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ADVANCED STEEL CONSTRUCTION 2023年第4期19卷 366-374页

作者： Wang, Zhang-Qi Zheng, Zhe Mengxiang, Jun-Wei Jiang, Wen-Qiang North China Elect Power Univ Dept Mech Engn Baoding Peoples R China North China Elect Power Univ Hebei Engn Res Ctr Adv Mfg & Intelligent Operat & Baoding Peoples R China North China Elect Power Univ Hebei Key Lab Elect Machinery Hlth Maintenance & F Baoding 071003 Peoples R China

Uncertainty exists widely in practical engineering. It is an important challenge in engineering structural analysis. In truss structures, the uncertainties of axial stiffness of bolted joints will significantly affect the mechanical behavior of the structure as the axial load is dominated by the member internal forces. Structural response analysis based on determined structural parameters is a common forward problem that can be solved by modeling analysis methods. However, the uncertainties parameter of axial stiffness of bolted joint cannot be determined during the design and analysis of truss structure in the direct nonlinear analysis method. Structural parameter identification based on structural response is a typical inverse problem in engineering, which is difficult to solve using traditional analysis tools. In this paper, an inverse model based on Graph Neural Network (GNN) is proposed. The feature encoding method for transforming truss structures into graph representations of GNN is defined. A parameterized acquisition method for large-scale datasets is presented, and an innovative inversion model based on GNN for the inversion of uncertain parameters of truss structures is proposed. The proposed method is shown to perform well with an inversion accuracy, and accurate results can be obtained with limited data sets. The inversion method has strong data mining capability and model interpretability, making it a promising direction for exploring engineering structural analysis.

关键词： Truss structures Uncertain parameters inversion method GNN

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Assessment of water inrush hazards in tunnels using the inversion method of full-decay induced polarization with physical law constraints

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ENGINEERING GEOLOGY 2025年 354卷

作者： Nie, Lichao Jia, Shixun Zhou, Wei Li, Zhiqiang Jing, Pengyu Zhang, Shuo Shandong Univ State Key Lab Tunnel Engn Jinan 250061 Shandong Peoples R China Shandong Univ Geotech & Struct Engn Res Ctr Jinan 250061 Shandong Peoples R China Shandong Univ Sch Civil Engn Jinan 250061 Shandong Peoples R China China Railway SIYUAN Survey & Design Grp Co Ltd Wuhan 430061 Hubei Peoples R China

Water hazards pose significant risks in complex geological conditions encountered during tunnel construction. Therefore, it is necessary to assess and detect water hazards in tunnels with greater precision. This paper proposes an inversion method of full-decay induced polarization (FDIP) with physical law constraints for assessing water inrush hazards. This method innovatively adds physical law constraints to the conventional FDIP inversion method based on equivalent resistivity. The physical law constraints comprise time-smoothing, the Weibull growth model, and spatial smoothing three constraints. Initially, the study presents two constraints for calculating equivalent resistivity, time-smoothing, and Weibull growth model constraints, to ensure smooth and monotonical resistivity changes over time. Additionally, the study incorporates a spatial smoothing constraint when deriving FDIP multi-parameters which guarantees gradual transitions of subsurface properties to avoid abrupt changes. The efficacy of the inversion method of FDIP with physical law constraints was confirmed by numerical simulations and a water diverting tunnel in Southwest China.

关键词： Water inrush Full-decay induced polarization(FDIP) Equivalent resistivity inversion method Physical law constraints Field experiment

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A Transformer Network Air Temperature and Humidity inversion method Based on ATMS Brightness Temperature Data

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Xiao, Chengwang Dong, Jian Dou, Haofeng Li, Yinan Wang, Wenjing Ren, Fengchao Cent South Univ Sch Elect Informat Changsha 410083 Peoples R China China Acad Space Technol Xian Xian 710100 Peoples R China

Accurately measuring and inverting air parameters, such as air temperature and humidity, is crucial for weather forecasting, climate research, and environmental monitoring. In this letter, we propose an inversion method based on the transformer model to accurately estimate the spatial distribution of air temperature and humidity. Compared with traditional methods, the transformer model demonstrates superior ability in capturing nonlinear relationships and spatial dependencies in observational data, thereby improving inversion accuracy. Experiments conducted on real observational data have shown that compared to traditional techniques, the proposed method achieves a reduction of over 4.8% in the root mean square error (RMSE) of air temperature and over 14.2% in humidity estimation, demonstrating its high accuracy and reliability in inverting air temperature and humidity. This method provides a new approach for advancing air parameter inversion technology.

