Cable-stayed bridges have been widely used in high-speed railway infrastructure.The accurate determination of cable’s representative temperatures is vital during the intricate processes of design,construction,and mai...Cable-stayed bridges have been widely used in high-speed railway infrastructure.The accurate determination of cable’s representative temperatures is vital during the intricate processes of design,construction,and maintenance of cable-stayed bridges.However,the representative temperatures of stayed cables are not specified in the existing design codes.To address this issue,this study investigates the distribution of the cable temperature and determinates its representative temperature.First,an experimental investigation,spanning over a period of one year,was carried out near the bridge site to obtain the temperature data.According to the statistical analysis of the measured data,it reveals that the temperature distribution is generally uniform along the cable cross-section without significant temperature gradient.Then,based on the limited data,the Monte Carlo,the gradient boosted regression trees(GBRT),and univariate linear regression(ULR)methods are employed to predict the cable’s representative temperature throughout the service life.These methods effectively overcome the limitations of insufficient monitoring data and accurately predict the representative temperature of the cables.However,each method has its own advantages and limitations in terms of applicability and accuracy.A comprehensive evaluation of the performance of these methods is conducted,and practical recommendations are provided for their application.The proposed methods and representative temperatures provide a good basis for the operation and maintenance of in-service long-span cable-stayed bridges.展开更多
A corrosion defect is recognized as one of the most severe phenomena for high-pressure pipelines,especially those served for a long time.Finite-element method and empirical formulas are thereby used for the strength p...A corrosion defect is recognized as one of the most severe phenomena for high-pressure pipelines,especially those served for a long time.Finite-element method and empirical formulas are thereby used for the strength prediction of such pipes with corrosion.However,it is time-consuming for finite-element method and there is a limited application range by using empirical formulas.In order to improve the prediction of strength,this paper investigates the burst pressure of line pipelines with a single corrosion defect subjected to internal pressure based on data-driven methods.Three supervised ML(machine learning)algorithms,including the ANN(artificial neural network),the SVM(support vector machine)and the LR(linear regression),are deployed to train models based on experimental data.Data analysis is first conducted to determine proper pipe features for training.Hyperparameter tuning to control the learning process is then performed to fit the best strength models for corroded pipelines.Among all the proposed data-driven models,the ANN model with three neural layers has the highest training accuracy,but also presents the largest variance.The SVM model provides both high training accuracy and high validation accuracy.The LR model has the best performance in terms of generalization ability.These models can be served as surrogate models by transfer learning with new coming data in future research,facilitating a sustainable and intelligent decision-making of corroded pipelines.展开更多
When assessing seismic liquefaction potential with data-driven models,addressing the uncertainties of establishing models,interpreting cone penetration tests(CPT)data and decision threshold is crucial for avoiding bia...When assessing seismic liquefaction potential with data-driven models,addressing the uncertainties of establishing models,interpreting cone penetration tests(CPT)data and decision threshold is crucial for avoiding biased data selection,ameliorating overconfident models,and being flexible to varying practical objectives,especially when the training and testing data are not identically distributed.A workflow characterized by leveraging Bayesian methodology was proposed to address these issues.Employing a Multi-Layer Perceptron(MLP)as the foundational model,this approach was benchmarked against empirical methods and advanced algorithms for its efficacy in simplicity,accuracy,and resistance to overfitting.The analysis revealed that,while MLP models optimized via maximum a posteriori algorithm suffices for straightforward scenarios,Bayesian neural networks showed great potential for preventing overfitting.Additionally,integrating decision thresholds through various evaluative principles offers insights for challenging decisions.Two case studies demonstrate the framework's capacity for nuanced interpretation of in situ data,employing a model committee for a detailed evaluation of liquefaction potential via Monte Carlo simulations and basic statistics.Overall,the proposed step-by-step workflow for analyzing seismic liquefaction incorporates multifold testing and real-world data validation,showing improved robustness against overfitting and greater versatility in addressing practical challenges.This research contributes to the seismic liquefaction assessment field by providing a structured,adaptable methodology for accurate and reliable analysis.展开更多
Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure.Therefore,it is particularly significant to identify the structural modal parameters accordi...Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure.Therefore,it is particularly significant to identify the structural modal parameters according to the monitoring data information in the structural health monitoring(SHM)system,so as to provide a scientific basis for structural damage identification and dynamic model modification.In view of this,this paper reviews methods for identifying structural modal parameters under environmental excitation and briefly describes how to identify structural damages based on the derived modal parameters.The paper primarily introduces data-driven modal parameter recognition methods(e.g.,time-domain,frequency-domain,and time-frequency-domain methods,etc.),briefly describes damage identification methods based on the variations of modal parameters(e.g.,natural frequency,modal shapes,and curvature modal shapes,etc.)and modal validation methods(e.g.,Stability Diagram and Modal Assurance Criterion,etc.).The current status of the application of artificial intelligence(AI)methods in the direction of modal parameter recognition and damage identification is further discussed.Based on the pre-vious analysis,the main development trends of structural modal parameter recognition and damage identification methods are given to provide scientific references for the optimized design and functional upgrading of SHM systems.展开更多
