Model Updating

Growing needs for French closed-captioning of live TV broadcasts in Canada cannot be met only with stenography-based technology because of a chronic shortage of skilled stenographers. Using speech recognition for live closed-captioning, however, requires several specific problems to be solved, such as the need for low-latency real-time recognition, remote operation, automated model updates, and collaborative work. In this paper we describe our solutions to these problems and the implementation of a live captioning system based on the CRIM speech recognizer. We report results from field deployment in several projects. The oldest in operation has been broadcasting real-time closed-captions for more than 2 years.

A Kalman filter was developed to overcome the problems caused by process drifting. Different types of models were used to predict response variables of an activated sludge waste-water treatment plant. These models were constructed using MLR, PCR, and PLS. The MLR-type regression coefficients were calculated for both the PCR and PLS models. After that, the Kalman filter was used to estimate these coefficients, recursively. Both the PCR and PLS 'inner relation' coefficient vectors were also estimated in this way and the results were then compared. The effect of the number of variables was also briefly studied. The testing was carried out using sequential process data. The prediction ability was measured by a Q 2 -value as a function of a lag in the updating of the coefficients. q

This chapter is devoted to the description of several methods found in the literature that were developed and applied to solve the problem of structural damage identification. Because of the economical and safety advantages of its solution, the damage identification problem has been attracting the attention of the Civil, Mechanical and Aeronautical Engineering communities for several decades. The present survey is mainly focused on the inverse problems methodology and only deals with methods based on structural dynamic characteristics. In this survey, the methods are categorised in chronological order of appearance in the literature and grouped by the dynamic characteristics (e.g. natural frequencies and mode shapes) and the mathematical techniques (e.g. least squares) in which they are based. Since each method is, typically, only appropriated to certain type of structures, their characteristics are also described. The nature of the data used (numerical, experimental or numerical-experimental) to perform the damage identification is also pointed out. availability of a universal method which can be applied to all kinds of structures and damages types.

Present paper describes the facilities of composite material properties identification technique using specimen vibration tests, genetic algorithms, finite elements analysis and specimen shape optimization. In identification process the elastic constants in a numerical model is updated so that the output from the numerical code fits the results from vibration testing. Main problem analysed in this paper is that Poisson's ratio is the worst determined elastic characteristic due to its low influence on specimen eigenfrequencies. It is shown that it is possible to increase its influence by choosing specific test specimen characteristics (side aspect ratio, orthotropy angle, etc.) via optimization routine. In this paper are presented test results of some experiments wherein glass‐epoxy and carbon‐epoxy material properties were identified. Santrauka Straipsnyje aprašomas kompozitiniu medžiagu tamprumo charakteristiku identifikavimo metodas naudojant bandiniu vibracinius bandymus, gen...

This paper describes ambient vibration based seismic evaluation procedure of an isolated highway bridge. The procedure includes finite element modeling, ambient vibration testing, finite element model updating and time history analysis. Gülburnu Highway Bridge located on the Giresun–Espiye state highway is selected as a case study. Three dimensional finite element model of the bridge is created by SAP2000 software to determine the dynamic characteristics analytically. Since input force is not measured, Operational Modal Analysis is applied to identify dynamic characteristics. Enhanced Frequency Domain Decomposition and Stochastic Subspace Identification methods are used to obtain experimental dynamic characteristics. Analytical and experimental dynamic characteristic are compared with each other and finite element model of the bridge is updated by changing of material properties to reduce the differences between the results. Analytical model of the bridge after model updating is analyzed using 1992 Erzincan earthquake record to determine the seismic behavior. EW, NS and UP components of the ground motion are applied to the bridge at the longitudinal, transverse and vertical directions, respectively. It is demonstrated that the ambient vibration measurements are enough to identify the most significant modes of highway bridges. Maximum differences between the natural frequencies are reduced averagely from 9% to 2% by model updating. It is seen from the earthquake analyses that friction pendulum isolators are very effective in reducing the displacements and internal forces.► We determine the seismic behavior of Gülburnu highway bridge using finite element analysis and ambient vibration tests. ► We examine changes in dynamic characteristics of the bridge. ► Changing of the some uncertain parameters decreases the differences between analytical and experimental dynamic characteristics. ► We demonstrate that the ambient vibration measurements are enough to identify the most significant modes of highway bridges.

