Fractures are typically characterized by roughness that significantlyaffects the mechanical and hydraulic characteristics of reservoirs.However,hydraulic fracturing mechanisms under the influenceof fracture morphology...Fractures are typically characterized by roughness that significantlyaffects the mechanical and hydraulic characteristics of reservoirs.However,hydraulic fracturing mechanisms under the influenceof fracture morphology remain largely unexplored.Leveraging the advantages of the finite-discrete element method(FDEM)for explicitly simulating fracture propagation and the strengths of the unifiedpipe model(UPM)for efficientlymodeling dual-permeability seepage,we propose a new hydromechanical(HM)coupling approach for modeling hydraulic fracturing.Validated against benchmark examples,the proposed FDEM-UPM model is further augmented by incorporating a Fourier-based methodology for reconstructing non-planar fractures,enabling quantitative analysis of hydraulic fracturing behavior within rough discrete fracture networks(DFNs).The FDEM-UPM model demonstrates computational advantages in accurately capturing transient hydraulic seepage phenomena,while the asynchronous time-stepping schemes between hydraulic and mechanical analyses substantially enhanced computational efficiencywithout compromising computational accuracy.Our results show that fracture morphology can affect both macroscopic fracture networks and microscopic interaction types between hydraulic fractures(HFs)and natural fractures(NFs).In an isotropic stress field,the initiation azimuth,propagation direction and microcracking mechanism are significantly influencedby fracture roughness.In an anisotropic stress field,HFs invariably propagate parallel to the direction of the maximum principal stress,reducing the overall complexity of the stimulated fracture networks.Additionally,stress concentration and perturbation attributed to fracture morphology tend to be compromised as the leak-off increases,while the breakdown and propagation pressures remain insensitive to fracture morphology.These findingsprovide new insights into the hydraulic fracturing mechanisms of fractured reservoirs containing complex rough DFNs.展开更多
Utilization of ceramic wastes to fabricate concrete can not only effectively dispose the wastes,but also reduce the energy and source consumptions.Therefore,we fabricated a green ultra high performance concrete using ...Utilization of ceramic wastes to fabricate concrete can not only effectively dispose the wastes,but also reduce the energy and source consumptions.Therefore,we fabricated a green ultra high performance concrete using ceramic waste powder(CWP)to replace 55%of cement,and ceramic waste aggregate(CWA)to replace 100%natural quartz sand.However,high content of ceramic wastes will harm the concrete performance including workability and mechanical properties.Therefore,a low-cost and low carbon nano-calcium carbonate(NC)was introduced to compensate for the defects caused by large amounts of CWP and CWA to workability and mechanical behavior.The experimental results show that the workability of ultra high performance concrete with large amounts of CWP and CWA(UHPCLCC)increases by 28.57%with NC content of 5%.Moreover,the flexural strengths,fracture energy,compressive strengths,and compressive toughness of UHPCLCC increase up to 29.6%,56.5%,20.4%,and 37.6%,respectively,which is caused by the nano-core effect of NC.展开更多
The current technical standards primarily relied on experience to judge the interfacial bonding properties between the self-compacting concrete filling layer and the steam-cured concrete precast slab in CRTS Ⅲ slab b...The current technical standards primarily relied on experience to judge the interfacial bonding properties between the self-compacting concrete filling layer and the steam-cured concrete precast slab in CRTS Ⅲ slab ballastless track structure.This study sought to enhance technical standards for evaluating interfacial bonding properties by suggesting the use of the splitting tensile strength to evaluate the impact of bubble defects.Specimens were fabricated through on-site experiment.The percent of each area of 6 cm^(2)or more bubble defect was 0 in most of specimens.When the cumulative area of all bub-ble defects reached 12%,the splitting tensile strength value was 0.67 MPa,which exceeded the minimum required value of 0.5 MPa for ensuring bonding interface adhesion.Furthermore,when the cumulative area of all bubble defects reached 8%,the splitting tensile strength value was 0.85 MPa,which exceeded the minimum required value of 0.8 MPa,thereby over-coming the negative impact of each area of 10 cm^(2) or more bubble defect.Additionally,keeping the cumulative area of each area of 6 cm^(2) or more bubble defect below 6%ensured adequate bonding strength and reduced the occurrence of specimens with lower splitting tensile strength values.展开更多
Landslide susceptibility prediction(LSP)is significantly affected by the uncertainty issue of landslide related conditioning factor selection.However,most of literature only performs comparative studies on a certain c...Landslide susceptibility prediction(LSP)is significantly affected by the uncertainty issue of landslide related conditioning factor selection.However,most of literature only performs comparative studies on a certain conditioning factor selection method rather than systematically study this uncertainty issue.Targeted,this study aims to systematically explore the influence rules of various commonly used conditioning factor selection methods on LSP,and on this basis to innovatively propose a principle with universal application for optimal selection of conditioning factors.An'yuan County in southern China is taken as example considering 431 landslides and 29 types of conditioning factors.Five commonly used factor selection methods,namely,the correlation analysis(CA),linear regression(LR),principal component analysis(PCA),rough set(RS)and artificial neural network(ANN),are applied to select the optimal factor combinations from the original 29 conditioning factors.The factor selection results are then used as inputs of four types of common machine learning models to construct 20 types of combined models,such as CA-multilayer perceptron,CA-random forest.Additionally,multifactor-based multilayer perceptron random forest models that selecting conditioning factors based on the proposed principle of“accurate data,rich types,clear significance,feasible operation and avoiding duplication”are constructed for comparisons.Finally,the LSP uncertainties are evaluated by the accuracy,susceptibility index distribution,etc.Results show that:(1)multifactor-based models have generally higher LSP performance and lower uncertainties than those of factors selection-based models;(2)Influence degree of different machine learning on LSP accuracy is greater than that of different factor selection methods.Conclusively,the above commonly used conditioning factor selection methods are not ideal for improving LSP performance and may complicate the LSP processes.In contrast,a satisfied combination of conditioning factors can be constructed according to the proposed principle.展开更多
The spatial distribution of overburden layer thickness(OLT)is crucial for landslide susceptibility prediction and slope stability analysis.Due to OLT spatial heterogeneity in hillslope regions,combined with the diffic...The spatial distribution of overburden layer thickness(OLT)is crucial for landslide susceptibility prediction and slope stability analysis.Due to OLT spatial heterogeneity in hillslope regions,combined with the difficulty and time consumption of OLT sample collection,accurately predicting OLT distribution remains a challenging.To address this,a novel framework has been developed.First,OLT samples are collected through field surveys,remote sensing,and geological drilling.Next,the heterogeneity of OLT’s spatial distribution is analyzed using the probability distribution of OLT samples and their horizontal and vertical distributions.The OLT samples are categorized and the small sample categories are expanded using the synthetic minority over-sampling technique(SMOTE).The slope position is selected as a key conditioning factor.Subsequently,16 conditioning factors are applied to construct OLT prediction model using the random forest regression algorithm.Weights are assigned to each OLT sample category to balance the uneven distribution of sample sizes.Finally,the Pearson correlation coefficient,mean absolute error(MAE),root mean square error(RMSE),and Lin’s concordance correlation coefficient(Lin’s CCC)are employed to validate the OLT prediction results.The Huangtan town serves as the case study.Results show:(1)heterogeneity analysis,SMOTE-based OLT sample expansion strategy and slope position selection can significantly mitigate the effect of spatial heterogeneity on OLT prediction.(2)The Pearson correlation coefficient,RMSE,MAE and Lin’s CCC values are 0.84,1.173,1.378 and 0.804,respectively,indicating excellent prediction performance.This research provides an effective solution for predicting OLT distribution in hillslope regions.展开更多
