Estimation of the anisotropy of hydraulic conductivity through 3D fracture networks using the directional geological entropy

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.

Uncertainties of landslide susceptibility prediction: Influences of random errors in landslide conditioning factors and errors reduction by low pass filter method

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.

Uncertainties in landslide susceptibility prediction:Influence rule of different levels of errors in landslide spatial position

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.

Effects of soil crust on the collapsing erosion of colluvial deposits with granite residual soil

Collapsing erosion is a unique phenomenon commonly observed on the granite residue hillslopes in the tropical and subtropical regions of southern China,characterized by its abrupt occurrence and significant erosion volumes.However,the impacts of soil crust conditions on the erosion of colluvial deposits with granite residual soils have only been studied to a limited extent.To address this issue,this study investigates the impacts of three soil crust conditions(i.e.,without crust,10-minute crust,and 20-minute crust)on gully morphology,rainfall infiltration,and runoff and sediment yield during slope erosion of colluvial deposits with granite residues(classified as Acrisols)in Yudu County,Ganzhou City,Jiangxi Province,China,using simulated rainfall tests and photographic methods.The results showed that as the strength of the soil crust increased,the capacity of moisture infiltration and the width and depth of the gully as well as the sediment concentration and yield ratio decreased;at the same time,the runoff ratio increased.The sediment yield in the without-crust test was found to be 1.24 and 1.43 times higher than that observed in the 10-minute crust and 20-minute crust tests,respectively.These results indicate that soil crusts can effectively prevent slope erosion and moisture infiltration,while providing valuable insights for the management of soil erosion in natural environments.

Frost deformation and microstructure evolution of porous rock under uniform and unidirectional freeze-thaw conditions

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.

Characteristic and optimization of ferrite-rich sulfoaluminate-based composite cement suitable for cold region tunnels

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.

Probabilistic back-analysis of rainfall-induced landslides for slope reliability prediction with multi-source information

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.

Earthquakes and Sustainable Infrastructure- Neodeterministic (NDSHA) Approach Guarantees Prevention Rather Than Cure

The reduction of seismic disaster risk hinges on the reliable assessment of seismic hazard.In the realm of seismic hazard assessment(SHA),there have historically been two methods:the probabilistic SHA(PSHA)and the deterministic SHA(DSHA)(Reiter,1990).Hazard is defined as inherent physical characteristic that poses potential threats to people,property,or environment.Within the context of seismic hazard,the main purpose of hazard analysis is to quantitatively assess ground shaking level at a site,through either DSHA or PSHA method.It is essential to quantitatively evaluate ground motion level at a target site.

Landslide susceptibility prediction using slope unit-based machine learning models considering the heterogeneity of conditioning factors

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.

Groundwater flow through fractured rocks and seepage control in geotechnical engineering: Theories and practices

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.

Uncertainties of landslide susceptibility prediction considering different landslide types

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.

Experimental study on the seismic performance of masonry wall reinforced by cement mortar and polypropylene band

Since masonry structures are prone to collapse in earthquakes,a novel joint reinforcement method with a polypropylene band(PP-band)and cement mortar(CM)has been put forward.Compared with the common reinforcement methods,this method not only facilitates construction but also ensures lower reinforcement cost.To systematically explore the influence of joint reinforcement on the seismic performance of masonry walls,quasi-static tests were carried out on six specimens with different reinforcement forms.The test results show that the joint action of PP-band and CM can significantly improve the specimen′s brittle failure characteristics and enhance the integrity of the specimen after cracking.Compared with the specimen without reinforcement,each of the seismic performance indexes of the joint reinforced specimen had obvious improvement.The maximum increased rate about peak load and ductility of the joint reinforced specimen is 100.6%and 233.4%,respectively.

Biofuel Recovery from Plantain and Banana Plant Wastes:Integration of Biochemical and Thermochemical Approach

Globally,fossil fuel dependence has created several environmental challenges and climate change.Hence,creating other alternative renewable and ecologically friendly bio-energy sources is necessary.Lignocellulosic biomass has gained significant attention recently as a renewable material for biofuel production.The large amounts of plantain and banana plant parts wasted after harvesting,as well as the peels generated daily by the fruit market and industries,demonstrate the potential of bioenergy resources.This review briefly assesses plantain and banana plant biomass(PBB)generated in the developing,developed,and underdeveloped countries,the consumable parts,and feasible products yield.It emphasized the advantages and disadvantages of the commonly adopted treatment technologies of composting,incineration,and landfilling.Further,the utilization of PBB as catalysts in biodiesel synthesis was briefly highlighted.To optimize recovery of biofuel,different integration routes of pyrolysis,anaerobic digestion,fermentation,hydrothermal carbonization,hydrothermal liquefaction,and hydrothermal gasification for the valorization of the PBB were proposed.The complex compounds present in the PBB(hemicellulose,cellulose,and lignin)can be converted into valuable bio-products such as methane gas and bio-ethanol for bioenergy,and nutrients to promote bioactive ingredients.The investigation of the viability and innovation potential of the integrated routes’technology is necessary to improve the circular bio-economy and the recovery of biofuels from biomass waste,particularly PBB.

