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American Control Conference

作者： Garrido, R Rivero-Angeles, FJ Martinez-Garcia, JC Martinez-Guerra, R Gomez-Gonzalez, B IPN CINVESTAV Dept Automat Control Mexico City 07300 DF Mexico

ISBN: (纸本)0780383354

Civil structures undergo hysteresis cycles due to cracking or yielding when subjected to severe strong motions. Seismic instrumentation, structural monitoring and system identification techniques have been used in the past years to assess damage. The present research proposes a gradient algorithm to simultaneously identify the combined driving and nonlinear restoring forces, and structural parameters, such as damping and stiffness in a single-degree-of-freedom structure. Simulations are carried out using the El Centro (1940) and the Mexico City (1985) seismic records. Encouraging results are obtained for the identification of the parameters and the estimation of the driving and restoring forces.

关键词： gradient methods structural engineering parameter estimation condition monitoring computerised monitoring nonlinear restoring force structural parameter estimation gradient algorithm civil structure hysteresis cycle seismic instrumentation structural monitoring system identification technique driving force

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A robust structural parameter estimation method using seismic response measurements

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structural CONTROL & HEALTH MONITORING 2020年第3期27卷 e2475-e2475页

作者： Kuleli, Muge Nagayama, Tomonori Univ Tokyo Dept Civil Engn Tokyo Japan

Although successful applications of data assimilation techniques such as Kalman filter to structural parameter identification problems exist in literature, they have been explored mostly via simulations because real-world application poses challenges. Current practice is to set the process and measurement noise covariance matrices by manual tuning and keeping them constant over the estimation period. However, when the matrices are not correct, Kalman filters are prone to suboptimal performance and/ or even divergence. In this paper, use of a stochastic approximation scheme along with the unscented Kalman filter is explored. The sensitivity of the initial filter parameter selection has been investigated through both simulations and full-scale laboratory experiments. Results show that by automatically updating the filter statistics, system states, including the structural parameters, are successfully estimated with a fast convergence rate, good accuracy, and trackability in a stable and objective manner. The method is further applied to in situ seismic response records of a box-girder bridge. The stiffness values of the rubber bearings are successfully identified.

关键词： seismic response stochastic approximation structural parameter estimation system identification unscented Kalman filter

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Optimal sensor placement for uncertain inverse problem of structural parameter estimation

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2021年 160卷 107914-107914页

作者： Liu, Jie Ouyang, Heng Han, Xu Liu, Guirong Hunan Univ Coll Mech & Vehicle Engn State Key Lab Adv Design & Mfg Vehicle Body Changsha 410082 Hunan Peoples R China Hebei Univ Technol Sch Mech Engn State Key Lab Reliabil & Intelligence Elect Equip Tianjin 300401 Peoples R China Univ Cincinnati Dept Aerosp Engn & Engn Mech Cincinnati OH 45221 USA

This paper presents an optimal sensor placement approach for the uncertain inverse prob-lem of structural parameter estimation, aiming to mitigate the ill-posedness problem that often exists in inverse procedures. The key idea is to select the sensor positions with the most sensitive measured responses to the structural parameters and the least correlated measured responses at the selected positions. Our optimization strategy is to convert the traditional minimum variance criterion of structural parameters to be identified into a new maximum independent mean-variance criterion of structural responses, so that the complex optimal sensor placement problem is transformed into a forward uncertainty propagation problem. Then, two orthogonal matching pursuit (OMP) methods based on Monte Carlo simulation (MCS) and dimension reduction integration (DRI) method are pre-sented in this work, in which the MCS and the much more efficient DRI method are employed to solve the forward uncertainty propagation problem, and the OMP methods in sample form and moment form are developed to determine the optimal sensor place -ment for the identification procedure by considering the uncertainties in the measured responses. A Markov Chain Monte Carlo algorithm is adopted to identify the distributions of structural parameters eventually. Numerical and experimental examples are presented to verify the practicability and effectiveness of the proposed methods. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Optimal sensor placement Uncertain inverse problem structural parameter estimation Orthogonal matching pursuit procedure Dimension reduction integration method Maximum independent mean-variance criterion

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Evaluation and Comparison of SEM, ESEM, and BSEM in Estimating structural Models with Potentially Unknown Cross-loadings

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structural EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL 2022年第3期29卷 327-338页

作者： Wei, Xiayan Huang, Jiasheng Zhang, Lijin Pan, Deng Pan, Junhao Sun Yat Sen Univ Guangzhou Peoples R China Zhejiang Univ Hangzhou Peoples R China Huazhong Univ Sci & Technol Wuhan Peoples R China

Cross-loadings are common in multidimensional instruments;however, they cannot be appropriately addressed in conventional structural equation modeling (SEM) owing to the assumption of zero cross-loadings in standard confirmatory factor analysis (CFA). Although it has been proposed that exploratory structural equation modeling (ESEM) and Bayesian structural equation modeling (BSEM) can address this issue more flexibly, their performance in structural parameter estimation has not been adequately compared. This study uses simulated data to evaluate and compare SEM, ESEM, and BSEM in estimating structural models under different manipulation conditions (i.e., sample size, target loading, cross-loading, and path coefficient). The results demonstrated that the performances of these approaches were similar in the case of zero cross-loadings. SEM performed worse as cross-loadings increased, and the performance of BSEM significantly depended on the accuracy of the priors for cross-loadings. ESEM was inferior to BSEM with correctly specified prior means for cross-loadings in most evaluation measures and exhibits unstable performance in conditions with small target loadings. Recommended strategies for selecting an appropriate modeling approach are discussed based on our findings.