The study of the shear behavior of bonded rock-cement interface is important for understanding the strength and stability of grouted rock masses.This research aims to reveal the failure mechanism behind the shear prop...The study of the shear behavior of bonded rock-cement interface is important for understanding the strength and stability of grouted rock masses.This research aims to reveal the failure mechanism behind the shear property of bonded rock-cement interfaces.For the study,sandstone and granite joint blocks with specific morphology were fabricated by using a three-dimensional(3D)engraving technique.Bonded rock-cement joints with asperity inclination angles of 15°,30°,and 45°were prepared.Shear tests were performed on these bonded rock-cement joints to investigate the shear response and failure modes considering the effect of applied normal stress and interface morphology.Meanwhile,the two-dimensional particle flow code(PFC2D)was utilized to model the entire shear process of bonded rock-cement interfaces.The macroscopic shear behavior and mesoscopic failure mechanism were comprehensively investigated by the laboratory test and numerical simulation.The results showed that the shear stress-displacement curves of bonded rock-cement joints exhibit two distinct peaks,and the shear stress evolution can be categorized into four stages including elastic growth,rapid stress drop,secondary stress growth,and progressive softening.Significantly,the number of acoustic emission events also exhibits two distinct peaks related to the double peak of the shear stress curves.The failure of bonded rock-cement interfaces is mainly induced by shear fractures,while the failure of rock and cement blocks is primarily caused by tensile fractures.The number of shear cracks in the bonded rock-cement interfaces reaches the peak when the shear stress reaches the primary peak;whereas as the shear stress continuously approaches the residual stage,the fracture of the bonded rock-cement joints is primarily characterized by tensile cracks in the blocks.展开更多
The geothermal resources in hot dry rock(HDR)are considered the future trend in geothermal energy extraction due to their abundant reserves.However,exploitation of the resources is fraught with complexity and technica...The geothermal resources in hot dry rock(HDR)are considered the future trend in geothermal energy extraction due to their abundant reserves.However,exploitation of the resources is fraught with complexity and technical challenges arising from their unique characteristics of high temperature,high strength,and low permeability.With the continuous advancement of artificial intelligence(AI)technology,intelligent algorithms such as machine learning and evolutionary algorithms are gradually replacing or assisting traditional research methods,providing new solutions for HDR geothermal resource exploitation.This study first provides an overview of HDR geothermal resource exploitation technologies and AI methods.Then,the latest research progress is systematically reviewed in AI applications in HDR geothermal reservoir characterization,deep drilling,heat production,and operational parameter optimization.Additionally,this study discusses the potential limitations of AI methods in HDR geothermal resource exploitation and highlights the corresponding opportunities for AI's application.Notably,the study proposes the framework of an intelligent HDR exploitation system,offering a valuable reference for future research and practice.展开更多
We optimized the gradation of cold recycled mixture(CRM)based on low-temperature performance.Firstly,the low-temperature crack resistance of CRM with different gradation and emulsified asphalt content was studied by i...We optimized the gradation of cold recycled mixture(CRM)based on low-temperature performance.Firstly,the low-temperature crack resistance of CRM with different gradation and emulsified asphalt content was studied by indirect tension(IDT)and semi-circular bending(SCB)test.Thereafter,the low-temperature performance evaluation index suitable for CRM was put forward.Then,the triangular coordinate statistical chart was used to analyze the optimal proportion of three grades of aggregate which are 2.36-4.75 mm,0.075-2.36 mm and below 0.075 mm.The results showed that the W_(f) and G_(f) could distinguish the low-temperature performance of CRM with different mixtures and emulsified asphalt dosage.For cold recycled fine aggregate,2.36-4.75 mm,0.075-2.36 mm and less than 0.075 mm account for 20%-25%,74.3%-80%and 5%-8%,respectively.The CRM with lower void fraction,higher W_(f) and G_(f) could be obtained.Based on the reported findings,it was suggested that the sieve passing percentage of 4.75,2.36,and 0.075 mm of CRM is 45%-55%,27%-52%and 1.5%-5%,respectively.展开更多
In recent years, the uncontrollable risks of urban production-living-ecological(PLE)space have increased sharply, making resilience enhancement essential for sustainable urban development. Based on the social-ecologic...In recent years, the uncontrollable risks of urban production-living-ecological(PLE)space have increased sharply, making resilience enhancement essential for sustainable urban development. Based on the social-ecological system(SES) theory, this study constructs an assessment framework for urban PLE space resilience by analyzing its inherent characteristics. The central urban area of Ganzhou city is taken as a case study to evaluate urban PLE space resilience and diagnose its obstacles. The results are as follows: The PLE space resilience in the central urban area of Ganzhou exhibits gradations and substantial spatial differentiation. The ecological space resilience in the study area was the highest, followed by that of production space, while living space resilience was the lowest. The primary factors influencing PLE space resilience are concentrated in the dimensions of robustness and adaptability. In particular, the robustness of the PLE space is relatively low. Based on these results, targeted spatial resilience governance strategies for the PLE space have been proposed. These strategies serve as theoretical and technical references for the study area. By adopting the PLE space perspective, this paper enriches resilience research and provide theoretical support for sustainable urban development.展开更多
The freeze-thaw cycles of frozen soil could significantly affect its thermo-hydro-mechanical-chemical(THMC)properties,causing the frost heaving and thawing settlement.The microscale essence is the water-ice phase tran...The freeze-thaw cycles of frozen soil could significantly affect its thermo-hydro-mechanical-chemical(THMC)properties,causing the frost heaving and thawing settlement.The microscale essence is the water-ice phase transition,but the microscale details are still poorly understood,especially at ultra-low temperatures.Nuclear magnetic resonance(NMR)technology and molecular dynamics(MD)simulation method were performed to explore the freeze-thaw behaviors of montmorillonite clay under temperature of 210e293 K.Then,the water-ice phase transition,freeze-thaw hysteresis,ice nucleation mechanism,and surface effect of clay at an atomistic level were discussed.A classification method of different types of unfrozen water through NMR experiment was proposed,including bulk,capillary,and bound water.Here,it is found that:(1)the freeze-thaw process of frozen soil at the macroscale was essentially the occurrence of ice-water phase transition at the microscale.(2)The freeze-thaw hysteresis was caused by different growth and melting rates of ice crystals,where the ice growth/nucleation on clay surface(i.e.freeze process)was more difficult to develop.(3)The surface effect of clay was essential for the ice nucleation and the existence of bound water.For example,little unfrozen water still existed in unfrozen soil even at 213 K.(4)For unsaturated frozen soil,the quasi-liquid water was an essential component of unfrozen water that cannot be ignored.This work could provide an atomistic insight to unravel the atomistic origin of the freeze-thaw mechanism of montmorillonite clay and complement relevant experimental evidence.展开更多
