Challenges in stratigraphic modeling arise from underground uncertainty.While borehole exploration is reliable,it remains sparse due to economic and site constraints.Electrical resistivity tomography(ERT)as a cost-eff...Challenges in stratigraphic modeling arise from underground uncertainty.While borehole exploration is reliable,it remains sparse due to economic and site constraints.Electrical resistivity tomography(ERT)as a cost-effective geophysical technique can acquire high-density data;however,uncertainty and nonuniqueness inherent in ERT impede its usage for stratigraphy identification.This paper integrates ERT and onsite observations for the first time to propose a novel method for characterizing stratigraphic profiles.The method consists of two steps:(1)ERT for prior knowledge:ERT data are processed by soft clustering using the Gaussian mixture model,followed by probability smoothing to quantify its depthdependent uncertainty;and(2)Observations for calibration:a spatial sequential Bayesian updating(SSBU)algorithm is developed to update the prior knowledge based on likelihoods derived from onsite observations,namely topsoil and boreholes.The effectiveness of the proposed method is validated through its application to a real slope site in Foshan,China.Comparative analysis with advanced borehole-driven methods highlights the superiority of incorporating ERT data in stratigraphic modeling,in terms of prediction accuracy at borehole locations and sensitivity to borehole data.Informed by ERT,reduced sensitivity to boreholes provides a fundamental solution to the longstanding challenge of sparse measurements.The paper further discusses the impact of ERT uncertainty on the proposed model using time-lapse measurements,the impact of model resolution,and applicability in engineering projects.This study,as a breakthrough in stratigraphic modeling,bridges gaps in combining geophysical and geotechnical data to address measurement sparsity and paves the way for more economical geotechnical exploration.展开更多
Metaheuristic algorithms are pivotal in cloud task scheduling. However, the complexity and uncertainty of the scheduling problem severely limit algorithms. To bypass this circumvent, numerous algorithms have been prop...Metaheuristic algorithms are pivotal in cloud task scheduling. However, the complexity and uncertainty of the scheduling problem severely limit algorithms. To bypass this circumvent, numerous algorithms have been proposed. The Hiking Optimization Algorithm (HOA) have been used in multiple fields. However, HOA suffers from local optimization, slow convergence, and low efficiency of late iteration search when solving cloud task scheduling problems. Thus, this paper proposes an improved HOA called CMOHOA. It collaborates with multi-strategy to improve HOA. Specifically, Chebyshev chaos is introduced to increase population diversity. Then, a hybrid speed update strategy is designed to enhance convergence speed. Meanwhile, an adversarial learning strategy is introduced to enhance the search capability in the late iteration. Different scenarios of scheduling problems are used to test the CMOHOA’s performance. First, CMOHOA was used to solve basic cloud computing task scheduling problems, and the results showed that it reduced the average total cost by 10% or more. Secondly, CMOHOA has been applied to edge fog cloud scheduling problems, and the results show that it reduces the average total scheduling cost by 2% or more. Finally, CMOHOA reduced the average total cost by 7% or more in scheduling problems for information transmission.展开更多
Evolutionary algorithms have been extensively utilized in practical applications.However,manually designed population updating formulas are inherently prone to the subjective influence of the designer.Genetic programm...Evolutionary algorithms have been extensively utilized in practical applications.However,manually designed population updating formulas are inherently prone to the subjective influence of the designer.Genetic programming(GP),characterized by its tree-based solution structure,is a widely adopted technique for optimizing the structure of mathematical models tailored to real-world problems.This paper introduces a GP-based framework(GPEAs)for the autonomous generation of update formulas,aiming to reduce human intervention.Partial modifications to tree-based GP have been instigated,encompassing adjustments to its initialization process and fundamental update operations such as crossover and mutation within the algorithm.By designing suitable function sets and terminal sets tailored to the selected evolutionary algorithm,and ultimately derive an improved update formula.The Cat Swarm Optimization Algorithm(CSO)is chosen as a case study,and the GP-EAs is employed to regenerate the speed update formulas of the CSO.To validate the feasibility of the GP-EAs,the comprehensive performance of the enhanced algorithm(GP-CSO)was evaluated on the CEC2017 benchmark suite.Furthermore,GP-CSO is applied to deduce suitable embedding factors,thereby improving the robustness of the digital watermarking process.The experimental results indicate that the update formulas generated through training with GP-EAs possess excellent performance scalability and practical application proficiency.展开更多
