The impact of different global and local variables in urban development processes requires a systematic study to fully comprehend the underlying complexities in them.The interplay between such variables is crucial for...The impact of different global and local variables in urban development processes requires a systematic study to fully comprehend the underlying complexities in them.The interplay between such variables is crucial for modelling urban growth to closely reflects reality.Despite extensive research,ambiguity remains about how variations in these input variables influence urban densification.In this study,we conduct a global sensitivity analysis(SA)using a multinomial logistic regression(MNL)model to assess the model’s explanatory and predictive power.We examine the influence of global variables,including spatial resolution,neighborhood size,and density classes,under different input combinations at a provincial scale to understand their impact on densification.Additionally,we perform a stepwise regression to identify the significant explanatory variables that are important for understanding densification in the Brussels Metropolitan Area(BMA).Our results indicate that a finer spatial resolution of 50 m and 100 m,smaller neighborhood size of 5×5 and 3×3,and specific density classes—namely 3(non-built-up,low and high built-up)and 4(non-built-up,low,medium and high built-up)—optimally explain and predict urban densification.In line with the same,the stepwise regression reveals that models with a coarser resolution of 300 m lack significant variables,reflecting a lower explanatory power for densification.This approach aids in identifying optimal and significant global variables with higher explanatory power for understanding and predicting urban densification.Furthermore,these findings are reproducible in a global urban context,offering valuable insights for planners,modelers and geographers in managing future urban growth and minimizing modelling.展开更多
Accurate quantification of carbon and water fluxes dynamics in arid and semi-arid ecosystems is a critical scientific challenge for regional carbon neutrality assessments and sustainable water resource management.In t...Accurate quantification of carbon and water fluxes dynamics in arid and semi-arid ecosystems is a critical scientific challenge for regional carbon neutrality assessments and sustainable water resource management.In this study,we developed a multi-flux global sensitivity discriminant index(D_(sen))by integrating the Biome-BGCMuSo model with eddy covariance flux observations.This index was combined with a Bayesian optimization algorithm to conduct parameter optimization.The results demonstrated that:(1)Sensitivity analysis identified 13 highly sensitive parameters affecting carbon and water fluxes.Among these,the canopy light extinction coefficient(k)and the fraction of leaf N in Rubisco(FLNR)exhibited significantly higher sensitivity to carbon fluxes(GPP,NEE,Reco;D_(sen)>10%)compared to water flux(ET).This highlights the strong dependence of carbon cycle simulations on vegetation physiological parameters.(2)The Bayesian optimization framework efficiently converged 30 parameter spaces within 50 iterations,markedly improving carbon fluxes simulation accuracy.The Kling-Gupta efficiency(KGE)values for Gross Primary Production(GPP),Net Ecosystem Exchange(NEE),and Total Respiration(Reco)increased by 44.94%,69.23%and 123%,respectively.The optimization prioritized highly sensitive parameters,underscoring the necessity of parameter sensitivity stratification.(3)The optimized model effectively reproduced carbon sink characteristics in mountain meadows during the growing season(cumulative NEE=-375 g C/m^(2)).It revealed synergistic carbon-water fluxes interactions governed by coupled photosynthesis-stomatal pathways and identified substrate supply limitations on heterotrophic respiration.This study proposes a novel multi-flux sensitivity index and an efficient optimization framework,elucidating the coupling mechanisms between vegetation physiological regulation(k,FLNR)and environmental stressors(VPD,SWD)in carbonwater cycles.The methodology offers a practical approach for arid ecosystem model optimization and provides theoretical insights for grassland management through canopy structure regulation and water-use efficiency enhancement.展开更多
As a means of quantitative interpretation,forward calculations of the global lithospheric magnetic field in the Spherical Harmonic(SH)domain have been widely used to reveal geophysical,lithological,and geothermal vari...As a means of quantitative interpretation,forward calculations of the global lithospheric magnetic field in the Spherical Harmonic(SH)domain have been widely used to reveal geophysical,lithological,and geothermal variations in the lithosphere.Traditional approaches either do not consider the non-axial dipolar terms of the inducing field and its radial variation or do so by means of complicated formulae.Moreover,existing methods treat the magnetic lithosphere either as an infinitesimally thin layer or as a radially uniform spherical shell of constant thickness.Here,we present alternative forward formulae that account for an arbitrarily high maximum degree of the inducing field and for a magnetic lithosphere of variable thickness.Our simulations based on these formulae suggest that the satellite magnetic anomaly field is sensitive to the non-axial dipolar terms of the inducing field but not to its radial variation.Therefore,in forward and inverse calculations of satellite magnetic anomaly data,the non-axial dipolar terms of the inducing field should not be ignored.Furthermore,our results show that the satellite magnetic anomaly field is sensitive to variability in the lateral thickness of the magnetized shell.In particular,we show that for a given vertically integrated susceptibility distribution,underestimating the thickness of the magnetic layer overestimates the induced magnetic field.This discovery bridges the greatest part of the alleged gap between the susceptibility values measured from rock samples and the susceptibility values required to match the observed magnetic field signal.We expect the formulae and conclusions of this study to be a valuable tool for the quantitative interpretation of the Earth's global lithospheric magnetic field,through an inverse or forward modelling approach.展开更多
The seismic data of the Laoshan Uplift in the South Yellow Sea Basin reveal a low signal-tonoise ratio and low refl ection signal energy in the deep Mesozoic–Paleozoic strata.The main reason is that the Mesozoic-Pale...The seismic data of the Laoshan Uplift in the South Yellow Sea Basin reveal a low signal-tonoise ratio and low refl ection signal energy in the deep Mesozoic–Paleozoic strata.The main reason is that the Mesozoic-Paleozoic marine carbonate rock strata are directly covered by the Cenozoic terrestrial clastic rock strata,which form a strong shielding layer.To obtain the reflection signals of the strata below the strong shielding layer,a one-way wave equation bidirectional illumination analysis of the main observation system parameters was conducted by analyzing the mechanism of the strong shielding layer.Low-frequency seismic sources are assumed to have a high illumination intensity on the reflection layer below the strong shielding layer.Accordingly,optimized acquisition parameter suggestions were proposed,and reacquisition was performed at the existing survey line locations in the Laoshan Uplift area.The imaging of the newly acquired data in the middle and deep layers was drastically improved.It revealed the unconformity between the Sinian and Cambrian under the strong shielding layer.The study yielded new insights into the tectonic and sedimentary evolution of the Lower Paleozoic in the South Yellow Sea.展开更多
