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Creep parameter inversion for high CFRDs based on improved BP neural network response surface method

SOFT COMPUTING 2022年第18期26卷 9527-9541页

作者： Zhou, Xinjie Sun, Xinjian Li, Yongye Zhang, Juntao Li, Zhigang Ma, Zhongxiang Guan, Zhixuan Zhang, Yuxiang Zhang, Pengtao Dalian Univ Technol State Key Lab Coastal & Offshore Engn Dalian 116024 Liaoning Peoples R China Dalian Univ Technol Fac Infrastruct Engn Sch Hydraul Engn Inst Earthquake Engn Dalian 116024 Liaoning Peoples R China Qinghai Univ Sch Water Resources & Elect Power Xining 810016 Qinghai Peoples R China Key Lab Ecol Protect & High Qual Dev Upper Yellow Xining Qinghai Peoples R China Construct Management Bur Xujixia Water Conservanc Delingha 817000 Peoples R China Yellow River Engn Consulting Co Ltd Zhengzhou 450003 Peoples R China

The creep parameters of rockfill materials obtained from engineering analogy method or indoor tests often cannot accurately reflect the long-term deformation of high Concrete-Faced Rockfill Dams (CFRDs). This paper introduces an optimized inversion method based on multi-population genetic algorithm-improved BP neural network and response surface method (MPGA-BPNN RSM). The parameters used for inversion are determined by parameter sensitivity analysis based on the statistical orthogonal test method. MPGA-BPNN RSM, validated by root-mean-square error, mean absolute percentage error, squared correlation coefficient (R-2), etc., completely reflects the response between the creep parameters and the settlement calculation values obtained by finite element method (FEM). MPGA optimized the objective function to obtain the optimal creep parameters. The results show that the settlement values of Xujixia CFRD calculated by FEM using the inversion parameters has great consistency with the monitored values both in size and in distribution, suggesting that the model parameters obtained by the introduced creep parameter inversion method are feasible and effective. The introduced method can improve the inversion efficiency and the prediction accuracy in FEM applications.

关键词： parameter inversion Sensitivity analysis MPGA-BPNN RSM Creep High CFRDs

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Structural parameter inversion of a gravity dam based on the dynamic response induced by an underwater explosion

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JOURNAL OF VIBRATION AND CONTROL 2023年第7-8期29卷 1577-1589页

作者： Liu, Yijia Lu Wenbo Wang, Gaohui Lyu Linmei Chen, Yeqing Wuhan Univ State Key Lab Water Resources & Hydropower Engn S Wuhan Peoples R China Acad Mil Sci Peoples Liberat Army Inst Def Engn Taipinglu 22 Beijing 100850 Peoples R China Wuhan Univ Key Lab Rock Mech Hydraul Struct Engn Minist Educ Wuhan Peoples R China

The overall mechanical behavior of the structure of a gravity dam is comprehensively reflected by modal information included in the measured vibration response. In this study, the dynamic elastic modulus of the dam body and foundation materials were inverted via an underwater explosion, and analyzed. First, the first three main frequencies and basic mode shapes of the dam were obtained based on the acceleration response induced by the explosion. Then, the support vector regression model optimized with the particle swarm optimization algorithm was used to invert the mechanical parameters. The results indicate that the objective function that considers dominant frequency and mode shape has a greater advantage in terms of anti-noise compared to when only the dominant frequency is considered. The inverted dynamic elastic modulus is about 1.5 times that of the static elastic modulus of the corresponding material, and the calculated modal values using the inversion parameters are basically consistent with the measured values. This method provides a new way of determining the dynamic parameters of gravity dams.

关键词： Gravity dam underwater explosion modal identification parameter inversion particle swarm optimization-Support vector regression

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Parallel finite layer method for land subsidence and its homotopy parameter inversion

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ENVIRONMENTAL MODELLING & SOFTWARE 2024年 176卷

作者： Wang, Shaowei Xu, Jin Xu, Jiangbo Changan Univ Sch Highway Xian 710064 Shaanxi Peoples R China Yantai Univ Sch Civil Engn Yantai 264005 Shandong Peoples R China

Land subsidence is caused by the interaction between groundwater flow and soil deformation. At present, in the field of land subsidence calculation, there is an increasing tendency to use Biot's consolidation theory to precisely simulate the coupling processes of flow and deformation. However, there are some practical problems with largescale numerical computation, such as large computational loads, low efficiency, and difficulty in obtaining parameters. The Finite Layer Method employs orthogonality of analytical functions to achieve decoupling among various series terms. And the method can be enhanced through parallel computing to optimize the efficiency of solving complex and extensive problems due to its decoupling property. Based on this, combined with the nonlinear homotopy method, a new approach for the reversal of land subsidence is proposed. The validity of the parallel program is confirmed through comparison with established solutions, and the impact of various factors on the efficiency of parallel computing is analyzed. The results indicate that the parallel approach enhances computational efficiency, while the homotopy inversion technique exhibits broad convergence characteristics.