关键词： BSEM cross-loadings ESEM structural parameter estimation

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Vibration-based estimation of boundary stiffness and axial tension in beams using added mass

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JOURNAL OF SOUND AND VIBRATION 2020年 487卷 115617-115617页

作者： Brons, Marie Thomsen, Jon Juel Tech Univ Denmark Dept Mech Engn Lyngby Denmark

Boundary stiffness and axial tension for a uniform Euler-Bernoulli beam model can be estimated by applying non-linear Gauss regression to measured natural frequencies. However, there are two major drawbacks in this method: it requires many measured natural frequencies to obtain reliable estimates, and for a lengthwise uniform beam cross-section, the boundary stiffness parameters may converge to swapped boundaries without mode shape measurements. This work suggests a simple novel concept to overcome these shortcomings: By attaching an external boundary mass with rotational inertia to one end of the beam, the natural frequencies will change without changing the boundary stiffness and axial tension. Combined with measurements without additional mass, this provides additional information to be used in a regression model, without imposing more unknown parameters: The mass adds a known asymmetry, permitting a distinction between left and right, and for each added mass the number of measured natural frequencies needed to obtain a reliable estimate is roughly halved. estimation of boundary stiffness and axial tension can be of interest in, e.g., bolted joints. A tightened bolt can be modeled as a beam, with unknown boundary parameters, where the main interest often would be to estimate the axial tension. Previously, an estimation method would be hard to apply in practice, as bolts are often mounted in complex structures with many interacting modes, making it difficult to measure many transverse natural frequencies of the bolt itself. By adding a mass, the method can potentially be applicable for estimating tension in bolted joints. The proposed method is validated using simulated noisy measurement data and tested with experimental measurements obtained for a real bolt tightened in a structure. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Beam theory Boundary conditions Axial tension structural parameter estimation Bolt tension

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Posterior density estimation for structural parameters using improved differential evolution adaptive Metropolis algorithm

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SMART STRUCTURES AND SYSTEMS 2015年第3期15卷 735-749页

作者： Zhou, Jin Mita, Akira Mei, Liu Keio Univ Dept Syst Design Engn Kohoku Ku Yokohama Kanagawa 2238522 Japan

The major difficulty of using Bayesian probabilistic inference for system identification is to obtain the posterior probability density of parameters conditioned by the measured response. The posterior density of structural parameters indicates how plausible each model is when considering the uncertainty of prediction errors. The Markov chain Monte Carlo (MCMC) method is a widespread medium for posterior inference but its convergence is often slow. The differential evolution adaptive Metropolis-Hasting (DREAM) algorithm boasts a population-based mechanism, which runs multiple different Markov chains simultaneously, and a global optimum exploration ability. This paper proposes an improved differential evolution adaptive Metropolis-Hasting algorithm (IDREAM) strategy to estimate the posterior density of structural parameters. The main benefit of IDREAM is its efficient MCMC simulation through its use of the adaptive Metropolis (AM) method with a mutation strategy for ensuring quick convergence and robust solutions. Its effectiveness was demonstrated in simulations on identifying the structural parameters with limited output data and noise polluted measurements.

关键词： structural identification differential evolution adaptive metropolis-hastings Markov chain Monte Carlo structural parameter estimation Bayesian posterior probability density

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Nondestructive testing for design verification of Boston's Central Artery underpinning frames and connections

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BRIDGE STRUCTURES 2008年第2期4卷 87-98页

作者： Santini-Bell, Erin Sanayei, Masoud Brenner, Brian Sipple, Jesse Blanchard, Adam Univ New Hampshire Dept Civil Engn Durham NH 03824 USA Tufts Univ Dept Civil & Environm Engn Medford MA 02155 USA Fay Spofford & Thorndike Burlington MA 01803 USA LeMessurier Consultants Cambridge MA 02139 USA

Prior to the demolition of the Boston Central Artery viaduct in March 2004, a research team implemented a programme of nondestructive testing for design verification of two structural steel highway bents. The tested support bents were used to underpin the original Interstate-93 Central Artery viaduct during construction of the new cutand-cover tunnel below it. Upon opening of the tunnels, traffic was rerouted from the elevated viaduct to the new tunnel, and the demolition process of the viaduct structure began. Two of the remaining support bents of the underpinning structure were fitted with sensors (strain gages, tiltmeters, slide wire potentiometers, and a 50 kip (222.4 kN) load cell) and loaded by a 50-ton (444.8 kN) crane. The measured structural response was compared to the expected response from finite element structural models, and the structural models were updated using parameter estimation techniques for design verification. Using as-built information, considering original design assumptions, and parameter estimation simulation results, the researchers selected a set of sensor types and locations for the nondestructive field test. Key design parameters of the underpinning finite element model such as connection stiffness values were successfully estimated using structural parameter estimation. As a result, the updated structural response correlated well with the collected nondestructive test data.

