this displacement monitoring method, two indoor landslide model tests and corresponding finite element simulation calculations were designed and conducted. MEMS technology was used to monitor the internal displacement of the soil for the test slopes, and the results were compared with the the finite element simulation outputs to evaluate the accuracy and reliability of the algorithm. Results The findings indicate that the MEMS-based soil landslide displacement monitoring achieved a minimum average relative error of 0.09% in the horizontal direction and 0.50% in the vertical direction, demonstrating high accuracy and suitability for practical engineering applications. Conclusion The research results provide a novel approach to soil landslide monitoring and provides a theoretical foundation for the integrating MEMS sensors in landslide and slope safety engineering.
Objective To investigate the spatial-temporal variations in landslide susceptibility due to human engineering activities in resettled urban areas. Methods This study focuses on the new urban area of Yunyang County in the Three Gorges Reservoir region. Landslide susceptibility time-varying index factors were introduced to map spatial-temporal susceptibility differences and explore the spatial-temporal evolution of landslide disasters during urbanization in resettled urban areas. First, the stacking ensemble model was selected as the static susceptibility evaluation model. Then, the InSAR deformation rates and land use types over three distinct time spans (namely, January 16, 2017, to August 27, 2018 (T1), September 20, 2018, to July 30, 2021 (T2), and August 23, 2021, to November 17, 2023 (T3)) were selected as time-varying factors. Last, the time-varying factors were combined with the static evaluation results to create susceptibility difference distribution maps for the different periods. Results The study revealed that introducing time-varying factors in the analysis of spatial-temporal susceptibility differences effectively reflects the impact of urbanization on landslide disasters. When the land type in the study area changed from non-engineering land to engineering land, the landslide susceptibility level generally increased, with grid shares of 61.3% and 67.1% in the two change stages, respectively. The temporal trends of the InSAR displacement time series curves for selected typical landslides in urban areas showed high spatial-temporal correlations with land type changes, further validating the reliability of this method. Conclusion The proposed research approach provides the basis for disaster prevention, mitigation, and regional planning during the urbanization process in resettled urban areas of the Three Gorges Reservoir region.
Objective In the aftermath of the "5.12" Wenchuan earthquake, the frequency of chain disasters, precipitated by debris flow blockages and subsequent breaching floods, significantly increased in the Bailong River basin. The region's distinctive geological conditions, characterized by numerous towns situated on canyon terraces and debris flow accumulation fans, further exacerbate its susceptibility to such chain disasters. This study aims to investigate the potential risks associated with debris flow blockages and flood chain disasters in the Bailong River basin, with a specific focus on the Zhaizi gully debris flow in Bailong River basin, Zhouqu County, Gansu Province. The objective is to elucidate the disaster-breeding characteristics, disaster-inducing conditions, and evolution patterns of the Zhaizi gully debris flow chain disaster, while also delineating the threat range posed by these chain disasters. Methods Through remote sensing interpretation and field surveys, a comprehensive database encompassing the topography, geomorphology, and material sources of the Zhaizi gully debris flow was established. This facilitated the identification of the development characteristics and various physical and mechanical parameters of the debris flow. Using the FLO-2D and HEC-RAS models, numerical simulations were performed under different rainfall frequencies (P=1%, 2%), yielding key parameters such as depth, flow velocity, and the threat range of the debris flow and breaching floods. These parameters facilitated the analysis of hazard intensity and potential risks associated with debris flows and breaching floods. Results Under a 100-year rainfall frequency, the maximum flow velocity of the Zhaizi gully debris flow can reach 11.96 m/s. The average thickness of the debris dam formed is approximately 10 m, leading to complete blockage of the Bailong River and the formation of a dammed lake with a capacity of 6.26 km3. The evolution of the breaching flood lasts approximately 12 hours, with the peak flow occurring about 30 minutes after breach. The impact range of the breaching flood extends from the downstream area of Fengdie Town in Zhouqu County, Gannan, along the main stream of the Bailong River, to the upstream section of Jigan Township in Wudu District, Longnan City, covering an area of 56.36 km2 and spanning a distance of approximately 97.4 km. Based on the simulation results, a preliminary discussion was conducted on a comprehensive risk prevention and control model for basin-wide debris flow disaster chains, integrating monitoring and mitigation measures. Conclusion This study highlights the limitations of traditional models in flood disaster assessment and enhances the understanding of cascading hazards induced by debris flow blockages. The findings provide valuable insights for the risk assessment and engineering design of mitigation projects for similar debris flow disaster chains in the middle and lower reaches of the Bailong River basin.