Hydraulic fracturing technology has achieved remarkable results in improving the production of tight gas reservoirs,but its effectiveness is under the joint action of multiple factors of complexity.Traditional analysi...Hydraulic fracturing technology has achieved remarkable results in improving the production of tight gas reservoirs,but its effectiveness is under the joint action of multiple factors of complexity.Traditional analysis methods have limitations in dealing with these complex and interrelated factors,and it is difficult to fully reveal the actual contribution of each factor to the production.Machine learning-based methods explore the complex mapping relationships between large amounts of data to provide datadriven insights into the key factors driving production.In this study,a data-driven PCA-RF-VIM(Principal Component Analysis-Random Forest-Variable Importance Measures)approach of analyzing the importance of features is proposed to identify the key factors driving post-fracturing production.Four types of parameters,including log parameters,geological and reservoir physical parameters,hydraulic fracturing design parameters,and reservoir stimulation parameters,were inputted into the PCA-RF-VIM model.The model was trained using 6-fold cross-validation and grid search,and the relative importance ranking of each factor was finally obtained.In order to verify the validity of the PCA-RF-VIM model,a consolidation model that uses three other independent data-driven methods(Pearson correlation coefficient,RF feature significance analysis method,and XGboost feature significance analysis method)are applied to compare with the PCA-RF-VIM model.A comparison the two models shows that they contain almost the same parameters in the top ten,with only minor differences in one parameter.In combination with the reservoir characteristics,the reasonableness of the PCA-RF-VIM model is verified,and the importance ranking of the parameters by this method is more consistent with the reservoir characteristics of the study area.Ultimately,the ten parameters are selected as the controlling factors that have the potential to influence post-fracturing gas production,as the combined importance of these top ten parameters is 91.95%on driving natural gas production.Analyzing and obtaining these ten controlling factors provides engineers with a new insight into the reservoir selection for fracturing stimulation and fracturing parameter optimization to improve fracturing efficiency and productivity.展开更多
We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpr...We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.展开更多
This study employs a data-driven methodology that embeds the principle of dimensional invariance into an artificial neural network to automatically identify dominant dimensionless quantities in the penetration of rod ...This study employs a data-driven methodology that embeds the principle of dimensional invariance into an artificial neural network to automatically identify dominant dimensionless quantities in the penetration of rod projectiles into semi-infinite metal targets from experimental measurements.The derived mathematical expressions of dimensionless quantities are simplified by the examination of the exponent matrix and coupling relationships between feature variables.As a physics-based dimension reduction methodology,this way reduces high-dimensional parameter spaces to descriptions involving only a few physically interpretable dimensionless quantities in penetrating cases.Then the relative importance of various dimensionless feature variables on the penetration efficiencies for four impacting conditions is evaluated through feature selection engineering.The results indicate that the selected critical dimensionless feature variables by this synergistic method,without referring to the complex theoretical equations and aiding in the detailed knowledge of penetration mechanics,are in accordance with those reported in the reference.Lastly,the determined dimensionless quantities can be efficiently applied to conduct semi-empirical analysis for the specific penetrating case,and the reliability of regression functions is validated.展开更多
The separation-of-variable(SOV)methods,such as the improved SOV method,the variational SOV method,and the extended SOV method,have been proposed by the present authors and coworkers to obtain the closed-form analytica...The separation-of-variable(SOV)methods,such as the improved SOV method,the variational SOV method,and the extended SOV method,have been proposed by the present authors and coworkers to obtain the closed-form analytical solutions for free vibration and eigenbuckling of rectangular plates and circular cylindrical shells.By taking the free vibration of rectangular thin plates as an example,this work presents the theoretical framework of the SOV methods in an instructive way,and the bisection–based solution procedures for a group of nonlinear eigenvalue equations.Besides,the explicit equations of nodal lines of the SOV methods are presented,and the relations of nodal line patterns and frequency orders are investigated.It is concluded that the highly accurate SOV methods have the same accuracy for all frequencies,the mode shapes about repeated frequencies can also be precisely captured,and the SOV methods do not have the problem of missing roots as well.展开更多
Soil improvement is one of the most important issues in geotechnical engineering practice.The wide application of traditional improvement techniques(cement/chemical materials)are limited due to damage ecological en-vi...Soil improvement is one of the most important issues in geotechnical engineering practice.The wide application of traditional improvement techniques(cement/chemical materials)are limited due to damage ecological en-vironment and intensify carbon emissions.However,the use of microbially induced calcium carbonate pre-cipitation(MICP)to obtain bio-cement is a novel technique with the potential to induce soil stability,providing a low-carbon,environment-friendly,and sustainable integrated solution for some geotechnical engineering pro-blems in the environment.This paper presents a comprehensive review of the latest progress in soil improvement based on the MICP strategy.It systematically summarizes and overviews the mineralization mechanism,influ-encing factors,improved methods,engineering characteristics,and current field application status of the MICP.Additionally,it also explores the limitations and correspondingly proposes prospective applications via the MICP approach for soil improvement.This review indicates that the utilization of different environmental calcium-based wastes in MICP and combination of materials and MICP are conducive to meeting engineering and market demand.Furthermore,we recommend and encourage global collaborative study and practice with a view to commercializing MICP technique in the future.The current review purports to provide insights for engineers and interdisciplinary researchers,and guidance for future engineering applications.展开更多
Ocean energy has progressively gained considerable interest due to its sufficient potential to meet the world’s energy demand,and the blade is the core component in electricity generation from the ocean current.Howev...Ocean energy has progressively gained considerable interest due to its sufficient potential to meet the world’s energy demand,and the blade is the core component in electricity generation from the ocean current.However,the widened hydraulic excitation frequency may satisfy the blade resonance due to the time variation in the velocity and angle of attack of the ocean current,even resulting in blade fatigue and destructively interfering with grid stability.A key parameter that determines the resonance amplitude of the blade is the hydrodynamic damping ratio(HDR).However,HDR is difficult to obtain due to the complex fluid-structure interaction(FSI).Therefore,a literature review was conducted on the hydrodynamic damping characteristics of blade-like structures.The experimental and simulation methods used to identify and obtain the HDR quantitatively were described,placing emphasis on the experimental processes and simulation setups.Moreover,the accuracy and efficiency of different simulation methods were compared,and the modal work approach was recommended.The effects of key typical parameters,including flow velocity,angle of attack,gap,rotational speed,and cavitation,on the HDR were then summarized,and the suggestions on operating conditions were presented from the perspective of increasing the HDR.Subsequently,considering multiple flow parameters,several theoretical derivations and semi-empirical prediction formulas for HDR were introduced,and the accuracy and application were discussed.Based on the shortcomings of the existing research,the direction of future research was finally determined.The current work offers a clear understanding of the HDR of blade-like structures,which could improve the evaluation accuracy of flow-induced vibration in the design stage.展开更多