Saline aquifers are considered as highly favored reservoirs for CO_(2)sequestration due to their favorable properties.Understanding the impact of saline aquifer properties on the migration and distribution of CO_(2)pl...Saline aquifers are considered as highly favored reservoirs for CO_(2)sequestration due to their favorable properties.Understanding the impact of saline aquifer properties on the migration and distribution of CO_(2)plume is crucial.This study focuses on four key parameters-permeability,porosity,formation pressure,and temperature-to characterize the reservoir and analyse the petrophysical and elastic response of CO_(2).First,we performed reservoir simulations to simulate CO_(2)saturation,using multiple sets of these four parameters to examine their significance on CO_(2)saturation and the plume migration speed.Subsequently,the effect of these parameters on the elastic properties is tested using rock physics theory.We established a relationship of compressional wave velocity(V_(p))and quality factor(Q_(p))with the four key parameters,and conducted a sensitivity analysis to test their sensitivity to V_(p) and Q_(p).Finally,we utilized visco-acoustic wave equation simulated time-lapse seismic data based on the computed V_(p) and Q_(p) models,and analysed the impact of CO_(2) saturation changes on seismic data.As for the above nu-merical simulations and analysis,we conducted sensitivity analysis using both homogeneous and heterogeneous models.Consistent results are found between homogeneous and heterogeneous models.The permeability is the most sensitive parameter to the CO_(2)saturation,while porosity emerges as the primary factor affecting both Q_(p) and V_(p).Both Q_(p) and V_(p) increase with the porosity,which contradicts the observations in gas reservoirs.The seismic simulations highlight significant variations in the seismic response to different parameters.We provided analysis for these observations,which serves as a valuable reference for comprehensive CO_(2)integrity analysis,time-lapse monitoring,injection planning and site selection.展开更多
While steady improvements have been achieved for the track forecasts of typhoons,there has been a lack of improvement for intensity forecasts.One challenge for intensity forecasts is to capture the rapid intensificati...While steady improvements have been achieved for the track forecasts of typhoons,there has been a lack of improvement for intensity forecasts.One challenge for intensity forecasts is to capture the rapid intensification(RI),whose nonlinear characteristics impose great difficulties for numerical models.The ensemble sensitivity analysis(ESA)method is used here to analyze the initial conditions that contribute to typhoon intensity forecasts,especially with RI.Six RI processes from five typhoons(Chaba,Haima,Meranti,Sarika,and Songda)in 2016,are applied with ESA,which also gives a composite initial condition that favors subsequent RI.Results from individual cases have generally similar patterns of ESA,but with different magnitudes,when various cumulus parameterization schemes are applied.To draw the initial conditions with statistical significance,sample-mean azimuthal components of ESA are obtained.Results of the composite sensitivity show that typhoons that experience RI in 24 h favor enhanced primary circulation from low to high levels,intensified secondary circulation with increased radial inflow at lower levels and increased radial outflow at upper levels,a prominent warm core at around 300 hPa,and increased humidity at low levels.As the forecast lead time increases,the patterns of ESA are retained,while the sensitivity magnitudes decay.Given the general and quantitative composite sensitivity along with associated uncertainties for different cumulus parameterization schemes,appropriate sampling of the composite sensitivity in numerical models could be beneficial to capturing the RI and improving the forecasting of typhoon intensity.展开更多
Kangbao County is located in the northwest of Bashang in Hebei Province,which is a sub-arid area in the middle temperate zone,with a cold and arid climate and frequent disastrous weather.The meteorological data of Kan...Kangbao County is located in the northwest of Bashang in Hebei Province,which is a sub-arid area in the middle temperate zone,with a cold and arid climate and frequent disastrous weather.The meteorological data of Kangbao County Meteorological Station from 1994 to 2023 were selected,and the meteorological elements such as air pressure,temperature,precipitation,wind,relative humidity,sunshine,thunderstorm,hail,gale,rainstorm,fog,and snow cover were counted.The climate background analysis and high-impact weather analysis were carried out in combination with the topographic characteristics,geographical location,and climate characteristics.The results of meteorological sensitivity survey in the park showed that industries such as food,agriculture and new energy are very sensitive to temperature.During the visit to the enterprises in the park,it was found that heavy precipitation,snow,strong winds and hail had a great impact on many industries,and it was recommended to carry out long-term planning and reasonable design of buildings.It should pay close attention to forecasts and early warnings,formulate emergency plans for high-impact weather defense,and actively take preventive measures.展开更多
In engineering systems,uncertainties in input parameters can significantly influence the output responses.This paper proposes a model distance-based approach to perform global sensitivity analysis for quantifying the ...In engineering systems,uncertainties in input parameters can significantly influence the output responses.This paper proposes a model distance-based approach to perform global sensitivity analysis for quantifying the influence of input uncertainties on multiple responses in an engineering system.The sensitivity indices are determined by comparing a reference model that incorporates all system uncertainties,with an altered model,where specific uncertainties are constrained.The proposed framework employs probability distance measures such as Hellinger distance,Kullback-Leibler divergence,and I2 norm which are based on joint probability density functions.The study also demonstrates the equivalence between the l2 norm-based approach and Sobol's analysis in multivariate sensitivity context.The proposed methodology effectively manages correlated random variables,accommodates both Gaussian and non-Gaussian distributions,and allows for the grouping of input variables.Ilustrative examples consist of static analysis of a truss system and dynamic analysis of a frame subjected to seismic excitation.The sensitivity indices are estimated using brute-force Monte Carlo simulations.The relative ranking of these sensitivity indices can be utilized to identify the most and least significant variables contributing to the response uncertainty.The numerical results show a consistent ranking of input variables across different probability measures,indicating the robustness of proposed framework.展开更多
The reuse of liquid propellant rocket engines has increased the difficulty of their control and estimation.State and parameter Moving Horizon Estimation(MHE)is an optimization-based strategy that provides the necessar...The reuse of liquid propellant rocket engines has increased the difficulty of their control and estimation.State and parameter Moving Horizon Estimation(MHE)is an optimization-based strategy that provides the necessary information for model predictive control.Despite the many advantages of MHE,long computation time has limited its applications for system-level models of liquid propellant rocket engines.To address this issue,we propose an asynchronous MHE method called advanced-multi-step MHE with Noise Covariance Estimation(amsMHE-NCE).This method computes the MHE problem asynchronously to obtain the states and parameters and can be applied to multi-threaded computations.In the background,the state and covariance estimation optimization problems are computed using multiple sampling times.In real-time,sensitivity is used to quickly approximate state and parameter estimates.A covariance estimation method is developed using sensitivity to avoid redundant MHE problem calculations in case of sensor degradation during engine reuse.The amsMHE-NCE is validated through three cases based on the space shuttle main engine system-level model,and we demonstrate that it can provide more accurate real-time estimates of states and parameters compared to other commonly used estimation methods.展开更多
