Relevant bibliographies by topics / Robus fitting

Academic literature on the topic 'Robus fitting'

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Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Robus fitting"

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Dunlap, Brett I. "Robust and variational fitting." Physical Chemistry Chemical Physics 2, no. 10 (2000): 2113–16. http://dx.doi.org/10.1039/b000027m.

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Elsaied, Hanan, and Roland Fried. "ROBUST FITTING OF INARCH MODELS." Journal of Time Series Analysis 35, no. 6 (June 27, 2014): 517–35. http://dx.doi.org/10.1111/jtsa.12079.

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Yu, Jieqi, Sanjeev R. Kulkarni, and H. Vincent Poor. "Robust ellipse and spheroid fitting." Pattern Recognition Letters 33, no. 5 (April 2012): 492–99. http://dx.doi.org/10.1016/j.patrec.2011.11.025.

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Domínguez-Soria, Víctor D., Gerald Geudtner, José Luis Morales, Patrizia Calaminici, and Andreas M. Köster. "Robust and efficient density fitting." Journal of Chemical Physics 131, no. 12 (September 28, 2009): 124102. http://dx.doi.org/10.1063/1.3216476.

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Ladrón de Guevara, I., J. Muñoz, O. D. de Cózar, and E. B. Blázquez. "Robust Fitting of Circle Arcs." Journal of Mathematical Imaging and Vision 40, no. 2 (December 22, 2010): 147–61. http://dx.doi.org/10.1007/s10851-010-0249-8.

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Chang, Chung, and R. Todd Ogden. "Robust fitting for neuroreceptor mapping." Statistics in Medicine 28, no. 6 (March 15, 2009): 1004–16. http://dx.doi.org/10.1002/sim.3510.

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Aigner, Martin, and Bert Jüttler. "Robust fitting of parametric curves." PAMM 7, no. 1 (December 2007): 1022201–2. http://dx.doi.org/10.1002/pamm.200700009.

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Tew, David P. "Communication: Quasi-robust local density fitting." Journal of Chemical Physics 148, no. 1 (January 7, 2018): 011102. http://dx.doi.org/10.1063/1.5013111.

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Welsh, A. H., and A. F. Ruckstuhl. "Robust fitting of the binomial model." Annals of Statistics 29, no. 4 (August 2001): 1117–36. http://dx.doi.org/10.1214/aos/1013699996.

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Vorobyov, S. A., Yue Rong, N. D. Sidiropoulos, and A. B. Gershman. "Robust iterative fitting of multilinear models." IEEE Transactions on Signal Processing 53, no. 8 (August 2005): 2678–89. http://dx.doi.org/10.1109/tsp.2005.850343.

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Dissertations / Theses on the topic "Robus fitting"

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Xing, Yanru. "Robust mixture regression model fitting by Laplace distribution." Kansas State University, 2013. http://hdl.handle.net/2097/16534.

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Master of Science
Department of Statistics
Weixing Song
A robust estimation procedure for mixture linear regression models is proposed in this report by assuming the error terms follow a Laplace distribution. EM algorithm is imple- mented to conduct the estimation procedure of missing information based on the fact that the Laplace distribution is a scale mixture of normal and a latent distribution. Finite sample performance of the proposed algorithm is evaluated by some extensive simulation studies, together with the comparisons made with other existing procedures in this literature. A sensitivity study is also conducted based on a real data example to illustrate the application of the proposed method.
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Truong, Ha-Giang. "Robust fitting: Assisted by semantic analysis and reinforcement learning." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2022. https://ro.ecu.edu.au/theses/2567.