Text mining has emerged as a powerful strategy for extracting domain knowledge structure from large amounts of text data.To date,most text mining methods are restricted to specific literature information,resulting in ...Text mining has emerged as a powerful strategy for extracting domain knowledge structure from large amounts of text data.To date,most text mining methods are restricted to specific literature information,resulting in incomplete knowledge graphs.Here,we report a method that combines citation analysis with topic modeling to describe the hidden development patterns in the history of science.Leveraging this method,we construct a knowledge graph in the field of Raman spectroscopy.The traditional Latent DirichletAllocation model is chosen as the baseline model for comparison to validate the performance of our model.Our method improves the topic coherence with a minimum growth rate of 100%compared to the traditional text mining method.It outperforms the traditional text mining method on the diversity,and its growth rate ranges from 0 to 126%.The results show the effectiveness of rule-based tokenizer we designed in solving the word tokenizer problem caused by entity naming rules in the field of chemistry.It is versatile in revealing the distribution of topics,establishing the similarity and inheritance relationships,and identifying the important moments in the history of Raman spectroscopy.Our work provides a comprehensive tool for the science of science research and promises to offer new insights into the historical survey and development forecast of a research field.展开更多
High-quality data is essential for the success of data-driven learning tasks.The characteristics,precision,and completeness of the datasets critically determine the reliability,interpretability,and effectiveness of su...High-quality data is essential for the success of data-driven learning tasks.The characteristics,precision,and completeness of the datasets critically determine the reliability,interpretability,and effectiveness of subsequent analyzes and applications,such as fault detection,predictive maintenance,and process optimization.However,for many industrial processes,obtaining sufficient high-quality data remains a significant challenge due to high costs,safety concerns,and practical constraints.To overcome these challenges,data augmentation has emerged as a rapidly growing research area,attracting considerable attention across both academia and industry.By expanding datasets,data augmentation techniques improve greater generalization and more robust performance in actual applications.This paper provides a comprehensive,multi-perspective review of data augmentation methods for industrial processes.For clarity and organization,existing studies are systematically grouped into four categories:small sample with low dimension,small sample with high dimension,large sample with low dimension,and large sample with high dimension.Within this framework,the review examines current research from both methodological and application-oriented perspectives,highlighting main methods,advantages,and limitations.By synthesizing these findings,this review offers a structured overview for scholars and practitioners,serving as a valuable reference for newcomers and experienced researchers seeking to explore and advance data augmentation techniques in industrial processes.展开更多
This paper reviews recent advancements in system identification methods for perturbed motorized systems,focusing on brushed DC motors,brushless DC motors,and permanent magnet synchronous motors.It examines data acquis...This paper reviews recent advancements in system identification methods for perturbed motorized systems,focusing on brushed DC motors,brushless DC motors,and permanent magnet synchronous motors.It examines data acquisition setups and evaluates conventional and metaheuristic optimization algorithms,highlighting their advantages,limitations,and applications.The paper explores emerging trends in model structures and parameter optimization techniques that address specific perturbations such as varying loads,noise,and friction.A comparative performance analysis is also included to assess several widely used optimization methods,including least squares(LS),particle swarm optimization(PSO),grey wolf optimizer(GWO),bat algorithm(BA),genetic algorithm(GA)and neural network for system identification of a specific case of a perturbed DC motor in both open-loop(OL)and closed-loop(CL)settings.Results show that GWO achieves the lowest error overall,excelling in OL scenarios,while PSO performs best in CL due to its synergy with feedback control.LS proves efficient in CL settings,whereas GA and BA rely heavily on feedback for improved performance.The paper also outlines potential research directions aimed at advancing motor modeling techniques,including integration of advanced machine learning methods,hybrid learning-based methods,and adaptive modeling techniques.These insights offer a foundation for advancing motor modeling techniques in real-world applications.展开更多
RNAs have important biological functions and the functions of RNAs are generally coupled to their structures, especiallytheir secondary structures. In this work, we have made a comprehensive evaluation of the performa...RNAs have important biological functions and the functions of RNAs are generally coupled to their structures, especiallytheir secondary structures. In this work, we have made a comprehensive evaluation of the performances of existingtop RNA secondary structure prediction methods, including five deep-learning (DL) based methods and five minimum freeenergy (MFE) based methods. First, we made a brief overview of these RNA secondary structure prediction methods.Afterwards, we built two rigorous test datasets consisting of RNAs with non-redundant sequences and comprehensivelyexamined the performances of the RNA secondary structure prediction methods through classifying the RNAs into differentlength ranges and different types. Our examination shows that the DL-based methods generally perform better thanthe MFE-based methods for RNAs with long lengths and complex structures, while the MFE-based methods can achievegood performance for small RNAs and some specialized MFE-based methods can achieve good prediction accuracy forpseudoknots. Finally, we provided some insights and perspectives in modeling RNA secondary structures.展开更多
Understanding the wind power potential of a site is essential for designing an optimal wind power conditioning system. The Weibull distribution and wind speed extrapolation methods are powerful mathematical tools for ...Understanding the wind power potential of a site is essential for designing an optimal wind power conditioning system. The Weibull distribution and wind speed extrapolation methods are powerful mathematical tools for efficiently predicting the frequency distribution of wind speeds at a site. Hourly wind speed and direction data were collected from the National Aeronautics and Space Administration (NASA) website for the period 2013 to 2023. MATLAB software was used to calculate the distribution parameters using the graphical method and to plot the corresponding curves, while WRPLOTView software was used to construct the wind rose. The average wind speed obtained is 3.33 m/s and can reach up to 5.71 m/s at a height of 100 meters. The wind energy is estimated to be 1315.30 kWh/m2 at a height of 100 meters. The wind rose indicates the prevailing winds (ranging from 3.60 m/s to 5.70 m/s) in the northeast-east direction.展开更多