Bridges are indispensable components of the infrastructure of modern society, and their assessment via techniques of structural dynamics is assuming greater importance. This assessment concerns performance of the as-built structure compared to the design and can also extend to the assessment of structural deterioration or damage. Simple validation of numerical results by dynamic testing has met some success; however, feedback from testing into analysis is usually crude, and only recently have systematic techniques been developed that can be supplied to such structures. This paper investigates the application of sensitivity-based model updating technology to the dynamic assessment of the Safti Link Bridge, a curved cable-stayed bridge in Singapore. Based on the measured modal data from prototype testing, the simulated dynamic properties obtained via finiteelement analysis have been significantly improved by modification of uncertain structural parameters such as Young's modulus of concrete and structural geometry. 1

Many applications involve repeatedly computing the optimal, maximum a posteriori (MAP) configuration of a graphical model as the model changes, often slowly or incrementally over time, e.g., due to input from a user. Small changes to the model often require updating only a small fraction of the MAP configuration, suggesting the possibility of performing updates faster than recomputing from scratch. In this paper we present an algorithm for efficiently performing such updates under arbitrary changes to the model. Our algorithm is within a logarithmic factor of the optimal and is asymptotically never slower than re-computing from-scratch: if a modification to the model requires m updates to the MAP configuration of n random variables, then our algorithm requires O(m log (n/m)) time; re-computing from scratch requires O(n) time. We evaluate the practical effectiveness of our algorithm by considering two problems in genomic signal processing, CpG region segmentation and protein sidechain packing, where a MAP configuration must be repeatedly updated. Our results show significant speedups over recomputing from scratch.

This study presents a method based on the Gauss-Newton optimization technique for continuous reservoir model updating with respect to production history and time-lapse seismic data in the form of zero offset amplitudes and amplitude versus offset (AVO) gradients. The main objective of the study is to test the feasibility of using these integrated data as input to reservoir parameter estimation problems. Using only production data or zero offset time-lapse seismic amplitudes as observation data in the parameter estimation process cannot properly limit the solution space. The emphasis of this work is to use the integrated data combined with empirical knowledge about rock types from laboratory measurements, to further constrain the inversion process. The algorithm written for this study consists of three parts: the reservoir simulator, the rock physics petro-elastic model and the optimization algorithm. The Gauss-Newton inversion is tested at a 2D semi-synthetic model inspired by real field data from offshore Norway. The algorithm reduces the misfit between the observed and simulated data which make it possible to estimate porosity and permeability distributions. The Gauss-Newton optimization technique is an efficient parameter estimation technique. However, the numerical estimation of the gradient is time consuming, and it can be prohibitive for practical applications. This method is suitable for distributed computing which considerably reduces the total optimization time. The amount of reduction depends mainly on the number of available processors.

A new method is presented for the finite element model updating of structures at the element level utilizing Frequency Response Function data. Response sensitivities with respect to the change of mass and stiffness parameters are indirectly evaluated using the decomposed form of the FRF. Solution of these sensitivity equations through the Least Square algorithm and weighting of these equations has been addressed to achieve parameter estimation with a high accuracy. Numerical examples using noise polluted data confirm that the proposed method can be an alternative to conventional model updating methods even in the presence of mass modeling errors.

Reducing the weight and production cost of chassis is considered to be one of the most important area of research in automotive manufacturing industry. However with lighter vehicle, the chassis is easily subjected to large vibration mainly coming from the engine and road irregularities. This paper looks into the application of dynamic analysis for verification of the complex FE model of truck chassis. The dynamic characteristic of truck chassis such as the natural frequency and mode shape is determined by finite element method. Experimental measurement by modal testing is then carried out to determine the accuracy of finite element analysis. Initial result indicated some discrepancies with regard to natural frequencies at various modes. However for mode shape analysis, both methods produced almost the same shape. As for getting a better agreement between both methods of analysis, modal updating had been carried out by adjusting parametric value of Modulus Young and mass density. It had been pointed out that the error was reduced to ± 2% through the model updating verified FE model has been obtained. Finally, the modifications of the verified FE truck chassis model has been suggested by consider adding the stiffener to reduce the vibration through chassis as well as improve the strength of structure.