Efficient slope reliability and sensitivity analysis using quantile-based first-order second-moment method

This paper introduces a novel approach for parameter sensitivity evaluation and efficient slope reliability analysis based on quantile-based first-order second-moment method(QFOSM).The core principles of the QFOSM are elucidated geometrically from the perspective of expanding ellipsoids.Based on this geometric interpretation,the QFOSM is further extended to estimate sensitivity indices and assess the significance of various uncertain parameters involved in the slope system.The proposed method has the advantage of computational simplicity,akin to the conventional first-order second-moment method(FOSM),while providing estimation accuracy close to that of the first-order reliability method(FORM).Its performance is demonstrated with a numerical example and three slope examples.The results show that the proposed method can efficiently estimate the slope reliability and simultaneously evaluate the sensitivity of the uncertain parameters.The proposed method does not involve complex optimization or iteration required by the FORM.It can provide a valuable complement to the existing approximate reliability analysis methods,offering rapid sensitivity evaluation and slope reliability analysis.

Editorial for advances in slope deformation and failure analysis methods

We are privileged to be invited by the Honorary Editor-in-Chief,Professor Qihu Qian,Editor-in-Chief,Professor Xia-Ting Feng,and the editorial staff of the Journal of Rock Mechanics and Geotechnical Engineering(JRMGE),to serve as Guest Editors for this Special Issue(SI).In literature there have been new advancements in monitoring techniques,advanced numerical modelling,optimization algorithms,artificial intelligence and data science introduced for slope engineering.The newly proposed approaches show great potential for improving slope design,construction,and risk assessment.

Model-based investigation on building thermal mass utilization and flexibility enhancement of air conditioning loads

Building air conditioning systems(ACs)can contribute to the stable operation of power grids by participating in peak load shaving programs,but the participants need a fast and accurate zone temperature prediction model,e.g.,the detailed room thermal-resistance(RC)model,to improve peak shaving effect and avoid obvious thermal discomfort.However,when applying the detailed room RC model to multi-zone buildings,conventional studies mostly consider the heat transfer among neighboring rooms,which contributes little to the prediction accuracy improvement,but leads to complicated model structure and heavy computation.Thus,a distributed RC model is developed for multi-zone buildings in this study.Compared to conventional models,the proposed model considers the total heat transfer between the building and the air,and ignores the heat transfer among indoor air in neighboring rooms through internal walls with heavy thermal mass,thereby having comparable temperature prediction accuracy,simpler structure,and stronger robustness.Based on the model,the effectiveness of passive pre-cooling strategies in reducing the air conditioning loads during peak periods is investigated.Results indicate that the thermal insulation performance of opaque building envelope is quite important to the flexibility enhancement of air conditioning loads.With an uninsulated building envelope,passive pre-cooling is useless for the peak load shaving.In comparison,well insulated opaque building envelope enables the building thermal mass to be utilized through passive pre-cooling,which leads to the air conditioning loads during peak periods being further reduced by about 12%.

Modifying effects and mechanisms of superfine stainless wires on microstructures and mechanical properties of ultra-high performance seawater sea-sand concrete

Ultra-high-performance seawater sea-sand concrete(UHPSSC)presents a prospective solution to address the natural resource shortage in marine infrastructure construction.To eliminate the corrosion risk of steel fibers and broaden the applicability of UHPSSC,this study investigates the mechanical properties and free chloride ion content as well as microstructures of UHPSSC reinforced with superfine stainless wires(SSWs)under natural curing.The results indicate that 1.5%SSWs can remarkably improve the flexural strength and toughness of UHPSSC by 127%and 1724%,respectively,and mitigate the long-term strength degradation of UHPSSC.The strong interfacial bond between SSW and UHPSSC improves the compactness of UHPSSC,thus reducing the growth space for Ca(OH)_(2) crystals and swelling hydration products generated by sulfate and magnesium ions.This can be supported by the observed reduction in the Ca/Si ratio of C–S–H gels,CH crystal orientation index,and porosity.Moreover,through mechanisms such as pull-out,rupture,overlapping network,and internal anchor interface,SSWs effectively prevent microcrack growth and propagation,transforming single long-connected microcracks into multiple-emission microcracks centered on SSW.Additionally,the free chloride ion content of the composites at 28 and 180 d meets the ACI 318-19 standard requirements for concrete exposed to seawater.This compliance is attributed to the chloride immobilization facilitated by Friedel’s salt and C–S–H gels within the interfaces around SSWs and sea-sand.Consequently,SSWs-reinforced UHPSSC exhibits considerable potential for applications in sustainable marine infrastructures,demanding long-term mechanical properties and high durability.