Significance Landslide disasters are widely distributed in China. Effective monitoring, early warning, and risk management measures are key to disaster prevention and mitigation. Progress Compared with conventional techniques, distributed fiber optic sensing (DFOS) technology has made significant progress in landslide monitoring in recent decades, owing to its strengths in distributed, long-distance, large-range, and multiparameter monitoring. This paper first introduces several representative fiber optic sensing technologies, then proposes the concept of a fiber optic neural sensing system for landslides, and last elaborates the working principles of various fiber optic sensors and their deployment methods. Two typical landslide monitoring cases using ultra-weak fiber Bragg grating (UWFBG) monitoring technology are introduced, and the current technical bottlenecks are discussed. Conclusions and Prospects The case studies show that the fiber optic neural sensing system can achieve remote, real-time, high-precision underground multiparameter data acquisition, accurately detect potential slip surfaces and other key interfaces. Additionally, multiphysical changes at these interfaces provide important data support for understanding the underground evolution of landslides, which offers new insights into landslide prediction and early warning.
Objective Mountain restoration is currently one of the major projects in environmental engineering. The backfill formed in artificial slopes is relatively loose and highly susceptible to the impact of rainfall intensity, leading to slope instability. Methods In this study, a combination of numerical simulation method and onsite monitoring technology was used to analyze the stability of artificial slopes formed during the restoration of Dingguanfeng Mountain. By establishing precise geological models, defining material parameters, and setting boundary conditions, the stability coefficients of the slope under the four different rainfall working conditions set were obtained, and the distribution characteristics of the seepage field and deformation field of the slope under different conditions were simulated. A real-time monitoring cloud platform was established on the site to monitor the surface horizontal displacement and deep horizontal displacement of the fill slope on site. The monitoring results were compared with those obtained from numerical simulation to quantitatively assess the slope stability under different working conditions. Results The results indicate that the stability coefficients of the fill slope under different rainfall conditions are greater than the critical factor for interface sliding. Under various working conditions, the pore water pressure at the slope toe and the portion close to the slope surface increases considerably. Seepage channels are mostly developed at the front edge of the slope body and the steep areas of the slope surface, and the stability of these areas is relatively more affected by rainfall. With the increase in rainfall intensity, the maximum horizontal displacement in the middle and lower parts of the slope gradually enlarges. The greater the amount of rainfall infiltrating into the slope within the same time, the more significant the reduction in shear strength, the larger the area with large horizontal displacement, and it gradually extends towards the front and rear edges of the slope. By comparing the numerical simulation results with the data obtained from on-site monitoring, it is discovered that there is a good consistency between them, and the slope is basically in a stable state. Conclusion Henceforth, for the data generated from numerical simulation analyses, they should be combined with on-site monitoring data to conduct a more comprehensive assessment of the engineering stability.