Economic losses and catastrophic casualties may occur once super high-rise structures are struck by low-probability but high-consequence scenarios of concurrent earthquakes and winds. Therefore, accurately predicting ...Economic losses and catastrophic casualties may occur once super high-rise structures are struck by low-probability but high-consequence scenarios of concurrent earthquakes and winds. Therefore, accurately predicting multi-hazard dynamic responses to super high-rise structures has significant engineering and scientific value. This study performed a parametric global sensitivity analysis (GSA) for multi-hazard dynamic response prediction of super high-rise structures using the multiple-degree-of-freedom shear (MFS) model. Polynomial chaos Kriging (PCK) was introduced to build a surrogate model that allowed GSA to be combined with Sobol’ indices. Monte Carlo simulation (MCS) is also conducted for the comparison to verify the accuracy and efficiency of the PCK method. Parametric sensitivity analysis is performed for a wide range of aleatory uncertainty (intensities of coupled multi-hazard), epistemic uncertainty (bending stiffness, k_(m);shear stiffness, kq;density, ρ;and damping ratio, ξ), probability distribution types, and coefficients of variation. The results indicate that epistemic uncertainty parameters, k_(m), ρ, and ξ dramatically affect the multi-hazard dynamic responses of super high-rise structures;in addition, Sobol’ indices between the normal and lognormal distributions are insignificant, while the variation levels have remarkably influenced the sensitivity indices.展开更多
Damage to electrical equipment in an earthquake can lead to power outage of power systems.Seismic fragility analysis is a common method to assess the seismic reliability of electrical equipment.To further guarantee th...Damage to electrical equipment in an earthquake can lead to power outage of power systems.Seismic fragility analysis is a common method to assess the seismic reliability of electrical equipment.To further guarantee the efficiency of analysis,multi-source uncertainties including the structure itself and seismic excitation need to be considered.A method for seismic fragility analysis that reflects structural and seismic parameter uncertainty was developed in this study.The proposed method used a random sampling method based on Latin hypercube sampling(LHS)to account for the structure parameter uncertainty and the group structure characteristics of electrical equipment.Then,logistic Lasso regression(LLR)was used to find the seismic fragility surface based on double ground motion intensity measures(IM).The seismic fragility based on the finite element model of an±1000 kV main transformer(UHVMT)was analyzed using the proposed method.The results show that the seismic fragility function obtained by this method can be used to construct the relationship between the uncertainty parameters and the failure probability.The seismic fragility surface did not only provide the probabilities of seismic damage states under different IMs,but also had better stability than the fragility curve.Furthermore,the sensitivity analysis of the structural parameters revealed that the elastic module of the bushing and the height of the high-voltage bushing may have a greater influence.展开更多
Purpose–To investigate the influence of vehicle operation speed,curve geometry parameters and rail profile parameters on wheel–rail creepage in high-speed railway curves and propose a multi-parameter coordinated opt...Purpose–To investigate the influence of vehicle operation speed,curve geometry parameters and rail profile parameters on wheel–rail creepage in high-speed railway curves and propose a multi-parameter coordinated optimization strategy to reduce wheel–rail contact fatigue damage.Design/methodology/approach–Taking a small-radius curve of a high-speed railway as the research object,field measurements were conducted to obtain track parameters and wheel–rail profiles.A coupled vehicle-track dynamics model was established.Multiple numerical experiments were designed using the Latin Hypercube Sampling method to extract wheel-rail creepage indicators and construct a parameter-creepage response surface model.Findings–Key service parameters affecting wheel–rail creepage were identified,including the matching relationship between curve geometry and vehicle speed and rail profile parameters.The influence patterns of various parameters on wheel–rail creepage were revealed through response surface analysis,leading to the establishment of parameter optimization criteria.Originality/value–This study presents the systematic investigation of wheel–rail creepage characteristics under multi-parameter coupling in high-speed railway curves.A response surface-based parameter-creepage relationship model was established,and a multi-parameter coordinated optimization strategy was proposed.The research findings provide theoretical guidance for controlling wheel–rail contact fatigue damage and optimizing wheel–rail profiles in high-speed railway curves.展开更多
This paper presents a sensitivity analysis method for analyzing the key factors affecting the stability problem of the transonic compressors.The adjoint method is integrated into the meridional stability model,a linea...This paper presents a sensitivity analysis method for analyzing the key factors affecting the stability problem of the transonic compressors.The adjoint method is integrated into the meridional stability model,a linear stability prediction model utilized to analyze the flow stability problem of the compressor,and the sensitivity analysis method is further developed for the flow stability problem of the compressor.The study selects the NASA Rot or 37,a transonic compressor,as the research object to verify the proposed method and explore the sensitive factors leading to the stall inception.The results of sensitivity analysis to both the flow parameters and the external source terms reveal that the stall inception is sensitive to the base flow field at the rotor tip and the stability margin of the compressor can be enhanced by improving the flow field at this region.Physical explanations are presented and discussed to correlate the three-dimensional flow field to the results obtained via the employed analysis method,which shows that flow structures and characteristics near the end-wall region,especially the tip leakage flow or the tip leakage vortex and its interaction with the shock wave,contribute to the stall inception.展开更多
In this study,an improved integrated radial basis function with nonuniform shape parameter is introduced.The proposed shape parameter varies in each support domain and is defined byθ=1/d_(max),where d_(max)is the max...In this study,an improved integrated radial basis function with nonuniform shape parameter is introduced.The proposed shape parameter varies in each support domain and is defined byθ=1/d_(max),where d_(max)is the maximum distance of any pair of nodes in the support domain.The proposed method is verified and shows good performance.The results are stable and accurate with any number of nodes and an arbitrary nodal distribution.Notably,the support domain should be large enough to obtain accurate results.This method is then applied for transient analysis of curved shell structures made from functionally graded materials with complex geometries.Through several numerical examples,the accuracy of the proposed approach is demonstrated and discussed.Additionally,the influence of various factors on the dynamic behavior of the structures,including the power-law index,different materials,loading conditions,and geometrical parameters of the structures,was investigated.展开更多