Purpose–For the commonly used concrete mix for railway tunnel linings,concrete model specimens were made,and springback and core drilling tests were conducted at different ages.The springback strength was measured to...Purpose–For the commonly used concrete mix for railway tunnel linings,concrete model specimens were made,and springback and core drilling tests were conducted at different ages.The springback strength was measured to the compressive strength of the core sample with a diameter of 100mm and a height-to-diameter ratio of 1:1.By comparing the measured strength values,the relationship between the measured values under different strength measurement methods was analyzed.Design/methodology/approach–A comparative test of the core drilling method and the rebound method was conducted on the side walls of tunnel linings in some under-construction railways to study the feasibility of the rebound method in engineering quality supervision and inspection.Findings–Tests showed that the rebound strength was positively correlated with the core drill strength.The core drill test strength was significantly higher than the rebound test strength,and the strength still increased after 56 days of age.The rebound method is suitable for the general survey of concrete strength during the construction process and is not suitable for direct supervision and inspection.Originality/value–By studying the correlation of test strength of tunnel lining concrete using two methods,the differences in test results of different methods are proposed to provide a reference for the test and evaluation of tunnel lining strength in railway engineering.展开更多
Efficient and accurate simulation of unsteady flow presents a significant challenge that needs to be overcome in computational fluid dynamics.Temporal discretization method plays a crucial role in the simulation of un...Efficient and accurate simulation of unsteady flow presents a significant challenge that needs to be overcome in computational fluid dynamics.Temporal discretization method plays a crucial role in the simulation of unsteady flows.To enhance computational efficiency,we propose the Implicit-Explicit Two-Step Runge-Kutta(IMEX-TSRK)time-stepping discretization methods for unsteady flows,and develop a novel adaptive algorithm that correctly partitions spatial regions to apply implicit or explicit methods.The novel adaptive IMEX-TSRK schemes effectively handle the numerical stiffness of the small grid size and improve computational efficiency.Compared to implicit and explicit Runge-Kutta(RK)schemes,the IMEX-TSRK methods achieve the same order of accuracy with fewer first derivative calculations.Numerical case tests demonstrate that the IMEX-TSRK methods maintain numerical stability while enhancing computational efficiency.Specifically,in high Reynolds number flows,the computational efficiency of the IMEX-TSRK methods surpasses that of explicit RK schemes by more than one order of magnitude,and that of implicit RK schemes several times over.展开更多
This study presents a data-driven approach to predict tailplane aerodynamics in icing conditions,supporting the ice-tolerant design of aircraft horizontal stabilizers.The core of this work is a low-cost predictive mod...This study presents a data-driven approach to predict tailplane aerodynamics in icing conditions,supporting the ice-tolerant design of aircraft horizontal stabilizers.The core of this work is a low-cost predictive model for analyzing icing effects on swept tailplanes.The method relies on a multi-fidelity data gathering campaign,enabling seamless integration into multidisciplinary aircraft design workflows.A dataset of iced airfoil shapes was generated using 2D inviscid methods across various flight conditions.High-fidelity CFD simulations were conducted on both clean and iced geometries,forming a multidimensional aerodynamic database.This 2D database feeds a nonlinear vortex lattice method to estimate 3D aerodynamic characteristics,following a'quasi-3D'approach.The resulting reduced-order model delivers fast aerodynamic performance estimates of iced tailplanes.To demonstrate its effectiveness,optimal ice-tolerant tailplane designs were selected from a range of feasible shapes based on a reference transport aircraft.The analysis validates the model's reliability,accuracy,and limitations concerning 3D ice shapes and aerodynamic characteristics.Most notably,the model offers near-zero computational cost compared to high-fidelity simulations,making it a valuable tool for efficient aircraft design.展开更多
In this paper,a new technique is introduced to construct higher-order iterative methods for solving nonlinear systems.The order of convergence of some iterative methods can be improved by three at the cost of introduc...In this paper,a new technique is introduced to construct higher-order iterative methods for solving nonlinear systems.The order of convergence of some iterative methods can be improved by three at the cost of introducing only one additional evaluation of the function in each step.Furthermore,some new efficient methods with a higher-order of convergence are obtained by using only a single matrix inversion in each iteration.Analyses of convergence properties and computational efficiency of these new methods are made and testified by several numerical problems.By comparison,the new schemes are more efficient than the corresponding existing ones,particularly for large problem sizes.展开更多
The feasibility of using a problem-dependent method to solve systems of second order ODEs is corroborated by an eigen-based theory and a methodology to develop such a numerical method is constructed.The key steps of t...The feasibility of using a problem-dependent method to solve systems of second order ODEs is corroborated by an eigen-based theory and a methodology to develop such a numerical method is constructed.The key steps of this methodology are to decouple a system of ODEs of second order into a set of uncoupled ODEs of second order;next,an eigen-dependent method is proposed to approximate the solution of each uncoupled ODE of second order.It is vital to transform all eigen-dependent methods to a problem-dependent method to bypass an Eigen analysis.The development of an eigen-dependent method plays a key role in this methodology so that slow eigenmodes can be accurately integrated while there is no instability or excessive amplitude growth in fast eigenmodes.This can explain why a problem-dependent method can simultaneously combine the explicitness of each step and A-stability.Consequently,huge computational efforts can be saved for solving nonlinear stiff problems.A new family of problem-dependent methods is developed in this work so that the feasibility of the proposed methodology can be affirmed.It has almost the same performance as that of the HHT-αmethod.However,it can save more than 99.5%of CPU demand in approximating a solution for a system of 1000 nonlinear second order ODEs.展开更多
基金Project(2017G006-N)supported by the Project of Science and Technology Research and Development Program of China Railway Corporation。
文摘Cable-stayed bridges have been widely used in high-speed railway infrastructure.The accurate determination of cable’s representative temperatures is vital during the intricate processes of design,construction,and maintenance of cable-stayed bridges.However,the representative temperatures of stayed cables are not specified in the existing design codes.To address this issue,this study investigates the distribution of the cable temperature and determinates its representative temperature.First,an experimental investigation,spanning over a period of one year,was carried out near the bridge site to obtain the temperature data.According to the statistical analysis of the measured data,it reveals that the temperature distribution is generally uniform along the cable cross-section without significant temperature gradient.Then,based on the limited data,the Monte Carlo,the gradient boosted regression trees(GBRT),and univariate linear regression(ULR)methods are employed to predict the cable’s representative temperature throughout the service life.These methods effectively overcome the limitations of insufficient monitoring data and accurately predict the representative temperature of the cables.However,each method has its own advantages and limitations in terms of applicability and accuracy.A comprehensive evaluation of the performance of these methods is conducted,and practical recommendations are provided for their application.The proposed methods and representative temperatures provide a good basis for the operation and maintenance of in-service long-span cable-stayed bridges.