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Many computer vision applications require robust model estimation from a set of observed data. However, these data usually contain outliers, due to imperfect data acquisition or pre-processing steps, which can reduce the performance of conventional model-fitting methods. Robust fitting is thus critical to make the model estimation robust against outliers and reach stable performance. All of the contributions made in this thesis are for maximum consensus. In robust model fitting, maximum consensus is one of the most popular criteria, which aims to estimate the model that is consistent to as many observations as possible, i.e. obtain the highest consensus. The thesis makes contributions in two aspects of maximum consensus, one is non-learning based approaches and the other is learning based approaches. The first motivation for our work is the remarkable progress in semantic segmentation in recent years. Semantic segmentation is a useful process and is usually available for scene understanding, medical image analysis, and virtual reality. We propose novel methods, which make use of semantic segmentation, to improve the efficiency of two robust non-learning based algorithms. Another motivation for our contributions is the advances in reinforcement learning. In the thesis, a novel unsupervised learning framework is proposed to learn (without labelled data) to solve robust estimation directly. In particular, we formulate robust fitting problem as a special case of goal-oriented learning, and adopt the Reinforcement Learning framework as the basis of our approach. Our approach is agnostic to the input features and can be generalized to various practical applications.
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Wang, Hanzi. "Robust statistics for computer vision : model fitting, image segmentation and visual motion analysis." Monash University, Dept. of Electrical and Computer Systems Engineering, 2004. http://arrow.monash.edu.au/hdl/1959.1/5345.

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Yang, Li. "Robust fitting of mixture of factor analyzers using the trimmed likelihood estimator." Kansas State University, 2014. http://hdl.handle.net/2097/18118.

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Master of Science
Department of Statistics
Weixin Yao
Mixtures of factor analyzers have been popularly used to cluster the high dimensional data. However, the traditional estimation method is based on the normality assumptions of random terms and thus is sensitive to outliers. In this article, we introduce a robust estimation procedure of mixtures of factor analyzers using the trimmed likelihood estimator (TLE). We use a simulation study and a real data application to demonstrate the robustness of the trimmed estimation procedure and compare it with the traditional normality based maximum likelihood estimate.
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Mordini, Nicola. "Multicentre study for a robust protocol in single-voxel spectroscopy: quantification of MRS signals by time-domain fitting algorithms." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amslaurea.unibo.it/7579/.

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Magnetic Resonance Spectroscopy (MRS) is an advanced clinical and research application which guarantees a specific biochemical and metabolic characterization of tissues by the detection and quantification of key metabolites for diagnosis and disease staging. The "Associazione Italiana di Fisica Medica (AIFM)" has promoted the activity of the "Interconfronto di spettroscopia in RM" working group. The purpose of the study is to compare and analyze results obtained by perfoming MRS on scanners of different manufacturing in order to compile a robust protocol for spectroscopic examinations in clinical routines. This thesis takes part into this project by using the GE Signa HDxt 1.5 T at the Pavillion no. 11 of the S.Orsola-Malpighi hospital in Bologna. The spectral analyses have been performed with the jMRUI package, which includes a wide range of preprocessing and quantification algorithms for signal analysis in the time domain. After the quality assurance on the scanner with standard and innovative methods, both spectra with and without suppression of the water peak have been acquired on the GE test phantom. The comparison of the ratios of the metabolite amplitudes over Creatine computed by the workstation software, which works on the frequencies, and jMRUI shows good agreement, suggesting that quantifications in both domains may lead to consistent results. The characterization of an in-house phantom provided by the working group has achieved its goal of assessing the solution content and the metabolite concentrations with good accuracy. The goodness of the experimental procedure and data analysis has been demonstrated by the correct estimation of the T2 of water, the observed biexponential relaxation curve of Creatine and the correct TE value at which the modulation by J coupling causes the Lactate doublet to be inverted in the spectrum. The work of this thesis has demonstrated that it is possible to perform measurements and establish protocols for data analysis, based on the physical principles of NMR, which are able to provide robust values for the spectral parameters of clinical use.
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Willersjö, Nyfelt Emil. "Comparison of the 1st and 2nd order Lee–Carter methods with the robust Hyndman–Ullah method for fitting and forecasting mortality rates." Thesis, Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-48383.

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The 1st and 2nd order Lee–Carter methods were compared with the Hyndman–Ullah method in regards to goodness of fit and forecasting ability of mortality rates. Swedish population data was used from the Human Mortality Database. The robust estimation property of the Hyndman–Ullah method was also tested with inclusion of the Spanish flu and a hypothetical scenario of the COVID-19 pandemic. After having presented the three methods and making several comparisons between the methods, it is concluded that the Hyndman–Ullah method is overall superior among the three methods with the implementation of the chosen dataset. Its robust estimation of mortality shocks could also be confirmed.
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Relvas, Carlos Eduardo Martins. "Modelos parcialmente lineares com erros simétricos autoregressivos de primeira ordem." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-28052013-182956/.