Objective To investigate the differences in anti-pull bearing characteristics between single anchor and group anchors foundations of transmission lines, Methods this study employs a combination of theoretical analysis and field experiments. First, based on the structural characteristics, the bearing mechanisms of singleand group anchors were analyzed in terms of force and deformation. The full-length bonded anchors, commonly used in transmission line projects, were selected as the research object, with the granite ground in Quanzhou selected as the test site. On-site full-scale tests were conducted on three single anchors and four group anchors. Displacement sensors were used to monitor foundation and ground deformation, while optical frequency domain reflectometry (OFDR) recorded the strain in the anchor rods. The load-displacement curve of the test foundation and the internal force distribution along the anchor interface were obtained. Finally, a comparative analysis of the deformation and failure mechanisms for both anchor types was performed. Results The results show that the load-displacement curve of a single anchor differs from that of group anchors, with plastic deformation being more pronounced in group anchors. In the initial stages of testing, anchor system displacement is primarily governed by the tension in the anchor bars, whereas at the end of the test, displacement is more influenced by slippage at the anchor-rock interface. The axial tension stress of the anchor rod decreases gradually with depth, reaching near-zero at depths of 2 to 3 m. The failure mode of a single anchor under tensile load is related to the saturated uniaxial compressive strength of rock, while the failure mode for group anchors is influenced by the number of single anchor. It is recommended that group anchors foundation tests be used to determine the bond strength at the anchor interface for design purposes in engineering applications. Conclusion The research findings can provide references for the selection and design of rock anchor foundations for transmission lines.
[Objective]The pore system of low-permeability sandstone reservoirs is intricate,and the distribution of pore-throat sizes is highly variable.The microscopic pore structure significantly influences the reservoir's petro-physical properties and plays a critical role in controlling fluid flow within sandstone reservoirs.Traditional approa-ches for evaluating pore structures primarily rely on morphological analyses of pore throat size distributions or regres-sion analyses of pore structure parameters.These methods are significantly affected by human bias and often lack precise evaluation frameworks.[Methods]Poroperm analysis,mercury injection capillary pressure,nuclear mag-netic resonance(NMR)measurements,and X-ray computed tomography(X-ray CT)scanning experiments were performed to characterize the pore structures of the Es4 low-permeability sandstones in the G oilfield,Bohai Bay Ba-sin.On this basis,15 parameters that reflect the microscopic features of low-permeability sandstones were selected,and four types of pore structures were classified by applying an unsupervised self-organizing-map neural network al-gorithm.[Results]The findings reveal that the Type Ⅰ pore structure predominantly features large pore throats,with a median throat radius(r50)ranging from 0.38 to 2.35 μm.This type exhibits excellent pore connectivity,contributing significantly to permeability.The petrophysical properties and pore connectivity of Type Ⅱ pore struc-tures are second only to those of Type Ⅰ pore structures.The movable fluid porosity ranges from 2.76％to 5.61％,and the median throat radius(r50)is primarily distributed in the range of 0.01 to 0.23 μm.Type Ⅲ pore structures display good pore connectivity along with considerable microscopic heterogeneity.The petrophysical prop-erties and seepage properties of Type Ⅲ pore structures are comparable to those of Type Ⅰ and Type Ⅱ pore struc-tures.The Type Ⅳ pore structures are characterized by small pore throats and poor microscopic connectivity,which hinders fluid movement within the sandstones.[Conclusion]The self-organizing map neural network algorithm ef-fectively classifies pore structure types in cases involving multiple parameters.The classification results are not af-fected by inaccurate user-defined information,and there is no limitation on the number of parameters involved in the training process,making the application effect in pore structure classification remarkable.The established pore structure evaluation scheme,which is based on a self-organizing feature map neural network algorithm,is vital for investigating the microscopic seepage behavior and reservoir quality of low-permeability sandstones.