文摘A corrosion defect is recognized as one of the most severe phenomena for high-pressure pipelines,especially those served for a long time.Finite-element method and empirical formulas are thereby used for the strength prediction of such pipes with corrosion.However,it is time-consuming for finite-element method and there is a limited application range by using empirical formulas.In order to improve the prediction of strength,this paper investigates the burst pressure of line pipelines with a single corrosion defect subjected to internal pressure based on data-driven methods.Three supervised ML(machine learning)algorithms,including the ANN(artificial neural network),the SVM(support vector machine)and the LR(linear regression),are deployed to train models based on experimental data.Data analysis is first conducted to determine proper pipe features for training.Hyperparameter tuning to control the learning process is then performed to fit the best strength models for corroded pipelines.Among all the proposed data-driven models,the ANN model with three neural layers has the highest training accuracy,but also presents the largest variance.The SVM model provides both high training accuracy and high validation accuracy.The LR model has the best performance in terms of generalization ability.These models can be served as surrogate models by transfer learning with new coming data in future research,facilitating a sustainable and intelligent decision-making of corroded pipelines.
文摘When assessing seismic liquefaction potential with data-driven models,addressing the uncertainties of establishing models,interpreting cone penetration tests(CPT)data and decision threshold is crucial for avoiding biased data selection,ameliorating overconfident models,and being flexible to varying practical objectives,especially when the training and testing data are not identically distributed.A workflow characterized by leveraging Bayesian methodology was proposed to address these issues.Employing a Multi-Layer Perceptron(MLP)as the foundational model,this approach was benchmarked against empirical methods and advanced algorithms for its efficacy in simplicity,accuracy,and resistance to overfitting.The analysis revealed that,while MLP models optimized via maximum a posteriori algorithm suffices for straightforward scenarios,Bayesian neural networks showed great potential for preventing overfitting.Additionally,integrating decision thresholds through various evaluative principles offers insights for challenging decisions.Two case studies demonstrate the framework's capacity for nuanced interpretation of in situ data,employing a model committee for a detailed evaluation of liquefaction potential via Monte Carlo simulations and basic statistics.Overall,the proposed step-by-step workflow for analyzing seismic liquefaction incorporates multifold testing and real-world data validation,showing improved robustness against overfitting and greater versatility in addressing practical challenges.This research contributes to the seismic liquefaction assessment field by providing a structured,adaptable methodology for accurate and reliable analysis.
基金supported by the Innovation Foundation of Provincial Education Department of Gansu(2024B-005)the Gansu Province National Science Foundation(22YF7GA182)the Fundamental Research Funds for the Central Universities(No.lzujbky2022-kb01)。
文摘Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure.Therefore,it is particularly significant to identify the structural modal parameters according to the monitoring data information in the structural health monitoring(SHM)system,so as to provide a scientific basis for structural damage identification and dynamic model modification.In view of this,this paper reviews methods for identifying structural modal parameters under environmental excitation and briefly describes how to identify structural damages based on the derived modal parameters.The paper primarily introduces data-driven modal parameter recognition methods(e.g.,time-domain,frequency-domain,and time-frequency-domain methods,etc.),briefly describes damage identification methods based on the variations of modal parameters(e.g.,natural frequency,modal shapes,and curvature modal shapes,etc.)and modal validation methods(e.g.,Stability Diagram and Modal Assurance Criterion,etc.).The current status of the application of artificial intelligence(AI)methods in the direction of modal parameter recognition and damage identification is further discussed.Based on the pre-vious analysis,the main development trends of structural modal parameter recognition and damage identification methods are given to provide scientific references for the optimized design and functional upgrading of SHM systems.