关键词： Humidity Temperature distribution Transformers Atmospheric modeling Brightness temperature Distributed databases Data models Tropical cyclones Temperature measurement Temperature dependence Air temperature and humidity inversion method transformer

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inversion method of hydraulic conductivity for steady-state problem based on reduced-order model constructed by improved greedy sampling method

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ADVANCES IN WATER RESOURCES 2022年 166卷

作者： Qian, Wuwen Chai, Junrui Zhao, Xinyu Niu, JingTai Xiao, Fang Deng, Zhiping Nanchang Inst Technol Coll Water Conservancy & Ecol Engn Nanchang 330099 Peoples R China Xian Univ Technol State Key Lab Ecohydraul Northwest Arid Reg China Xian 710048 Peoples R China

To solve the time-consuming parameter inversion problem based on swarm evolution algorithm, this study proposes an improved greedy sampling method (GSM) (IGSM) based on model reduction technology to rapidly estimate the hydraulic conductivity of steady-state seepage problem. Different from the traditional GSM, IGSM neither needs to estimate the posterior error bound of the reduced-order model (ROM) nor does it require a training sample set to train the ROM. Instead, the observation data are directly applied to the training process of ROM. Each iterative process of IGSM includes a parameter inversion process to obtain the parameters that best match the observation information and use it to update the current ROM. The improved differential evolution algorithm (e.g., MMRDE) is used as the search algorithm, and the resulting algorithm is denoted as IGSMMMRDE. After comparative testing, IGSM-MMRDE was found to have higher accuracy and lower calculation cost than GSM-MMRDE. In addition, IGSM-MMRDE is also capable of high-dimensional parameter inversion problems and parameter inversion with a wider search range. After applying IGSM-MMRDE to the inversion of the rock mass hydraulic conductivity of the natural seepage field in the dam site area of a hydropower station, IGSM-MMRDE converges quickly and takes much less time than ADINA-MMRDE based on the full-order model (approximately 0.31% of ADINA-MMRDE). The performance results are even better than the ADINA-MMRDE using the fixed evolutionary generation. Therefore, the advantages of using IGSM in the rapid inversion of the initial seepage field of large-scale projects are extremely evident.

关键词： Reduced-order Greedy snapshot Hydraulic conductivity inversion method Steady-state seepage

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inversion method of the Young's Modulus Field and Poisson's Ratio Field for Rock and Its Test Application

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MATERIALS 2022年第15期15卷 5463-5463页

作者： Yin, Yanchun Liu, Guangyan Zhao, Tongbin Ma, Qinwei Wang, Lu Zhang, Yubao Beijing Inst Technol Sch Aerosp Engn Beijing 100081 Peoples R China Shandong Univ Sci & Technol Coll Energy & Min Engn Qingdao 266590 Peoples R China

As one typical heterogeneous material, the heterogeneity of rock micro parameters has an important effect on its macro mechanical behavior. The study of the heterogeneity of micro parameters is more important to reveal the root cause of deformation and failure. However, as a typical heterogeneous material, the current testing and inversion method is not suitable for micro parameters measurement for the rock. Aiming at obtaining the distribution of micro Young's modulus and micro Poisson's ratio of the rock, based on the digital image correlation method (DIC) and finite element method (FEM), this paper proposed a parameter field inversion method, namely the DF-PF inversion method. Its inversion accuracy is verified using numerical simulation and laboratory uniaxial compression test. Considering the influences of heterogeneity, stress state and dimension difference, the average inversion error of Young's modulus field and Poisson's ratio field are below 10%, and the proportion of elements with an error of less than 15% accounts for more than 86% in the whole specimen model. Compared with the conventional measuring method, the error of macro Young's modulus and macro Poisson's ratio calculated by the DF-PF inversion method is less than 2.8% and 9.07%, respectively. Based on the statistical analysis of Young's modulus field and Poisson's ratio field, the parameter homogeneity and quantitative function relation between the micro parameter and the principal strain can also be obtained in laboratory tests. The DF-PF inversion method provides a new effective method of testing Young's modulus field and Poisson's ratio field of the rocks under complex stress states.