This paper presents an open and integrated tool environment that enables engineers to effectively search, in a CAD solid model form, for a mechanism design with optimal kinematic and dynamic performance. In order to demonstrate the feasibility of such an environment, design parameterization that supports capturing design intents in product solid models must be available, and advanced modeling, simulation, and optimization technologies implemented in engineering software tools must be incorporated. In this paper, the design parameterization capabilities developed previously have been applied to support design optimization of engineering products, including a High Mobility Multi-purpose Wheeled Vehicle (HMMWV). In the proposed environment, Pro/ENGINEER and SolidWorks are supported for product model representation, DADS (Dynamic Analysis and Design System) is employed for dynamic simulation of mechanical systems including ground vehicles, and DOT (Design Optimization Tool) is included for a batch mode design optimization. In addition to the commercial tools, a number of software modules have been implemented to support the integration; e.g., interface modules for data retrieval, and model update modules for updating CAD and simulation models in accordance with design changes. Note that in this research, the overall finite difference method has been adopted to support design sensitivity analysis.

Structural monitoring is essential for ensuring the structural safety performance during the service life. The process is of paramount importance in case of the offshore jacket-type platforms due to the underwater structural parts subjected to the marine environmental conditions. This work is an experimental investigation on a laboratory model of a jacket platform with the objective of establishing a baseline finite element (FE) model for long-term structural health monitoring for this type of structures. A robust damage diagnosis system is also developed which is less sensitive to both the measurements and the modeling uncertainties. Experimental vibration tests are conducted on a physical platform model to obtain dynamic characteristics and then, the initial FE-model of the intact structure is developed to determine them numerically. Some differences between numerically and experimentally identified characteristics emerge due to various uncertainties in the FE-model and measured vibration data. To minimize these differences, initial FE-model is updated according to the experimental results. The updated FE-model is employed to predict the changes in the dynamic characteristics under variety of damage scenarios which are imposed by reducing the stiffness at the components of the model. Fuzzy logic system (FLS) and probabilistic analysis is developed for linguistic classification of damage and global damage diagnosis. Incorporation of the FLS fault isolation technique into FE-model updating method are proposed and evaluated for two different FLS methods to develop a vigorous damage diagnosis method. The efficiency of the technique is validated by different damage scenarios foreseen on the physical model. This technique is shown to be effective for diagnosing the presence of degradation and quantify it.

The potential of combining finite element (FE) analysis with on-site measurement through finite element model updating is indisputable. However, simplified initial models and too few measurements can lead to updated model parameters which conceal inaccurate modelling assumptions rather than improve estimates of the actual structural parameters. Therefore, the methodology proposed aims primarily to eliminate inaccurate modelling simplification by means of manual model refinements before parameters are estimated by non-linear optimization. In addition, multi-response objective functions are introduced, which allow combing different types of measurements to obtain a solid basis for parameter estimation. The proposed methodology was applied to one of the world's largest single-arch bridges, the new Svinesund Bridge, and disclosed a need to use a non-linear model in order to estimate the structural parameters more accurately. The resultant model could reproduce the measurements with significantly improved accuracy without assigning unrealistic values to model parameters.

The bell-tower of the San Vittore church in Arcisate has been studied by Politecnico di Milano since 2007. After a brief description of past operational modal tests and FE modeling, the paper focuses on the results provided by a simple dynamic monitoring system installed inside the tower for several months. The modal identification from continuously recorded acceleration data has been performed using an automated operational modal analysis procedure, based on the covariance-driven Stochastic Subspace Identification method. Since the identified natural frequencies revealed cyclic evolution over short-and long-time periods, mainly due to thermal effects, two methods aimed at detecting the structural anomalies were adopted: (1) the first one is based on the use of appropriate statistical models to predict the modal frequency fluctuation; (2) the second one consists in the continuous update of the FE model based on the temperature de-trended frequencies.