A novel porous shock absorption layer is put forward in this study, and the shock absorption performance of the porous shock absorption layer is evaluated based on three-dimensional pseudo-static analysis. The modifie...A novel porous shock absorption layer is put forward in this study, and the shock absorption performance of the porous shock absorption layer is evaluated based on three-dimensional pseudo-static analysis. The modified reaction acceleration method is adopted and validated in the three-dimensional model. Seven ground motions are selected and the peak ground acceleration is adjusted to 0.2 g, 0.4 g and 0.6 g. The impact of the void ratio and thickness of the porous shock absorption layer is studied, while the surrounding rock grade and tunnel depth are also investigated. The numerical results show that the porous shock absorption layer has good shock absorption performance and can effectively reduce the maximum internal force of the secondary lining, but it cannot reduce the maximum horizontal relative displacement of the secondary lining. The circumferential rubber strip in the porous shock absorption layer will reduce shock absorption performance. The results of parameter analysis indicate that the shock absorption performance of the porous shock absorption layer increases with the increase of the void ratio and thickness, and it has good shock absorption performance under different surrounding rock grades and tunnel depths.展开更多
This paper presents a framework for constructing surrogate models for sensitivity analysis of structural dynamics behavior.Physical models involving deformation,such as collisions,vibrations,and penetration,are devel-...This paper presents a framework for constructing surrogate models for sensitivity analysis of structural dynamics behavior.Physical models involving deformation,such as collisions,vibrations,and penetration,are devel-oped using the material point method.To reduce the computational cost of Monte Carlo simulations,response surface models are created as surrogate models for the material point system to approximate its dynamic behavior.An adaptive randomized greedy algorithm is employed to construct a sparse polynomial chaos expansion model with a fixed order,effectively balancing the accuracy and computational efficiency of the surrogate model.Based on the sparse polynomial chaos expansion,sensitivity analysis is conducted using the global finite difference and Sobol methods.Several examples of structural dynamics are provided to demonstrate the effectiveness of the proposed method in addressing structural dynamics problems.展开更多
In recent years,incidents of simultaneous exceedance of PM_(2.5)and O_(3) concentrations,termed PM_(2.5)and O_(3) co-pollution events,have frequently occurred in China.This study conducted atmospheric circulation anal...In recent years,incidents of simultaneous exceedance of PM_(2.5)and O_(3) concentrations,termed PM_(2.5)and O_(3) co-pollution events,have frequently occurred in China.This study conducted atmospheric circulation analysis on two typical co-pollution events in Beijing,occurring from July 22 to July 28,2019,and from April 25 to May 2,2020.These events were categorized into pre-trough southerly airflow type(Type 1)and post-trough northwest flow type(Type 2).Subsequently,sensitivity analyses using the GRAPES-CUACE adjoint model were performed to quantify the contributions of precursor emissions from Beijing and surrounding areas to PM_(2.5)and O_(3) concentrations in Beijing for two types of co-pollution.The results indicated that the spatiotemporal distribution of sensitive source region varied among different circulation types.Primary PM_(2.5)(PPM_(2.5))emissions from Hebei contributed the most to the 24-hour average PM_(2.5)(24-h PM_(2.5))peak concentration(41.6%-45.4%),followed by Beijing emissions(31%-35.7%).The maximum daily 8-hour average ozone peak concentration was primarily influenced by the emissions from Hebei and Beijing,with contribution ratios respectively of 32.8%-44.8% and 29%-42.1%.Additionally,NO_(x)emissions were the main contributors in Type 1,while both NO_(x)and VOCs emissions contributed similarly in Type 2.The iterative emission reduction experiments for two types of co-pollution indicated that Type 1 required emission reductions in NO_(x)(52.4%-71.8%)and VOCs(14.1%-33.8%)only.In contrast,Type 2 required combined emission reductions in NO_(x)(37.0%-65.1%),VOCs(30.7%-56.2%),and PPM_(2.5)(31%-46.9%).This study provided a reference for controlling co-pollution events and improving air quality in Beijing.展开更多
Accurate determination of rock mass parameters is essential for ensuring the accuracy of numericalsimulations. Displacement back-analysis is the most widely used method;however, the reliability of thecurrent approache...Accurate determination of rock mass parameters is essential for ensuring the accuracy of numericalsimulations. Displacement back-analysis is the most widely used method;however, the reliability of thecurrent approaches remains unsatisfactory. Therefore, in this paper, a multistage rock mass parameterback-analysis method, that considers the construction process and displacement losses is proposed andimplemented through the coupling of numerical simulation, auto-machine learning (AutoML), andmulti-objective optimization algorithms (MOOAs). First, a parametric modeling platform for mechanizedtwin tunnels is developed, generating a dataset through extensive numerical simulations. Next, theAutoML method is utilized to establish a surrogate model linking rock parameters and displacements.The tunnel construction process is divided into multiple stages, transforming the rock mass parameterback-analysis into a multi-objective optimization problem, for which multi-objective optimization algorithmsare introduced to obtain the rock mass parameters. The newly proposed rock mass parameterback-analysis method is validated in a mechanized twin tunnel project, and its accuracy and effectivenessare demonstrated. Compared with traditional single-stage back-analysis methods, the proposedmodel decreases the average absolute percentage error from 12.73% to 4.34%, significantly improving theaccuracy of the back-analysis. Moreover, although the accuracy of back analysis significantly increaseswith the number of construction stages considered, the back analysis time is acceptable. This studyprovides a new method for displacement back analysis that is efficient and accurate, thereby paving theway for precise parameter determination in numerical simulations.展开更多
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...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.展开更多
Battery production is crucial for determining the quality of electrode,which in turn affects the manufactured battery performance.As battery production is complicated with strongly coupled intermediate and control par...Battery production is crucial for determining the quality of electrode,which in turn affects the manufactured battery performance.As battery production is complicated with strongly coupled intermediate and control parameters,an efficient solution that can perform a reliable sensitivity analysis of the production terms of interest and forecast key battery properties in the early production phase is urgently required.This paper performs detailed sensitivity analysis of key production terms on determining the properties of manufactured battery electrode via advanced data-driven modelling.To be specific,an explainable neural network named generalized additive model with structured interaction(GAM-SI)is designed to predict two key battery properties,including electrode mass loading and porosity,while the effects of four early production terms on manufactured batteries are explained and analysed.The experimental results reveal that the proposed method is able to accurately predict battery electrode properties in the mixing and coating stages.In addition,the importance ratio ranking,global interpretation and local interpretation of both the main effects and pairwise interactions can be effectively visualized by the designed neural network.Due to the merits of interpretability,the proposed GAM-SI can help engineers gain important insights for understanding complicated production behavior,further benefitting smart battery production.展开更多
基金funded by the INTER program and cofunded by the Fond National de la Recherche,Luxembourg(FNR)and the Fund for Scientific Research-FNRS,Belgium(F.R.S-FNRS),T.0233.20-‘Sustainable Residential Densification’project(SusDens,2020–2024).