关键词： rock Young's modulus Poisson's ratio parameters field inversion method

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Electrical-Elastic Joint inversion method for Fracture Characterization in Anisotropic Media

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2023年 61卷 1页

作者： Guo, Chen Fan, Zhenzhen Yang, Zhifang Yan, Xinfei Ling, Bowen Changan Univ Sch Informat Engn Xian 710064 Peoples R China CNPC PetroChina Res Inst Petr Explorat & Dev Beijing 100083 Peoples R China Res Inst Petr Explorat & Dev Beijing 100083 Peoples R China PetroChina Key Lab Geophys Beijing 100083 Peoples R China Chinese Acad Sci Inst Mech Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Engn Sci Beijing 100049 Peoples R China

Fracture networks are omnipresent in unconventional energy reservoirs. The inversion of fractures is of vital importance to oil and gas exploration and production. Most of the existing inversion methods are developed based on homogeneous media theory and rely on a solitary physical descriptor. For instance, one commonly employed single-property inversion approach is the determination of water saturation through the use of the media's electrical conductivity. With the fast development of multiphysics geological survey, a joint inversion framework that is suitable for anisotropic fractured media is needed. In this article, we propose an electrical-elastic joint inversion method involving both electrical tensor and elastic tensor to invert the fracture characteristics (e.g., fracture shape, inclination angle, and porosity). We conduct numerical experiments with two-phase geometries containing idealized ellipsoidal fractures. The resistivity tensor and Young's moduli of different directions are calculated and used to construct an anisotropy diagram and a joint inversion chart. The method is validated by comparing the predicted fracture geometry with the actual geometry of the fracture embedded in media. Both ideal homogeneous media and digital rock samples are used to test the inversion framework. A comparison between the single- and the joint-property inversion is also presented, and the joint-property inversion shows a higher accuracy in predicting fracture volume and tilting angle. This work indicates that the proposed electrical-elastic joint method can capture the anisotropy of the formation rock, and the multiphysics inversion framework exhibits the potential to recover fracture features with high fidelity.

关键词： Electrical and elastic properties fractured media inversion method joint property

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An inversion method for identifying the convection heat transfer coefficients of friction stir welding using a surrogate model

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2023年第11-12期126卷 5353-5369页

作者： Meng, Shaofei Liu, Haitao Yue, Wei Xiao, Juliang Huang, Tian Ni, Yanbing Tianjin Univ Key Lab Mech Theory & Equipment Design Minist Educ Tianjin 300354 Peoples R China Univ Warwick Sch Engn Coventry CV4 7AL England

This paper focuses on identifying the convection heat transfer coefficients of the friction stir welding (FSW) temperature field from the temperature measuring data. Firstly, the numerical model of the FSW temperature field is constructed by utilizing the Lagrangian formulation. Then a novel inversion method combined with the surrogate model is proposed. The surrogate model based on proper orthogonal decomposition and radial basis function (RBF) is applied to approximate the input-output relationship of the numerical model. To improve the calculation efficiency and accuracy of the inversion method, an iterative updating method of the surrogate model combined with parallel computing technology is adopted. Subsequently, the effect of the type of RBF and the measurement position of the thermocouple on the calculation accuracy of the inversion analysis method are analyzed. The conclusions indicate that all the RBFs used in this paper are suitable for constructing the surrogate model of the temperature field. With the thermocouple measuring point close to the weld center, the maximum RMSE calculated by the numerical model decreases from 10.8 degrees C to 7.0 degrees C, but the maximum relative error of the peak temperature changes slightly, about 7.5%. The results illustrate that the identified convection heat transfer coefficients allow predictions on the temperature field of FSW which are consistent with the experimental data.

关键词： Friction stir welding inversion method Convection heat transfer coefficients Surrogate model Temperature field

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Study on the inversion method of Rendezvous Parameters Using Near-Field Echo Characteristics 14

Study on the Inversion Method of Rendezvous Parameters Using...

