Dam deformation monitoring data analysis using space-time Kalman filter

Noise filtering, data predicting, and unmonitored data interpolating are important to dam deformation data analysis. However, traditional methods generally process single point monitoring data separately, without considering the spatial correlation between points. In this paper, the Space-Time Kalman Filter (STKF), a dynamic spatio-temporal filtering model, is used as a spatio-temporal data analysis method for dam deformation. There were three main steps in the method applied in this paper. The first step was to determine the Kriging spatial fields based on the characteristics of dam deformation. Next, the observation noise covariance, system noise covariance, the initial mean vector state, and its covariance were estimated using the Expectation Maximization algorithm (EM algorithm) in the second step. In the third step, we filtered the observation noise, interpolated the whole dam unmonitored data in space and time domains, and predicted the deformation for the whole dam using the Kalman filter recursion algorithm. The simulation data and Wuqiangxi dam deformation monitoring data were used to verify the STKF method. The results show that the STKF not only can filter the deformation data noise in both the temporal and spatial domain effectively, but also can interpolate and predict the deformation for the whole da

Modelling the relationship between water level and vertical displacements on the Yamula Dam, Turkey

International audienceMonitoring deformation pattern of dams is often one of the most effective ways to understand their safety status. The main objective of the study is to find the extent to which rising reservoir level affects the mechanism of deformation of The Yamula Dam under certain change in the reservoir level conditions during to the first filling period. Three different deformation analysis techniques, namely static, kinematic and dynamic, were used to analyze four geodetic monitoring records consisting of vertical displacements of nine object points established on the Dam and six reference points surrounding of it, to see whether the rising reservoir level have a role in the vertical deformations during the first filling period. The largest vertical displacements were in the middle of the dam. There is an apparent linear relationship between the dam subsidence and the reservoir level. A dynamic deformation model was developed to model this situation. The model infers a causative relationship between the reservoir level and the dam deformations. The analysis of the results determines the degree of the correlation between the change in the reservoir level and the observed structural deformation of the dam

Bridge structure deformation prediction based on GNSS data using Kalman-ARIMA-GARCH model

Bridges are an essential part of the ground transportation system. Health monitoring is fundamentally important for the safety and service life of bridges. A large amount of structural information is obtained from various sensors using sensing technology, and the data processing has become a challenging issue. To improve the prediction accuracy of bridge structure deformation based on data mining and to accurately evaluate the time-varying characteristics of bridge structure performance evolution, this paper proposes a new method for bridge structure deformation prediction, which integrates the Kalman filter, autoregressive integrated moving average model (ARIMA), and generalized autoregressive conditional heteroskedasticity (GARCH). Firstly, the raw deformation data is directly pre-processed using the Kalman filter to reduce the noise. After that, the linear recursive ARIMA model is established to analyze and predict the structure deformation. Finally, the nonlinear recursive GARCH model is introduced to further improve the accuracy of the prediction. Simulation results based on measured sensor data from the Global Navigation Satellite System (GNSS) deformation monitoring system demonstrated that: (1) the Kalman filter is capable of denoising the bridge deformation monitoring data; (2) the prediction accuracy of the proposed Kalman-ARIMA-GARCH model is satisfactory, where the mean absolute error increases only from 3.402 mm to 5.847 mm with the increment of the prediction step; and (3) in comparision to the Kalman-ARIMA model, the Kalman-ARIMA-GARCH model results in superior prediction accuracy as it includes partial nonlinear characteristics (heteroscedasticity); the mean absolute error of five-step prediction using the proposed model is improved by 10.12%. This paper provides a new way for structural behavior prediction based on data processing, which can lay a foundation for the early warning of bridge health monitoring system based on sensor data using sensing technolog

Bayesian Parameter Determination of a CT-Test Described by a Viscoplastic-Damage Model Considering the Model Error