文摘The impact of different global and local variables in urban development processes requires a systematic study to fully comprehend the underlying complexities in them.The interplay between such variables is crucial for modelling urban growth to closely reflects reality.Despite extensive research,ambiguity remains about how variations in these input variables influence urban densification.In this study,we conduct a global sensitivity analysis(SA)using a multinomial logistic regression(MNL)model to assess the model’s explanatory and predictive power.We examine the influence of global variables,including spatial resolution,neighborhood size,and density classes,under different input combinations at a provincial scale to understand their impact on densification.Additionally,we perform a stepwise regression to identify the significant explanatory variables that are important for understanding densification in the Brussels Metropolitan Area(BMA).Our results indicate that a finer spatial resolution of 50 m and 100 m,smaller neighborhood size of 5×5 and 3×3,and specific density classes—namely 3(non-built-up,low and high built-up)and 4(non-built-up,low,medium and high built-up)—optimally explain and predict urban densification.In line with the same,the stepwise regression reveals that models with a coarser resolution of 300 m lack significant variables,reflecting a lower explanatory power for densification.This approach aids in identifying optimal and significant global variables with higher explanatory power for understanding and predicting urban densification.Furthermore,these findings are reproducible in a global urban context,offering valuable insights for planners,modelers and geographers in managing future urban growth and minimizing modelling.
基金jointly funded by the National Natural Science Foundation of China(Grant No.42161024)the Central Financial Forestry and Grassland Science and Technology Extension Demonstration Project(2025)(Grant No.Xin[2025]TG 09)。
文摘Accurate quantification of carbon and water fluxes dynamics in arid and semi-arid ecosystems is a critical scientific challenge for regional carbon neutrality assessments and sustainable water resource management.In this study,we developed a multi-flux global sensitivity discriminant index(D_(sen))by integrating the Biome-BGCMuSo model with eddy covariance flux observations.This index was combined with a Bayesian optimization algorithm to conduct parameter optimization.The results demonstrated that:(1)Sensitivity analysis identified 13 highly sensitive parameters affecting carbon and water fluxes.Among these,the canopy light extinction coefficient(k)and the fraction of leaf N in Rubisco(FLNR)exhibited significantly higher sensitivity to carbon fluxes(GPP,NEE,Reco;D_(sen)>10%)compared to water flux(ET).This highlights the strong dependence of carbon cycle simulations on vegetation physiological parameters.(2)The Bayesian optimization framework efficiently converged 30 parameter spaces within 50 iterations,markedly improving carbon fluxes simulation accuracy.The Kling-Gupta efficiency(KGE)values for Gross Primary Production(GPP),Net Ecosystem Exchange(NEE),and Total Respiration(Reco)increased by 44.94%,69.23%and 123%,respectively.The optimization prioritized highly sensitive parameters,underscoring the necessity of parameter sensitivity stratification.(3)The optimized model effectively reproduced carbon sink characteristics in mountain meadows during the growing season(cumulative NEE=-375 g C/m^(2)).It revealed synergistic carbon-water fluxes interactions governed by coupled photosynthesis-stomatal pathways and identified substrate supply limitations on heterotrophic respiration.This study proposes a novel multi-flux sensitivity index and an efficient optimization framework,elucidating the coupling mechanisms between vegetation physiological regulation(k,FLNR)and environmental stressors(VPD,SWD)in carbonwater cycles.The methodology offers a practical approach for arid ecosystem model optimization and provides theoretical insights for grassland management through canopy structure regulation and water-use efficiency enhancement.
基金supported by the National Natural Science Foundation of China(Grant Nos.42250103 and 42174090)the Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education(Grant No.GLAB2023ZR02)the Ministry of Science and Technology(MOST)Special Fund from the State Key Laboratory of Geological Processes and Mineral Resources(Grant No.MSFGPMR2022-4)。
文摘As a means of quantitative interpretation,forward calculations of the global lithospheric magnetic field in the Spherical Harmonic(SH)domain have been widely used to reveal geophysical,lithological,and geothermal variations in the lithosphere.Traditional approaches either do not consider the non-axial dipolar terms of the inducing field and its radial variation or do so by means of complicated formulae.Moreover,existing methods treat the magnetic lithosphere either as an infinitesimally thin layer or as a radially uniform spherical shell of constant thickness.Here,we present alternative forward formulae that account for an arbitrarily high maximum degree of the inducing field and for a magnetic lithosphere of variable thickness.Our simulations based on these formulae suggest that the satellite magnetic anomaly field is sensitive to the non-axial dipolar terms of the inducing field but not to its radial variation.Therefore,in forward and inverse calculations of satellite magnetic anomaly data,the non-axial dipolar terms of the inducing field should not be ignored.Furthermore,our results show that the satellite magnetic anomaly field is sensitive to variability in the lateral thickness of the magnetized shell.In particular,we show that for a given vertically integrated susceptibility distribution,underestimating the thickness of the magnetic layer overestimates the induced magnetic field.This discovery bridges the greatest part of the alleged gap between the susceptibility values measured from rock samples and the susceptibility values required to match the observed magnetic field signal.We expect the formulae and conclusions of this study to be a valuable tool for the quantitative interpretation of the Earth's global lithospheric magnetic field,through an inverse or forward modelling approach.
基金“High precision prestack reverse time depth migration imaging of long array seismic data in the East China Sea Shelf Basin”of the National Natural Science Foundation of China(No.42106207)“Seismic acquisition technology for deep strata under strong shielding layers in the sea and rugged seabed”of Laoshan Laboratory Science and Technology Innovation Project(No.LSKJ202203404)“Research on the compensation methods of the middledeep weak seismic reflections in the South Yellow Sea based on multi-resolution HHT time-frequency analysis”of the National Natural Science Foundation of China(No.42106208).
文摘The seismic data of the Laoshan Uplift in the South Yellow Sea Basin reveal a low signal-tonoise ratio and low refl ection signal energy in the deep Mesozoic–Paleozoic strata.The main reason is that the Mesozoic-Paleozoic marine carbonate rock strata are directly covered by the Cenozoic terrestrial clastic rock strata,which form a strong shielding layer.To obtain the reflection signals of the strata below the strong shielding layer,a one-way wave equation bidirectional illumination analysis of the main observation system parameters was conducted by analyzing the mechanism of the strong shielding layer.Low-frequency seismic sources are assumed to have a high illumination intensity on the reflection layer below the strong shielding layer.Accordingly,optimized acquisition parameter suggestions were proposed,and reacquisition was performed at the existing survey line locations in the Laoshan Uplift area.The imaging of the newly acquired data in the middle and deep layers was drastically improved.It revealed the unconformity between the Sinian and Cambrian under the strong shielding layer.The study yielded new insights into the tectonic and sedimentary evolution of the Lower Paleozoic in the South Yellow Sea.