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14th International Symposium on Antennas Propagation and EM Theory

作者： Cui, Juntao Gao, Sicheng Zhang, Chi Wen, Yongdiao Li, Gang Li, Tie Sci & Technol Electromech Dynam Control Lab Xian Peoples R China Xian Inst Electromech Informat Technol Xian Peoples R China

ISBN: (纸本)9798350353129;9798350353136

In this paper, a rendezvous parameter inversion method based on one-dimensional convolutional neural network is proposed by using near-field rendezvous echo data. The simulated near-field rendezvous echo data of typical targets are preprocessed and data enhanced to form a training set. A one-dimensional convolutional neural network for inversion of rendezvous parameters is designed and trained. The influence of different network structures on the accuracy of inversion parameters is analyzed. The results show that the inversion method based on near-field echo characteristics has good robustness and accuracy.

关键词： rendezvous echo rendezvous parameter inversion method convolutional neural network

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inversion method for working behavior of prestressed anchor cables in dam foundation driven by structural health monitoring data

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STRUCTURES 2024年 70卷

作者： Chen, Bo Niu, Hanyi Gu, Hao Yan, Tao Ge, Panmeng Hohai Univ State Key Lab Hydrol Water Resources & Hydraul Eng Nanjing 210098 Peoples R China Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing Peoples R China

Accurate knowledge of the working behavior of prestressed anchor cables is essential to ensure the long-term safe operation of dams. However, due to the lack of effective monitoring means, it is hard to analyze the working performance of anchor cables, and there is still a lack of methods to accurately quantify the anchorage effectiveness of prestressed anchor cables in actual hydraulic engineering projects. To solve this problem, this paper presents an inversion method to accurately quantify the behavior of prestressed anchor cables in dam foundations using structural health monitoring data. Firstly, Ensemble Empirical Mode Decomposition (EEMD) and Singular Spectrum Analysis (SSA) are used to denoise monitoring sequences. Secondly, the hydraulic pressure deformation component is separated by constructing a statistical model reflecting the relationship between dam deformation and environmental factors. Meanwhile, a three-dimensional finite element model (FEM) of the dam and its foundation is established in which the water load and anchoring force are applied step by step to simulate the deformation values of the dam under different combinations of working conditions. And a BP neural network is used to fit the finite element forward process. Finally, the inferred values of the anchorage force are derived by comparing the deformation separated by the statistical model with the deformation calculated by the FEM. In order to minimize the fitting difference of the two series of deformations, an improved grey wolf optimization algorithm (IGWO) is introduced to optimize the inverse calculation. The method is applied to a multiple arch dam foundation anchorage project, and its feasibility and effectiveness are verified. The proposed method is capable of providing effective support for ensuring the safe service of prestressed anchor cables of dams.

关键词： inversion method Structural health monitoring Finite element simulation Prestressed anchor cable Multiple arch dam

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AC-IBFGS-Based inversion method for Estimating the Quasi-Viscoelastic Parameters of Arch Dams

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IEEE ACCESS 2022年 10卷 68151-68160页

作者： Gu, Hao Yang, Meng Gu, Chongshi Huang, Xiaofei Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210098 Peoples R China Nanjing Hydraul Res Inst Nanjing 210029 Peoples R China Hohai Univ State Key Lab Hydrol Water Resources & Hydraul En Nanjing 210098 Peoples R China Nanjing Hydraul Res Inst State Key Lab Hydrol Water Resources & Hydraul En Nanjing 210029 Peoples R China Hohai Univ Lib Dept Informat Consulting Nanjing 210098 Peoples R China

This article investigates an inverse problem for variable physical and mechanical comprehensive parameters. A back analysis method based on an improved BFGS (Broyden-Fletcher-Goldfarb Shanno-Newtonian iteration) and AC (adjustment coefficient) is proposed to estimate the physical and mechanical comprehensive parameters of complex concrete arch dam structures. These quasi-viscoelastic variable physical and mechanical comprehensive parameters of concrete arch dam structures are established based on in situ monitoring data and test results. By studying the constitutive model of concrete arch dam structures and the corresponding characterization method of the gradual changes of those parameters, a method utilizing adjustment coefficient is established to determine the initial values of the physical and mechanical comprehensive parameters and an adjoin method combined with BFGS Newtonian iteration method is proposed to inverse the variable physical and mechanical comprehensive parameters of concrete arch dam structures and analyze the variation regulation of these parameters. The proposed inversion analysis method was successfully implemented for the second-highest concrete double curvature arch dam. The results indicate that the proposed method has high precision and strong generalizability.

关键词： Dams Monitoring Strain Data models Safety Load modeling Behavioral sciences Comprehensive parameters monitoring data quasi-viscoelastic inversion method adjoint method arch dam

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