Structural Identification and Damage Identification Using Output-Only Vibration Measurements

This dissertation studied the structural identification and damage detection of civil engineering structures. Several issues regarding structural health monitoring were addressed. The data-driven subspace identification algorithm was investigated for modal identification of bridges using output-only data. This algorithm was tested through a numerical truss bridge with abrupt damage as well as a real concrete highway bridge with actual measurements. Stabilization diagrams were used to analyze the identified results and determine the modal characteristics. The identification results showed that this identification method is quite effective and accurate. The influence of temperature fluctuation on the frequencies of a highway concrete bridge was investigated using ambient vibration data over a one-year period of a highway bridge under health monitoring. The data were fitted by nonlinear and linear regression models, which were then analyzed. The substructure identification by using an adaptive Kalman filter was investigated by applying numerical studies of a shear building, a frame structure, and a truss structure. The stiffness and damping were identified successfully from limited acceleration responses, while the abrupt damages were identified as well. Wavelet analysis was also proposed for damage detection of substructures, and was shown to be able to approximately locate such damages. Delamination detection of concrete slabs by modal identification from the output-only data was proposed and carried out through numerical studies and experimental modal testing. It was concluded that the changes in modal characteristics can indicate the presence and severity of delamination. Finite element models of concrete decks with different delamination sizes and locations were established and proven to be reasonable. Pounding identification can provide useful early warning information regarding the potential damage of structures. This thesis proposed to use wavelet scalograms of dynamic response to identify the occurrence of pounding. Its applications in a numerical example as well as shaking table tests of a bridge showed that the scalograms can detect the occurrence of pounding very well. These studies are very useful for vibration-based structural health monitoring

Evolutionary divide-and-conquer strategy for identification of structural systems and moving forces

Ph.DDOCTOR OF PHILOSOPH

Data-Driven and Model-Based Methods with Physics-Guided Machine Learning for Damage Identification

Structural health monitoring (SHM) has been widely used for structural damage diagnosis and prognosis of a wide range of civil, mechanical, and aerospace structures. SHM methods are generally divided into two categories: (1) model-based methods; (2) data-driven methods. Compared with data-driven SHM, model-based methods provide an updated physics-based numerical model that can be used for damage prognosis when long-term data is available. However, the performance of model-based methods is susceptible to modeling error in establishing the numerical model, which is usually unavoidable due to model simplification and omission. The major challenge of data-driven SHM methods lies in data insufficiency, e.g., lack of data covering as many as possible damage states, especially for large-scale structures. Hence, multi-site damage identification using data-driven methods can be more challenging as pattern recognition theoretically requires sufficient data from each damage scenario. The main objectives of this dissertation are to: (1) integrate model-based and data-driven SHM methods so that their shortcomings can be weakened while their respective merits can be preserved when implementing damage identification; (2) improve the accuracy of data-driven methods for multi-site damage identification with limited measured data. To achieve the first research objective, physics-guided machine learning (PGML) is proposed to improve the performance of pattern recognition in data-driven SHM with insufficient measured data. The results of model-based SHM (i.e., FE model updating) are taken as an implicit representation of physics underlying the monitored structure, which is incorporated into the learning process of a neural network model with the physics guidance introduced into the loss function. In addition to PGML, transfer learning (TL) is used to bridge the gap between the numerical and experimental domains of SHM. The distribution difference and manifold discrepancy between the two domains is minimized through TL as a means of domain adaptation. To improve the performance of multi-site damage identification in data-driven SHM, multi-label classification (MLC) and constrained independent component analysis(cICA) methods are applied to investigate the correlations between damage cases sharing common damaged sites. Finally, as a case study, a two-step strategy of identifying structural damage of offshore wind turbines via FE model updating is proposed

Artificial neural networks for vibration based inverse parametric identifications: A review

Vibration behavior of any solid structure reveals certain dynamic characteristics and property parameters of that structure. Inverse problems dealing with vibration response utilize the response signals to find out input factors and/or certain structural properties. Due to certain drawbacks of traditional solutions to inverse problems, ANNs have gained a major popularity in this field. This paper reviews some earlier researches where ANNs were applied to solve different vibration-based inverse parametric identification problems. The adoption of different ANN algorithms, input-output schemes and required signal processing were denoted in considerable detail. In addition, a number of issues have been reported, including the factors that affect ANNs’ prediction, as well as the advantage and disadvantage of ANN approaches with respect to general inverse methods Based on the critical analysis, suggestions to potential researchers have also been provided for future scopes

A uniformly sampled genetic algorithm with gradient search for system identification

Ph.DDOCTOR OF PHILOSOPH

Interval and Fuzzy Computing in Neural Network for System Identification Problems

