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Robust deformation monitoring of bridge structures using MEMS accelerometers and image-assisted total stations / Mohammad Omidalizarandi (2020)  

Public Titre : Robust deformation monitoring of bridge structures using MEMS accelerometers and image-assisted total stations Type de document : Thèse/HDR Auteurs : Mohammad Omidalizarandi, Auteur Editeur : Munich : Bayerische Akademie der Wissenschaften Année de publication : 2020 Collection : DGK - C, ISSN 0065-5325 num. 859 Importance : 260 p. Format : 21 x 30 cm Note générale : bibliographie Diese Arbeit ist gleichzeitig veröffentlicht in: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Universität Hannover, ISSN 0174-1454, Nr. 366 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] accéléromètre [Termes IGN] algorithme espérance-maximisation [Termes IGN] auscultation d'ouvrage [Termes IGN] méthode robuste [Termes IGN] microsystème électromécanique [Termes IGN] pont [Termes IGN] surveillance d'ouvrage [Termes IGN] tachéomètre électronique [Termes IGN] topométrie de précision Résumé : (auteur) Today, short- and long-term structural health monitoring (SHM) of bridge structures has received considerable attention. However, permanent, cost-effective, and reliable monitoring are still challenging issues. From a surveying or civil engineer's point of view, vibration-based SHM is often carried out by inspecting the changes in the dynamic responses of bridge structures known as modal parameters, such as eigen frequencies, eigenforms and modal damping. The use of cost-effective micro-electro-mechanical-systems (MEMS) accelerometers with a high sampling frequency is becoming more affordable and feasible for the aforementioned monitoring task. Within this dissertation, a three-step scenario is proposed to choose a suitable MEMS accelerometer despite of its purchase price, measurement range and sampling frequency. Firstly, a robust calibration procedure is proposed and implemented to model MEMS related systematic errors such as biases, scale factors, and non-orthogonality angles between the axes. Secondly, a controlled excitation experiment is conducted by using a high-precision shaker. Thirdly, a statictest experiment is accomplished over a long period. Robust, accurate, and automatic estimation of the modal parameters is particularly challenging when vibration measurements are contaminated with a high coloured measurement noise, e.g., due to cost-effective MEMS acceleration data. This is even more challenging when the structure is continuously under imposed forces due to moving vehicles or wind. For this purpose, a robust and automatic vibration analysis procedure the so–called robust time domain modal parameter identification (RT-MPI) approach is proposed and implemented. It is a novel approach in the sense of automatic excitation (e.g. ambient) window selection, automatic and reliable identification of initial eigen frequencies even closely spaced ones as well as robustly and accurately estimating the modal parameters. To estimate frequencies, damping ratio coefficients, amplitudes, and phase shifts, an observation model consisting of a damped harmonic oscillation (DHO) model, an autoregressive model of coloured measurement noise and a stochastic model in the form of the heavy-tailed family of scaled t-distributions with unknown degree of freedom and scale factor, is employed. The aforementioned three parametric models are jointly adjusted by means of a generalised expectation maximisation (GEM) algorithm. The proposed RT-MPI algorithm is also able to estimate amplitudes in a metric unit and with a high accuracy for the recorded acceleration data by means of double integration of the DHO model. The eigenforms are characterised in a subsequent step, and by using the estimated parameters from the GEM algorithm. In addition, having amplitudes in the metric unit allows to characterise deflection eigen forms in their true scales for selected excitation windows within short time intervals. The deformation/displacement monitoring by merely using the MEMS accelerometer is challenging, since it suffers from accuracy degradation with time for absolute position/displacement estimates. Therefore, the MEMS accelerometers and an image-assisted total station (IATS) are fused by performing one-dimensional (1D) coordinate update within the Kalman filtering framework. To generate 1D displacement data from the IATS, video frames of a passive target, that is attached to a bridge structure, are captured by means of a telescope camera of the IATS. A passive target centroid detection algorithm is proposed and implemented, which is robust and reliable with respect to poor environmental conditions, such as low lighting, dusty situations, and skewed angle targets. Next, an angular conversion factor of the telescope camera is calibrated, which allows to convert the generated displacement data from pixel to metric unit. Experiments are performed in four case studies including simulation, controlled excitation and two real applications of a footbridge structure and a synthetic bridge. The estimated modal parameters are compared and validated by their true values as well as their corresponding estimates obtained from reference sensors such as reference accelerometer, geophone, and laser tracker. Additionally, the estimated eigen frequencies and damping ratio coefficients are compared with a well-known covariance driven stochastic subspace identification (SSI-COV) approach. The results show that the MEMS accelerometers are suitable for identifying all occurring eigen frequencies of the bridge structures. Moreover, the vibration analysis procedure demonstrates that amplitudes are estimated in submillimetre range accuracy, frequencies with an accuracy of better than 0.1 Hz and damping ratio coefficients with an accuracy of better than 0.1 and 0.2%for modal and system damping, respectively. The analysis reveals the superiority of the proposed RT-MPI algorithm compared to the SSI-COV algorithm. Finally, a high accurate displacement time series at the level of submillimetre is generated by fusion of the IATS and the MEMS measurements. Numéro de notice : 17680 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Thèse étrangère En ligne : https://dgk.badw.de/fileadmin/user_upload/Files/DGK/docs/c-859.pdf Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98021