关键词： Land subsidence Finite layer method Parallel computing parameter inversion Homotopy method

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Biot's equations-based reservoir parameter inversion using deep neural networks

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JOURNAL OF GEOPHYSICS AND ENGINEERING 2021年第6期18卷 862-874页

作者： Xiong, Fansheng Yong, Heng Chen, Hua Wang, Han Shen, Weidong Inst Appl Phys & Computat Math Beijing 100094 Peoples R China Inst Appl Phys & Computat Math Lab Computat Phys Beijing 100094 Peoples R China Peking Univ HEDPS Ctr Appl Phys & Technol Beijing 100871 Peoples R China Peking Univ Coll Engn Beijing 100871 Peoples R China

Reservoir parameter inversion from seismic data is an important issue in rock physics. The traditional optimisation-based inversion method requires high computational expense, and the process exhibits subjectivity due to the nonuniqueness of generated solutions. This study proposes a deep neural network (DNN)-based approach as a new means to analyse the sensitivity of seismic attributes to basic rock-physics parameters and then realise fast parameter inversion. First, synthetic data of inputs (reservoir properties) and outputs (seismic attributes) are generated using Biot's equations. Then, a forward DNN model is trained to carry out a sensitivity analysis. One can in turn investigate the influence of each rock-physics parameter on the seismic attributes calculated by Biot's equations, and the method can also be used to estimate and evaluate the accuracy of parameter inversion. Finally, DNNs are applied to parameter inversion. Different scenarios are designed to study the inversion accuracy of porosity, bulk and shear moduli of a rock matrix considering that the input quantities are different. It is found that the inversion of porosity is relatively easy and accurate, while more information is needed to make the inversion more accurate for bulk and shear moduli. From the presented results, the new approach makes it possible to realise accurate and pointwise inverse modelling with high efficiency for actual data interpretation and analysis.

关键词： Biot's equations deep neural network training and test sensitivity analysis parameter inversion

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Study on deformation mechanism and parameter inversion of a reservoir bank slope during initial impoundment

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ACTA GEOTECHNICA 2023年第8期18卷 4353-4374页

作者： Zhuang, Wenyu Liu, Yaoru Zhang, Rujiu Hou, Shaokang Yang, Qiang Tsinghua Univ State Key Lab Hydrosci & Engn Beijing 100084 Peoples R China

The stability of reservoir bank slope during impoundment is significant for the safe operation of hydropower stations. The deformation evolution of a slope adjacent to dam is analyzed based on the field monitoring data. The main influencing factors and spatial distribution of the slope deformation during impoundment are identified based on the multiple regression model and K-means cluster analysis, respectively. Subsequently, the deformation mechanism of the slope is analyzed by three-dimensional nonlinear finite element method. Then, an inversion analysis method based on improved adaptive genetic algorithm and back propagation neural network is proposed. The measured deformations of 44 monitoring points are used to inverse a total of 27 mechanical parameters, including deformation parameters, strength parameters and Biot coefficients of 9 materials. Finally, the influence of sample numbers on the prediction accuracy and the robustness of the neural network are discussed. The results indicate that the deformation rate of the slope is substantially associated with the impounding process. The water pressure component and aging component of the deformation account for a relatively high proportion while the temperature component is negligible. The deformation is mainly affected by material softening and effective stress of shallow-buried fractured rock mass, in which the latter dominates. The water load on the dam surface is the deformation inducement of the deep rock mass. The proposed inversion method can reasonably obtain the weakening rate of materials and make the numerical model accurately fit in with the actual state of the slope.

关键词： Deformation mechanism IAGA-BPNN Multiple regression parameter inversion Reservoir bank slope Water-rock interaction

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Internal Wave parameter inversion at Malin Shelf Edge Based on the Nonlinear Schrodinger Equation

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3rd International Conference on Machinery Electronics and Control Engineering (ICMECE 2013)

作者： Li, Xiaoyong Wang, Jing Sun, Meiling Ma, Ruiling Meng, Junmin Ocean Univ China Coll Informat Sci & Engn 238 Songling Rd Qingdao 266100 Shandong Peoples R China First Inst Oceanog State Ocean Adm Qingdao 266061 Shandong Peoples R China

ISBN: (纸本)9783037859032

We established a deep-sea internal wave detection model based on the nonlinear Schrodinger (NLS) equation and Synthetic Aperture Radar (SAR) images, and applied the model to the Malin Shelf edge, located at UK Continental Shelf, west of Scotland, to retrieve internal wave parameters. We selected the SAR images of internal waves at Malin Shelf edge, combined NLS equation with the action spectrum balance equation and Bragg scattering model, retrieved the amplitudes and phase velocities of the internal waves at Malin Shelf edge, and compared these data with those retrieved by the model based on KdV equation and those observed at the same period. The results show that the error between the data retrieved by our model and the measured data is very small, while the difference between the data retrieved by the detection model based on KdV equation and the measured data is significant. In addition, the phase velocities, calculated in our model and the model based on KdV equation, are both close to the measured data. Consequently, our model is valid and more accurate for the parameter inversion of internal waves in deep-sea area.