The state of materials and accordingly the properties of structures are changing over the period of use, which may influence the reliability and quality of the structure during its life-time. Therefore identification of the model parameters of the system is a topic which has attracted attention in the content of structural health monitoring. The parameters of a constitutive model are usually identified by minimization of the difference between model response and experimental data. However, the measurement errors and differences in the specimens lead to deviations in the determined parameters. In this article, the Choboche model with a damage is used and a stochastic simulation technique is applied to generate artificial data which exhibit the same stochastic behavior as experimental data. Then the model and damage parameters are identified by applying the sequential Gauss-Markov-Kalman filter (SGMKF) approach as this method is determined as the most efficient method for time consuming finite element model updating problems among filtering and random walk approaches. The parameters identified using this Bayesian approach are compared with the true parameters in the simulation, and further, the efficiency of the identification method is discussed. The aim of this study is to observe whether the mentioned method is suitable and efficient to identify the model and damage parameters of a material model, as a highly non-linear model, for a real structural specimen using a limited surface displacement measurement vector gained by Digital Image Correlation (DIC) and to see how much information is indeed needed to estimate the parameters accurately even by considering the model error and whether this approach can also practically be used for health monitoring purposes before the occurrence of severe damage and collaps

The use of the Kalman filter in the automated segmentation of EIT lung images

In this paper, we present a new pipeline for the fast and accurate segmentation of impedance images of the lungs using electrical impedance tomography (EIT). EIT is an emerging, promising, non-invasive imaging modality that produces real-time, low spatial but high temporal resolution images of impedance inside a body. Recovering impedance itself constitutes a nonlinear ill-posed inverse problem, therefore the problem is usually linearized, which produces impedance-change images, rather than static impedance ones. Such images are highly blurry and fuzzy along object boundaries. We provide a mathematical reasoning behind the high suitability of the Kalman filter when it comes to segmenting and tracking conductivity changes in EIT lung images. Next, we use a two-fold approach to tackle the segmentation problem. First, we construct a global lung shape to restrict the search region of the Kalman filter. Next, we proceed with augmenting the Kalman filter by incorporating an adaptive foreground detection system to provide the boundary contours for the Kalman filter to carry out the tracking of the conductivity changes as the lungs undergo deformation in a respiratory cycle. The proposed method has been validated by using performance statistics such as misclassified area, and false positive rate, and compared to previous approaches. The results show that the proposed automated method can be a fast and reliable segmentation tool for EIT imaging