With the widespread adoption of hydraulic fracturing technology in oil and gas resource development,improving the accuracy and efficiency of fracturing simulations has become a critical research focus.This paper propo...With the widespread adoption of hydraulic fracturing technology in oil and gas resource development,improving the accuracy and efficiency of fracturing simulations has become a critical research focus.This paper proposes an improved fluid flow algorithm,aiming to enhance the computational efficiency of hydraulic fracturing simulations while ensuring computational accuracy.The algorithm optimizes the aperture law and iteration criteria,focusing on improving the domain volume and crack pressure update strategy,thereby enabling precise capture of dynamic borehole pressure variations during injection tests.The effectiveness of the algorithm is verified through three flow-solid coupling cases.The study also analyzes the effects of borehole size,domain volume,and crack pressure update strategy on fracturing behavior.Furthermore,the performance of the improved algorithm in terms of crack propagation rate,micro-crack formation,and fluid pressure distribution was further evaluated.The results indicate that while large-size boreholes delay crack initiation,the cracks propagate more rapidly once formed.Additionally,the optimized domain volume calculation and crack pressure update strategy significantly shorten the pressure propagation stage,promote crack propagation,and improve computational efficiency.展开更多
Current dynamic finite element model updating methods are not efficient or restricted to the problem of local optima. To circumvent these, a novel updating method which integrates the meta-model and the genetic algori...Current dynamic finite element model updating methods are not efficient or restricted to the problem of local optima. To circumvent these, a novel updating method which integrates the meta-model and the genetic algorithm is proposed. Experimental design technique is used to determine the best sampling points for the estimation of polynomial coefficients given the order and the number of independent variables. Finite element analyses are performed to generate the sampling data. Regression analysis is then used to estimate the response surface model to approximate the functional relationship between response features and design parameters on the entire design space. In the fitness evaluation of the genetic algorithm, the response surface model is used to substitute the finite element model to output features with given design parameters for the computation of fitness for the individual. Finally, the global optima that corresponds to the updated design parameter is acquired after several generations of evolution. In the application example, finite element analysis and modal testing are performed on a real chassis model. The finite element model is updated using the proposed method. After updating, root-mean-square error of modal frequencies is smaller than 2%. Furthermore, prediction ability of the updated model is validated using the testing results of the modified structure. The root-mean-square error of the prediction errors is smaller than 2%.展开更多
The dynamic characteristics of bridge structures, such as the natural frequencies, mode shapes and model damping ratio, are the basis of structural dynamic computation, seismic analysis, vibration control and structur...The dynamic characteristics of bridge structures, such as the natural frequencies, mode shapes and model damping ratio, are the basis of structural dynamic computation, seismic analysis, vibration control and structural health condition monitoring. In this paper, a three-dimensional finite-element model is established for a highway bridge over a railway on No.312 National Highway and the ambient test is carried out in site, the dynamic characteristics of the bridge are studied using the finite-element analysis and ambient vibration measurements. Comparison between the theoretical and experimental results shows that the frequency differences of the modes range between 0.44% and 8.77%. If the measurement is more reliable, the finite element model updating is necessary. Thus, a set of design variables is selected based on sensitivity analysis, then the finite element model of the bridge is updated based on optimization algorithm. The results of model updating show that the proposed updating method in this paper is more simple and effective, the updated finite element model can reflect the dynamic characteristics of the bridge better, the analytical results can provide the theoretical basis for damage identification and health condition monitoring of the bridge.展开更多
基金the financial support from the National Key R&D Program of China(Grant No.2021YFC3001003)Science and Technology Development Fund,Macao SAR(File No.0056/2023/RIB2)Guangdong Provincial Department of Science and Technology(Grant No.2022A0505030019).
