Andrew W. Smyth
Columbia University
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Featured researches published by Andrew W. Smyth.
International Journal of Non-linear Mechanics | 2002
Andrew W. Smyth; Sami F. Masri; Elias B. Kosmatopoulos; A. G. Chassiakos; T. K. Caughey
Abstract Adaptive estimation procedures have gained significant attention by the research community to perform real-time identification of non-linear hysteretic structural systems under arbitrary dynamic excitations. Such techniques promise to provide real-time, robust tracking of system response as well as the ability to track time variation within the system being modeled. An overview of some of the authors’ previous work in this area is presented, along with a discussion of some of the emerging issues being tackled with regard to this class of problems. The trade-offs between parametric-based modeling and non-parametric modeling of non-linear hysteretic dynamic system behavior are discussed. Particular attention is given to (1) the effects of over- and under-parameterization on parameter convergence and system output tracking performance, (2) identifiability in multi-degree-of-freedom structural systems, (3) trade-offs in setting user-defined parameters for adaptive laws, and (4) the effects of noise on measurement integration. Both simulation and experimental results indicating the performance of the parametric and non-parametric methods are presented and their implications are discussed in the context of adaptive structures and structural health monitoring.
Earthquake Spectra | 2004
Andrew W. Smyth; George Deodatis; Guillermo Franco; Howard Kunreuther; Hilmi Luş; Esra Mete; Nano Seeber
In the wake of the 1999 earthquake destruction in Turkey, the urgent need has arisen to evaluate the benefits of loss mitigation measures that could be undertaken to strengthen the existing housing stock. In this study, a benefit-cost analysis methodology is introduced for the comparative evaluation of several seismic retrofitting measures applied to a representative apartment building located in Istanbul. The analysis is performed probabilistically through the development of fragility curves of the structure in its different retrofitted configurations. By incorporating the probabilistic seismic hazard for the region, expected direct losses can be estimated for arbitrary time horizons. By establishing realistic cost estimates of the retrofitting schemes and costs of direct losses, one can then estimate the net present value of the various retrofitting measures. The analysis in this work implies that, even when considering only direct losses, all of the retrofitting measures considered are desirable for all but the very shortest time horizons. This conclusion is valid for a wide range of estimates regarding costs of mitigation, discount rates, number of fatalities, and cost of human life. The general methodology developed here for a single building can be extended to an entire region by incorporating additional structural types, soil types, retrofitting measures, more precise space- and time-dependent seismic hazard estimates, etc. It is hoped that this work can serve as a benchmark for more realistic and systematic benefit-cost analyses for earthquake damage mitigation.
International Journal of Non-linear Mechanics | 2004
Sami F. Masri; John P. Caffrey; T. K. Caughey; Andrew W. Smyth; A. G. Chassiakos
Abstract Building on the basic idea behind the Restoring Force Method for the non-parametric identification of non-linear systems, a general procedure is presented for the direct identification of the state equation of complex non-linear systems. No information about the system mass is required, and only the applied excitation(s) and resulting acceleration are needed to implement the procedure. Arbitrary non-linear phenomena spanning the range from polynomial non-linearities to the noisy Duffing–van der Pol oscillator (involving product-type non-linearities and multiple excitations) or hysteretic behavior such as the Bouc–Wen model can be handled without difficulty. In the case of polynomial-type non-linearities, the approach yields virtually exact results for sufficiently rich excitations. For other types of non-linearities, the approach yields the optimum (in least-squares sense) representation in non-parametric form of the dominant interaction forces induced by the motion of the system. Several examples involving synthetic data corresponding to a variety of highly non-linear phenomena are presented to demonstrate the utility as well as the range of validity of the proposed approach.
Journal of Applied Mechanics | 1998
A. G. Chassiakos; Sami F. Masri; Andrew W. Smyth; T. K. Caughey
Using adaptive estimation approaches, a method is presented for the on-line identification of hysteretic systems under arbitrary dynamic environments The availability of such an identification approach is crucial for the on-line control and monitoring of nonlinear structural systems to be actively controlled. In spite of the challenges encountered in the identification of the hereditary nature of the restoring force of such nonlinear systems, it is shown through the use of simulation studies and experimental measurements that the proposed approach can yield reliable estimates of the hysteretic restoring force under a very wide range of excitation levels and response ranges.
Journal of Applied Mechanics | 2001
Elias B. Kosmatopoulos; Andrew W. Smyth; Sami F. Masri; A. G. Chassiakos
The availability of methods for on-line estimation and identification of structures is crucial for the monitoring and active control of time-varying nonlinear structural systems. Adaptive estimation approaches that have recently appeared in the literature for on-line estimation and identification of hysteretic systems under arbitrary dynamic environments are in general model based. In these approaches, it is assumed that the unknown restoring forces are modeled by nonlinear differential equations (which can represent general non-linear characteristics, including hysteretic phenomena). The adaptive methods estimate the parameters of the nonlinear differential equations on line. Adaptation of the parameters is done by comparing the prediction of the assumed model to the response measurement, and using the prediction error to change the system parameters. In this paper, a new methodology is presented which is not model based. The new approach solves the problem of estimating/identifying the restoring forces without assuming any model of the restoring forces dynamics, and without postulating any structure on the form of the underlying nonlinear dynamics. The new approach uses the Volterra/Wiener neural networks (VWNN) which are capable of learning input/output nonlinear dynamics, in combination with adaptive filtering and estimation techniques. Simulations and experimental results from a steel structure and from a reinforced-concrete structure illustrate the power and efficiency of the proposed method.