Objective The RM hydropower station is proposed to be constructed in the upper reaches of the Lancang River. Its impoundment and operation might result in deformation and instability of the RS giant accumulation on the left bank near the dam, thereby triggering landslide-induced wave disasters and endangering the safety of the key hydraulic structures and the downstream residents. Methods This study combines extensive geological surveys and physical mechanics experiments to investigate the potential instability and failure modes of RS accumulation under reservoir filling. On this basis, a three-dimensional numerical model of the entire river channel from the RS accumlation to the dam section was established. An analysis of the dynamic evolution of landslide-wave chain disasters caused by RS accumulation was conducted, and parameters such as the initial wave height, wave height along the opposite bank, propagation characteristics, wave height at the dam front, and wave height climbing along the dam were predicted. Results The results indicate that as the reservoir water level is gradually elevated to an altitude of 2 800 meters, the RS accumulation is most likely to undergo a large-scale instability failure. The rear tensile fracture boundary is the crushed stone and soil layer of the accumulation within a certain range above the reservoir water level, and the front shear boundary is the fine-grained layer in the middle and lower parts of the accumulation. After the instability failure of the accumulation, it induces a landslide-induced wave. The height of the first wave peaks near the water entry point, approximately 31.5 meters, and lasts for about 15 seconds. As the wave propagates downstream, the wave height decreases by 39.5% at the No. 1 river bay, reaching the dam in approximately 147 s and continuing to climb along the dam slope. The climbing wave height is approximately 2.6 m and lasts for 180 s, with no risk of overtopping by the initial or subsequent smaller waves. After being impeded by the dam, the initial wave propagates upstream, creating a backflow phenomenon, which, combined with subsequent waves, forms a locally high wave area. At monitoring point P5, the maximum backflow wave height reaches approximately 4.56 m and lasts for 219 s. Conclusion During the wave propagation process, the topography of the river bay and backflow phenomena significantly accelerate the energy dissipation of the waves, effectively reducing the risk of wave impact and secondary disasters.
In the sedimentary environment shaped by river-tidal bidirectional flow,thin coal seams are typically lo-cated within the oil-and gas-bearing strata of the Pinghu Formation in the PB area of the Xihu Sag.These coal seams significantly influence the seismic amplitude,phase,and frequency of the reservoirs.[Objective]To bet-terclarify the specific impact of coal seam development in the PB area on reservoir identification and effective identi-fication techniques,[Methods]this paper focuses on analyzing seismic and logging data to evaluate the prestack and poststack seismic response characteristics of the reservoirs.We then investigate the seismic response character-istics of the reflection coefficient after the coal seam is removed and perform poststack seismic forward modeling based on the wave equation.This analysis elucidates the influence of coal seams on seismic responses.Finally,rock physics multiparameter intersection analysis is employed to identify the sensitive parameters and establish the threshold values for reservoir lithology identification within the study area.[Results]When the reservoir thickness is less than the tuning thickness,the seismic data can be rotated by 90°,which reduces the impact of the coal seam on the reservoir diminishes.[Conclusion]For thin reservoirs with associated thin coal seams in the study area,we adopt a three-step high-quality reservoir prediction process that involves broadband spectrum inversion,specifically prestack Vp/Vs and prestack AVOG.This approach allows amore effective description of the distribution range of a-erated sand bodies in the study area.
Objective Differential deformation of urban land surfaces can threaten or damage surface infrastructure, leading to fractures and distortions. Monitoring spatial differential deformation and assessing associated risk levels are crucial for urban safety management. Methods This study employs Sentinel-1 satellite data and the time series InSAR techniques to analyze surface deformation over time, enabling the derivation of spatial-temporal deformation gradients. Hazard and vulnerability assessment factors are calculated using an analytic hierarchy process, integrating data such as nighttime light remote sensing, land use, and Chinese building height datasets.A macroscopic risk assessment is conducted, with supplementary microscopic-levelanalysis to assess building risks and identify potential high-risk areas. Comparison experiments verify the effectiveness of the research. Conclusion Significant deformation disparities are identified between the eastern Chaoyang District and the northwestern Tongzhou District. In addition, high-risk areas are observed around the Capital International Airport region and the vicinity of Anding South Street. Therefore, the study highlights the importance of multisource data for effectively monitoring differential deformation to ensureurban safe.