This research is focused on the calculation of a reasonable detonator delay time for realizing cut blast vibration control.First,the viscoelastic rock mass parameters corresponding to the engineering rock mass quality...This research is focused on the calculation of a reasonable detonator delay time for realizing cut blast vibration control.First,the viscoelastic rock mass parameters corresponding to the engineering rock mass quality classification were determined based on wave theory of Kelvin medium.Then,a calculation model was obtained for the millisecond-delay cut blast vibration in Kelvin media using the Starfield charge superposition principle.Further,the influence of the delay time on the cut blast vibration was quantitatively analyzed and a method for calculating the reasonable cut blasting millisecond delay time is proposed according to the principle of dimensional analysis.Finally,field tests were used to verify the applicability of the method.The results show that 5 ms to 20 ms is a better detonator delay time range and cut blasting vibration can be effectively controlled using the delay time calculated by the calculation model described in this paper.展开更多
Groundwater flow through fractured rocks has been recognized as an important issue in many geotechnical engineering practices.Several key aspects of fundamental mechanisms,numerical modeling and engineering applicatio...Groundwater flow through fractured rocks has been recognized as an important issue in many geotechnical engineering practices.Several key aspects of fundamental mechanisms,numerical modeling and engineering applications of flow in fractured rocks are discussed.First,the microscopic mechanisms of fluid flow in fractured rocks,especially under the complex conditions of non-Darcian flow,multiphase flow,rock dissolution,and particle transport,have been revealed through a com-bined effort of visualized experiments and theoretical analysis.Then,laboratory and field methods of characterizing hydraulic properties(e.g.intrinsic permeability,inertial permeability,and unsaturated flow parameters)of fractured rocks in different flow regimes have been proposed.Subsequently,high-performance numerical simulation approaches for large-scale modeling of groundwater flow in frac-tured rocks and aquifers have been developed.Numerical procedures for optimization design of seepage control systems in various settings have also been proposed.Mechanisms of coupled hydro-mechanical processes and control of flow-induced deformation have been discussed.Finally,three case studies are presented to illustrate the applications of the improved theoretical understanding,characterization methods,modeling approaches,and seepage and deformation control strategies to geotechnical engi-neering projects.展开更多
To perform landslide susceptibility prediction(LSP),it is important to select appropriate mapping unit and landslide-related conditioning factors.The efficient and automatic multi-scale segmentation(MSS)method propose...To perform landslide susceptibility prediction(LSP),it is important to select appropriate mapping unit and landslide-related conditioning factors.The efficient and automatic multi-scale segmentation(MSS)method proposed by the authors promotes the application of slope units.However,LSP modeling based on these slope units has not been performed.Moreover,the heterogeneity of conditioning factors in slope units is neglected,leading to incomplete input variables of LSP modeling.In this study,the slope units extracted by the MSS method are used to construct LSP modeling,and the heterogeneity of conditioning factors is represented by the internal variations of conditioning factors within slope unit using the descriptive statistics features of mean,standard deviation and range.Thus,slope units-based machine learning models considering internal variations of conditioning factors(variant slope-machine learning)are proposed.The Chongyi County is selected as the case study and is divided into 53,055 slope units.Fifteen original slope unit-based conditioning factors are expanded to 38 slope unit-based conditioning factors through considering their internal variations.Random forest(RF)and multi-layer perceptron(MLP)machine learning models are used to construct variant Slope-RF and Slope-MLP models.Meanwhile,the Slope-RF and Slope-MLP models without considering the internal variations of conditioning factors,and conventional grid units-based machine learning(Grid-RF and MLP)models are built for comparisons through the LSP performance assessments.Results show that the variant Slopemachine learning models have higher LSP performances than Slope-machine learning models;LSP results of variant Slope-machine learning models have stronger directivity and practical application than Grid-machine learning models.It is concluded that slope units extracted by MSS method can be appropriate for LSP modeling,and the heterogeneity of conditioning factors within slope units can more comprehensively reflect the relationships between conditioning factors and landslides.The research results have important reference significance for land use and landslide prevention.展开更多
Most literature related to landslide susceptibility prediction only considers a single type of landslide,such as colluvial landslide,rock fall or debris flow,rather than different landslide types,which greatly affects...Most literature related to landslide susceptibility prediction only considers a single type of landslide,such as colluvial landslide,rock fall or debris flow,rather than different landslide types,which greatly affects susceptibility prediction performance.To construct efficient susceptibility prediction considering different landslide types,Huichang County in China is taken as example.Firstly,105 rock falls,350 colluvial landslides and 11 related environmental factors are identified.Then four machine learning models,namely logistic regression,multi-layer perception,support vector machine and C5.0 decision tree are applied for susceptibility modeling of rock fall and colluvial landslide.Thirdly,three different landslide susceptibility prediction(LSP)models considering landslide types based on C5.0 decision tree with excellent performance are constructed to generate final landslide susceptibility:(i)united method,which combines all landslide types directly;(ii)probability statistical method,which couples analyses of susceptibility indices under different landslide types based on probability formula;and(iii)maximum comparison method,which selects the maximum susceptibility index through comparing the predicted susceptibility indices under different types of landslides.Finally,uncertainties of landslide susceptibility are assessed by prediction accuracy,mean value and standard deviation.It is concluded that LSP results of the three coupled models considering landslide types basically conform to the spatial occurrence patterns of landslides in Huichang County.The united method has the best susceptibility prediction performance,followed by the probability method and maximum susceptibility method.More cases are needed to verify this result in-depth.LSP considering different landslide types is superior to that taking only a single type of landslide into account.展开更多