基金supported by the State Key Laboratory of Offshore Oil and Gas Exploitation, Open Fund Project (No. CCL2023RCPS0162RQN)the primary funding, National Natural Science Foundation of China (No. ZX20230400)
文摘Saline aquifers are considered as highly favored reservoirs for CO_(2)sequestration due to their favorable properties.Understanding the impact of saline aquifer properties on the migration and distribution of CO_(2)plume is crucial.This study focuses on four key parameters-permeability,porosity,formation pressure,and temperature-to characterize the reservoir and analyse the petrophysical and elastic response of CO_(2).First,we performed reservoir simulations to simulate CO_(2)saturation,using multiple sets of these four parameters to examine their significance on CO_(2)saturation and the plume migration speed.Subsequently,the effect of these parameters on the elastic properties is tested using rock physics theory.We established a relationship of compressional wave velocity(V_(p))and quality factor(Q_(p))with the four key parameters,and conducted a sensitivity analysis to test their sensitivity to V_(p) and Q_(p).Finally,we utilized visco-acoustic wave equation simulated time-lapse seismic data based on the computed V_(p) and Q_(p) models,and analysed the impact of CO_(2) saturation changes on seismic data.As for the above nu-merical simulations and analysis,we conducted sensitivity analysis using both homogeneous and heterogeneous models.Consistent results are found between homogeneous and heterogeneous models.The permeability is the most sensitive parameter to the CO_(2)saturation,while porosity emerges as the primary factor affecting both Q_(p) and V_(p).Both Q_(p) and V_(p) increase with the porosity,which contradicts the observations in gas reservoirs.The seismic simulations highlight significant variations in the seismic response to different parameters.We provided analysis for these observations,which serves as a valuable reference for comprehensive CO_(2)integrity analysis,time-lapse monitoring,injection planning and site selection.
基金supported by the National Natural Science Foundation of China[grant numbers 42192553 and 41922036]the Fundamental Research Funds for the Central Universities–Cemac“GeoX”Interdisciplinary Program[grant number 020714380207]。
文摘While steady improvements have been achieved for the track forecasts of typhoons,there has been a lack of improvement for intensity forecasts.One challenge for intensity forecasts is to capture the rapid intensification(RI),whose nonlinear characteristics impose great difficulties for numerical models.The ensemble sensitivity analysis(ESA)method is used here to analyze the initial conditions that contribute to typhoon intensity forecasts,especially with RI.Six RI processes from five typhoons(Chaba,Haima,Meranti,Sarika,and Songda)in 2016,are applied with ESA,which also gives a composite initial condition that favors subsequent RI.Results from individual cases have generally similar patterns of ESA,but with different magnitudes,when various cumulus parameterization schemes are applied.To draw the initial conditions with statistical significance,sample-mean azimuthal components of ESA are obtained.Results of the composite sensitivity show that typhoons that experience RI in 24 h favor enhanced primary circulation from low to high levels,intensified secondary circulation with increased radial inflow at lower levels and increased radial outflow at upper levels,a prominent warm core at around 300 hPa,and increased humidity at low levels.As the forecast lead time increases,the patterns of ESA are retained,while the sensitivity magnitudes decay.Given the general and quantitative composite sensitivity along with associated uncertainties for different cumulus parameterization schemes,appropriate sampling of the composite sensitivity in numerical models could be beneficial to capturing the RI and improving the forecasting of typhoon intensity.
文摘Kangbao County is located in the northwest of Bashang in Hebei Province,which is a sub-arid area in the middle temperate zone,with a cold and arid climate and frequent disastrous weather.The meteorological data of Kangbao County Meteorological Station from 1994 to 2023 were selected,and the meteorological elements such as air pressure,temperature,precipitation,wind,relative humidity,sunshine,thunderstorm,hail,gale,rainstorm,fog,and snow cover were counted.The climate background analysis and high-impact weather analysis were carried out in combination with the topographic characteristics,geographical location,and climate characteristics.The results of meteorological sensitivity survey in the park showed that industries such as food,agriculture and new energy are very sensitive to temperature.During the visit to the enterprises in the park,it was found that heavy precipitation,snow,strong winds and hail had a great impact on many industries,and it was recommended to carry out long-term planning and reasonable design of buildings.It should pay close attention to forecasts and early warnings,formulate emergency plans for high-impact weather defense,and actively take preventive measures.
文摘In engineering systems,uncertainties in input parameters can significantly influence the output responses.This paper proposes a model distance-based approach to perform global sensitivity analysis for quantifying the influence of input uncertainties on multiple responses in an engineering system.The sensitivity indices are determined by comparing a reference model that incorporates all system uncertainties,with an altered model,where specific uncertainties are constrained.The proposed framework employs probability distance measures such as Hellinger distance,Kullback-Leibler divergence,and I2 norm which are based on joint probability density functions.The study also demonstrates the equivalence between the l2 norm-based approach and Sobol's analysis in multivariate sensitivity context.The proposed methodology effectively manages correlated random variables,accommodates both Gaussian and non-Gaussian distributions,and allows for the grouping of input variables.Ilustrative examples consist of static analysis of a truss system and dynamic analysis of a frame subjected to seismic excitation.The sensitivity indices are estimated using brute-force Monte Carlo simulations.The relative ranking of these sensitivity indices can be utilized to identify the most and least significant variables contributing to the response uncertainty.The numerical results show a consistent ranking of input variables across different probability measures,indicating the robustness of proposed framework.
基金supported by the National Natural Science Foundation of China(Nos.62120106003 and 62173301)。
文摘The reuse of liquid propellant rocket engines has increased the difficulty of their control and estimation.State and parameter Moving Horizon Estimation(MHE)is an optimization-based strategy that provides the necessary information for model predictive control.Despite the many advantages of MHE,long computation time has limited its applications for system-level models of liquid propellant rocket engines.To address this issue,we propose an asynchronous MHE method called advanced-multi-step MHE with Noise Covariance Estimation(amsMHE-NCE).This method computes the MHE problem asynchronously to obtain the states and parameters and can be applied to multi-threaded computations.In the background,the state and covariance estimation optimization problems are computed using multiple sampling times.In real-time,sensitivity is used to quickly approximate state and parameter estimates.A covariance estimation method is developed using sensitivity to avoid redundant MHE problem calculations in case of sensor degradation during engine reuse.The amsMHE-NCE is validated through three cases based on the space shuttle main engine system-level model,and we demonstrate that it can provide more accurate real-time estimates of states and parameters compared to other commonly used estimation methods.