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Neste trabalho, apresentamos os modelos simétricos parcialmente lineares AR(1), que generalizam os modelos parcialmente lineares para a presença de erros autocorrelacionados seguindo uma estrutura de autocorrelação AR(1) e erros seguindo uma distribuição simétrica ao invés da distribuição normal. Dentre as distribuições simétricas, podemos considerar distribuições com caudas mais pesadas do que a normal, controlando a curtose e ponderando as observações aberrantes no processo de estimação. A estimação dos parâmetros do modelo é realizada por meio do critério de verossimilhança penalizada, que utiliza as funções escore e a matriz de informação de Fisher, sendo todas essas quantidades derivadas neste trabalho. O número efetivo de graus de liberdade e resultados assintóticos também são apresentados, assim como procedimentos de diagnóstico, destacando-se a obtenção da curvatura normal de influência local sob diferentes esquemas de perturbação e análise de resíduos. Uma aplicação com dados reais é apresentada como ilustração.
In this master dissertation, we present the symmetric partially linear models with AR(1) errors that generalize the normal partially linear models to contain autocorrelated errors AR(1) following a symmetric distribution instead of the normal distribution. Among the symmetric distributions, we can consider heavier tails than the normal ones, controlling the kurtosis and down-weighting outlying observations in the estimation process. The parameter estimation is made through the penalized likelihood by using score functions and the expected Fisher information. We derive these functions in this work. The effective degrees of freedom and asymptotic results are also presented as well as the residual analysis, highlighting the normal curvature of local influence under different perturbation schemes. An application with real data is given for illustration.
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Cai, Zhipeng. "Consensus Maximization: Theoretical Analysis and New Algorithms." Thesis, 2020. http://hdl.handle.net/2440/127452.

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The core of many computer vision systems is model fitting, which estimates a particular mathematical model given a set of input data. Due to the imperfection of the sensors, pre-processing steps and/or model assumptions, computer vision data usually contains outliers, which are abnormally distributed data points that can heavily reduce the accuracy of conventional model fitting methods. Robust fitting aims to make model fitting insensitive to outliers. Consensus maximization is one of the most popular paradigms for robust fitting, which is the main research subject of this thesis. Mathematically, consensus maximization is an optimization problem. To understand the theoretical hardness of this problem, a thorough analysis about its computational complexity is first conducted. Motivated by the theoretical analysis, novel techniques that improve different types of algorithms are then introduced. On one hand, an efficient and deterministic optimization approach is proposed. Unlike previous deterministic approaches, the proposed one does not rely on the relaxation of the original optimization problem. This property makes it much more effective at refining an initial solution. On the other hand, several techniques are proposed to significantly accelerate consensus maximization tree search. Tree search is one of the most efficient global optimization approaches for consensus maximization. Hence, the proposed techniques greatly improve the practicality of globally optimal consensus maximization algorithms. Finally, a consensus-maximization-based method is proposed to register terrestrial LiDAR point clouds. It demonstrates how to surpass the general theoretical hardness by using special problem structure (the rotation axis returned by the sensors), which simplify the problem and lead to application-oriented algorithms that are both efficient and globally optimal.
Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 2020
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Yu, Xinming. "Robust estimation for range image segmentation and fitting." Thesis, 1993. http://spectrum.library.concordia.ca/4144/1/NN84686.pdf.

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In the dissertation a new robust estimation technique for range image segmentation and fitting has been developed. The performance of the algorithm has been considerably improved by incorporating the genetic algorithm. The new robust estimation method randomly samples range image points and solves equations determined by these points for parameters of selected primitive type. From K samples we measure RESidual Consensus (RESC) to choose one set of sample points which determines an equation best fitting the largest homogeneous surface patch in the current processing region. The residual consensus is measured by a compressed histogram method which can be used at various noise levels. After obtaining surface parameters of the best fitting and the residuals of each point in the current processing region, a boundary list searching method is used to extract this surface patch out of the processing region and to avoid further computation. Since the RESC method can tolerate more than 80% of outliers, it is a substantial improvement over the least median squares method. The method segments range image into planar and quadratic surfaces, and works very well even in smoothly connected curve regions. A genetic algorithm is used to accelerate the random search. A large number of offline average performance experiments on GA are carried out to investigate different types of GAs and the influence of control parameters. A steady state GA works better than a generational replacement GA. The algorithms have been validated on the large set of synthetic and real range images.
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Le, Huu Minh. "New algorithmic developments in maximum consensus robust fitting." Thesis, 2018. http://hdl.handle.net/2440/115183.