Objective and methods In this study, the hydrochemical method and self-organized neural network (SOM-KM) coupling method were employed to identify recharge sources and reveal the water-rich mechanism in the karst groundwater-rich area of the Changxiao karst water system in Jinan City. The contribution ratio of karst groundwater recharge sources in the karst groundwater-rich area was quantitatively calculated using the end-element mixed model. The enrichment mechanism of karst groundwater is explored by combining with topography, geological structure, stratigraphic lithology, and catchment conditions. Results The results showed that the karst groundwater in the catchment drainage area had similar water chemistry to that in the southern recharge area, the karst groundwater in the lateral runoff area, and the Yellow River, indicating a close hydraulic connection. This implies that the karst groundwater in the catchment drainage area is recharged by three sources: The southern mountain area, the karst groundwater in the lateral runoff area, and the Yellow River. The contribution ratios of the three components are 75.09%, 21.02%, and 3.89%, respectively. Carbonate rocks are widely distributed, and fissured karst is well developed in the accumulation and discharge areas, especially in the Maji-Xiaoli-Guide area. Moreover, there are abundant karst groundwater recharge sources in this area. During the runoff process of karst groundwater from southeast to northwest, it is impeded by sandstone and mudstone in the north. As a result, it accumulates in the contact zone between soluble rock and insoluble rock, thus forming impeded-type karst groundwater-rich structures. Conclusion Revealing the enrichment mechanism of karst groundwater in the Changxiao karst water system can provide scientific support for accurate calculations of recoverable resources and the protection of the springs in Jinan.
The extensive distribution and complex material composition of soil-rock mixture slopes in China have attracted significant attention from scholars. Objective This study aims to scientifically and rationally assess the impact of the spatial variability of soil parameters on the stability of soil-rock mixture slopes. Methods Based on the random field theory, the effective shear strength parameters cohesion c and internal friction angle φ are selected as random variables. The local averaging method is used to simulate the random field, with random field parameter generation conducted in MATLAB. Python scripts are employed to map the random field parameters to the soil-rock mixture slope via finite element software, accounting for the actual shape and content of block rocks in the soil-rock mixture. The strength reduction method is then applied to calculate the slope stability safety factor. Results The results reveal that the stability safety factor of soil-rock mixture slopes follows a normal distribution. As the block stone content increases, the mean value of the stability safety factor rises from 1.005 to 1.095, reflecting a transition from shallow to deep failure. For block stone content of 35%, the stability safety factor reaches 1.334 for larger block stones and 1.064 for smaller ones. Compared to deterministic calculation results, incorporating the spatial variability of the soil parameters yields higher stability safety factor. Conclusion Therefore, in soil-rock mixture slope stability analyses, the spatial variability of effective shear strength parameters must be fully considered to prevent overly conservative designs.
Objective In high geostress or complex geological conditions, tunnel convergence frequently exceeds the threshold, resulting in damage to support structures and, in extreme cases, tunnel collapse. Accurately predicting the deformation trend and convergence of surrounding rock during tunnel construction is crucial to ensuring the safety of workers and improving construction efficiency. Traditional single prediction models struggle to adapt to the dynamic nature of tunnel convergence, limiting their predictive accuracy. Methods To address this, this study introduces a dynamic prediction model for tunnel convergence based on continuous Bayesian updating and an optimal model selection strategy. Utilizing real-time monitoring data of tunnel convergence deformation, the parameters in three empirical models are continuously updated and refined. The optimal model is then selected to predict the final convergence deformation of the surrounding rock and quantify its associated uncertainty. Results The model was tested on 16 measurement points across 9 sections of the Baima Tunnel, achieving a mean relative error of only 3.22% between the predicted and monitored final convergence rates. Conclusion Additionally, with just 10 days of observed data, the model can forecast the final convergence deformation for up to 40 days post-excavation, offering valuable technical support for preventing squeezing disasters in the full-section tunnel excavation.