关键词： Nonlinear Schrodinger Equation Internal Wave parameter inversion Synthetic Aperture Radar Deep-Sea

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parameter inversion method for target plate constitutive model based on BP neural network

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INTERNATIONAL JOURNAL OF IMPACT ENGINEERING 2024年 186卷

作者： Zheng, Zhao Yuzhuo, Zhang Rui, Tan Dongyu, Li Nanjing Univ Sci & Technol Sch Energy & Power Engn Nanjing 210094 Jiangsu Peoples R China

In this paper, a neural network-based inversion method that can be used to streamline the acquisition of polymethyl methacrylate (PMMA) material constitutive model parameters for explosive cutting simulations is presented. This approach mitigates the need for multiple tests, a requirement of traditional methods. Initially, three categories of the damage, penetration depth, impact fracture thickness and spallation damage thickness, were distinguished and quantified by cutting the14 mm PMMA target plate with a 2.5 mm wide linear shaped charge, and the adjustment interval for the constitutive model parameters could be determined by combining the explosive cutting experiment data and the empirical parameters of the Johnson Holmquist Ceramics (JH-2) constitutive model. Subsequently, the numerical simulations of the cutting process of the performed target plate tests were carried out using LS-DYNA, and a dataset of target plate damage incorporating the three types of damage data was amassed. Ultimately, a neural network model correlating the parameters of the PMMA target plate constitutive model with the damage data was developed and trained using the plate damage dataset, and several supplementary experiments as well as finite element numerical simulations involving a 4.2 mm wide linear shaped charge cutting a 19 mm PMMA target plate were conducted. The numerical results displayed minimal variance from the experimental results in terms of fracture characteristics and damage data, suggesting that the inverted JH-2 constitutive model parameters can be effectively applied to PMMA target plate explosive cutting simulations. The parameter inversion method allows for the acquisition of more accurate material constitutive model parameters with fewer experiments and will be beneficial for further theoretical studies and practical applications of target plates in the future.

关键词： Neural network Explosive cutting Finite element analysis parameter inversion Target plate constitutive model

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Study on parameter inversion of Displacement Based on Homotopy Theory Genetic Algorithms

Study on Parameter Inversion of Displacement Based on Homoto...

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1st International Symposium on Computational Intelligence and Design

作者： Wen Jian-hua Chen Jun-ming Wu Dai-hua Wuhan Univ Technol Civil & Architecture Dept Wuhan 430070 Peoples R China Wuhan Univ Technol Sch Sci Wuhan 430070 Peoples R China

ISBN: (纸本)9780769533117

The principle and the main characteristics of Genetic Algorithm (GA) were stated. Crossover was a main process producing new generation through genetic genes recombination at the whole exploration space in GA. But some traditional crossover mechanism made the extent and profundity of cross among operators limited. There were some thorny issues such as crossover efficiency slowly, some operator perdition no chance crossing, the population diversity playing down quickly etc. In order to improve genetic cross-strategy, Homotopy Theory Genetic Algorithm (HTGA) was presented by combining the improved homotopy theory and GA. Homotopy genetic curve directed GA genes operating which enhanced genes' interior exchange of population. Then the HTGA was applied to the multi parameters inversion of cavern displacement in geotechnical engineering. Numerical example shows that HTGA is of strong ability to obtain global optimization solution, and its performances are superior to those of single GA.

关键词： parameter inversion Homotopy Theory Genetic Algorithm cavern displacement Homotopy Genetic curve

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Rock and Soil Mechanics Model parameter inversion Method Based on Computational Intelligence

Rock and Soil Mechanics Model Parameter Inversion Method Bas...

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IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA)

作者： Yu Rongchun Guangxi Vocat & Tech Inst Ind Nanning 530001 Peoples R China

ISBN: (纸本)9781479969890

The optimal estimation of geotechnical mechanics model parameters is observed by comparing the difference of the model of information data and the theoretical model. parameter inversion method based on gradient search method drawback is that there is no guarantee that the search to the global optimal solution, the main reason of which lies in the existence of observation error and model error. By defining the objective function, the parameters identification inverse problem into optimization problem. A numerical example and the engineering practical application results show that the established parameter inversion method based on computational intelligence has good robustness and global convergence properties.

关键词： Geotechnical Engineering parameter inversion Computational Intelligence Genetic Algorithm

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Design and Validation of a Parallel parameter inversion for Program Based on Genetic Algorithm

Design and Validation of a Parallel Parameter Inversion for ...

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6th International Symposium on Intelligence Computation and Applications (ISICA 2012)

作者： Cao, Yuan Wang, Wenke Wang, Tieliang Liu, Feng Changan Univ Xian Peoples R China NW Inst Nucl Technol Xian Peoples R China

ISBN: (纸本)9783642342882

Aiming at the demand for model parameter calibration of numerical simulation, a parallel parameter inversion program based on genetic algorithm is designed, and the inversion effect of this program is validated by two examples. The results show that this program has strong versatility, good input and output interfaces, high inversion precision and high calculated efficiency, so automatic parameter inversion can be realized by coupling of this program with various forward programs.

关键词： Genetic Algorithm Parallel parameter inversion Program Design

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