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In many computer vision applications, the task of robustly estimating the set of parameters of a geometric model is a fundamental problem. Despite the longstanding research efforts on robust model fitting, there remains significant scope for investigation. For a large number of geometric estimation tasks in computer vision, maximum consensus is the most popular robust fitting criterion. This thesis makes several contributions in the algorithms for consensus maximization. Randomized hypothesize-and-verify algorithms are arguably the most widely used class of techniques for robust estimation thanks to their simplicity. Though efficient, these randomized heuristic methods do not guarantee finding good maximum consensus estimates. To improve the randomize algorithms, guided sampling approaches have been developed. These methods take advantage of additional domain information, such as descriptor matching scores, to guide the sampling process. Subsets of the data that are more likely to result in good estimates are prioritized for consideration. However, these guided sampling approaches are ineffective when good domain information is not available. This thesis tackles this shortcoming by proposing a new guided sampling algorithm, which is based on the class of LP-type problems and Monte Carlo Tree Search (MCTS). The proposed algorithm relies on a fundamental geometric arrangement of the data to guide the sampling process. Specifically, we take advantage of the underlying tree structure of the maximum consensus problem and apply MCTS to efficiently search the tree. Empirical results show that the new guided sampling strategy outperforms traditional randomized methods. Consensus maximization also plays a key role in robust point set registration. A special case is the registration of deformable shapes. If the surfaces have the same intrinsic shapes, their deformations can be described accurately by a conformal model. The uniformization theorem allows the shapes to be conformally mapped onto a canonical domain, wherein the shapes can be aligned using a M¨obius transformation. The problem of correspondence-free M¨obius alignment of two sets of noisy and partially overlapping point sets can be tackled as a maximum consensus problem. Solving for the M¨obius transformation can be approached by randomized voting-type methods which offers no guarantee of optimality. Local methods such as Iterative Closest Point can be applied, but with the assumption that a good initialization is given or these techniques may converge to a bad local minima. When a globally optimal solution is required, the literature has so far considered only brute-force search. This thesis contributes a new branch-and-bound algorithm that solves for the globally optimal M¨obius transformation much more efficiently. So far, the consensus maximization problems are approached mainly by randomized algorithms, which are efficient but offer no analytical convergence guarantee. On the other hand, there exist exact algorithms that can solve the problem up to global optimality. The global methods, however, are intractable in general due to the NP-hardness of the consensus maximization. To fill the gap between the two extremes, this thesis contributes two novel deterministic algorithms to approximately optimize the maximum consensus criterion. The first method is based on non-smooth penalization supported by a Frank-Wolfe-style optimization scheme, and another algorithm is based on Alternating Direction Method of Multipliers (ADMM). Both of the proposed methods are capable of handling the non-linear geometric residuals commonly used in computer vision. As will be demonstrated, our proposed methods consistently outperform other heuristics and approximate methods.
Thesis (Ph.D.) (Research by Publication) -- University of Adelaide, School of Computer Science, 2018
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Books on the topic "Robus fitting"

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Weinberg, Jonathan M. Knowledge, Noise, and Curve-Fitting. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198724551.003.0016.

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The psychology of the ‘Gettier effect’ appears robust—but complicated. Contrary to initial reports, more recent and thorough work by several groups of researchers indicates strongly that it is in fact found widely across cultures. Nonetheless, I argue that the pattern of psychological results should not at all be taken to settle the epistemological questions about the nature of knowledge. For the Gettier effect occurs both intermittently and with sensitivity to epistemically irrelevant factors. In short, the effect is noisy. And good principles of model selection indicate that, the noisier one’s data, the more one should prefer simpler curves over those that may be more complicated yet hew closer to the data. While we should not endorse K=JTB at this time, nonetheless the question ‘Is knowledge really just justified true belief?’ ought to be treated as once again in play.
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Book chapters on the topic "Robus fitting"

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Chin, Tat-Jun, David Suter, Shin-Fang Ch’ng, and James Quach. "Quantum Robust Fitting." In Computer Vision – ACCV 2020, 485–99. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69525-5_29.