The frost deterioration and deformation of porous rock are commonly investigated under uniform freeze-thaw(FT)conditions.However,the unidirectional FT condition,which is also prevalent in engineering practice,has rece...The frost deterioration and deformation of porous rock are commonly investigated under uniform freeze-thaw(FT)conditions.However,the unidirectional FT condition,which is also prevalent in engineering practice,has received limited attention.Therefore,a comparative study on frost deformation and microstructure evolution of porous rock under both uniform and unidirectional FT conditions was performed.Firstly,frost deformation experiments of rock were conducted under cyclic uniform and unidirectional FT action,respectively.Results illustrate that frost deformation of saturated rock exhibits isotropic characteristics under uniform FT cycles,while it shows anisotropic characteristics under unidirectional FT condition with both the frost heaving strain and residual strain along FT direction much higher than those perpendicular to FT direction.Moreover,the peak value and residual value of cumulative frost strain vary as logarithmic functions with cycle number under both uniform and unidirectional FT conditions.Subsequently,the microstructure evolution of rock suffered cyclic uniform and unidirectional FT action were measured.Under uniform FT cycles,newly generated pores uniformly distribute in rock and pore structure of rock remains isotropic in micro scale,and thus the frost deformation shows isotropic characteristics in macro scale.Under unidirectional FT cycles,micro-cracks or pore belts generate with their orientation nearly perpendicular to the FT direction,and rock structure gradually becomes anisotropic in micro scale,resulting in the anisotropic characteristics of frost deformation in macro scale.展开更多
In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken a...In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.展开更多
To develop suitable grouting materials for water conveyance tunnels in cold regions,firstly,this study investigated the performance evolution of ferrite-rich sulfoaluminate-based composite cement(FSAC grouting materia...To develop suitable grouting materials for water conveyance tunnels in cold regions,firstly,this study investigated the performance evolution of ferrite-rich sulfoaluminate-based composite cement(FSAC grouting material)at 20 and 3℃.The results show that low temperature only delays the strength development of FSAC grouting material within the first 3 d.Then,the effect of four typical early strength synergists on the early properties of FSAC grouting material was evaluated to optimize the early(£1 d)strength at 3℃.The most effective synergist,Ca(HCOO)_(2),which enhances the low-temperature early strength without compromising fluidity was selected based on strength and fluidity tests.Its micro-mechanism was analyzed by XRD,TG,and SEM methods.The results reveal that the most suitable dosage range is 0.3 wt%−0.5 wt%.Proper addition of Ca(HCOO)_(2)changed the crystal morphology of the hydration products,decreased the pore size and formed more compact hydration products by interlocking and overlapping.However,excessive addition of Ca(HCOO)_(2)inhibited the hydration reaction,resulting in a simple and loose structure of the hydration products.The research results have reference value for controlling surrounding rock deformation and preventing water and mud inrushes during the excavation in cold region tunnels.展开更多
With an extension of the geological entropy concept in porous media,the approach called directional entrogram is applied to link hydraulic behavior to the anisotropy of the 3D fracture networks.A metric called directi...With an extension of the geological entropy concept in porous media,the approach called directional entrogram is applied to link hydraulic behavior to the anisotropy of the 3D fracture networks.A metric called directional entropic scale is used to measure the anisotropy of spatial order in different directions.Compared with the traditional connectivity indexes based on the statistics of fracture geometry,the directional entropic scale is capable to quantify the anisotropy of connectivity and hydraulic conductivity in heterogeneous 3D fracture networks.According to the numerical analysis of directional entrogram and fluid flow in a number of the 3D fracture networks,the hydraulic conductivities and entropic scales in different directions both increase with spatial order(i.e.,trace length decreasing and spacing increasing)and are independent of the dip angle.As a result,the nonlinear correlation between the hydraulic conductivities and entropic scales from different directions can be unified as quadratic polynomial function,which can shed light on the anisotropic effect of spatial order and global entropy on the heterogeneous hydraulic behaviors.展开更多
The accuracy of landslide susceptibility prediction(LSP)mainly depends on the precision of the landslide spatial position.However,the spatial position error of landslide survey is inevitable,resulting in considerable ...The accuracy of landslide susceptibility prediction(LSP)mainly depends on the precision of the landslide spatial position.However,the spatial position error of landslide survey is inevitable,resulting in considerable uncertainties in LSP modeling.To overcome this drawback,this study explores the influence of positional errors of landslide spatial position on LSP uncertainties,and then innovatively proposes a semi-supervised machine learning model to reduce the landslide spatial position error.This paper collected 16 environmental factors and 337 landslides with accurate spatial positions taking Shangyou County of China as an example.The 30e110 m error-based multilayer perceptron(MLP)and random forest(RF)models for LSP are established by randomly offsetting the original landslide by 30,50,70,90 and 110 m.The LSP uncertainties are analyzed by the LSP accuracy and distribution characteristics.Finally,a semi-supervised model is proposed to relieve the LSP uncertainties.Results show that:(1)The LSP accuracies of error-based RF/MLP models decrease with the increase of landslide position errors,and are lower than those of original data-based models;(2)70 m error-based models can still reflect the overall distribution characteristics of landslide susceptibility indices,thus original landslides with certain position errors are acceptable for LSP;(3)Semi-supervised machine learning model can efficiently reduce the landslide position errors and thus improve the LSP accuracies.展开更多
Probabilistic back-analysis is an important means to infer the statistics of uncertain soil parameters,making the slope reliability assessment closer to the engineering reality.However,multi-source information(includi...Probabilistic back-analysis is an important means to infer the statistics of uncertain soil parameters,making the slope reliability assessment closer to the engineering reality.However,multi-source information(including test data,monitored data,field observation and slope survival records)is rarely used in current probabilistic back-analysis.Conducting the probabilistic back-analysis of spatially varying soil parameters and slope reliability prediction under rainfalls by integrating multi-source information is a challenging task since thousands of random variables and high-dimensional likelihood function are usually involved.In this paper,a framework by integrating a modified Bayesian Updating with Subset simulation(mBUS)method with adaptive Conditional Sampling(aCS)algorithm is established for the probabilistic back-analysis of spatially varying soil parameters and slope reliability prediction.Within this framework,the high-dimensional probabilistic back-analysis problem can be easily tackled,and the multi-source information(e.g.monitored pressure heads and slope survival records)can be fully used in the back-analysis.A real Taoyuan landslide case in Taiwan,China is investigated to illustrate the effectiveness and performance of the established framework.The findings show that the posterior knowledge of soil parameters obtained from the established framework is in good agreement with the field observations.Furthermore,the updated knowledge of soil parameters can be utilized to reliably predict the occurrence probability of a landslide caused by the heavy rainfall event on September 12,2004 or forecast the potential landslides under future rainfalls in the Fuhsing District of Taoyuan City,Taiwan,China.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.52574103 and 42277150).
