The use of heterogeneous, non-collocated measurements for nonlinear structural system identification is explored herein. In particular, this paper considers the example of sensor heterogeneity arising from the fact that both acceleration and displacement are measured at various locations of the structural system. The availability of non-collocated data might often arise in the identification of systems where the displacement data may be provided through global positioning systems (GPS). The well-known extended Kalman filter (EKF) is often used to deal with nonlinear system identification. However, as suggested in (J. Eng. Mech. 1999; 125(2):133-142), the EKF is not effective in the case of highly nonlinear problems. Instead, two techniques are examined herein, the unscented Kalman filter method (UKF), proposed by Julier and Uhlman, and the particle filter method, also known as sequential Monte Carlo method (SMC). The two methods are compared and their efficiency is evaluated through the example of a three degree-offreedom system, involving a Bouc-Wen hysteretic component, where the availability of displacement and acceleration measurements for different DOFs is assumed. displacement response measurements is essential for the effective monitoring of structural response and the determination of the parameters governing it. Displacement and/or strain information in particular is of great importance when it comes to permanent deformations.The availability of acceleration data is usually ensured since this is what is commonly measured. However, most nonlinear models are functions of displacement and velocity and hence the convenience of acquiring access to those signals becomes evident. In practice, velocities and displacements can be acquired by integrating the accelerations although the latter technique presents some drawbacks. The recent advances in technology have provided us with new methods of obtaining accurate position information, through global position system receivers for instance. In this paper the potential of exploiting combined displacement and acceleration information for different degrees of freedom of a structure (non-collocated, heterogeneous measurements) is explored. In addition, the influence of displacement data availability is investigated in Section 5.3.The nonlinearity of the problem (both in the dynamics and in the measurement equations as will be shown) requires the use of sophisticated computational tools. Many techniques have been proposed for nonlinear applications in Civil Engineering, including the least squares estimation (LSE) [1,2], the extended Kalman filter (EKF) [3][4][5], the unscented Kalman filter (UKF) [6,7] and the sequential Monte Carlo methods (particle filters, PF) [8][9][10][11]. The adaptive LSE schemes depend on measured data from the structural system response. Since velocity and displacement are not often readily available, for their implementation these signals have to be obtained by integration and/or differentiation schemes. As mentioned above, this poses difficu...

Summary Damage identification forms a key objective in structural health monitoring. Several state‐of‐the‐art review papers regarding progress in this field up to 2011 have been published. This paper summarizes the recent progress between 2011 and 2017 in the area of damage identification methods for bridge structures. This paper is organized based on the classification of bridge infrastructure in terms of fundamental structural systems, namely, beam bridges, truss bridges, arch bridges, cable‐stayed bridges, and suspension bridges. The overview includes theoretical developments, enhanced simulation attempts, laboratory‐scale implementations, full‐scale validation, and the summary for each type of bridges. Based on the offered review, some challenges, suggestions, and future trends in damage identification are proposed. The work can be served as a basis for both academics and practitioners, who seek to implement damage identification methods in next‐generation structural health monitoring systems.

SUMMARY The question of a priori observability of a dynamic system, that is, whether the states of a system can be identified given a particular set of measured quantities is of utmost importance in multiple disciplines including biology, economics, and engineering. More often than not, some of the parameters of the system need to be identified, and thus the issue of identifiability, that is, whether the measurements result in unique or finite solutions for the values of the parameters, is of interest. Identifiability arises in conjunction with the question of observability, when the notion of states may be augmented to include both the actual state variables of the dynamic system and its parameters. This results in the formulation of a nonlinear augmented system even though the dynamic equations of motion of the original system might be linear. In this work, three methods for the observability and identifiability of nonlinear dynamic systems are considered. More specifically, for a system whose state and measurement equations are analytic, the geometric Observability Rank Condition, which is based on Lie derivatives may be used. If the equations are rational, algebraic methods are also available. These include the algebraic observability methods and the algebraic identifiability algorithms which determine the finiteness or uniqueness of the solutions for the parameters. The aforementioned methods are used to study the observability and identifiability of suitable problems in civil engineering and highlight the connections between them and the corresponding concepts. Copyright © 2014 John Wiley & Sons, Ltd.

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The application of polynomial chaos expansions (PCEs) to the propagation of uncertainties in stochastic dynamical models is well-known to face challenging issues. The accuracy of PCEs degenerates quickly in time. Thus maintaining a sufficient level of long term accuracy requires the use of high-order polynomials. In numerous cases, it is even infeasible to obtain accurate metamodels with regular PCEs due to the fact that PCEs cannot represent the dynamics. To overcome the problem, an original numerical approach was recently proposed that combines PCEs and non-linear autoregressive with exogenous input (NARX) models, which are a universal tool in the field of system identification. The approach relies on using NARX models to mimic the dynamical behaviour of the system and dealing with the uncertainties using PCEs.The PC-NARX model was built by means of heuristic genetic algorithms. This paper aims at introducing the least angle regression (LAR) technique for computing PC-NARX models, which consists in solving two linear regression problems. The proposed approach is validated with structural mechanics case studies, in which uncertainties arising from both structures and excitations are taken into account. Comparison with Monte Carlo simulation and regular PCEs is also carried out to demonstrate the effectiveness of the proposed approach.Keywords: surrogate models -polynomial chaos expansions -nonlinear autoregressive with exogenous input (NARX) models -Monte Carlo simulation -dynamical systems 1 arXiv:1604.07627v1 [stat.ME] 26 Apr 2016Modern engineering and applied sciences have greatly benefited from the rapid increase of available computational power. Indeed, computational models allow for an accurate representation of complex physical phenomena, e.g. fluid and structural dynamics. Physical systems are in nature prone to aleatory uncertainties, such as the natural variability of system properties, excitations and boundary conditions. In order to obtain reliable predictions from numerical simulations, it is of utmost importance to take into account these uncertainties. In this context, uncertainty quantification has gained particular interest in the last decade. The propagation of uncertainties from the input to the output of the model is commonly associated with sampling-based methods, e.g. Monte Carlo simulation, which are robust, however not suitable when only a small number of simulations is affordable or available. Metamodelling techniques can be employed to circumvent this issue. Polynomial chaos expansions (PCEs) (Ghanem and Spanos, 2003;Soize and Ghanem, 2004) are a powerful metamodelling technique that is widely used in numerous disciplines. However, it is well-known that PCEs exhibit difficulties when being applied to stochastic dynamical models (Wan and Karniadakis, 2006a;Gerritsma et al., 2010).Many efforts have been focused on solving this problem. Wan and Karniadakis (2006a,b) proposed multi-element PCEs, which rely on the decomposition of the random input space into sub-domains and the local comp...

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This algorithm is designed for non-destructive assessment of structural components. Trial flaws are modeled using the XFEM as the forward problem and genetic algorithms (GAs) are employed as the optimization method to converge to the true flaw location and size. The main advantage of the approach is that XFEM alleviates the need for re-meshing the domain at every new iteration of the inverse solution process and GAs have proven to be robust and efficient optimization techniques in particular for this type of problems.In this paper the XFEM-GA methodology is applied to elastostatic problems where flaws are considered as straight cracks, circular holes and non-regular-shaped holes. Measurements are obtained from strain sensors that are attached to the surface of the structure at specific locations and provide the target solution to the GA. The results show convergence robustness and accuracy provided that a sufficient number of sensors are employed and sufficiently large flaws are considered.