Experimental and numerical investigation of droplet flow mechanisms at fracture intersections

Understanding unsaturated flow behaviors in fractured rocks is essential for various applications.A fundamental process in this regard is flow splitting at fracture intersections.However,the impact of geometrical properties of fracture intersections on flow splitting is still unclear.This work investigates the combined influence of geometry(intersection angle,fracture apertures,and inclination angle),liquid droplet length,inertia,and dynamic wetting properties on liquid splitting dynamics at fracture intersections.A theoretical model of liquid splitting is developed,considering the factors mentioned above,and numerically solved to predict the flow splitting behavior.The model is validated against carefullycontrolled visualized experiments.Our results reveal two distinct splitting behaviors,separated by a critical droplet length.These behaviors shift from a monotonic to a non-monotonic trend with decreasing inclination angle.A comprehensive analysis further clarifies the impacts of the key factors on the splitting ratio,which is defined as the percentage of liquid volume entering the branch fracture.The splitting ratio decreases with increasing inclination angle,indicating a decrease in the gravitational effect on the branch fracture,which is directly proportional to the intersection angle.A non-monotonic relationship exists between the splitting ratio and the aperture ratio of the branch fracture to the main fracture.The results show that as the intersection angle decreases,the splitting ratio increases.Additionally,the influence of dynamic contact angles decreases with increasing intersection angle.These findings enhance our understanding of the impact of geometry on flow dynamics at fracture intersections.The proposed model provides a foundation for simulating and predicting unsaturated flow in complex fractured networks.

Uncertainties in landslide susceptibility prediction modeling:A review on the incompleteness of landslide inventory and its influence rules

Landslide inventory is an indispensable output variable of landslide susceptibility prediction(LSP)modelling.However,the influence of landslide inventory incompleteness on LSP and the transfer rules of LSP resulting error in the model have not been explored.Adopting Xunwu County,China,as an example,the existing landslide inventory is first obtained and assumed to contain all landslide inventory samples under ideal conditions,after which different landslide inventory sample missing conditions are simulated by random sampling.It includes the condition that the landslide inventory samples in the whole study area are missing randomly at the proportions of 10%,20%,30%,40%and 50%,as well as the condition that the landslide inventory samples in the south of Xunwu County are missing in aggregation.Then,five machine learning models,namely,Random Forest(RF),and Support Vector Machine(SVM),are used to perform LSP.Finally,the LSP results are evaluated to analyze the LSP uncertainties under various conditions.In addition,this study introduces various interpretability methods of machine learning model to explore the changes in the decision basis of the RF model under various conditions.Results show that(1)randomly missing landslide inventory samples at certain proportions(10%–50%)may affect the LSP results for local areas.(2)Aggregation of missing landslide inventory samples may cause significant biases in LSP,particularly in areas where samples are missing.(3)When 50%of landslide samples are missing(either randomly or aggregated),the changes in the decision basis of the RF model are mainly manifested in two aspects:first,the importance ranking of environmental factors slightly differs;second,in regard to LSP modelling in the same test grid unit,the weights of individual model factors may drastically vary.

The role of polyurethane foam compressible layer in the mechanical behaviour of multi-layer yielding supports for deep soft rock tunnels

The polyurethane foam(PU)compressible layer is a viable solution to the problem of damage to the secondary lining in squeezing tunnels.Nevertheless,the mechanical behaviour of the multi-layer yielding supports has not been thoroughly investigated.To fill this gap,large-scale model tests were conducted in this study.The synergistic load-bearing mechanics were analyzed using the convergenceconfinement method.Two types of multi-layer yielding supports with different thicknesses(2.5 cm,3.75 cm and 5 cm)of PU compressible layers were investigated respectively.Digital image correlation(DIC)analysis and acoustic emission(AE)techniques were used for detecting the deformation fields and damage evolution of the multi-layer yielding supports in real-time.Results indicated that the loaddisplacement relationship of the multi-layer yielding supports could be divided into the crack initiation,crack propagation,strain-hardening,and failure stages.Compared with those of the stiff support,the toughness,deformability and ultimate load of the yielding supports were increased by an average of 225%,61%and 32%,respectively.Additionally,the PU compressible layer is positioned between two primary linings to allow the yielding support to have greater mechanical properties.The analysis of the synergistic bearing effect suggested that the thickness of PU compressible layer and its location significantly affect the mechanical properties of the yielding supports.The use of yielding supports with a compressible layer positioned between the primary and secondary linings is recommended to mitigate the effects of high geo-stress in squeezing tunnels.