文摘Fractures are typically characterized by roughness that significantlyaffects the mechanical and hydraulic characteristics of reservoirs.However,hydraulic fracturing mechanisms under the influenceof fracture morphology remain largely unexplored.Leveraging the advantages of the finite-discrete element method(FDEM)for explicitly simulating fracture propagation and the strengths of the unifiedpipe model(UPM)for efficientlymodeling dual-permeability seepage,we propose a new hydromechanical(HM)coupling approach for modeling hydraulic fracturing.Validated against benchmark examples,the proposed FDEM-UPM model is further augmented by incorporating a Fourier-based methodology for reconstructing non-planar fractures,enabling quantitative analysis of hydraulic fracturing behavior within rough discrete fracture networks(DFNs).The FDEM-UPM model demonstrates computational advantages in accurately capturing transient hydraulic seepage phenomena,while the asynchronous time-stepping schemes between hydraulic and mechanical analyses substantially enhanced computational efficiencywithout compromising computational accuracy.Our results show that fracture morphology can affect both macroscopic fracture networks and microscopic interaction types between hydraulic fractures(HFs)and natural fractures(NFs).In an isotropic stress field,the initiation azimuth,propagation direction and microcracking mechanism are significantly influencedby fracture roughness.In an anisotropic stress field,HFs invariably propagate parallel to the direction of the maximum principal stress,reducing the overall complexity of the stimulated fracture networks.Additionally,stress concentration and perturbation attributed to fracture morphology tend to be compromised as the leak-off increases,while the breakdown and propagation pressures remain insensitive to fracture morphology.These findingsprovide new insights into the hydraulic fracturing mechanisms of fractured reservoirs containing complex rough DFNs.
基金Funded by the National Science Foundation of China(No.52368031)the China Postdoctoral Science Foundation(No.2022M713497)+1 种基金the Jiangxi Provincial Natural Science Foundation(No.20252BAC250115)the Jiangxi Provincial Department of Transportation Science and Technology Project(No.2022H0017)。
文摘Utilization of ceramic wastes to fabricate concrete can not only effectively dispose the wastes,but also reduce the energy and source consumptions.Therefore,we fabricated a green ultra high performance concrete using ceramic waste powder(CWP)to replace 55%of cement,and ceramic waste aggregate(CWA)to replace 100%natural quartz sand.However,high content of ceramic wastes will harm the concrete performance including workability and mechanical properties.Therefore,a low-cost and low carbon nano-calcium carbonate(NC)was introduced to compensate for the defects caused by large amounts of CWP and CWA to workability and mechanical behavior.The experimental results show that the workability of ultra high performance concrete with large amounts of CWP and CWA(UHPCLCC)increases by 28.57%with NC content of 5%.Moreover,the flexural strengths,fracture energy,compressive strengths,and compressive toughness of UHPCLCC increase up to 29.6%,56.5%,20.4%,and 37.6%,respectively,which is caused by the nano-core effect of NC.
基金supported by a grant from China railway corporation science and technology research and development plan project(Grant No.2017G005-B)funding support by Wuyi University’s Hong Kong and Macao Joint Research and Development Fund(Grants No.2021WGALH15)funding support by the Innovation and Technology Commission of Hong Kong SAR Government to the Hong Kong Branch of National Rail Transit Electrification and Automation Engineering Technology Research Center(Grant No.K-BBY1).
文摘The current technical standards primarily relied on experience to judge the interfacial bonding properties between the self-compacting concrete filling layer and the steam-cured concrete precast slab in CRTS Ⅲ slab ballastless track structure.This study sought to enhance technical standards for evaluating interfacial bonding properties by suggesting the use of the splitting tensile strength to evaluate the impact of bubble defects.Specimens were fabricated through on-site experiment.The percent of each area of 6 cm^(2)or more bubble defect was 0 in most of specimens.When the cumulative area of all bub-ble defects reached 12%,the splitting tensile strength value was 0.67 MPa,which exceeded the minimum required value of 0.5 MPa for ensuring bonding interface adhesion.Furthermore,when the cumulative area of all bubble defects reached 8%,the splitting tensile strength value was 0.85 MPa,which exceeded the minimum required value of 0.8 MPa,thereby over-coming the negative impact of each area of 10 cm^(2) or more bubble defect.Additionally,keeping the cumulative area of each area of 6 cm^(2) or more bubble defect below 6%ensured adequate bonding strength and reduced the occurrence of specimens with lower splitting tensile strength values.
基金funded by the Natural Science Foundation of China(Grant Nos.42377164 and 41972280)the Badong National Observation and Research Station of Geohazards(Grant No.BNORSG-202305).
文摘Landslide susceptibility prediction(LSP)is significantly affected by the uncertainty issue of landslide related conditioning factor selection.However,most of literature only performs comparative studies on a certain conditioning factor selection method rather than systematically study this uncertainty issue.Targeted,this study aims to systematically explore the influence rules of various commonly used conditioning factor selection methods on LSP,and on this basis to innovatively propose a principle with universal application for optimal selection of conditioning factors.An'yuan County in southern China is taken as example considering 431 landslides and 29 types of conditioning factors.Five commonly used factor selection methods,namely,the correlation analysis(CA),linear regression(LR),principal component analysis(PCA),rough set(RS)and artificial neural network(ANN),are applied to select the optimal factor combinations from the original 29 conditioning factors.The factor selection results are then used as inputs of four types of common machine learning models to construct 20 types of combined models,such as CA-multilayer perceptron,CA-random forest.Additionally,multifactor-based multilayer perceptron random forest models that selecting conditioning factors based on the proposed principle of“accurate data,rich types,clear significance,feasible operation and avoiding duplication”are constructed for comparisons.Finally,the LSP uncertainties are evaluated by the accuracy,susceptibility index distribution,etc.Results show that:(1)multifactor-based models have generally higher LSP performance and lower uncertainties than those of factors selection-based models;(2)Influence degree of different machine learning on LSP accuracy is greater than that of different factor selection methods.Conclusively,the above commonly used conditioning factor selection methods are not ideal for improving LSP performance and may complicate the LSP processes.In contrast,a satisfied combination of conditioning factors can be constructed according to the proposed principle.
基金funded by the Natural Science Foundation of China(No.42407241,42272326 and 52222905)Jiangxi Provincial Natural Science Foundation(Nos.20242BAB20241,20242BAB23052 and 20242BAB24001).