Objective, Methods This study focus on debris flows in Huangniba gully, Muli County, Liangshan Prefecture, Sichuan Province, utilizing a mass flow numerical simulation platform. Through field investigations and the construction of numerical models, we analyze the mechanisms driving debris flow formation and evolution, aiming to invert these mechanisms.Based on this foundation, we assess debris flow hazards, develop a vulnerability model for masonry structures under different damage modes, and establish a dynamic process-based debris flow risk assessment method. Results The risk assessment indicates that, for a 20-year return period, very high- and high-risk zones for debris flow encompass 0.15×104 m2 and 1.68×104 m2, affecting 10 and 13 buildings, respectively. For a 50-year return period, the areas of very high- and high-risk zones expand by 40% and 70.8%, with 2 and 4 additional buildings affected. Moreover, for a 100-year return period, these zones increase by 113.3% and 132.1%, respectively, affecting 11 and 5 more buildings compared to the 20-year scenario. Conclusion Furthermore, the erosion-incorporating debris flow dynamics model developed in this study accurately represents the debris flow events in Huangniba gully. Additionally, the vulnerability assessment model for masonry structures was validated against other debris flow events, confirming its enhanced feasibility. These findings provide a foundation for quantitative risk prediction in Huangniba gully.
Objective Infrasound is an effective approach for debris flow warning. Traditional threshold-based warning methods focus solely on individual infrasound characteristics, which can lead to false alarms or missed detections. Thus, incorporating multiple time-frequency characteristics is essential to improve warning accuracy. Methods Infrasound data from Caodianshui Village, Beijing, were analyzed to differentiate the infrasound characteristics of debris flows from environmental background. A random forest algorithm was employed to establish an infrasound warning model for debris flows. Results The effective pressure of debris flow infrasound ranges from 0.4 to 1.0 Pa, while environmental infrasound typical remains below 0.1 Pa, though noise can raise it above 0.4 Pa.Noise infrasound energy is primarily concentrated below 6 Hz, whereas debris flow infrasound exhibits significantly higher energy in the 6-15 Hz. Therefore, comprehensive time-frequency characteristics, especially the energy in the range of 6-15 Hz, should be considered when identifying debris flow infrasound. Using effective infrasound pressure, infrasound pressure within 6-15 Hz, zero crossing rate, dominant frequency, and its amplitude as characteristic variables, a debris flow warning model was constructed based on a random forest algorithm. The model achieved an AUC of 0.99, with a 90% recognition accuracy for test data, a 15% improvement over threshold methods. Conclusion The random forest-based infrasound warning model substantially improves warning accuracy for debris flows and is applicable to typical basins in the Beijing mountainous areas. This approach offers a valuable reference for infrasound-based debris flow warning research in other areas.
Objective This study aims to investigate the causes of typical soil landslides in eastern Jiangxi and assess the influence of rainfall on slope stability in the region. Methods Using the Ziwu landslide in Guangfeng District, Shangrao City, Jiangxi Province as a case study, this paper conducted a comprehensive analysis of rainfall data and surface displacement monitoring. A two-dimensional (2D) mechanical model was established using GeoStudio finite element software to simulate the deformation of small residual slope soil landslides under different rainfall conditions. This study analyzes the local rainfall characteristics, modeled four typical rainfall patterns across five scenarios, applied multiple linear regression to fit the data, and developed an I-D-Fs evaluation model. Results The results show that (1) the peak values of earth pressure and soil moisture content are 16.8 kPa and 16.3%, respectively, with a 3 to 5 days lag between rainfall onset and the increase in these values during the early stages of rainfall; (2) rainfall is the primary trigger for landslides, which progress through three distinct phases: Creeping of the front slope, pulling of the rear slope, and sudden sliding of the whole slope; and (3) rainfall patterns significantly impact slope stability, necessitating seasonal monitoring and early warning systems. During periods of low rainfall, uniform rainfall is the most detrimental, decreasing the stability coefficients by 2% compared to other patterns. During heave rainfall, frontal rainfall is the most hazardous, decreasing the stability coefficient by 8% compared to other patterns. Conclusion These results provide a scientific basis for the monitoring and early warning of shallow soil landslides.