文摘Challenges in stratigraphic modeling arise from underground uncertainty.While borehole exploration is reliable,it remains sparse due to economic and site constraints.Electrical resistivity tomography(ERT)as a cost-effective geophysical technique can acquire high-density data;however,uncertainty and nonuniqueness inherent in ERT impede its usage for stratigraphy identification.This paper integrates ERT and onsite observations for the first time to propose a novel method for characterizing stratigraphic profiles.The method consists of two steps:(1)ERT for prior knowledge:ERT data are processed by soft clustering using the Gaussian mixture model,followed by probability smoothing to quantify its depthdependent uncertainty;and(2)Observations for calibration:a spatial sequential Bayesian updating(SSBU)algorithm is developed to update the prior knowledge based on likelihoods derived from onsite observations,namely topsoil and boreholes.The effectiveness of the proposed method is validated through its application to a real slope site in Foshan,China.Comparative analysis with advanced borehole-driven methods highlights the superiority of incorporating ERT data in stratigraphic modeling,in terms of prediction accuracy at borehole locations and sensitivity to borehole data.Informed by ERT,reduced sensitivity to boreholes provides a fundamental solution to the longstanding challenge of sparse measurements.The paper further discusses the impact of ERT uncertainty on the proposed model using time-lapse measurements,the impact of model resolution,and applicability in engineering projects.This study,as a breakthrough in stratigraphic modeling,bridges gaps in combining geophysical and geotechnical data to address measurement sparsity and paves the way for more economical geotechnical exploration.
基金supported by the National Natural Science Foundation of China (52275480)the Guizhou Provincial Science and Technology Program of Qiankehe Zhongdi Guiding ([2023]02)+1 种基金the Guizhou Provincial Science and Technology Program of Qiankehe Platform Talent Project (GCC[2023]001)the Guizhou Provincial Science and Technology Project of Qiankehe Platform Project (KXJZ[2024]002).
文摘Metaheuristic algorithms are pivotal in cloud task scheduling. However, the complexity and uncertainty of the scheduling problem severely limit algorithms. To bypass this circumvent, numerous algorithms have been proposed. The Hiking Optimization Algorithm (HOA) have been used in multiple fields. However, HOA suffers from local optimization, slow convergence, and low efficiency of late iteration search when solving cloud task scheduling problems. Thus, this paper proposes an improved HOA called CMOHOA. It collaborates with multi-strategy to improve HOA. Specifically, Chebyshev chaos is introduced to increase population diversity. Then, a hybrid speed update strategy is designed to enhance convergence speed. Meanwhile, an adversarial learning strategy is introduced to enhance the search capability in the late iteration. Different scenarios of scheduling problems are used to test the CMOHOA’s performance. First, CMOHOA was used to solve basic cloud computing task scheduling problems, and the results showed that it reduced the average total cost by 10% or more. Secondly, CMOHOA has been applied to edge fog cloud scheduling problems, and the results show that it reduces the average total scheduling cost by 2% or more. Finally, CMOHOA reduced the average total cost by 7% or more in scheduling problems for information transmission.
文摘Evolutionary algorithms have been extensively utilized in practical applications.However,manually designed population updating formulas are inherently prone to the subjective influence of the designer.Genetic programming(GP),characterized by its tree-based solution structure,is a widely adopted technique for optimizing the structure of mathematical models tailored to real-world problems.This paper introduces a GP-based framework(GPEAs)for the autonomous generation of update formulas,aiming to reduce human intervention.Partial modifications to tree-based GP have been instigated,encompassing adjustments to its initialization process and fundamental update operations such as crossover and mutation within the algorithm.By designing suitable function sets and terminal sets tailored to the selected evolutionary algorithm,and ultimately derive an improved update formula.The Cat Swarm Optimization Algorithm(CSO)is chosen as a case study,and the GP-EAs is employed to regenerate the speed update formulas of the CSO.To validate the feasibility of the GP-EAs,the comprehensive performance of the enhanced algorithm(GP-CSO)was evaluated on the CEC2017 benchmark suite.Furthermore,GP-CSO is applied to deduce suitable embedding factors,thereby improving the robustness of the digital watermarking process.The experimental results indicate that the update formulas generated through training with GP-EAs possess excellent performance scalability and practical application proficiency.
