Structural system identification in time domain using measured acceleration

he main advantage of a UAV over manned aircraft lies in remote control or autonomous flight. They are low cost and presents lower risk to the operator and environment. Remote control and fully autonomous flight are difficult to get used to for pilots since controlling aircraft without sensing aircraft states is very challenging. On the other hand autonomous control requires complex control and error handling algorithms to ensure that the vehicle is safely recovered in the event of a system failure. Moreover, UAVs are very appropriate to use for flight control system design studies due to their low cost and low risk of damage or harm. Therefore, an accurate mathematical model of UAV dynamic is necessary for pilot training and control system design. However, obtaining an accurate model of UAV is not easy. Aerodynamic modeling techniques such as using CFD or wind tunnel testing can be too costly for a UAV project. These models cannot accurately match the actual aircraft due to accumulated uncertainties and modeling simplifications. Therefore practical ways of identifying the flight dynamic properties of small fixed wing UAVs accurately are important. In this paper, an overview of the on-going research on bare airframe identification of a UAV at Turkish Aerospace Industries, Inc. (TAI) is presented. Two different approaches are studied to identify UAV dynamics using flight test data: time domain and frequency domain identifications. The flight test data used in this study are obtained from the remotely piloted flight of the UAV with autopilot disengaged. The frequency domain identification is based on the methods of Ref 1 using CIFER and time domain identification is obtained by using methods of Ref 2 and its software FVSysID.

This paper characterizes the extensive research activities conducted in the Earthquake Engineering Laboratory of University of Nevada, Reno, in the field of dynamic monitoring and system identification of three 1/3-scale two-span bridges. The first part of the study briefly presents the verification of target-tracking Digital Image Correlation (DIC) results as compared to conventional sensors, e.g. string potentiometers and triaxial accelerometers from one of the three bridge tests. Structural system identification is presented in the second part for the other two bridges with focus on determining structural model parameters based on the DIC measured response data. All bridges were tested under bidirectional earthquake loading using the multiple shake table array. However, the system identification used data collected from the white noise runs before and after the seismic tests. A quasi-linear response of the system was assumed because of the low intensity white noise base excitation, and the modal parameters were estimated accordingly. Using the structural vibration data recorded by target-tracking DIC at various locations on the bridges, five system identification methods were applied to analyze the modal parameters of the tested bridges. The results were used to estimate the frequency, damping ratio, and mode shapes, of the bridges in two states. The initial state is before seismic testing and the end state is the damaged state after the completion of the seismic tests. The results show that the applied methods provide a reasonable estimate of the natural frequency and damping ratio of the bridge systems in the original and damaged states.

Summary Identifiability of parameters in structural system identification (SSI) is of primary importance in any SSI method. It depends on the number and the location of the measurements, which is linked with sensor configuration. In this paper, under the framework of SSI by observability method (OM), the number of necessary measurements to identify all parameters of structural system was clarified first. Then, an example was solved step by step to show the lacking constraints among unknowns in SSI by OM. In a frame example, it was found that no measurement set having as many measurements as the number of unknowns was able to identify all parameters. To further understand this phenomenon, the observability of a simply supported beam was analyzed in an exhaustive way using 252 possible measurement sets. Three quarters of these sets were not able to identify all the parameters. In order to solve this issue, for the very first time, SSI by constrained observability method (COM), which appends the nonlinear constraints to SSI by OM, was proposed. With SSI by COM applied, the observability of the structural parameters with respect to the 252 sets was greatly improved. Finally, the efficacy of SSI by COM was verified by a 13-story frame building.