Real-time Loss Estimation for Instrumented Buildings

Motivation. A growing number of buildings have been instrumented to measure and record earthquake motions and to transmit these records to seismic-network data centers to be archived and disseminated for research purposes. At the same time, sensors are growing smaller, less expensive to install, and capable of sensing and transmitting other environmental parameters in addition to acceleration. Finally, recently developed performance-based earthquake engineering methodologies employ structural-response information to estimate probabilistic repair costs, repair durations, and other metrics of seismic performance. The opportunity presents itself therefore to combine these developments into the capability to estimate automatically in near-real-time the probabilistic seismic performance of an instrumented building, shortly after the cessation of strong motion. We refer to this opportunity as (near-) real-time loss estimation (RTLE). Methodology. This report presents a methodology for RTLE for instrumented buildings. Seismic performance is to be measured in terms of probabilistic repair cost, precise location of likely physical damage, operability, and life-safety. The methodology uses the instrument recordings and a Bayesian state-estimation algorithm called a particle filter to estimate the probabilistic structural response of the system, in terms of member forces and deformations. The structural response estimate is then used as input to component fragility functions to estimate the probabilistic damage state of structural and nonstructural components. The probabilistic damage state can be used to direct structural engineers to likely locations of physical damage, even if they are concealed behind architectural finishes. The damage state is used with construction cost-estimation principles to estimate probabilistic repair cost. It is also used as input to a quantified, fuzzy-set version of the FEMA-356 performance-level descriptions to estimate probabilistic safety and operability levels. CUREE demonstration building. The procedure for estimating damage locations, repair costs, and post-earthquake safety and operability is illustrated in parallel demonstrations by CUREE and Kajima research teams. The CUREE demonstration is performed using a real 1960s-era, 7-story, nonductile reinforced-concrete moment-frame building located in Van Nuys, California. The building is instrumented with 16 channels at five levels: ground level, floors 2, 3, 6, and the roof. We used the records obtained after the 1994 Northridge earthquake to hindcast performance in that earthquake. The building is analyzed in its condition prior to the 1994 Northridge Earthquake. It is found that, while hindcasting of the overall system performance level was excellent, prediction of detailed damage locations was poor, implying that either actual conditions differed substantially from those shown on the structural drawings, or inappropriate fragility functions were employed, or both. We also found that Bayesian updating of the structural model using observed structural response above the base of the building adds little information to the performance prediction. The reason is probably that Real-Time Loss Estimation for Instrumented Buildings ii structural uncertainties have only secondary effect on performance uncertainty, compared with the uncertainty in assembly damageability as quantified by their fragility functions. The implication is that real-time loss estimation is not sensitive to structural uncertainties (saving costly multiple simulations of structural response), and that real-time loss estimation does not benefit significantly from installing measuring instruments other than those at the base of the building. Kajima demonstration building. The Kajima demonstration is performed using a real 1960s-era office building in Kobe, Japan. The building, a 7-story reinforced-concrete shearwall building, was not instrumented in the 1995 Kobe earthquake, so instrument recordings are simulated. The building is analyzed in its condition prior to the earthquake. It is found that, while hindcasting of the overall repair cost was excellent, prediction of detailed damage locations was poor, again implying either that as-built conditions differ substantially from those shown on structural drawings, or that inappropriate fragility functions were used, or both. We find that the parameters of the detailed particle filter needed significant tuning, which would be impractical in actual application. Work is needed to prescribe values of these parameters in general. Opportunities for implementation and further research. Because much of the cost of applying this RTLE algorithm results from the cost of instrumentation and the effort of setting up a structural model, the readiest application would be to instrumented buildings whose structural models are already available, and to apply the methodology to important facilities. It would be useful to study under what conditions RTLE would be economically justified. Two other interesting possibilities for further study are (1) to update performance using readily observable damage; and (2) to quantify the value of information for expensive inspections, e.g., if one inspects a connection with a modeled 50% failure probability and finds that the connect is undamaged, is it necessary to examine one with 10% failure probability

Separablity of deformations and measurement noises of GPS time series with modified Kalman filter for landslide monitoring in real-time