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Frühwirth, Rudolf, and Are Strandlie. "Track Fitting." In Pattern Recognition, Tracking and Vertex Reconstruction in Particle Detectors, 103–27. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65771-0_6.

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AbstractTrack fitting is an application of established statistical estimation procedures with well-known properties. For a long time, estimators based on the least-squares principle were—with some notable exceptions—the principal methods for track fitting. More recently, robust and adaptive methods have found their way into the reconstruction programs. The first section of the chapter presents least-squares regression, the extended Kalman filter, regression with breakpoints, general broken lines and the triplet fit. The following section discusses robust regression by the M-estimator, the deterministic annealing filter, and the Gaussian-sum filter for electron reconstruction. The next section deals with linearized fits of space points to circles and helices. The chapter concludes with a section on track quality and shows how to test the track hypothesis, how to detect outliers, and how to find kinks in a track.
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Frühwirth, Rudolf, and Are Strandlie. "Vertex Fitting." In Pattern Recognition, Tracking and Vertex Reconstruction in Particle Detectors, 143–58. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65771-0_8.

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AbstractThe methods used for vertex fitting are closely related to the ones used in track fitting. The chapter describes least-squares estimators as well as robust and adaptive estimators. Furthermore, it is shown how the vertex fit can be extended to a kinematic fit by imposing additional constraints on the tracks participating in the fit.
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Ieng, Sio-Song, Jean-Philippe Tarel, and Pierre Charbonnier. "Evaluation of Robust Fitting Based Detection." In Lecture Notes in Computer Science, 341–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24671-8_27.

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Enqvist, Olof, Erik Ask, Fredrik Kahl, and Kalle Åström. "Robust Fitting for Multiple View Geometry." In Computer Vision – ECCV 2012, 738–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33718-5_53.

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Storer, Markus, Peter M. Roth, Martin Urschler, Horst Bischof, and Josef A. Birchbauer. "Efficient Robust Active Appearance Model Fitting." In Communications in Computer and Information Science, 229–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11840-1_17.

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Wang, Hanzi, and David Suter. "Robust Fitting by Adaptive-Scale Residual Consensus." In Lecture Notes in Computer Science, 107–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24672-5_9.

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Cruz Hernández, Heriberto, and Luis Gerardo de la Fraga. "A Multi-objective Robust Ellipse Fitting Algorithm." In NEO 2016, 141–58. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64063-1_6.

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Chai, Dengfeng, and Qunsheng Peng. "Image Feature Detection as Robust Model Fitting." In Computer Vision – ACCV 2006, 673–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11612704_67.

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Li, Zhaoxi, Cai Meng, Dingzhe Li, and Limin Liu. "Robust Ellipse Fitting with an Auxiliary Normal." In Lecture Notes in Computer Science, 601–12. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87355-4_50.

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Conference papers on the topic "Robus fitting"

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Liu, Yuqing, Philip Diwakar, Ismat El Jaouhari, and Dan Lin. "Sweeplus®: An Integrated Solution to Pipe Vibration Failures." In ASME 2019 Pressure Vessels & Piping Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/pvp2019-93023.

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Abstract This paper discusses our efforts to develop a robust branch fitting that can withstand AIV related to higher sound power levels than a standard contour fitting and accommodate FIV related vibration loads, without compromising project cost and schedule. Numerical simulation results and experimental test data are presented to substantiate our claims that a sweeplus® performs much better than any other contoured branch fittings available in the market with respect to any AIV, AR and FIV related risks. The aerodynamically shaped new fitting can withstand higher sound power level (PWL) limits in AIV, has smoother curvature to reduce flow separation, vortex formation and shedding thereby lowering peak stress concentrations and avoiding AR and FIV related risk, has an extended fatigue life, while adhering to ASME B16.9 Code requirements.
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de la Fraga, Luis Gerardo, and Gustavo M. Lopez Dominguez. "Robust fitting of ellipses with heuristics." In 2010 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2010. http://dx.doi.org/10.1109/cec.2010.5586304.