基金funded by the Key Research and Development Program of Shaanxi,China(No.2024GX-YBXM-503)the National Natural Science Foundation of China(No.51974254)。
文摘Hydraulic fracturing technology has achieved remarkable results in improving the production of tight gas reservoirs,but its effectiveness is under the joint action of multiple factors of complexity.Traditional analysis methods have limitations in dealing with these complex and interrelated factors,and it is difficult to fully reveal the actual contribution of each factor to the production.Machine learning-based methods explore the complex mapping relationships between large amounts of data to provide datadriven insights into the key factors driving production.In this study,a data-driven PCA-RF-VIM(Principal Component Analysis-Random Forest-Variable Importance Measures)approach of analyzing the importance of features is proposed to identify the key factors driving post-fracturing production.Four types of parameters,including log parameters,geological and reservoir physical parameters,hydraulic fracturing design parameters,and reservoir stimulation parameters,were inputted into the PCA-RF-VIM model.The model was trained using 6-fold cross-validation and grid search,and the relative importance ranking of each factor was finally obtained.In order to verify the validity of the PCA-RF-VIM model,a consolidation model that uses three other independent data-driven methods(Pearson correlation coefficient,RF feature significance analysis method,and XGboost feature significance analysis method)are applied to compare with the PCA-RF-VIM model.A comparison the two models shows that they contain almost the same parameters in the top ten,with only minor differences in one parameter.In combination with the reservoir characteristics,the reasonableness of the PCA-RF-VIM model is verified,and the importance ranking of the parameters by this method is more consistent with the reservoir characteristics of the study area.Ultimately,the ten parameters are selected as the controlling factors that have the potential to influence post-fracturing gas production,as the combined importance of these top ten parameters is 91.95%on driving natural gas production.Analyzing and obtaining these ten controlling factors provides engineers with a new insight into the reservoir selection for fracturing stimulation and fracturing parameter optimization to improve fracturing efficiency and productivity.
基金supported by National Key Research and Development Program (2019YFA0708301)National Natural Science Foundation of China (51974337)+2 种基金the Strategic Cooperation Projects of CNPC and CUPB (ZLZX2020-03)Science and Technology Innovation Fund of CNPC (2021DQ02-0403)Open Fund of Petroleum Exploration and Development Research Institute of CNPC (2022-KFKT-09)
文摘We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.
基金supported by the National Natural Science Foundation of China(Grant Nos.12272257,12102292,12032006)the special fund for Science and Technology Innovation Teams of Shanxi Province(Nos.202204051002006).
文摘This study employs a data-driven methodology that embeds the principle of dimensional invariance into an artificial neural network to automatically identify dominant dimensionless quantities in the penetration of rod projectiles into semi-infinite metal targets from experimental measurements.The derived mathematical expressions of dimensionless quantities are simplified by the examination of the exponent matrix and coupling relationships between feature variables.As a physics-based dimension reduction methodology,this way reduces high-dimensional parameter spaces to descriptions involving only a few physically interpretable dimensionless quantities in penetrating cases.Then the relative importance of various dimensionless feature variables on the penetration efficiencies for four impacting conditions is evaluated through feature selection engineering.The results indicate that the selected critical dimensionless feature variables by this synergistic method,without referring to the complex theoretical equations and aiding in the detailed knowledge of penetration mechanics,are in accordance with those reported in the reference.Lastly,the determined dimensionless quantities can be efficiently applied to conduct semi-empirical analysis for the specific penetrating case,and the reliability of regression functions is validated.
基金supported by the National Natural Science Foundation of China(12172023).
文摘The separation-of-variable(SOV)methods,such as the improved SOV method,the variational SOV method,and the extended SOV method,have been proposed by the present authors and coworkers to obtain the closed-form analytical solutions for free vibration and eigenbuckling of rectangular plates and circular cylindrical shells.By taking the free vibration of rectangular thin plates as an example,this work presents the theoretical framework of the SOV methods in an instructive way,and the bisection–based solution procedures for a group of nonlinear eigenvalue equations.Besides,the explicit equations of nodal lines of the SOV methods are presented,and the relations of nodal line patterns and frequency orders are investigated.It is concluded that the highly accurate SOV methods have the same accuracy for all frequencies,the mode shapes about repeated frequencies can also be precisely captured,and the SOV methods do not have the problem of missing roots as well.
基金funded by the National Natural Science Foundation of China(No.41962016)the Natural Science Foundation of NingXia(Nos.2023AAC02023,2023A1218,and 2021AAC02006).
文摘Soil improvement is one of the most important issues in geotechnical engineering practice.The wide application of traditional improvement techniques(cement/chemical materials)are limited due to damage ecological en-vironment and intensify carbon emissions.However,the use of microbially induced calcium carbonate pre-cipitation(MICP)to obtain bio-cement is a novel technique with the potential to induce soil stability,providing a low-carbon,environment-friendly,and sustainable integrated solution for some geotechnical engineering pro-blems in the environment.This paper presents a comprehensive review of the latest progress in soil improvement based on the MICP strategy.It systematically summarizes and overviews the mineralization mechanism,influ-encing factors,improved methods,engineering characteristics,and current field application status of the MICP.Additionally,it also explores the limitations and correspondingly proposes prospective applications via the MICP approach for soil improvement.This review indicates that the utilization of different environmental calcium-based wastes in MICP and combination of materials and MICP are conducive to meeting engineering and market demand.Furthermore,we recommend and encourage global collaborative study and practice with a view to commercializing MICP technique in the future.The current review purports to provide insights for engineers and interdisciplinary researchers,and guidance for future engineering applications.
基金Supported by the National Natural Science Foundation of China(Nos.52222904 and 52309117)China Postdoctoral Science Foundation(Nos.2022TQ0168 and 2023M731895).