Risks to cultural heritage and related losses should be mitigated before disasters such as earthquakes happen. Risks can be addressed by various means, from raising cultural attention of authorities to documenting artistic or historical value of an object. The main contribution of structural engineering to cultural heritage concerns regular maintenance and monitoring for risk reduction. Risk mitigation of historical buildings, as a part of more general concept of conservation, involves different disciplines. Teams need to be multidisciplinary and information deriving from historical, metric, stylistic, structural, seismic, geotechnical and physical analysis may contribute to achievement of an overall comprehension of cultural assets. The synergic action of characterization and monitoring techniques are essential factors to understand, on one hand mechanisms and consequences of degradation and, on other hand, to provide reliable and well-grounded guidelines for definition of technical interventions to prevent/ to stop degradation phenomena, to restore functionality and use of historical building/ artifact, or to predict, mitigate and even control response to accidental events, including strong motions. In this field, an important role is played not only by analytical aspects, but also by development and validation of innovative materials and systems for conservation. International deontological guidelines on conservation of cultural heritage define structural rehabilitation of heritage structures as cure of a sick person, hence the heritage structures require anamnesis, diagnosis, therapy and controls, corresponding respectively to searches for significant data and information, individuation of causes of damage and decay, choice of remedial measures and control of efficiency of interventions. Moreover, same codes state that: the best therapy is preventive maintenance, which can only be achieved via monitoring of structure. In this thesis work a few topical issues of structural modelling, monitoring and assessment of historic masonry buildings were addressed, with particular emphasis on dynamic testing and identification. The possible connections with other disciplines are analysed and discussed throughout text. In this framework, outline of thesis includes an introductive first chapter in which context established by most recent codes and guidelines concerning architectural heritage conservation is duly reviewed and analysed. The importance of attaining a knowledge of structure is also discussed. The second chapter sets up scene, in which it introduces principal issues of seismic risk and safety assessment of architectural heritage. Firstly, a brief overview is given of seismic risk and of geological and geotechnical aspects as related to ancient heritage. Successively, viability of performance-based approaches, for application to seismic assessment of architectural heritage, is discussed also in light of a few recent proposals. In this context, fundamentals of structural health monitoring are also reported. Chapter 3 is intended to stress importance of modal testing as an effective tool for ancient structures characterisation, so it starts with a state-of-the-art on linear system identification methods with emphasis on output-only techniques. In particular, time domain and joint time-frequency domain identification techniques are introduced and deeply analysed. Model updating is then addressed and its connection with operational modal analysis is underlined. Finally, a few noteworthy examples of linear identification and model updating of architectural heritage structures are reported. Chapter 4 is about dynamic and seismic behaviour of domes. The coverage focuses on three ideal benchmarks on reconciling geometric survey with dynamic monitoring. The analyses concerned structures with oval shape domes, such as Sanctuary of Vicoforte, S. Caterina in Casale Monferrato and S. Agostino in L'Aquila. The final products are virtual models which were enabled to predict linear dynamic response under earthquake excitation. Chapter 5 inspects modelling strategies suited for masonry under intense seismic excitations. The state-of-the-art covers both models for equivalent static analysis and models which operates in dynamics. A model allowing for stiffness degradation, pinching and hysteresis is then proposed, whose formulation admits extensions to multiple degree of freedom systems. The proposal originates from well-known Bouc-Wen model. Chapter 6 deals with non-linear identification methods. In perspective, also non-linear identification is expected to become a powerful tool in context of structural and seismic reliability assessment, especially in light of increasing levels of knowledge and prediction capabilities which recent standards strive for. Unfortunately, non-linear identification is to date a specialized and challenging matter, and it has been seldom applied to full-scale structures. In this chapter, special emphasis is given to on-line implementations, with several numerical examples showing potential of non-linear as well as hysteretic system identification. The last chapter presents an experimental application of non-linear identification. A scaled model of a two-span masonry arch bridge has been artificially damaged and monitored at each damage step. A non-linear identification has been performed from shaker tests data. Results of experimental campaign will be used to corroborate a non-linear and hysteretic model of bridge endowed with prediction capabilities

Experimental modal analysis of a single-link flexible robotic manipulator with curved geometry using applied system identification methods

Estimating the governing equation parameter values is essential for integrating experimental data with scientific theory to understand, validate, and predict the dynamics of complex systems. In this work, we propose a new method for structural system identification (SI), uncertainty quantification, and validation directly from data. Inspired by generative modeling frameworks, a neural network maps random noise to physically meaningful parameters. These parameters are then used in the known equation of motion to obtain fake accelerations, which are compared to real training data via a mean square error loss. To simultaneously validate the learned parameters, we use independent validation datasets. The generated accelerations from these datasets are evaluated by a discriminator network, which determines whether the output is real or fake, and guides the parameter-generator network. The uncertainty in each parameter is quantified by fitting a Gaussian distribution to each parameter, then computing the 95% confidence bounds. Analytical and real experiments show the parameter estimation accuracy and model validation for different nonlinear structural systems.

Bayesian inference provides a powerful approach to system identification and damage assessment for structures. The application of Bayesian method is motivated by the fact that inverse problems in structural engineering, including structural health monitoring, are typically ill-conditioned and ill-posed when using noisy incomplete data because of various sources of modeling uncertainties. One should not just search for a single “optimal” value for the vector of model parameters but rather attempt to describe the whole family of plausible model parameters based on measured data using a Bayesian probabilistic framework. In this article, the fundamental principles of Bayesian analysis and computation are summarized; then a review is given of recent state-of-the-art practices of Bayesian inference in system identification and damage assessment for civil infrastructure. Discussions of the benefits and deficiencies of these approaches, as well as potentially useful avenues for future studies, are also provided. Our focus is on meeting challenges that arise from system identification and damage assessment for the civil infrastructure but our presented theories also have a considerably broader applicability for inverse problems in science and technology.