关键词： nondestructive testing model updating structural parameter estimation design verification

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Statistical Revealed Preference Models for Bipartite Networks

Statistical Revealed Preference Models for Bipartite Network...

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作者： Yeung, Fiona Chehong University of California Los Angeles

学位级别：Ph.D.

This dissertation focuses on investigating the driving factors behind the formation of connections in large two-mode networks. Assuming that network participants maximize their benefits, or "utilities", over their choices of connections, our primary research interest is to estimate a set of latent parameters that explains their "preferences" for choices of linkages. Discrete-choice models are incorporated into the proposed estimation framework to model decision-making behaviors. Most generative models for random graphs are based on the specification of a joint probability distribution over the observed pairings, with an emphasis on the structural properties of the networks. The method proposed here, however, takes into account the role of decision making and therefore offers insight into the rationale for the choices of connection. Understanding such decisions may in turn provide insights into any intervention that can induce the network connectivity into a more desirable state. The interest of this dissertation is limited to large bipartite networks in which edges occur only between nodes from different sets where the decision to form an edge is mutual. A non-transferable utility (NTU) setting is assumed and isolated nodes are allowed. The dissertation also includes an investigation of the statistical properties of one-to-many and many-to-many relationships and the specification of their statistical models. Inference for the preference parameters is then performed for the proposed statistical models, and simulations are used to evaluate their performance.

关键词： discrete choice models econometrics computational statistics network science graph theory matching theory (in economics) game theory structural parameter estimation

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Modal-based uncertainty quantification for deterministically estimated structural parameters in low-fidelity model updating of complex connections

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PROBABILISTIC ENGINEERING MECHANICS 2024年 77卷

作者： Mehrkash, Milad Santini-Bell, Erin Univ New Hampshire Dept Civil & Environm Engn Durham NH 03824 USA

Modeling complex joints in structures entails significant time and effort, necessitating simplifications. Epistemic uncertainties arising from low-fidelity modeling can be quantified through probabilistic model updating. However, finding a surrogate physical model to represent simplified joint configurations poses challenges. Additionally, establishing a Bayesian formulation capable of incorporating structural parameters of connections is necessary. This study employs a validated simplifying parameterization approach for surrogate modeling of complex semi-rigid connections in a benchmark laboratory steel grid. It proposes a modal probabilistic Bayesian methodology to quantify uncertainties in the structure's joints. Three modal-based objective functions are utilized for finite element model updating. The modal properties of the structure are extracted by experimental modal analysis during an impact test, which will be utilized in the model updating process. Deterministic and probabilistic structural parameter estimations are integrated to enhance the robustness of the Bayesian technique. Furthermore, a guideline for selecting optimal hyperparameters is provided. Results demonstrate that utilizing deterministically estimated parameters as prior knowledge can facilitate and improve modal probabilistic model updating for structures with complex joints. Also, it is found that despite significant simplifications of joints, structural parameter tolerance around the maximum a posteriori estimate in surrogate models remains low.

关键词： Uncertainty quantification Bayesian model updating structural parameter estimation Complex connection Low-fidelity modeling Modal analysis

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Updating structural parameters with Spatially Incomplete Measurements Using Subspace System Identification

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JOURNAL OF ENGINEERING MECHANICS 2017年第7期143卷 1-19页

作者： Park, Seung-Keun Noh, Hae Young Korea Inst Sci & Technol Informat Supercomp Modeling & Simulat Ctr 245 Daehak Ro Daejeon 34141 South Korea Carnegie Mellon Univ Dept Civil & Environm Engn Pittsburgh PA 15213 USA

Civil infrastructures are subjected to various loads over their lifetime, which leads to structural degradation. To understand and predict these dynamic behaviors of physical infrastructures, many mathematical structural models have been developed, such as subspace system identification. These models often require structural responses from all degrees of freedom (DOFs) to estimate structural parameters. However, in practice, it is often difficult, if not impossible, to make such measurements owing to sensing constraints, a lack of data, or an excessive number of DOFs as in large-scale civil structures. This lack of measurements in space results in ill-posed problems with nonunique solutions. To address this challenge, this paper presents a structural parameter estimation algorithm that incorporates spatially incomplete measurements and inaccurate prior information on structural parameters within a subspace system identification framework. Additional constraints are imposed using prior information, and the prior information is updated with a new estimation. To sequentially update the parameters, the process is repeated as more measurements are collected. The proposed method is evaluated using a numerical model of a 5-story shear building for two damage scenarios with measurement noise. The structural parameters are estimated with 85-99% accuracy with spatially incomplete measurements (40-80%), and the iterative updating further improves these accuracies. (C) 2017 American Society of Civil Engineers.

关键词： structural parameter estimation Spatially incomplete measurements Subspace system identification Inaccurate prior information Iterative model updating

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