基金supported by the National Natural Science Foundation of China(Nos.52164001,52064006,52004072 and 52364004)the Science and Technology Support Project of Guizhou(Nos.[2020]4Y044,[2021]N404 and[2021]N511)+1 种基金the Guizhou Provincial Science and Technology Foundation(No.GCC[2022]005-1),Talents of Guizhou University(No.201901)the Special Research Funds of Guizhou University(Nos.201903,202011,and 202012).
文摘With the widespread adoption of hydraulic fracturing technology in oil and gas resource development,improving the accuracy and efficiency of fracturing simulations has become a critical research focus.This paper proposes an improved fluid flow algorithm,aiming to enhance the computational efficiency of hydraulic fracturing simulations while ensuring computational accuracy.The algorithm optimizes the aperture law and iteration criteria,focusing on improving the domain volume and crack pressure update strategy,thereby enabling precise capture of dynamic borehole pressure variations during injection tests.The effectiveness of the algorithm is verified through three flow-solid coupling cases.The study also analyzes the effects of borehole size,domain volume,and crack pressure update strategy on fracturing behavior.Furthermore,the performance of the improved algorithm in terms of crack propagation rate,micro-crack formation,and fluid pressure distribution was further evaluated.The results indicate that while large-size boreholes delay crack initiation,the cracks propagate more rapidly once formed.Additionally,the optimized domain volume calculation and crack pressure update strategy significantly shorten the pressure propagation stage,promote crack propagation,and improve computational efficiency.
文摘Current dynamic finite element model updating methods are not efficient or restricted to the problem of local optima. To circumvent these, a novel updating method which integrates the meta-model and the genetic algorithm is proposed. Experimental design technique is used to determine the best sampling points for the estimation of polynomial coefficients given the order and the number of independent variables. Finite element analyses are performed to generate the sampling data. Regression analysis is then used to estimate the response surface model to approximate the functional relationship between response features and design parameters on the entire design space. In the fitness evaluation of the genetic algorithm, the response surface model is used to substitute the finite element model to output features with given design parameters for the computation of fitness for the individual. Finally, the global optima that corresponds to the updated design parameter is acquired after several generations of evolution. In the application example, finite element analysis and modal testing are performed on a real chassis model. The finite element model is updated using the proposed method. After updating, root-mean-square error of modal frequencies is smaller than 2%. Furthermore, prediction ability of the updated model is validated using the testing results of the modified structure. The root-mean-square error of the prediction errors is smaller than 2%.
基金Supported by the National Natural Science Foundation of China(50378041)the Program for New Century Excellent Talents of Ministry of Educationof China (2004)
文摘The dynamic characteristics of bridge structures, such as the natural frequencies, mode shapes and model damping ratio, are the basis of structural dynamic computation, seismic analysis, vibration control and structural health condition monitoring. In this paper, a three-dimensional finite-element model is established for a highway bridge over a railway on No.312 National Highway and the ambient test is carried out in site, the dynamic characteristics of the bridge are studied using the finite-element analysis and ambient vibration measurements. Comparison between the theoretical and experimental results shows that the frequency differences of the modes range between 0.44% and 8.77%. If the measurement is more reliable, the finite element model updating is necessary. Thus, a set of design variables is selected based on sensitivity analysis, then the finite element model of the bridge is updated based on optimization algorithm. The results of model updating show that the proposed updating method in this paper is more simple and effective, the updated finite element model can reflect the dynamic characteristics of the bridge better, the analytical results can provide the theoretical basis for damage identification and health condition monitoring of the bridge.