文摘The spatial distribution of overburden layer thickness(OLT)is crucial for landslide susceptibility prediction and slope stability analysis.Due to OLT spatial heterogeneity in hillslope regions,combined with the difficulty and time consumption of OLT sample collection,accurately predicting OLT distribution remains a challenging.To address this,a novel framework has been developed.First,OLT samples are collected through field surveys,remote sensing,and geological drilling.Next,the heterogeneity of OLT’s spatial distribution is analyzed using the probability distribution of OLT samples and their horizontal and vertical distributions.The OLT samples are categorized and the small sample categories are expanded using the synthetic minority over-sampling technique(SMOTE).The slope position is selected as a key conditioning factor.Subsequently,16 conditioning factors are applied to construct OLT prediction model using the random forest regression algorithm.Weights are assigned to each OLT sample category to balance the uneven distribution of sample sizes.Finally,the Pearson correlation coefficient,mean absolute error(MAE),root mean square error(RMSE),and Lin’s concordance correlation coefficient(Lin’s CCC)are employed to validate the OLT prediction results.The Huangtan town serves as the case study.Results show:(1)heterogeneity analysis,SMOTE-based OLT sample expansion strategy and slope position selection can significantly mitigate the effect of spatial heterogeneity on OLT prediction.(2)The Pearson correlation coefficient,RMSE,MAE and Lin’s CCC values are 0.84,1.173,1.378 and 0.804,respectively,indicating excellent prediction performance.This research provides an effective solution for predicting OLT distribution in hillslope regions.
基金supported by the National Natural Science Foundation of China(Grant Nos.52369019,52004127)the Young Elite Scientists Sponsorship Program by JXAST(Grant No.2023QT06).
文摘The study of the shear behavior of bonded rock-cement interface is important for understanding the strength and stability of grouted rock masses.This research aims to reveal the failure mechanism behind the shear property of bonded rock-cement interfaces.For the study,sandstone and granite joint blocks with specific morphology were fabricated by using a three-dimensional(3D)engraving technique.Bonded rock-cement joints with asperity inclination angles of 15°,30°,and 45°were prepared.Shear tests were performed on these bonded rock-cement joints to investigate the shear response and failure modes considering the effect of applied normal stress and interface morphology.Meanwhile,the two-dimensional particle flow code(PFC2D)was utilized to model the entire shear process of bonded rock-cement interfaces.The macroscopic shear behavior and mesoscopic failure mechanism were comprehensively investigated by the laboratory test and numerical simulation.The results showed that the shear stress-displacement curves of bonded rock-cement joints exhibit two distinct peaks,and the shear stress evolution can be categorized into four stages including elastic growth,rapid stress drop,secondary stress growth,and progressive softening.Significantly,the number of acoustic emission events also exhibits two distinct peaks related to the double peak of the shear stress curves.The failure of bonded rock-cement interfaces is mainly induced by shear fractures,while the failure of rock and cement blocks is primarily caused by tensile fractures.The number of shear cracks in the bonded rock-cement interfaces reaches the peak when the shear stress reaches the primary peak;whereas as the shear stress continuously approaches the residual stage,the fracture of the bonded rock-cement joints is primarily characterized by tensile cracks in the blocks.
基金Open Research Fund of Key Laboratory of Deep Earth Science and Engineering,Grant/Award Number:DESEYU202303Fundamental Research Funds for the Central Universities,Grant/Award Number:DUT24GJ205。
文摘The geothermal resources in hot dry rock(HDR)are considered the future trend in geothermal energy extraction due to their abundant reserves.However,exploitation of the resources is fraught with complexity and technical challenges arising from their unique characteristics of high temperature,high strength,and low permeability.With the continuous advancement of artificial intelligence(AI)technology,intelligent algorithms such as machine learning and evolutionary algorithms are gradually replacing or assisting traditional research methods,providing new solutions for HDR geothermal resource exploitation.This study first provides an overview of HDR geothermal resource exploitation technologies and AI methods.Then,the latest research progress is systematically reviewed in AI applications in HDR geothermal reservoir characterization,deep drilling,heat production,and operational parameter optimization.Additionally,this study discusses the potential limitations of AI methods in HDR geothermal resource exploitation and highlights the corresponding opportunities for AI's application.Notably,the study proposes the framework of an intelligent HDR exploitation system,offering a valuable reference for future research and practice.
基金Funded by the Key Research and Development Plan of Jiangxi Province (No. 20223BBG74002)the Natural Science Foundation of China (Nos. 51778483, 51978521)the Fundamental Research Funds for the Central Universities (No. DUT24RC (3)100)。
文摘We optimized the gradation of cold recycled mixture(CRM)based on low-temperature performance.Firstly,the low-temperature crack resistance of CRM with different gradation and emulsified asphalt content was studied by indirect tension(IDT)and semi-circular bending(SCB)test.Thereafter,the low-temperature performance evaluation index suitable for CRM was put forward.Then,the triangular coordinate statistical chart was used to analyze the optimal proportion of three grades of aggregate which are 2.36-4.75 mm,0.075-2.36 mm and below 0.075 mm.The results showed that the W_(f) and G_(f) could distinguish the low-temperature performance of CRM with different mixtures and emulsified asphalt dosage.For cold recycled fine aggregate,2.36-4.75 mm,0.075-2.36 mm and less than 0.075 mm account for 20%-25%,74.3%-80%and 5%-8%,respectively.The CRM with lower void fraction,higher W_(f) and G_(f) could be obtained.Based on the reported findings,it was suggested that the sieve passing percentage of 4.75,2.36,and 0.075 mm of CRM is 45%-55%,27%-52%and 1.5%-5%,respectively.
基金Social Science Foundation Project of Jiangxi Province,No.24GL61D。
文摘In recent years, the uncontrollable risks of urban production-living-ecological(PLE)space have increased sharply, making resilience enhancement essential for sustainable urban development. Based on the social-ecological system(SES) theory, this study constructs an assessment framework for urban PLE space resilience by analyzing its inherent characteristics. The central urban area of Ganzhou city is taken as a case study to evaluate urban PLE space resilience and diagnose its obstacles. The results are as follows: The PLE space resilience in the central urban area of Ganzhou exhibits gradations and substantial spatial differentiation. The ecological space resilience in the study area was the highest, followed by that of production space, while living space resilience was the lowest. The primary factors influencing PLE space resilience are concentrated in the dimensions of robustness and adaptability. In particular, the robustness of the PLE space is relatively low. Based on these results, targeted spatial resilience governance strategies for the PLE space have been proposed. These strategies serve as theoretical and technical references for the study area. By adopting the PLE space perspective, this paper enriches resilience research and provide theoretical support for sustainable urban development.
基金financially supported by the Open Fund of State Key Laboratory of Frozen Soil Engineering(Grant No.SKLFSE202104)the Natural Science Foundation of GuangDong Basic and Applied Basic Research Foundation(Grant No.2024A1515011853)the Research Grants Council(RGC)of Hong Kong Special Administrative Region Government(HKSARG)of China(Grant Nos.:N_PolyU534/20).