基金Dalian Municipal Natural Science Foundation under Grant No.2019RD01。
文摘Economic losses and catastrophic casualties may occur once super high-rise structures are struck by low-probability but high-consequence scenarios of concurrent earthquakes and winds. Therefore, accurately predicting multi-hazard dynamic responses to super high-rise structures has significant engineering and scientific value. This study performed a parametric global sensitivity analysis (GSA) for multi-hazard dynamic response prediction of super high-rise structures using the multiple-degree-of-freedom shear (MFS) model. Polynomial chaos Kriging (PCK) was introduced to build a surrogate model that allowed GSA to be combined with Sobol’ indices. Monte Carlo simulation (MCS) is also conducted for the comparison to verify the accuracy and efficiency of the PCK method. Parametric sensitivity analysis is performed for a wide range of aleatory uncertainty (intensities of coupled multi-hazard), epistemic uncertainty (bending stiffness, k_(m);shear stiffness, kq;density, ρ;and damping ratio, ξ), probability distribution types, and coefficients of variation. The results indicate that epistemic uncertainty parameters, k_(m), ρ, and ξ dramatically affect the multi-hazard dynamic responses of super high-rise structures;in addition, Sobol’ indices between the normal and lognormal distributions are insignificant, while the variation levels have remarkably influenced the sensitivity indices.
基金National Key R&D Program of China under Grant Nos.2018YFC1504504 and 2018YFC0809404。
文摘Damage to electrical equipment in an earthquake can lead to power outage of power systems.Seismic fragility analysis is a common method to assess the seismic reliability of electrical equipment.To further guarantee the efficiency of analysis,multi-source uncertainties including the structure itself and seismic excitation need to be considered.A method for seismic fragility analysis that reflects structural and seismic parameter uncertainty was developed in this study.The proposed method used a random sampling method based on Latin hypercube sampling(LHS)to account for the structure parameter uncertainty and the group structure characteristics of electrical equipment.Then,logistic Lasso regression(LLR)was used to find the seismic fragility surface based on double ground motion intensity measures(IM).The seismic fragility based on the finite element model of an±1000 kV main transformer(UHVMT)was analyzed using the proposed method.The results show that the seismic fragility function obtained by this method can be used to construct the relationship between the uncertainty parameters and the failure probability.The seismic fragility surface did not only provide the probabilities of seismic damage states under different IMs,but also had better stability than the fragility curve.Furthermore,the sensitivity analysis of the structural parameters revealed that the elastic module of the bushing and the height of the high-voltage bushing may have a greater influence.
基金sponsored by the National Natural Science Foundation of China(Grant No.52405443)the Technology Research and Development Plan of China Railway(Grant No.N2023G063)the Fund of China Academy of Railway Sciences Corporation Limited(Grant No.2023YJ054).
文摘Purpose–To investigate the influence of vehicle operation speed,curve geometry parameters and rail profile parameters on wheel–rail creepage in high-speed railway curves and propose a multi-parameter coordinated optimization strategy to reduce wheel–rail contact fatigue damage.Design/methodology/approach–Taking a small-radius curve of a high-speed railway as the research object,field measurements were conducted to obtain track parameters and wheel–rail profiles.A coupled vehicle-track dynamics model was established.Multiple numerical experiments were designed using the Latin Hypercube Sampling method to extract wheel-rail creepage indicators and construct a parameter-creepage response surface model.Findings–Key service parameters affecting wheel–rail creepage were identified,including the matching relationship between curve geometry and vehicle speed and rail profile parameters.The influence patterns of various parameters on wheel–rail creepage were revealed through response surface analysis,leading to the establishment of parameter optimization criteria.Originality/value–This study presents the systematic investigation of wheel–rail creepage characteristics under multi-parameter coupling in high-speed railway curves.A response surface-based parameter-creepage relationship model was established,and a multi-parameter coordinated optimization strategy was proposed.The research findings provide theoretical guidance for controlling wheel–rail contact fatigue damage and optimizing wheel–rail profiles in high-speed railway curves.
基金supported by the National Natural Science Foundation of China(Nos.52306036,52325602 and U2441279)the National Science and Technology Major Project,China(Nos.Y2022-Ⅱ-0003-0006 and Y2022-Ⅱ-0002-0005)supported by the Project of National Key Laboratory of Science and Technology on Aero-Engine Aero-thermodynamics,China(Nos.6142702200101 and 2024-CXPT-GF-JJ-88-0103)。
文摘This paper presents a sensitivity analysis method for analyzing the key factors affecting the stability problem of the transonic compressors.The adjoint method is integrated into the meridional stability model,a linear stability prediction model utilized to analyze the flow stability problem of the compressor,and the sensitivity analysis method is further developed for the flow stability problem of the compressor.The study selects the NASA Rot or 37,a transonic compressor,as the research object to verify the proposed method and explore the sensitive factors leading to the stall inception.The results of sensitivity analysis to both the flow parameters and the external source terms reveal that the stall inception is sensitive to the base flow field at the rotor tip and the stability margin of the compressor can be enhanced by improving the flow field at this region.Physical explanations are presented and discussed to correlate the three-dimensional flow field to the results obtained via the employed analysis method,which shows that flow structures and characteristics near the end-wall region,especially the tip leakage flow or the tip leakage vortex and its interaction with the shock wave,contribute to the stall inception.
基金Ho Chi Minh City University of Technology (HCMUT), VNU-HCM for supporting this study
文摘In this study,an improved integrated radial basis function with nonuniform shape parameter is introduced.The proposed shape parameter varies in each support domain and is defined byθ=1/d_(max),where d_(max)is the maximum distance of any pair of nodes in the support domain.The proposed method is verified and shows good performance.The results are stable and accurate with any number of nodes and an arbitrary nodal distribution.Notably,the support domain should be large enough to obtain accurate results.This method is then applied for transient analysis of curved shell structures made from functionally graded materials with complex geometries.Through several numerical examples,the accuracy of the proposed approach is demonstrated and discussed.Additionally,the influence of various factors on the dynamic behavior of the structures,including the power-law index,different materials,loading conditions,and geometrical parameters of the structures,was investigated.
