Damage Detection of Structural Systems with Noisy Incomplete Input and Response Measurements

A probabilistic approach for damage detection is presented using noisy incomplete input and response measurements that is an extension of a Bayesian system identification approach developed by the authors. This situation may be encountered, for example, during low-level ambient vibrations when a structure is instrumented with accelerometers that measure the input ground motion and structural response at a few locations but the wind excitation is not measured. A substructuring approach is used for the parameterization of the mass and stiffness distributions. Damage is defined to be a reduction of the substructure stiffness parameters compared with those of the undamaged structure. By using the proposed probabilistic methodology, the probability of various damage levels in each substructure can be calculated based on the available data. A four-story benchmark building subjected to wind and ground shaking is considered in order to demonstrate the proposed approach

Structural Health Monitoring of a Reinforced Concrete Building during the Severe Typhoon Vicente in 2012

The goal of this study is to investigate the structural performance of reinforced concrete building under the influence of severe typhoon. For this purpose, full-scale monitoring of a 22-story reinforced concrete building was conducted during the entire passage process of a severe typhoon “Vicente.” Vicente was the eighth tropical storm developed in the Western North Pacific Ocean and the South China Sea in 2012. Moreover, it was the strongest and most devastating typhoon that struck Macao since 1999. The overall duration of the typhoon affected period that lasted more than 70 hours and the typhoon eye region covered Macao for around one hour. The wind and structural response measurements were acquired throughout the entire typhoon affected period. The wind characteristics were analyzed using the measured wind data including the wind speed and wind direction time histories. Besides, the structural response measurements of the monitored building were utilized for modal identification using the Bayesian spectral density approach. Detailed analysis of the field data and the typhoon generated effects on the structural performance are discussed

Unified probabilistic approach for model updating and damage detection

A probabilistic approach for model updating and damage detection of structural systems is presented using noisy incomplete input and incomplete response measurements. The situation of incomplete input measurements may be encountered, for example, during low-level ambient vibrations when a structure is instrumented with accelerometers that measure the input ground motion and the structural response at a few instrumented locations but where other excitations, e.g., due to wind, are not measured. The method is an extension of a Bayesian system identification approach developed by the authors. A substructuring approach is used for the parameterization of the mass, damping and stiffness distributions. Damage in a substructure is defined as stiffness reduction established through the observation of a reduction in the values of the various substructure stiffness parameters compared with their initial values corresponding to the undamaged structure. By using the proposed probabilistic methodology, the probability of various damage levels in each substructure can be calculated based on the available dynamic data. Examples using a single-degree-of-freedom oscillator and a 15-story building are considered to demonstrate the proposed approach

A non-iterative partitioned computational method with the energy conservation property for time-variant dynamic systems

A non-iterative partitioned computational method with the energy conservation property is proposed in this study for calculating a large class of time-variant dynamic systems comprising multiple subsystems. The velocity continuity conditions are first assumed in all interfaces of the partitioned subsystems to resolve the interface link forces. The Newmark integration scheme is subsequently employed to independently calculate the responses of each system based on the obtained link forces. The proposed method is thus divided into two computational modules: multi-partitioned structural analyzers and an interface solver, providing a modular solution for time-variant systems. The proposed method resolves the long-standing problem of iterative computation required in partitioned time-variant systems. More specifically, the proposed method eliminates the need for time-variant matrix formation and the utilization of complex iterative procedures in partitioned computations, which significantly improves computational efficiency. The derivation process and theoretical demonstration of the proposed method are thoroughly presented through a representative example, i.e., a vehicle-rail-sleeper-ballast time-variant system. The proposed method's accuracy, energy conservation property, and efficiency are systematically demonstrated in comparison with the results of the global model, highlighting its superior performance. A more general example provided in Appendix C demonstrates that the proposed method is not confined to the analysis of vehicle-rail-sleeper-ballast systems but applies to other structural dynamic systems

A review of point set registration: from pairwise registration to groupwise registration

Abstract: This paper presents a comprehensive literature review on point set registration. The state-of-the-art modeling methods and algorithms for point set registration are discussed and summarized. Special attention is paid to methods for pairwise registration and groupwise registration. Some of the most prominent representative methods are selected to conduct qualitative and quantitative experiments. From the experiments we have conducted on 2D and 3D data, CPD-GL pairwise registration algorithm [1] and JRMPC groupwise registration algorithm [2,3] seem to outperform their rivals both in accuracy and computational complexity. Furthermore, future research directions and avenues in the area are identified