In the last two decades, many studies have reported the effectiveness of Experimental Modal Analysis and Finite Element Model Updating in mechanical and aerospace engineering, where they represent useful tools for Structural Health recognition and can provide an information base for detection, assessment and quantification of structural damage due to exercise actions and exceptional events.The applications of Damage Detection techniques to civil constructions left some still not well clarified points, due to the great variety of structural typologies, material properties, boundary conditions and possible damage patterns, as in the case of seismic damage.In order to design post-earthquake rehabilitation interventions, in fact a careful knowledge of the damage distribution and residual structural capacity is obviously needed and this can be very expensive and time consuming. However, the application of vibration based damage identification techniques to high ductile structures designed according to seismic capacity approach can improve the feasibility, reliability and efficiency of these methods.In the present study, Finite Element Model Updating procedures based on vibration measurements were used to detect, assess and quantify the structural damage of a high ductile steel–concrete composite frame subjected to increasing seismic damage by means of pseudo dynamic and cyclic test at the Joint Research Centre (Ispra, Italy). The updating process, repeated for three damage levels, has been applied to different finite element structural models (addressing different modelling strategies), allowing a comprehensive description and quantification of the progressive degradation of beam-to-column joints, devoted to dissipate the seismic energy by design.

We introduce quadratic two-parameter eigenvalue problem and show that we can linearize it as a singular two-parameter eigenvalue problem. This problem, together with another example that comes from model updating, shows the need for nu- merical methods for singular two-parameter eigenvalue problems and for a better understanding of such problems. There are various numerical methods for two-parameter eigenvalue problems, but

This paper presents an application of near-infrared (NIR) multivariate calibration for the determination of content uniformity of pharmaceutical tablets. Data used in this study were acquired from 3 NIR instruments obtained from 2 different vendors. One of these spectrometers is a dispersive NIR system configured for reflectance measurements while the other two, are Fourier transform (FT) based configured for both transmittance and reflectance. The performance of partial least squares (PLS) calibration models generated from the primary instrument (A) was investigated using samples measured on secondary instruments (B and C). The cross-instrument predictions without standardization or updating was not successful in the reflectance mode for both instrument B and C producing root mean square error of prediction (RMSEP) values of 2.78 and 5.78%, respectively. Similar deterioration of the calibration model was evidenced for instrument B in the transmittance mode with an RMSEP value of 2.65%. To minimize the effect of instrument response variations, piecewise direct standardization (PDS) was used. This lowered the RMSEP in the reflectance mode for the secondary instruments B and C to 1.19 and 1.58%, respectively. Similar improvement was obtained in the transmittance mode for instrument B producing RMSEP of 1.12%. For cases in which the stability of drug substance is poor and the on-demand manufacture of transfer samples is difficult, a model updating strategy that uses placebo samples is proposed. This technique also outperformed the direct cross-instrument predictions with RMSEP values of 1.25 and 1.70% for instruments B and C, respectively, in the reflectance mode and 1.42% for instrument B in the transmittance mode. #

A novel keyword propagation method is proposed for image retrieval based on a recently developed manifold-ranking algorithm. In contrast to existing methods which train a binary classifier for each keyword, our keyword model is constructed in a straightforward manner by exploring the relationship among all images in the feature space in the learning stage. In relevance feedback, the feedback information can be naturally incorporated to refine the retrieval result by additional propagation processes. In order to speed up the convergence of the query concept, we adopt two active learning schemes to select images during relevance feedback. Furthermore, by means of keyword model update, the system can be self-improved constantly. The updating procedure can be performed online during relevance feedback without extra offline training. Systematic experiments on a general-purpose image database consisting of 5 000 Corel images validate the effectiveness of the proposed method.