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Yu, Jieqi, Sanjeev R. Kulkarni, and H. Vincent Poor. "Robust fitting of ellipses and spheroids." In 2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers. IEEE, 2009. http://dx.doi.org/10.1109/acssc.2009.5470160.

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Guruswami, Venkatesan, and David Zuckerman. "Robust Fourier and Polynomial Curve Fitting." In 2016 IEEE 57th Annual Symposium on Foundations of Computer Science (FOCS). IEEE, 2016. http://dx.doi.org/10.1109/focs.2016.75.

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"SIMULTANEOUS ROBUST FITTING OF MULTIPLE CURVES." In International Conference on Computer Vision Theory and Applications. SciTePress - Science and and Technology Publications, 2007. http://dx.doi.org/10.5220/0002040801750182.

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Attila Sarhegyi. "Robust Sine Wave Fitting in ADC Testing." In 2006 IEEE Instrumentation and Measurement Technology. IEEE, 2006. http://dx.doi.org/10.1109/imtc.2006.236674.

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Sarhegyi, Attila, and Istvan Kollar. "Robust Sine Wave Fitting in ADC Testing." In IEEE Instrumentation and Measurement Technology Conference. IEEE, 2006. http://dx.doi.org/10.1109/imtc.2006.328246.

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Arellano, Claudia, and Rozenn Dahyot. "Robust Bayesian fitting of 3D morphable model." In the 10th European Conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2534008.2534013.

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Huang, Weiduo. "Robust Conicoid Fitting in Converting GPS Height." In 2009 International Conference on Information Engineering and Computer Science. IEEE, 2009. http://dx.doi.org/10.1109/iciecs.2009.5364106.

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Hanzi Wang. "Maximum kernel density estimator for robust fitting." In ICASSP 2008 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/icassp.2008.4518377.

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Reports on the topic "Robus fitting"

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Rahmani, Mehran, Xintong Ji, and Sovann Reach Kiet. Damage Detection and Damage Localization in Bridges with Low-Density Instrumentations Using the Wave-Method: Application to a Shake-Table Tested Bridge. Mineta Transportation Institute, September 2022. http://dx.doi.org/10.31979/mti.2022.2033.

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This study presents a major development to the wave method, a methodology used for structural identification and monitoring. The research team tested the method for use in structural damage detection and damage localization in bridges, the latter being a challenging task. The main goal was to assess capability of the improved method by applying it to a shake-table-tested prototype bridge with sparse instrumentation. The bridge was a 4-span reinforced concrete structure comprising two columns at each bent (6 columns total) and a flat slab. It was tested to failure using seven biaxial excitations at its base. Availability of a robust and verified method, which can work with sparse recording stations, can be valuable for detecting damage in bridges soon after an earthquake. The proposed method in this study includes estimating the shear (cS) and the longitudinal (cL) wave velocities by fitting an equivalent uniform Timoshenko beam model in impulse response functions of the recorded acceleration response. The identification algorithm is enhanced by adding the model’s damping ratio to the unknown parameters, as well as performing the identification for a range of initial values to avoid early convergence to a local minimum. Finally, the research team detect damage in the bridge columns by monitoring trends in the identified shear wave velocities from one damaging event to another. A comprehensive comparison between the reductions in shear wave velocities and the actual observed damages in the bridge columns is presented. The results revealed that the reduction of cS is generally consistent with the observed distribution and severity of damage during each biaxial motion. At bents 1 and 3, cS is consistently reduced with the progression of damage. The trends correctly detected the onset of damage at bent 1 during biaxial 3, and damage in bent 3 during biaxial 4. The most significant reduction was caused by the last two biaxial motions in bents 1 and 3, also consistent with the surveyed damage. In bent 2 (middle bent), the reduction trend in cS was relatively minor, correctly showing minor damage at this bent. Based on these findings, the team concluded that the enhanced wave method presented in this study was capable of detecting damage in the bridge and identifying the location of the most severe damage. The proposed methodology is a fast and inexpensive tool for real-time or near real-time damage detection and localization in similar bridges, especially those with sparsely deployed accelerometers.
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You might also be interested in the extended bibliographies on the topic 'Robus fitting' for particular source types:
Journal articles Dissertations / Theses
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