Journal of Engineering Mechanics-asce | 2012
M. N. Chatzis; Andrew W. Smyth
The rocking motion of a solid block on a moving deformable base is a dynamic problem that, despite its apparent simplicity, involves a number of complex dynamic phenomena such as impacts, sliding, geometric and material nonlinearities and, under some circumstances, chaotic behavior. For this reason, since the first model proposed by G.W. Housner in 1963, a number of alternative models have been proposed for its mathematical simulation. In this work, two new models are developed for the simulation of a rigid body experiencing a 2D rocking motion on a moving deformable base. The first model, the concentrated springs model, simulates the ground as tensionless vertical springs with vertical dampers placed at each of the two bottom corners of the body, whereas the second, the Winkler model, simulates the ground as a continuous medium of tensionless vertical springs with vertical dampers. Both models take into consideration sliding (with the use of both a penalty method and an analytical formulation for frictio...
Journal of Vibration and Control | 2017
Saeed Eftekhar Azam; Eleni Chatzi; Costas Papadimitriou; Andrew W. Smyth
In this study, a novel dual implementation of the Kalman filter proposed by Eftekhar Azam et al. (2014, 2015) is experimentally validated for simultaneous estimation of the states and input of structural systems. By means of numerical simulations, it has been shown that the proposed method outperforms existing techniques in terms of robustness and accuracy for the estimated displacement and velocity time histories. Herein, dynamic response measurements, in the form of displacement and acceleration time histories from a small-scale laboratory building structure excited at the base by a shake table, are considered for evaluating the performance of the proposed Dual Kalman filter and in order to compare this with available alternatives, such as the augmented Kalman filter (Lourens et al., 2012b) and the Gillijn De Moore filter (GDF) (2007b). The suggested Bayesian approach requires the availability of a physical model of the system in addition to output-only measurements from limited degrees of freedom. Two categories of such physical models are herein studied to evaluate the effect of model error on the filter performances; the first, is a model that comprises identified modal parameters, i.e., natural frequencies, mode shapes, modal damping ratios and modal participation factors; the second, is a model that is extracted from a recently developed subspace identification procedure, namely the transformed stochastic subspace identification method. The results are encouraging for the further use of the dual Kalman filter and its available alternatives for addressing the important problems of full response reconstruction and fatigue estimation in the entire body of linear structures, using a limited number of output-only vibration measurements.
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering | 2016
Thaleia Kontoroupi; Andrew W. Smyth
AbstractThe quality of structural parameter identification in nonlinear systems using Bayesian estimators, such as the unscented Kalman filter (UKF), depends heavily on the assumptions about the state and observation noise processes. In most practical situations though, the noise statistics are not known a priori. While the literature is rich in the area of offline approaches to noise estimation (often as part of model updating in general; the focus is not necessarily on noise parameters), there seems to be shortage of online implementations, which would be useful in structural health monitoring. Assuming that both noises (in the state and observation equations) are additive Gaussian processes, this study investigates how their statistics could be adaptively estimated online during the identification. By introducing certain distributional assumptions for the unknown noise parameters which exploit conjugacy, noise updating is simplified and is suitable for online applications. The proposed method is valida...
Advances in Science and Technology | 2008
Satish Nagarajaiah; Shirley J. Dyke; Jerome P. Lynch; Andrew W. Smyth; Anil K. Agrawal; Michael D. Symans; Erik A. Johnson
Structural Health Monitoring (SHM) is an important and growing field in civil engineering. The goal of SHM techniques is to identify, quantify and locate damage in structures. In light of the aging infrastructure and recent failures of important bridges, long-term monitoring techniques are being increasing investigated and adopted. In addition to SHM, structural control (SC) is increasingly adopted in modern structures around the world. In the past two decades a number of SC techniques, including, passive, semi-active, and active control methods have been developed and adopted in civil engineering–particularly, in infrastructure such as important tall buildings, critical facilities, and long span bridges. Both SHM and SC technology face significant challenges due to the size and scale of civil engineering structures. In response of these challenges researchers in the U.S.A and around the world have developed new and innovative techniques.This paper summarizes some of the ongoing research in the U.S.A. in the area of monitoring, damage detection and control in civil engineering structures.
international symposium on neural networks | 2007
Jin-Song Pei; Eric Mai; Joseph P. Wright; Andrew W. Smyth
A prototype-based initialization methodology is proposed to approximate functions that are used to characterize nonlinear stress-strain, moment-curvature, and load-displacement relationships, as well as restoring forces and time histories in engineering mechanics applications. Three prototypes are defined by exploiting the capabilities of linear sums of sigmoidal functions. By using the proposed prototypes either individually or combinatorially, successful training can take place for ten specific types of nonlinear functions and far beyond when the required number of hidden nodes and initial values of weights and biases can always be derived before the training starts. Some mathematical insights to this initialization methodology and a few prototypes are offered, while training examples are provided to demonstrate a clear procedure that is used to implement this methodology. With the derived numbers of hidden nodes in each approximation, applying the Nguyen-Widrow algorithm is enabled and the training performance is compared between the existing and the proposed initialization options.