Smart Sensing for Structural Health Monitoring (S3HM) is a collaborative research project running under the ESF (European Science Foundation) Eurocores S3T program.The objective of the project is to combine the most recent developments in sensor and actuator technology, networks, signal processing and computer techniques to explore new avenues in structural health monitoring (SHM). The consortium involves 5 research centres with complementary expertise: smart materials and structures, signal processing, identification, model updating, damage modelling, wave propagation. The project aims at addressing the following issues: using smart sensing technologies for SHM, extracting features relevant to damage and immune to variability, designing robust methods for autonomous SHM systems.

The key issues in CO 2 sequestration involve accurate monitoring, from the injection stage to the prediction and verification of CO 2 movement over time, for environmental considerations. '4-D seismics' is a natural non-intrusive monitoring technique which involves 3-D time-lapse seismic surveys. Successful monitoring of CO 2 movement requires a proper description of the physical properties of a porous reservoir. We investigate the importance of poroelasticity by contrasting poroelastic simulations with elastic and acoustic simulations. Discrepancies highlight a poroelastic signature that cannot be captured using an elastic or acoustic theory and that may play a role in accurately imaging and quantifying injected CO 2 . We focus on timelapse crosswell imaging and model updating based on Fréchet derivatives, or finite-frequency sensitivity kernels, which define the sensitivity of an observable to the model parameters. We compare results of time-lapse migration imaging using acoustic, elastic (with and without the use of Gassmann's formulae) and poroelastic models. Our approach highlights the influence of using different physical theories for interpreting seismic data, and, more importantly, for extracting the CO 2 signature from seismic waveforms. We further investigate the differences between imaging with the direct compressional wave, as is commonly done, versus using both direct compressional (P) and shear (S) waves. We conclude that, unlike direct P-wave traveltimes, a combination of direct P-and S-wave traveltimes constrains most parameters. Adding P-and S-wave amplitude information does not drastically improve parameter sensitivity, but it does improve spatial resolution of the injected CO 2 zone. The main advantage of using a poroelastic theory lies in direct sensitivity to fluid properties. Simulations are performed using a spectral-element method, and finite-frequency sensitivity kernels are calculated using an adjoint method.

Accurate models are necessary in critical applications. Key parameters in dynamic systems often change during their life cycle due to repair and replacement of parts or environmental changes. This dissertation presents a new approach to update system models, accounting for these changes. The approach uses frequency domain data and a neural network to produce estimates of the parameters being updated, yielding a model representative of the measured data. Current iterative methods developed to solve the model updating problem rely on minimization techniques to find the set of model parameters that yield the best match between experimental and analytical responses. Since the minimization procedure requires a fair amount of computation time, it makes the existing techniques infeasible for use as part of an adaptive control scheme correcting the model parameters as the system changes. They also require either mode shape expansion or model reduction before they can be applied, introducing errors in the procedure. Furthermore, none of the existing techniques has been applied to nonlinear systems. The neural network estimates the parameters being updated quickly and accurately without the need to measure all degrees of freedom of the system. This avoids the use of mode shape expansion or model reduction techniques, and allows for its implementation as part of an adaptive control scheme. The proposed technique is also capable of updating weakly nonlinear systems. Numerical simulations and experimental results show that the proposed method has good accuracy and generalization properties, and it is therefore, a suitable alternative for the solution of the model updating problem of this class of systems. My family is essential to me. Their unconditional love and support kept me going during the hard times. My parents' dedication, hard work, and perseverance made it possible to educate myself and my siblings. Thanks Mom, Dad, Márcio, and Miriam. One of my best decisions was to come to the U.S. and work with Dr. Inman. I learned many things from him during these years. I thank him for letting me try my own ideas, even when he did not agree with them. These were great years of my life, and they would not have been so without him.