基金Science and Technology Plan Project of Xizang Autonomous Region,China under Grant No.XZ202501YD0007。
文摘A novel porous shock absorption layer is put forward in this study, and the shock absorption performance of the porous shock absorption layer is evaluated based on three-dimensional pseudo-static analysis. The modified reaction acceleration method is adopted and validated in the three-dimensional model. Seven ground motions are selected and the peak ground acceleration is adjusted to 0.2 g, 0.4 g and 0.6 g. The impact of the void ratio and thickness of the porous shock absorption layer is studied, while the surrounding rock grade and tunnel depth are also investigated. The numerical results show that the porous shock absorption layer has good shock absorption performance and can effectively reduce the maximum internal force of the secondary lining, but it cannot reduce the maximum horizontal relative displacement of the secondary lining. The circumferential rubber strip in the porous shock absorption layer will reduce shock absorption performance. The results of parameter analysis indicate that the shock absorption performance of the porous shock absorption layer increases with the increase of the void ratio and thickness, and it has good shock absorption performance under different surrounding rock grades and tunnel depths.
基金support from the National Natural Science Foundation of China(Grant Nos.52174123&52274222).
文摘This paper presents a framework for constructing surrogate models for sensitivity analysis of structural dynamics behavior.Physical models involving deformation,such as collisions,vibrations,and penetration,are devel-oped using the material point method.To reduce the computational cost of Monte Carlo simulations,response surface models are created as surrogate models for the material point system to approximate its dynamic behavior.An adaptive randomized greedy algorithm is employed to construct a sparse polynomial chaos expansion model with a fixed order,effectively balancing the accuracy and computational efficiency of the surrogate model.Based on the sparse polynomial chaos expansion,sensitivity analysis is conducted using the global finite difference and Sobol methods.Several examples of structural dynamics are provided to demonstrate the effectiveness of the proposed method in addressing structural dynamics problems.
基金supported by the National Key Research and Development Program of China(No.2022YFC3701205)the National Natural Science Foundation of China(No.41975173)the Science and Technology Development Fund of the Chinese Academy of Meteorological Sciences(No.2021KJ011)。
文摘In recent years,incidents of simultaneous exceedance of PM_(2.5)and O_(3) concentrations,termed PM_(2.5)and O_(3) co-pollution events,have frequently occurred in China.This study conducted atmospheric circulation analysis on two typical co-pollution events in Beijing,occurring from July 22 to July 28,2019,and from April 25 to May 2,2020.These events were categorized into pre-trough southerly airflow type(Type 1)and post-trough northwest flow type(Type 2).Subsequently,sensitivity analyses using the GRAPES-CUACE adjoint model were performed to quantify the contributions of precursor emissions from Beijing and surrounding areas to PM_(2.5)and O_(3) concentrations in Beijing for two types of co-pollution.The results indicated that the spatiotemporal distribution of sensitive source region varied among different circulation types.Primary PM_(2.5)(PPM_(2.5))emissions from Hebei contributed the most to the 24-hour average PM_(2.5)(24-h PM_(2.5))peak concentration(41.6%-45.4%),followed by Beijing emissions(31%-35.7%).The maximum daily 8-hour average ozone peak concentration was primarily influenced by the emissions from Hebei and Beijing,with contribution ratios respectively of 32.8%-44.8% and 29%-42.1%.Additionally,NO_(x)emissions were the main contributors in Type 1,while both NO_(x)and VOCs emissions contributed similarly in Type 2.The iterative emission reduction experiments for two types of co-pollution indicated that Type 1 required emission reductions in NO_(x)(52.4%-71.8%)and VOCs(14.1%-33.8%)only.In contrast,Type 2 required combined emission reductions in NO_(x)(37.0%-65.1%),VOCs(30.7%-56.2%),and PPM_(2.5)(31%-46.9%).This study provided a reference for controlling co-pollution events and improving air quality in Beijing.
基金supported by the National Natural Science Foundation of China(Grant Nos.52090081,52079068)the State Key Laboratory of Hydroscience and Hydraulic Engineering(Grant No.2021-KY-04).
文摘Accurate determination of rock mass parameters is essential for ensuring the accuracy of numericalsimulations. Displacement back-analysis is the most widely used method;however, the reliability of thecurrent approaches remains unsatisfactory. Therefore, in this paper, a multistage rock mass parameterback-analysis method, that considers the construction process and displacement losses is proposed andimplemented through the coupling of numerical simulation, auto-machine learning (AutoML), andmulti-objective optimization algorithms (MOOAs). First, a parametric modeling platform for mechanizedtwin tunnels is developed, generating a dataset through extensive numerical simulations. Next, theAutoML method is utilized to establish a surrogate model linking rock parameters and displacements.The tunnel construction process is divided into multiple stages, transforming the rock mass parameterback-analysis into a multi-objective optimization problem, for which multi-objective optimization algorithmsare introduced to obtain the rock mass parameters. The newly proposed rock mass parameterback-analysis method is validated in a mechanized twin tunnel project, and its accuracy and effectivenessare demonstrated. Compared with traditional single-stage back-analysis methods, the proposedmodel decreases the average absolute percentage error from 12.73% to 4.34%, significantly improving theaccuracy of the back-analysis. Moreover, although the accuracy of back analysis significantly increaseswith the number of construction stages considered, the back analysis time is acceptable. This studyprovides a new method for displacement back analysis that is efficient and accurate, thereby paving theway for precise parameter determination in numerical simulations.
基金supported by the National Natural Science Foundation of China(Grant Nos.52109144,52025094 and 52222905).
文摘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.
基金supported by the National Natural Science Foundation of China (62373224,62333013,U23A20327)。
文摘Battery production is crucial for determining the quality of electrode,which in turn affects the manufactured battery performance.As battery production is complicated with strongly coupled intermediate and control parameters,an efficient solution that can perform a reliable sensitivity analysis of the production terms of interest and forecast key battery properties in the early production phase is urgently required.This paper performs detailed sensitivity analysis of key production terms on determining the properties of manufactured battery electrode via advanced data-driven modelling.To be specific,an explainable neural network named generalized additive model with structured interaction(GAM-SI)is designed to predict two key battery properties,including electrode mass loading and porosity,while the effects of four early production terms on manufactured batteries are explained and analysed.The experimental results reveal that the proposed method is able to accurately predict battery electrode properties in the mixing and coating stages.In addition,the importance ratio ranking,global interpretation and local interpretation of both the main effects and pairwise interactions can be effectively visualized by the designed neural network.Due to the merits of interpretability,the proposed GAM-SI can help engineers gain important insights for understanding complicated production behavior,further benefitting smart battery production.