文摘Ocean energy has progressively gained considerable interest due to its sufficient potential to meet the world’s energy demand,and the blade is the core component in electricity generation from the ocean current.However,the widened hydraulic excitation frequency may satisfy the blade resonance due to the time variation in the velocity and angle of attack of the ocean current,even resulting in blade fatigue and destructively interfering with grid stability.A key parameter that determines the resonance amplitude of the blade is the hydrodynamic damping ratio(HDR).However,HDR is difficult to obtain due to the complex fluid-structure interaction(FSI).Therefore,a literature review was conducted on the hydrodynamic damping characteristics of blade-like structures.The experimental and simulation methods used to identify and obtain the HDR quantitatively were described,placing emphasis on the experimental processes and simulation setups.Moreover,the accuracy and efficiency of different simulation methods were compared,and the modal work approach was recommended.The effects of key typical parameters,including flow velocity,angle of attack,gap,rotational speed,and cavitation,on the HDR were then summarized,and the suggestions on operating conditions were presented from the perspective of increasing the HDR.Subsequently,considering multiple flow parameters,several theoretical derivations and semi-empirical prediction formulas for HDR were introduced,and the accuracy and application were discussed.Based on the shortcomings of the existing research,the direction of future research was finally determined.The current work offers a clear understanding of the HDR of blade-like structures,which could improve the evaluation accuracy of flow-induced vibration in the design stage.
基金supported by the National Natural Science Foundation of China(T2222002,22032004)the Fundamental Research Funds for the Central Universities(Xiamen University:No.20720240053)State Key Laboratory of Vaccines for Infectious Diseases,Xiang An Biomedicine Laboratory(2023XAKJ0103074).
文摘Text mining has emerged as a powerful strategy for extracting domain knowledge structure from large amounts of text data.To date,most text mining methods are restricted to specific literature information,resulting in incomplete knowledge graphs.Here,we report a method that combines citation analysis with topic modeling to describe the hidden development patterns in the history of science.Leveraging this method,we construct a knowledge graph in the field of Raman spectroscopy.The traditional Latent DirichletAllocation model is chosen as the baseline model for comparison to validate the performance of our model.Our method improves the topic coherence with a minimum growth rate of 100%compared to the traditional text mining method.It outperforms the traditional text mining method on the diversity,and its growth rate ranges from 0 to 126%.The results show the effectiveness of rule-based tokenizer we designed in solving the word tokenizer problem caused by entity naming rules in the field of chemistry.It is versatile in revealing the distribution of topics,establishing the similarity and inheritance relationships,and identifying the important moments in the history of Raman spectroscopy.Our work provides a comprehensive tool for the science of science research and promises to offer new insights into the historical survey and development forecast of a research field.
基金supported by the Postdoctoral Fellowship Program(Grade B)of China(GZB20250435)the National Natural Science Foundation of China(62403270).
文摘High-quality data is essential for the success of data-driven learning tasks.The characteristics,precision,and completeness of the datasets critically determine the reliability,interpretability,and effectiveness of subsequent analyzes and applications,such as fault detection,predictive maintenance,and process optimization.However,for many industrial processes,obtaining sufficient high-quality data remains a significant challenge due to high costs,safety concerns,and practical constraints.To overcome these challenges,data augmentation has emerged as a rapidly growing research area,attracting considerable attention across both academia and industry.By expanding datasets,data augmentation techniques improve greater generalization and more robust performance in actual applications.This paper provides a comprehensive,multi-perspective review of data augmentation methods for industrial processes.For clarity and organization,existing studies are systematically grouped into four categories:small sample with low dimension,small sample with high dimension,large sample with low dimension,and large sample with high dimension.Within this framework,the review examines current research from both methodological and application-oriented perspectives,highlighting main methods,advantages,and limitations.By synthesizing these findings,this review offers a structured overview for scholars and practitioners,serving as a valuable reference for newcomers and experienced researchers seeking to explore and advance data augmentation techniques in industrial processes.
基金supported by the Malaysia Ministry of Higher Education under Fundamental Research Grant Scheme with Project Code:FRGS/1/2024/TK07/USM/02/3.
文摘This paper reviews recent advancements in system identification methods for perturbed motorized systems,focusing on brushed DC motors,brushless DC motors,and permanent magnet synchronous motors.It examines data acquisition setups and evaluates conventional and metaheuristic optimization algorithms,highlighting their advantages,limitations,and applications.The paper explores emerging trends in model structures and parameter optimization techniques that address specific perturbations such as varying loads,noise,and friction.A comparative performance analysis is also included to assess several widely used optimization methods,including least squares(LS),particle swarm optimization(PSO),grey wolf optimizer(GWO),bat algorithm(BA),genetic algorithm(GA)and neural network for system identification of a specific case of a perturbed DC motor in both open-loop(OL)and closed-loop(CL)settings.Results show that GWO achieves the lowest error overall,excelling in OL scenarios,while PSO performs best in CL due to its synergy with feedback control.LS proves efficient in CL settings,whereas GA and BA rely heavily on feedback for improved performance.The paper also outlines potential research directions aimed at advancing motor modeling techniques,including integration of advanced machine learning methods,hybrid learning-based methods,and adaptive modeling techniques.These insights offer a foundation for advancing motor modeling techniques in real-world applications.
基金supported by grants from the National Science Foundation of China(Grant Nos.12375038 and 12075171 to ZJT,and 12205223 to YLT).
文摘RNAs have important biological functions and the functions of RNAs are generally coupled to their structures, especiallytheir secondary structures. In this work, we have made a comprehensive evaluation of the performances of existingtop RNA secondary structure prediction methods, including five deep-learning (DL) based methods and five minimum freeenergy (MFE) based methods. First, we made a brief overview of these RNA secondary structure prediction methods.Afterwards, we built two rigorous test datasets consisting of RNAs with non-redundant sequences and comprehensivelyexamined the performances of the RNA secondary structure prediction methods through classifying the RNAs into differentlength ranges and different types. Our examination shows that the DL-based methods generally perform better thanthe MFE-based methods for RNAs with long lengths and complex structures, while the MFE-based methods can achievegood performance for small RNAs and some specialized MFE-based methods can achieve good prediction accuracy forpseudoknots. Finally, we provided some insights and perspectives in modeling RNA secondary structures.