Increase of population and growing of societal and commercial activities with limited land available in a modern city leads to construction up of tall/high-rise buildings. As such, it is important to investigate about the health of the structure after the occurrence of manmade or natural disasters such as earthquakes etc. A direct mathematical expression for parametric study or system identification of these structures is not always possible. Actually System Identification (SI) problems are inverse vibration problems consisting of coupled linear or non-linear differential equations that depend upon the physics of the system. It is also not always possible to get the solutions for these problems by classical methods. Few researchers have used different methods to solve the above mentioned problems. But difficulties are faced very often while finding solution to these problems because inverse problem generally gives non-unique parameter estimates. To overcome these difficulties alternate soft computing techniques such as Artificial Neural Networks (ANNs) are being used by various researchers to handle the above SI problems. It is worth mentioning that traditional neural network methods have inherent advantage because it can model the experimental data (input and output) where good mathematical model is not available. Moreover, inverse problems have been solved by other researchers for deterministic cases only. But while performing experiments it is always not possible to get the data exactly in crisp form. There may be some errors that are due to involvement of human or experiment. Accordingly, those data may actually be in uncertain form and corresponding methodologies need to be developed. It is an important issue about dealing with variables, parameters or data with uncertain value. There are three classes of uncertain models, which are probabilistic, fuzzy and interval. Recently, fuzzy theory and interval analysis are becoming powerful tools for many applications in recent decades. It is known that interval and fuzzy computations are themselves very complex to handle. Having these in mind one has to develop efficient computational models and algorithms very carefully to handle these uncertain problems. As said above, in general we may not obtain the corresponding input and output values (experimental) exactly or in crisp form but we may have only uncertain information of the data. Hence, investigations are needed to handle the SI problems where data is available in uncertain form. Identification methods with crisp (exact) data are known and traditional neural network methods have already been used by various researchers. But when the data are in uncertain form then traditional ANN may not be applied. Accordingly, new ANN models need to be developed which may solve the targeted uncertain SI problems. Hence present investigation targets to develop powerful methods of neural network based on interval and fuzzy theory for the analysis and simulation with respect to the uncertain system identification problems. In this thesis, these uncertain data are assumed as interval and fuzzy numbers. Accordingly, identification methodologies are developed for multistorey shear buildings by proposing new models of Interval Neural Network (INN) and Fuzzy Neural Network (FNN) models which can handle interval and fuzzified data respectively. It may however be noted that the developed methodology not only be important for the mentioned problems but those may very well be used in other application problems too. Few SI problems have been solved in the present thesis using INN and FNN model which are briefly described below. From initial design parameters (namely stiffness and mass in terms of interval and fuzzy) corresponding design frequencies may be obtained for a given structural problem viz. for a multistorey shear structure. The uncertain (interval/fuzzy) frequencies may then be used to estimate the present structural parameter values by the proposed INN and FNN. Next, the identification has been done using vibration response of the structure subject to ambient vibration with interval/fuzzy initial conditions. Forced vibration with horizontal displacement in interval/fuzzified form has also been used to investigate the identification problem. Moreover this study involves SI problems of structures (viz. shear buildings) with respect to earthquake data in order to know the health of a structure. It is well known that earthquake data are both positive and negative. The Interval Neural Network and Fuzzy Neural Network model may not handle the data with negative sign due to the complexity in interval and fuzzy computation. As regards, a novel transformation method have been developed to compute response of a structural system by training the model for Indian earthquakes at Chamoli and Uttarkashi using uncertain (interval/fuzzified) ground motion data. The simulation may give an idea about the safety of the structural system in case of future earthquakes. Further a single layer interval and fuzzy neural network based strategy has been proposed for simultaneous identification of the mass, stiffness and damping of uncertain multi-storey shear buildings using series/cluster of neural networks. It is known that training in MNN and also in INN and FNN are time consuming because these models depend upon the number of nodes in the hidden layer and convergence of the weights during training. As such, single layer Functional Link Neural Network (FLNN) with multi-input and multi-output model has also been proposed to solve the system identification problems for the first time. It is worth mentioning that, single input single output FLNN had been proposed by previous authors. In FLNN, the hidden layer is replaced by a functional expansion block for enhancement of the input patterns using orthogonal polynomials such as Chebyshev, Legendre and Hermite, etc. The computations become more efficient than the traditional or classical multi-layer neural network due to the absence of hidden layer. FLNN has also been used for structural response prediction of multistorey shear buildings subject to earthquake ground motion. It is seen that FLNN can very well predict the structural response of different floors of multi-storey shear building subject to earthquake data. Comparison of results among Multi layer Neural Network (MNN), Chebyshev Neural Network (ChNN), Legendre Neural Network (LeNN), Hermite Neural Network (HNN) and desired are considered and it is found that Functional Link Neural Network models are more effective and takes less computation time than MNN. In order to show the reliability, efficacy and powerfulness of INN, FNN and FLNN models variety of problems have been solved here. Finally FLNN is also extended to interval based FLNN which is again proposed for the first time to the best of our knowledge. This model is implemented to estimate the uncertain stiffness parameters of a multi-storey shear building. The parameters are identified here using uncertain response of the structure subject to ambient and forced vibration with interval initial condition and horizontal displacement also in interval form

Damage identification in bridge structures : review of available methods and case studies

Bridges are integral parts of the infrastructure and play a major role in civil engineering. Bridge health monitoring is necessary to extend the life of a bridge and retain safety. Periodic monitoring contributes significantly in keeping these structures operational and extends structural integrity. Different researchers have proposed different methods for identifying bridge damages based on different theories and laboratory tests. Several review papers have been published in the literature on the identification of damage and crack in bridge structures in the last few decades. In this paper, a review of literature on damage identification in bridge structures based on different methods and theories is carried out. The aim of this paper is to critically evaluate different methods that have been proposed to detect damages in different bridges. Different papers have been carefully reviewed, and the gaps, limitations, and superiority of the methods used are identified. Furthermore, in most of the reviews, future applications and several sustainable methods which are necessary for bridge monitoring are covered. This study significantly contributes to the literature by critically examining different methods, giving guidelines on the methods that identify the damages in bridge structures more accurately, and serving as a good reference for other researchers and future works