文摘The freeze-thaw cycles of frozen soil could significantly affect its thermo-hydro-mechanical-chemical(THMC)properties,causing the frost heaving and thawing settlement.The microscale essence is the water-ice phase transition,but the microscale details are still poorly understood,especially at ultra-low temperatures.Nuclear magnetic resonance(NMR)technology and molecular dynamics(MD)simulation method were performed to explore the freeze-thaw behaviors of montmorillonite clay under temperature of 210e293 K.Then,the water-ice phase transition,freeze-thaw hysteresis,ice nucleation mechanism,and surface effect of clay at an atomistic level were discussed.A classification method of different types of unfrozen water through NMR experiment was proposed,including bulk,capillary,and bound water.Here,it is found that:(1)the freeze-thaw process of frozen soil at the macroscale was essentially the occurrence of ice-water phase transition at the microscale.(2)The freeze-thaw hysteresis was caused by different growth and melting rates of ice crystals,where the ice growth/nucleation on clay surface(i.e.freeze process)was more difficult to develop.(3)The surface effect of clay was essential for the ice nucleation and the existence of bound water.For example,little unfrozen water still existed in unfrozen soil even at 213 K.(4)For unsaturated frozen soil,the quasi-liquid water was an essential component of unfrozen water that cannot be ignored.This work could provide an atomistic insight to unravel the atomistic origin of the freeze-thaw mechanism of montmorillonite clay and complement relevant experimental evidence.
基金National Natural Science Foundation of China under Grant Nos.51979205 and 51939008。
文摘This research is focused on the calculation of a reasonable detonator delay time for realizing cut blast vibration control.First,the viscoelastic rock mass parameters corresponding to the engineering rock mass quality classification were determined based on wave theory of Kelvin medium.Then,a calculation model was obtained for the millisecond-delay cut blast vibration in Kelvin media using the Starfield charge superposition principle.Further,the influence of the delay time on the cut blast vibration was quantitatively analyzed and a method for calculating the reasonable cut blasting millisecond delay time is proposed according to the principle of dimensional analysis.Finally,field tests were used to verify the applicability of the method.The results show that 5 ms to 20 ms is a better detonator delay time range and cut blasting vibration can be effectively controlled using the delay time calculated by the calculation model described in this paper.
基金The financial supports from the National Natural Science Foundation of China(Grant Nos.51988101,51925906 and 52122905)are gratefully acknowledged.
文摘Groundwater flow through fractured rocks has been recognized as an important issue in many geotechnical engineering practices.Several key aspects of fundamental mechanisms,numerical modeling and engineering applications of flow in fractured rocks are discussed.First,the microscopic mechanisms of fluid flow in fractured rocks,especially under the complex conditions of non-Darcian flow,multiphase flow,rock dissolution,and particle transport,have been revealed through a com-bined effort of visualized experiments and theoretical analysis.Then,laboratory and field methods of characterizing hydraulic properties(e.g.intrinsic permeability,inertial permeability,and unsaturated flow parameters)of fractured rocks in different flow regimes have been proposed.Subsequently,high-performance numerical simulation approaches for large-scale modeling of groundwater flow in frac-tured rocks and aquifers have been developed.Numerical procedures for optimization design of seepage control systems in various settings have also been proposed.Mechanisms of coupled hydro-mechanical processes and control of flow-induced deformation have been discussed.Finally,three case studies are presented to illustrate the applications of the improved theoretical understanding,characterization methods,modeling approaches,and seepage and deformation control strategies to geotechnical engi-neering projects.
基金funded by the Natural Science Foundation of China(Grant Nos.41807285,41972280 and 52179103).
文摘To perform landslide susceptibility prediction(LSP),it is important to select appropriate mapping unit and landslide-related conditioning factors.The efficient and automatic multi-scale segmentation(MSS)method proposed by the authors promotes the application of slope units.However,LSP modeling based on these slope units has not been performed.Moreover,the heterogeneity of conditioning factors in slope units is neglected,leading to incomplete input variables of LSP modeling.In this study,the slope units extracted by the MSS method are used to construct LSP modeling,and the heterogeneity of conditioning factors is represented by the internal variations of conditioning factors within slope unit using the descriptive statistics features of mean,standard deviation and range.Thus,slope units-based machine learning models considering internal variations of conditioning factors(variant slope-machine learning)are proposed.The Chongyi County is selected as the case study and is divided into 53,055 slope units.Fifteen original slope unit-based conditioning factors are expanded to 38 slope unit-based conditioning factors through considering their internal variations.Random forest(RF)and multi-layer perceptron(MLP)machine learning models are used to construct variant Slope-RF and Slope-MLP models.Meanwhile,the Slope-RF and Slope-MLP models without considering the internal variations of conditioning factors,and conventional grid units-based machine learning(Grid-RF and MLP)models are built for comparisons through the LSP performance assessments.Results show that the variant Slopemachine learning models have higher LSP performances than Slope-machine learning models;LSP results of variant Slope-machine learning models have stronger directivity and practical application than Grid-machine learning models.It is concluded that slope units extracted by MSS method can be appropriate for LSP modeling,and the heterogeneity of conditioning factors within slope units can more comprehensively reflect the relationships between conditioning factors and landslides.The research results have important reference significance for land use and landslide prevention.
基金funded by the Natural Science Foundation of China(Grant Nos.52079062 and 41807285)the Interdisciplinary Innovation Fund of Natural Science,Nanchang University,China(Grant No.9167-28220007-YB2107).
文摘Most literature related to landslide susceptibility prediction only considers a single type of landslide,such as colluvial landslide,rock fall or debris flow,rather than different landslide types,which greatly affects susceptibility prediction performance.To construct efficient susceptibility prediction considering different landslide types,Huichang County in China is taken as example.Firstly,105 rock falls,350 colluvial landslides and 11 related environmental factors are identified.Then four machine learning models,namely logistic regression,multi-layer perception,support vector machine and C5.0 decision tree are applied for susceptibility modeling of rock fall and colluvial landslide.Thirdly,three different landslide susceptibility prediction(LSP)models considering landslide types based on C5.0 decision tree with excellent performance are constructed to generate final landslide susceptibility:(i)united method,which combines all landslide types directly;(ii)probability statistical method,which couples analyses of susceptibility indices under different landslide types based on probability formula;and(iii)maximum comparison method,which selects the maximum susceptibility index through comparing the predicted susceptibility indices under different types of landslides.Finally,uncertainties of landslide susceptibility are assessed by prediction accuracy,mean value and standard deviation.It is concluded that LSP results of the three coupled models considering landslide types basically conform to the spatial occurrence patterns of landslides in Huichang County.The united method has the best susceptibility prediction performance,followed by the probability method and maximum susceptibility method.More cases are needed to verify this result in-depth.LSP considering different landslide types is superior to that taking only a single type of landslide into account.
基金This research was supported by the National Natural Science Foundation of China(52108370)Jiangxi Provincial Natural Science Foundation(No.20212BAB214062,20224BAB204061).