Robust external calibration of terrestrial laser scanner and digital camera for structural monitoring / Mohammad Omidalizarandi in Journal of applied geodesy, vol 13 n° 2 (April 2019)  

Public [article]  inJournal of applied geodesy > vol 13 n° 2 (April 2019) . - pp 105 - 130 Titre : Robust external calibration of terrestrial laser scanner and digital camera for structural monitoring Type de document : Article/Communication Auteurs : Mohammad Omidalizarandi, Auteur ; Boris Kargoll, Auteur ; Jens-André Paffenholz, Auteur ; Ingo Neumann, Auteur Année de publication : 2019 Article en page(s) : pp 105 - 130 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] algorithme espérance-maximisation [Termes IGN] déformation de la croute terrestre [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] élément d'orientation externe [Termes IGN] méthode des moindres carrés [Termes IGN] modèle de Gauss-Helmert [Termes IGN] modèle de Gauss-Markov [Termes IGN] orthoimage [Termes IGN] semis de points Résumé : (Auteur) In the last two decades, the integration of a terrestrial laser scanner (TLS) and digital photogrammetry, besides other sensors integration, has received considerable attention for deformation monitoring of natural or man-made structures. Typically, a TLS is used for an area-based deformation analysis. A high-resolution digital camera may be attached on top of the TLS to increase the accuracy and completeness of deformation analysis by optimally combining points or line features extracted both from three-dimensional (3D) point clouds and captured images at different epochs of time. For this purpose, the external calibration parameters between the TLS and digital camera needs to be determined precisely. The camera calibration and internal TLS calibration are commonly carried out in advance in the laboratory environments. The focus of this research is to highly accurately and robustly estimate the external calibration parameters between the fused sensors using signalised target points. The observables are the image measurements, the 3D point clouds, and the horizontal angle reading of a TLS. In addition, laser tracker observations are used for the purpose of validation. The functional models are determined based on the space resection in photogrammetry using the collinearity condition equations, the 3D Helmert transformation and the constraint equation, which are solved in a rigorous bundle adjustment procedure. Three different adjustment procedures are developed and implemented: (1) an expectation maximization (EM) algorithm to solve a Gauss-Helmert model (GHM) with grouped t-distributed random deviations, (2) a novel EM algorithm to solve a corresponding quasi-Gauss-Markov model (qGMM) with t-distributed pseudo-misclosures, and (3) a classical least-squares procedure to solve the GHM with variance components and outlier removal. The comparison of the results demonstrates the precise, reliable, accurate and robust estimation of the parameters in particular by the second and third procedures in comparison to the first one. In addition, the results show that the second procedure is computationally more efficient than the other two. Numéro de notice : A2019-145 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1515/jag-2018-0038 Date de publication en ligne : 02/02/2019 En ligne : https://doi.org/10.1515/jag-2018-0038 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92472 [article]