文摘Understanding the wind power potential of a site is essential for designing an optimal wind power conditioning system. The Weibull distribution and wind speed extrapolation methods are powerful mathematical tools for efficiently predicting the frequency distribution of wind speeds at a site. Hourly wind speed and direction data were collected from the National Aeronautics and Space Administration (NASA) website for the period 2013 to 2023. MATLAB software was used to calculate the distribution parameters using the graphical method and to plot the corresponding curves, while WRPLOTView software was used to construct the wind rose. The average wind speed obtained is 3.33 m/s and can reach up to 5.71 m/s at a height of 100 meters. The wind energy is estimated to be 1315.30 kWh/m2 at a height of 100 meters. The wind rose indicates the prevailing winds (ranging from 3.60 m/s to 5.70 m/s) in the northeast-east direction.
文摘Purpose–For the commonly used concrete mix for railway tunnel linings,concrete model specimens were made,and springback and core drilling tests were conducted at different ages.The springback strength was measured to the compressive strength of the core sample with a diameter of 100mm and a height-to-diameter ratio of 1:1.By comparing the measured strength values,the relationship between the measured values under different strength measurement methods was analyzed.Design/methodology/approach–A comparative test of the core drilling method and the rebound method was conducted on the side walls of tunnel linings in some under-construction railways to study the feasibility of the rebound method in engineering quality supervision and inspection.Findings–Tests showed that the rebound strength was positively correlated with the core drill strength.The core drill test strength was significantly higher than the rebound test strength,and the strength still increased after 56 days of age.The rebound method is suitable for the general survey of concrete strength during the construction process and is not suitable for direct supervision and inspection.Originality/value–By studying the correlation of test strength of tunnel lining concrete using two methods,the differences in test results of different methods are proposed to provide a reference for the test and evaluation of tunnel lining strength in railway engineering.
基金supported by the National Natural Science Foundation of China(No.92252201)the Fundamental Research Funds for the Central Universitiesthe Academic Excellence Foundation of Beihang University(BUAA)for PhD Students。
文摘Efficient and accurate simulation of unsteady flow presents a significant challenge that needs to be overcome in computational fluid dynamics.Temporal discretization method plays a crucial role in the simulation of unsteady flows.To enhance computational efficiency,we propose the Implicit-Explicit Two-Step Runge-Kutta(IMEX-TSRK)time-stepping discretization methods for unsteady flows,and develop a novel adaptive algorithm that correctly partitions spatial regions to apply implicit or explicit methods.The novel adaptive IMEX-TSRK schemes effectively handle the numerical stiffness of the small grid size and improve computational efficiency.Compared to implicit and explicit Runge-Kutta(RK)schemes,the IMEX-TSRK methods achieve the same order of accuracy with fewer first derivative calculations.Numerical case tests demonstrate that the IMEX-TSRK methods maintain numerical stability while enhancing computational efficiency.Specifically,in high Reynolds number flows,the computational efficiency of the IMEX-TSRK methods surpasses that of explicit RK schemes by more than one order of magnitude,and that of implicit RK schemes several times over.
基金funding from the Department of Industrial Engineering,University of Naples FedericoⅡ,Italy。
文摘This study presents a data-driven approach to predict tailplane aerodynamics in icing conditions,supporting the ice-tolerant design of aircraft horizontal stabilizers.The core of this work is a low-cost predictive model for analyzing icing effects on swept tailplanes.The method relies on a multi-fidelity data gathering campaign,enabling seamless integration into multidisciplinary aircraft design workflows.A dataset of iced airfoil shapes was generated using 2D inviscid methods across various flight conditions.High-fidelity CFD simulations were conducted on both clean and iced geometries,forming a multidimensional aerodynamic database.This 2D database feeds a nonlinear vortex lattice method to estimate 3D aerodynamic characteristics,following a'quasi-3D'approach.The resulting reduced-order model delivers fast aerodynamic performance estimates of iced tailplanes.To demonstrate its effectiveness,optimal ice-tolerant tailplane designs were selected from a range of feasible shapes based on a reference transport aircraft.The analysis validates the model's reliability,accuracy,and limitations concerning 3D ice shapes and aerodynamic characteristics.Most notably,the model offers near-zero computational cost compared to high-fidelity simulations,making it a valuable tool for efficient aircraft design.
基金Supported by the National Natural Science Foundation of China(12061048)NSF of Jiangxi Province(20232BAB201026,20232BAB201018)。
文摘In this paper,a new technique is introduced to construct higher-order iterative methods for solving nonlinear systems.The order of convergence of some iterative methods can be improved by three at the cost of introducing only one additional evaluation of the function in each step.Furthermore,some new efficient methods with a higher-order of convergence are obtained by using only a single matrix inversion in each iteration.Analyses of convergence properties and computational efficiency of these new methods are made and testified by several numerical problems.By comparison,the new schemes are more efficient than the corresponding existing ones,particularly for large problem sizes.
文摘The feasibility of using a problem-dependent method to solve systems of second order ODEs is corroborated by an eigen-based theory and a methodology to develop such a numerical method is constructed.The key steps of this methodology are to decouple a system of ODEs of second order into a set of uncoupled ODEs of second order;next,an eigen-dependent method is proposed to approximate the solution of each uncoupled ODE of second order.It is vital to transform all eigen-dependent methods to a problem-dependent method to bypass an Eigen analysis.The development of an eigen-dependent method plays a key role in this methodology so that slow eigenmodes can be accurately integrated while there is no instability or excessive amplitude growth in fast eigenmodes.This can explain why a problem-dependent method can simultaneously combine the explicitness of each step and A-stability.Consequently,huge computational efforts can be saved for solving nonlinear stiff problems.A new family of problem-dependent methods is developed in this work so that the feasibility of the proposed methodology can be affirmed.It has almost the same performance as that of the HHT-αmethod.However,it can save more than 99.5%of CPU demand in approximating a solution for a system of 1000 nonlinear second order ODEs.