The separation of the deformations and measurement noise of GPS coordinate time series and accuracy improvement of GPS real-time coordinates are major aspects of the thesis. In order to reduce the influence of the colored noise in the GPS position time series, three different methods have been compared: the Finite Impulse Response (FIR) filter, the Kalman filter model, and the sequential algorithm. Among these three methods, the Kalman filter is investigated in detail. The GPS real-time series contains the colored noise, yet the Kalman filter model requires white noise. The state vector can be augmented by appending to the state vector components of the shaping filter which can describe the long term movement of the colored noise. Thus the deformation analysis based on the Kalman filter model with a shaping filter technique, has been applied in the different movement trends of GPS real-time series. From the results, the Kalman filter model with a shaping filter can be widely used to process the GPS short baseline time series in real-time. The precise position coordinate can be obtained and the deformation epoch can be detected in time and with high reliability. It can be applied in the early warning system of the natural hazards. The detection of a deformation with less time delay and the improvement of reliability of detecting deformation epoch is another key issue of the investigation. The proposed model makes use of the statistical criterion (MDL criterion) comparison instead of the hypothesis test. Considering the affection of colored noise in the GPS time series the multiple Kalman filters model was augmented by shaping filters which describe the long-term movement of the colored noise. By the GPS experiments, it has been verified that the proposed models have the ability to better capture the deformation epoch and to improve the reliability of detecting the deformation epoch. The proposed models can be used to detect stepwise changes of a variety of fields in real-time or near real-time.Schwerpunkte dieser Arbeit sind die Trennung von tatsächlicher Bewegung und Messrauschen in GPS-Koordinatenzeitreihen und die Genauigkeitssteigerung von Echtzeit-GPS-Koordinaten. Zur Verringerung des Einflusses von farbigem Rauschen bei Zeitreihen von GPS-Positionen wurden drei verschiedene Verfahren verglichen: FIR-Filter (Finite Impulse Response),Kalman-Filter-Modell und Sequentielle Ausgleichung. Von diesen drei Verfahren wird das Kalman-Filter genauer untersucht. In Echtzeit-GPS-Datenreihen ist farbiges Rauschen enthalten, das Kalman-Filter hingegen erfordert weißes Rauschen. Die Zustandsschätzung erfolgt durch die Erweiterung des Zustandsvektors um die shaping-Filter-Komponenten, die den langfristigen Einfluss des farbigen Rauschprozesses beschreiben. Dementsprechend wurde die Bewegungsanalyse durch ein Kalman-Filter-Modell mit shaping-Filter-Verfahren auf verschiedene Rauschprozesse von Echtzeit-GPS-Zeitreihen angewandt. Das Ergebnis ist, dass ein Kalman-Filter mit shaping-Filter kann häufig zur Echtzeitauswertung von Zeitreihen kurzer GPS-Basislinien genutzt werden. Die genauen Positionskoordinaten lassen sich bestimmen, und, eine Bewegungsepoche kann rechtzeitig und mit einer hohen Zuverlässigkeit bestimmt werden. Ein Einsatz in Frühwarnsystemen vor Naturgefahren ist möglich. Die Erkennung von Bewegung mit geringer Zeitverzögerung und die Steigerung der Detektionszu-verlässigkeit von Bewegungsepochen sind weitere Untersuchungsschwerpunkte. Der vorgeschlagene Ansatz nutzt statt eines Hypothesentests den Vergleich eines statistischen Kriteriums (Minimum Desciption Length). In Anbetracht des farbigen Rauschens, das in GPS-Zeitreihen enthalten ist, wurde das multiple Kalman-Filter um shaping-Filter erweitert, die den langfristigen Einfluss des farbigen Rauschens beschreiben. Durch GPS- Experiment konnte nachgewiesen werden, dass die vorgeschlagenen Modelle eine verbesserte Deformationserkennung und eine Steigerung der Zuverlässigkeit bezüglich der Deformationsepochendetektion ermöglichen. Diese erlauben die Erkennung stufenförmiger Änderungen bei vielfältigen Anwendungen und zur Vorhersage einiger Naturkatastrophenereignisse in Echtzeit beziehungsweise Nahezu-Echtzeit

Variational-based data assimilation to simulate sediment concentration in the Lower Yellow River, China

The heavy sediment load of the Yellow River makes it difficult to simulate sediment concentration using classic numerical models. In this paper, on the basis of the classic one-dimensional numerical model of open channel flow, a variational-based data assimilation method is introduced to improve the simulation accuracy of sediment concentration and to estimate parameters in sediment carrying capacity. In this method, a cost function is introduced first to determine the difference between the sediment concentration distributions and available field observations. A one-dimensional suspended sediment transport equation, assumed as a constraint, is integrated into the cost function. An adjoint equation of the data assimilation system is used to solve the minimum problem of the cost function. Field data observed from the Yellow River in 2013 are used to test the proposed method. When running the numerical model with the data assimilation method, errors between the calculations and the observations are analyzed. Results show that (1) the data assimilation system can improve the prediction accuracy of suspended sediment concentration; (2) the variational inverse data assimilation is an effective way to estimate the model parameters, which are poorly known in previous research; and (3) although the available observations are limited to two cross sections located in the central portion of the study reach, the variational-based data assimilation system has a positive effect on the simulated results in the portion of the model domain in which no observations are available