Accurate models are necessary in critical applications. Key parameters in dynamic systems often change during their life cycle due to repair and replacement of parts or environmental changes. This dissertation presents a new approach to update system models, accounting for these changes. The approach uses frequency domain data and a neural network to produce estimates of the parameters being updated, yielding a model representative of the measured data. Current iterative methods developed to solve the model updating problem rely on minimization techniques to find the set of model parameters that yield the best match between experimental and analytical responses. Since the minimization procedure requires a fair amount of computation time, it makes the existing techniques infeasible for use as part of an adaptive control scheme correcting the model parameters as the system changes. They also require either mode shape expansion or model reduction before they can be applied, introducing errors in the procedure. Furthermore, none of the existing techniques has been applied to nonlinear systems. The neural network estimates the parameters being updated quickly and accurately without the need to measure all degrees of freedom of the system. This avoids the use of mode shape expansion or model reduction techniques, and allows for its implementation as part of an adaptive control scheme. The proposed technique is also capable of updating weakly nonlinear systems. Numerical simulations and experimental results show that the proposed method has good accuracy and generalization properties, and it is therefore, a suitable alternative for the solution of the model updating problem of this class of systems. My family is essential to me. Their unconditional love and support kept me going during the hard times. My parents' dedication, hard work, and perseverance made it possible to educate myself and my siblings. Thanks Mom, Dad, Márcio, and Miriam. One of my best decisions was to come to the U.S. and work with Dr. Inman. I learned many things from him during these years. I thank him for letting me try my own ideas, even when he did not agree with them. These were great years of my life, and they would not have been so without him.

A wide program of structural assessment has been carried out by Politecnico di Milano on the historic bell tower of the church Chiesa Collegiata in Arcisate (Varese, northern Italy). Within this context, the first part of the paper summarizes the results obtained from the application of the Stochastic Subspace Identification method to ambient response data collected in two dynamic tests, carried out in June 2007 and June 2008, respectively. Subsequently, the paper presents the vibration-based methodology developed for the calibration of a 3D Finite Element model of the tower, consisting in the successive application of systematic manual tuning, sensitivity analysis and simple system identification algorithm.

In this paper, a new model updating scheme is introduced to adjust the system matrices of a finite-element model by using experimental operating deflection shapes (ODS). An ODS is defined here as the response vector when the system is driven at a given degree of freedom with a unit force of fixed frequency. The proposed algorithm adjusts the numerical model in an iterative way. The matrix equilibrium equation is solved by first taking into account the frequency shift that appears between the non-updated finite element model and the experimental structure. In this way, numerical instabilities observed in state-of-the-art methods are avoided. We present results on two well-known numerical and experimental benchmark cases. They show the good convergence properties of the proposed approach.

The objective of this paper is to summarize the latest techniques, applications, methodologies, use of different software, and development of codes, in the field of Living Probabilistic Safety Assessment (LPSA) and Risk Monitoring (RM). The paper consists of three parts, in first part definitions, history, aims, uses of LPSA, and a new detailed model for LPSA have been explain. In second part brief introduction, worldwide use of RM, benefits and draw backs of software in RM, and in final part comparison between LPSA and RM have been discussed. The study also makes recommendations for further use and development of these two techniques in present and future NPPs.

To evaluate the cyclic behaviour under different loading conditions using the kinematic and isotropic hardening theory of steel a Chaboche visco-plastic material model is employed. The parameters of a constitutive model are usually identified by minimization of the distance between model response and experimental data. However, measurement errors and differences in the specimens lead to deviations in the determined parameters. In this article the Choboche model is used and a stochastic simulation technique is applied to generate artificial data which exhibit the same stochastic behaviour as experimental data. Then the model parameters are identified by applying a variaty of Bayes's theorem. Identified parameters are compared with the true parameters in the simulation and the efficiency of the identification method is discussed.

Two monuments in Portugal are being monitoring by the University of Minho: the Clock Tower of Mogadouro and the Church of Jerónimos Monastery, in Lisbon. Vibration sensors and temperature and relative air humidity sensors are installed in the two monuments. Operational modal analysis is being used to estimate the modal parameters, followed by statistical analysis to evaluate the environmental effects on the dynamic response. The aim is to explore damage assessment in masonry structures at an early stage by vibration signatures as a part of a heath monitoring process to preserve the historical constructions. The paper presents all the preceding dynamic analysis steps before the monitoring task, which includes the installation of the monitoring system, the system identification and subsequent FE model updating analysis, the automatic modal identification and the investigation of the influence of the environment on the identified modal parameters.