Model Selection, Identification and Robust Control for Dynamical Systems

To fully exploit new technologies for response mitigation and structural health monitoring, improved system identification and controller design methodologies are desirable that explicitly treat all the inherent uncertainties. In this thesis, a probabilistic framework is presented for model selection, identification and robust control of smart structural systems under dynamical loads, such as those induced by wind or earthquakes. First, a probabilistic based approach is introduced for selecting the most plausible class of models for a dynamical system using its response measurements. The proposed approach allows for quantitatively comparing the plausibility of different classes of models among a specified set of classes. Then, two probabilistic identification techniques are presented. The first one is for modal identification using nonstationary response measurements and the second one is for updating nonlinear models using incomplete noisy measurements only. These methods allow for updating of the uncertainties associated with the values of the parameters controlling the dynamic behavior of the structure by using noisy response measurements only. The probabilistic framework is very well-suited for solving this nonunique problem and the updated probabilistic description of the system can be used to design a robust controller of the system. It can also be used for structural health monitoring. Finally, a reliability-based stochastic robust control approach is used to design the controller for an active control system. Feedback of the incomplete response at earlier time steps is used, without any state estimation. The optimal controller is chosen by minimizing the robust failure probability over a set of possible models for the system. Here, failure means excessive levels of one or more response quantities representative of the performance of the structure and the control devices. When calculating the robust failure probability, the plausibility of each model as a representation of the system's dynamic behavior is quantified by a probability distribution over the set of possible models; this distribution is initially based on engineering judgement, but it can be updated using the aforementioned system identification approaches if dynamic data become available from the structure. Examples are presented to illustrate the proposed controller design procedure, which includes the procedure of model selection, identification and robust control for smart structures.</p

Structural modal identification using ambient dynamic data

This thesis mainly focuses on two problems: The first one is to update the modal parameters of linear structures and their associated uncertainties by utilizing ambient dynamic response data following a Bayesian probabilistic framework. Another issue is how to select the optimal location of sensors. In the first part of this thesis (Chapter 2 - Chapter 4), the problem of identification of the modal parameters of linear structural models using measured ambient response time histories is addressed. Three Bayesian probabilistic frameworks for modal updating are introduced which allow one to obtain not only the optimal values of the updated modal parameters but also their associated uncertainties, calculated from their joint probability distribution. Each of these chapters is corresponding to a different approach. Calculation of the uncertainties of the identified modal parameters is very important if one plans to proceed with a subsequent step of updating the theoretical finite element model based on the modal estimates. These three new approaches will be referred to as Bayesian fast Fourier transform approach (BFFTA), Bayesian spectral density approach (BSDA) and Bayesian Time-domain approach (BTDA). It is found that the updated PDF can be well approximated by a Gaussian distribution centered at the optimal parameters at which the posterior PDF is maximized. Examples using simulated data are presented to illustrate the proposed methods. Another issue addressed in this thesis is for making decisions regarding the optimal location of sensors for modal/model identification. Uncertainties are quantified using probability distributions, and the Bayesian spectral density approach is utilized for deriving appropriate expressions for the updated probability density function (PDF) of the modal/model parameters based on measured ambient response time histories. The optimal sensor configuration is selected as the one that minimizes the information entropy which is a unique measure of the uncertainties in the modal/model parameters. The information entropy measure is also extended to handle large uncertainties expected in the pre-test nominal model of a structure. Genetic algorithms are well-suited for solving the resulting discrete optimization problem. In experimental design, the proposed information entropy can be used to design cost-effective modal/model experiments by comparing and evaluating the benefits from placing additional sensors on the structure in relation to the improvement in the quality of the modal/model parameters identification

Bayesian spectral density approach for modal updating using ambient data

The problem of identification of the modal parameters of a structural model using measured ambient response time histories is addressed. A Bayesian spectral density approach (BSDA) for modal updating is presented which uses the statistical properties of a spectral density estimator to obtain not only the optimal values of the updated modal parameters but also their associated uncertainties by calculating the posterior joint probability distribution of these parameters. Calculation of the uncertainties of the identified modal parameters is very important if one plans to proceed with the updating of a theoretical finite element model based on modal estimates. It is found that the updated PDF of the modal parameters can be well approximated by a Gaussian distribution centred at the optimal parameters at which the posterior PDF is maximized. Examples using simulated data are presented to illustrate the proposed method. Copyright (C) 2001 John Wiley & Sons, Ltd