• Smart Structures and Systems
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• 제22권5호
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• pp.631-641
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• 2018

This paper outlines a computational procedure for the effective merging of diverse sensor measurements, displacement and acceleration signals in particular, in order to successfully monitor and simulate the current health condition of civil structures under dynamic loadings. In particular, it investigates a Kalman Filter implementation for the Heterogeneous Data Fusion of displacement and acceleration response signals of a structural system toward dynamic identification purposes. The procedure is perspectively aimed at enhancing extensive remote displacement measurements (commonly affected by high noise), by possibly integrating them with a few standard acceleration measurements (considered instead as noise-free or corrupted by slight noise only). Within the data fusion analysis, a Kalman Filter algorithm is implemented and its effectiveness in improving noise-corrupted displacement measurements is investigated. The performance of the filter is assessed based on the RMS error between the original (noise-free, numerically-determined) displacement signal and the Kalman Filter displacement estimate, and on the structural modal parameters (natural frequencies) that can be extracted from displacement signals, refined through the combined use of displacement and acceleration recordings, through inverse analysis algorithms for output-only modal dynamics identification, based on displacements.

• Smart Structures and Systems
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• 제4권3호
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• pp.319-342
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• 2008

Identification of the nonlinear hysteretic behavior of a reinforced concrete (RC) bridge pier subjected to earthquake loads is carried out based on acceleration measurements of the earthquake motion and bridge responses. The modified Takeda model is used to describe the hysteretic behavior of the RC pier with a small number of parameters, in which the nonlinear behavior is described in logical forms rather than analytical expressions. Hence, the modified extended Kalman filter is employed to construct the state transition matrix using a finite difference scheme. The sequential modified extended Kalman filter algorithm is proposed to identify the unknown parameters and the state vector separately in two steps, so that the size of the problem for each identification procedure may be reduced and possible numerical problems may be avoided. Mode superposition with a modal sorting technique is also proposed to reduce the size of the identification problem for the nonlinear dynamic system with multi-degrees of freedom. Example analysis is carried out for a continuous bridge with a RC pier subjected to earthquake loads in the longitudinal and transverse directions.

• Structural Engineering and Mechanics
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• 제56권4호
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• pp.625-648
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• 2015

Modern super-tall buildings are more sensitive to strong winds. The evaluation of wind loads for the design of these buildings is of primary importance. A direct monitoring of wind forces acting on super-tall structures is quite difficult to be realized. Indirect measurements interpreted by inverse techniques are therefore favourable since dynamic response measurements are easier to be carried out. To this end, a Kalman filtering based inverse approach is developed in this study so as to estimate the wind loads on super-tall buildings based on limited structural responses. The optimum solution of Kalman filter gain by solving the Riccati equation is used to update the identification accuracy of external loads. The feasibility of the developed estimation method is investigated through the wind tunnel test of a typical super-tall building by using a Synchronous Multi-Pressure Scanning System. The effects of crucial factors such as the type of wind-induced response, the covariance matrix of noise, errors of structural modal parameters and levels of noise involved in the measurements on the wind load estimations are examined through detailed parametric study. The effects of the number of vibration modes on the identification quality are studied and discussed in detail. The made observations indicate that the proposed inverse approach is an effective tool for predicting the wind loads on super-tall buildings.

• Structural Engineering and Mechanics
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• 제64권4호
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• pp.449-459
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• 2017

High-rise buildings are generally sensitive to strong winds. The evaluation of wind loads for the structural design, structural health monitoring (SHM), and vibration control of high-rise buildings is of primary importance. Nevertheless, it is difficult or even infeasible to measure the wind loads on an existing building directly. In this regard, a new inverse method for evaluating wind loads on high-rise buildings is developed in this study based on a discrete-time Kalman filter. The unknown structural responses are identified in conjunction with the wind loads on the basis of limited structural response measurements. The algorithm is applicable for estimating wind loads using different types of wind-induced response. The performance of the method is comprehensively investigated based on wind tunnel testing results of two high-rise buildings with typical external shapes. The stability of the proposed algorithm is evaluated. Furthermore, the effects of crucial factors such as cross-section shapes of building, the wind-induced response type, errors of structural modal parameters, covariance matrix of noise, noise levels in the response measurements and number of vibration modes on the identification accuracy are examined through a detailed parametric study. The research outputs of the proposed study will provide valuable information to enhance our understanding of the effects of wind on high-rise buildings and improve codes of practice.