Accurate knowledge of the interplanetary Galactic Cosmic Ray (GCR) environment is critical to planning and operating manned space flight to the moon and beyond. In the early 1990s, Badhwar and OÕNeill developed a GCR model based on balloon and satellite data from 1954 to 1992. Since August 1997 the Advanced Composition Explorer (ACE) has provided significantly more accurate GCR energy spectra due to its much larger collection power.The original Badhwar-OÕNeill Model, updated with the new ACE data, should provide interplanetary mission planners with highly accurate GCR environment data for radiation protection for astronauts and radiation hardness assurance for electronic equipment.

Nowadays, many non-destructive damage detection methods for determining the location and severity of damage in the field of health monitoring are considered in order to reduce the cost of maintenance and improve safety and reliability of structure. In this paper, damage specification is obtained by sensitivity-based updating approach. By applying changes on sensitivity matrix and using measured flexibility data, it is concluded that the results of proposed method are more accurate and efficient than the old modal flexibility methods. The mass modeling error and measurement error of flexibility and natural frequency are calculated in order to ensure the accuracy and robustness of proposed method for 2-D finite element truss and frame model. Close index, measuring the performance of the method, and the coefficient of variation, which represents the distribution of response, are used. Compared with Wang method, the proposed method is capable of accurately localizing and quantifying damage in all scenarios.

A Bayesian methodology to construct probabilistic seismic demand models for the components of a structural system is developed. Existing deterministic models and observational data are used. The demand models are combined with previously developed capacity models for reinforced concrete (RC) bridge columns to estimate the seismic fragilities of bridge components and systems. The approach properly accounts for all relevant uncertainties, including model error. Application to two bridge examples typical of modern California practice is presented.

This research present a multi-direction bridge finite element model updating method based on the static and dynamic test. A fiber optics structural health monitoring system was installed on the bridge site and 73 fiber optic sensors captured the static and dynamic data in local-level. A portable accelerometer system was used to record the ambient loading test and 15 force-balanced accelerometers were placed along bridge center to record the bridge global behavior. The original model was built according to the construction draw. The bridge model was updating by using multi-level test data. A new multi-direction model updating approach was established to separate the model updating into several stages based on the member's direction. In each stage, the uni-direction members were updating in local-global level. This study found the multi-direction model updating can reduce the number of objective functions and variables in each stage and bridge model updating in the uni-direction has limited influence on the other directions. It is necessary to update steel girder bridge's finite element model in the multi-direction in order to ensure the model's accuracy.

A Bayesian methodology to construct probabilistic seismic demand models for the components of a structural system is developed. Existing deterministic models and observational data are used. The demand models are combined with previously developed capacity models for reinforced concrete (RC) bridge columns to estimate the seismic fragilities of bridge components and systems. The approach properly accounts for all relevant uncertainties, including model error. Application to two bridge examples typical of modern California practice is presented.

The problem for damage detection and localisation is approached employing two different techniques for fault localisation and quantification. A case study on a reinforced concrete beam with distributed damage of crack accumulation is studied. The measured frequency response functions are used as vibration data for the structure, on the basis of which the localisation procedures are performed. The considered localisation procedures are model based and use a FE model of the structure. One of the approaches suggests the use of a residual energy criterion for the fault localisation, followed by an updating procedure for an eventual quantification of the defect. The other procedure employs pattern recognition techniques for a primary localisation of the damage in a part of the structure, followed by an identification procedure for more accurate localisation and quantification. A comparison of the results and performance of both techniques is provided.

Modelling complex concrete column-beam connection with hybrid fibre reinforced plastic (FRP) reinforcement properly requires understanding of the behaviour of such component and supporting from some experimental data for model updating and refinement. This paper, through a comprehensive experimental work, investigates the behaviour of reinforced concrete frame specimens designed to represent the column-beam connections in plane frames. As a follow-up to the previous reported work, it focuses on details of experimental analyses, in particular, a comprehensive strain analysis. Results of the analysis show that designed hybrid FRP reinforcement greatly improve the stiffness and load carrying capacity of its concrete counterpart. It also delays the crack initiation at the joint through confinement due to FRP reinforcement.