文摘The frost deterioration and deformation of porous rock are commonly investigated under uniform freeze-thaw(FT)conditions.However,the unidirectional FT condition,which is also prevalent in engineering practice,has received limited attention.Therefore,a comparative study on frost deformation and microstructure evolution of porous rock under both uniform and unidirectional FT conditions was performed.Firstly,frost deformation experiments of rock were conducted under cyclic uniform and unidirectional FT action,respectively.Results illustrate that frost deformation of saturated rock exhibits isotropic characteristics under uniform FT cycles,while it shows anisotropic characteristics under unidirectional FT condition with both the frost heaving strain and residual strain along FT direction much higher than those perpendicular to FT direction.Moreover,the peak value and residual value of cumulative frost strain vary as logarithmic functions with cycle number under both uniform and unidirectional FT conditions.Subsequently,the microstructure evolution of rock suffered cyclic uniform and unidirectional FT action were measured.Under uniform FT cycles,newly generated pores uniformly distribute in rock and pore structure of rock remains isotropic in micro scale,and thus the frost deformation shows isotropic characteristics in macro scale.Under unidirectional FT cycles,micro-cracks or pore belts generate with their orientation nearly perpendicular to the FT direction,and rock structure gradually becomes anisotropic in micro scale,resulting in the anisotropic characteristics of frost deformation in macro scale.
基金This work is funded by the National Natural Science Foundation of China(Grant Nos.42377164 and 52079062)the National Science Fund for Distinguished Young Scholars of China(Grant No.52222905).
文摘In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.
基金Projcet(52279119)supported by the National Natural Science Foundation of ChinaProject(XZ202201ZY0021G)supported by the Science and Technology Planning Project of Xizang Autonomous Region,China+1 种基金Project(2019QZKK0904)supported by the Second Xizang Plateau Scientific Expedition and Research Program of ChinaProject(51922104)supported by the National Natural Science Foundation for Distinguished Young Scholars of China。
文摘To develop suitable grouting materials for water conveyance tunnels in cold regions,firstly,this study investigated the performance evolution of ferrite-rich sulfoaluminate-based composite cement(FSAC grouting material)at 20 and 3℃.The results show that low temperature only delays the strength development of FSAC grouting material within the first 3 d.Then,the effect of four typical early strength synergists on the early properties of FSAC grouting material was evaluated to optimize the early(£1 d)strength at 3℃.The most effective synergist,Ca(HCOO)_(2),which enhances the low-temperature early strength without compromising fluidity was selected based on strength and fluidity tests.Its micro-mechanism was analyzed by XRD,TG,and SEM methods.The results reveal that the most suitable dosage range is 0.3 wt%−0.5 wt%.Proper addition of Ca(HCOO)_(2)changed the crystal morphology of the hydration products,decreased the pore size and formed more compact hydration products by interlocking and overlapping.However,excessive addition of Ca(HCOO)_(2)inhibited the hydration reaction,resulting in a simple and loose structure of the hydration products.The research results have reference value for controlling surrounding rock deformation and preventing water and mud inrushes during the excavation in cold region tunnels.
基金supported by the National Natural Science Foundation of China(Nos.42077243,52209148,and 52079062).
文摘With an extension of the geological entropy concept in porous media,the approach called directional entrogram is applied to link hydraulic behavior to the anisotropy of the 3D fracture networks.A metric called directional entropic scale is used to measure the anisotropy of spatial order in different directions.Compared with the traditional connectivity indexes based on the statistics of fracture geometry,the directional entropic scale is capable to quantify the anisotropy of connectivity and hydraulic conductivity in heterogeneous 3D fracture networks.According to the numerical analysis of directional entrogram and fluid flow in a number of the 3D fracture networks,the hydraulic conductivities and entropic scales in different directions both increase with spatial order(i.e.,trace length decreasing and spacing increasing)and are independent of the dip angle.As a result,the nonlinear correlation between the hydraulic conductivities and entropic scales from different directions can be unified as quadratic polynomial function,which can shed light on the anisotropic effect of spatial order and global entropy on the heterogeneous hydraulic behaviors.
基金the National Natural Science Foundation of China(Grant Nos.42377164 and 52079062)the Interdisciplinary Innovation Fund of Natural Science,Nanchang University(Grant No.9167-28220007-YB2107).
文摘The accuracy of landslide susceptibility prediction(LSP)mainly depends on the precision of the landslide spatial position.However,the spatial position error of landslide survey is inevitable,resulting in considerable uncertainties in LSP modeling.To overcome this drawback,this study explores the influence of positional errors of landslide spatial position on LSP uncertainties,and then innovatively proposes a semi-supervised machine learning model to reduce the landslide spatial position error.This paper collected 16 environmental factors and 337 landslides with accurate spatial positions taking Shangyou County of China as an example.The 30e110 m error-based multilayer perceptron(MLP)and random forest(RF)models for LSP are established by randomly offsetting the original landslide by 30,50,70,90 and 110 m.The LSP uncertainties are analyzed by the LSP accuracy and distribution characteristics.Finally,a semi-supervised model is proposed to relieve the LSP uncertainties.Results show that:(1)The LSP accuracies of error-based RF/MLP models decrease with the increase of landslide position errors,and are lower than those of original data-based models;(2)70 m error-based models can still reflect the overall distribution characteristics of landslide susceptibility indices,thus original landslides with certain position errors are acceptable for LSP;(3)Semi-supervised machine learning model can efficiently reduce the landslide position errors and thus improve the LSP accuracies.
文摘Probabilistic back-analysis is an important means to infer the statistics of uncertain soil parameters,making the slope reliability assessment closer to the engineering reality.However,multi-source information(including test data,monitored data,field observation and slope survival records)is rarely used in current probabilistic back-analysis.Conducting the probabilistic back-analysis of spatially varying soil parameters and slope reliability prediction under rainfalls by integrating multi-source information is a challenging task since thousands of random variables and high-dimensional likelihood function are usually involved.In this paper,a framework by integrating a modified Bayesian Updating with Subset simulation(mBUS)method with adaptive Conditional Sampling(aCS)algorithm is established for the probabilistic back-analysis of spatially varying soil parameters and slope reliability prediction.Within this framework,the high-dimensional probabilistic back-analysis problem can be easily tackled,and the multi-source information(e.g.monitored pressure heads and slope survival records)can be fully used in the back-analysis.A real Taoyuan landslide case in Taiwan,China is investigated to illustrate the effectiveness and performance of the established framework.The findings show that the posterior knowledge of soil parameters obtained from the established framework is in good agreement with the field observations.Furthermore,the updated knowledge of soil parameters can be utilized to reliably predict the occurrence probability of a landslide caused by the heavy rainfall event on September 12,2004 or forecast the potential landslides under future rainfalls in the Fuhsing District of Taoyuan City,Taiwan,China.