<div class="htmlview paragraph">Structural system identification was applied to small sample problems and to large automotive structures. The technique combines mass and stiffness matrices from a finite element model with experimental mode shapes and natural frequencies to produce improved mass and stiffness matrices. In numerical experiments on small problems the procedure enabled us to identify regions of original finite element models where changes of model parameters were required to bring finite element predictions into better agreement with exact results. Also, node point displacements were calculated for small structures subjected to time-dependent sinusoidal loads, and it was found that predictions of displacements from “improved” models were more accurate than original finite element model predictions when forcing frequencies were within the range of experimental frequencies used with structural system identification calculations. When applied to two large automotive structures. regions within the finite element models identified as candidates for parameter changes to, in one case, bring about a shift in predicted frequencies, and, in the other case, improve correlation with test results, were found to be consistent with our engineering judgement.</div>

In the system identification using finite element method (FEM), system responses of overall degree of freedoms (DOFs) are necessary. Because of the limitation of sensor and other experiment equipment, the responses of unspecified DOFs have to be contained in the design variables. This increase of the design variable makes difficult to solve the inverse problem. It is one of the solutions about the problem of limited responses that the responses of unspecified DOFs are represented by the responses of specified DOFs, using the condensation method. In the previous study, we applied iterative inverse perturbation method (IIPM) to enhance the efficiency and the solution convergence of the structural system identification problems using the condensation method. So we efficiently identify structural system through solving the optimization problem with design variables which have the same number of elements. However, if the size of problem developed to analyze practical model is increased, the number of design variables which had to be considered in solving process is extremely increased. To identify large structural system, optimization strategy to efficiently change design variables during the iteration of optimization is required. In this study, we suggest two optimization strategies which are adaptive sub-domain method and genetic concept method. Numerical examples are presented to verify the efficiency of the proposed methods and to compare with those methods.

Sparse and low-rank methods in structural system identification and monitoring

<p> The structural response of any structure is traditionally predicted by simplistic physics-based models. In these models the mechanical and geometrical properties are assumed as known. Nevertheless, this is not the case in most actual structures where the values of the actual properties (such as axial or flexural stiffness) are unknown due to uncertainties in the materials, in the construction methods or in the stress state. In some cases, a calibration of the computer models is required to improve de accuracy of the predicted structural response. To carry out this task Structural System Identification (SSI) methods can be used.</p><p> In this paper, a new parametric method for SSI from static excitation response is proposed. This method is based on the observability of the structure, understanding as such the problem of identifying which is the subset of characteristics of the structure, such as Young’s modulus, area, inertia, and/or products of them (axial or flexural stiffnesses) that may be uniquely determined when a subset of deflections and forces at nodes is provided. This method has lead to the development of a powerful mathematical tool to deal efficiently with polynomial systems of equations with a number of applications in many fields of science and engineering.</p><p> To illustrate the applicability of the proposed method, the analysis of the SSI of two structures of growing complexity is presented.</p>

Accurate finite element (FE) models are required in many applications of civil engineering. Non-structural elements (NSEs) often interfere with the main structure, altering its stiffness and modal signature. Neglecting such interaction in modelling, although a common design practice, may lead to unreliable predictions of future events and biased interpretations of in-field dynamic tests. In the current literature, the role of NSEs in vibration-based structural system identification (SSI) is well documented for NSEs working in parallel (P-NSEs) with the main structure (e.g. masonry infills in buildings, pavements or railings in bridges and footbridges) but is totally unexplored for NSEs working in series (S-NSEs) with the main structure (e.g. non-structural appendages such as chimneys, parapets, tanks, but also partitions and claddings in their out-of-plane modes). Presenting various numerical and experimental case studies, the present paper shows how in-series NSEs, through augmenting the modal model and by chance resonating with some structural mode, might significantly alter the dynamic behaviour of the main structure, and severely invalidate SSI if not properly accounted for while performing modelling and dynamic identification.

Structural system identification is a critical task in resilience assessments, especially following a natural hazard. In this paper, we propose PIDynNet, a novel physicsinformed approach that produces an ordinary differential equation-constrained neural network model for structural system identification. PIDynNet improves the estimation of structural parameters of nonlinear structural systems by embedding an auxiliary physicsbased loss term into the overall loss function as well as a supervised data-driven loss term. The proposed framework has the generalization capability to predict nonlinear structural response given unseen ground excitations. Two nonlinear numerical experiments are conducted to demonstrate the advantage of PIDynNet over other identification methods in problems with or without latent variables.

The structural responses and vibration properties such as frequencies and mode shapes are directly related to the structural mass and stiffness. Changing structural conditions will affect the structural responses and vibration properties. Structural conditions can thus be identified through measurements of structural vibration. However, certain uncertainties associated with structure models, changing ambient environmental conditions, changing loading conditions, equipment noises and various signal processing techniques used in extracting structural vibration properties, etc. is inevitable. The influences of these uncertainties on structural vibration properties might be more significant than that of the structural damage, therefore, cause false identification or result in the true structural damage not identifiable. This paper studies the applicability and reliability of a few popularly used modal identification methods including time-domain, frequency-domain and time-frequency domain methods for civil structural system identification. The results indicate that on average a 2% to 3% error tends to be yielded by using different signal processing techniques for vibration frequencies identification when the signal is not heavily noise contaminated, which should be taken into account when estimating the damage detection results. The applicability of the time-frequency domain methods to the identification of time-varying system either with sudden change or continuous variance is also discussed.

Errors of the third kind in engineering