Global assessment of structural conditions is important for structural health monitoring system. In particular, online or almost online structural parametric identification, based on vibration data measured from sensors, has received considerable attention recently. However, the problem becomes more challenging when the structure is complex and the number of degree-of-freedom (DOF) is large. A newly proposed time domain analysis methodology, referred to as the sequential nonlinear LSE (SNLSE) approach, has been studied and shown to be useful for the online tracking of parameters for structures with small DOFs. In this paper, the SNLSE approach will be applied for global assessment of an experimental cable-stay bridge model with large DOFs. A dynamic equivalent model of the bridge will be established and finite element analysis will be carried out to formulate the equation of motion. Numerical analysis will be conducted with different simulated damage scenarios and limited number of response data is considered. The capability of the proposed SNLSE approach in identifying the structural parameters and assessing the structural conditions will be verified.
This paper aims to evaluate the effectiveness of MR damper for vibration mitigation of stay cable under complex wind excitations. The MR damper, RD-1005-03, provided by Lord Company was used, a semi-active control algorithm based on the universal design curve for linear dampers and the bilinear mechanical model of the MR damper was developed, and simulation study was carried out for the cable-MR damper system. Firstly, fluctuating wind field was generated using the method of weighted amplitude wave superposition (WAWS) and Kaimal spectrum and the time-history sample curve of turbulent wind speed of stay cable was obtained. Then the dynamic response of the cable-MR damper system was computed with the proposed semi-active control algorithm applied for mitigating the vibration of stay cable. Finally, the effectiveness of MR damper for controlling cable vibration was assessed by comparing the dynamic responses of stay cable before and after damper installation.
KEYWORDS: Sensors, Error analysis, Matrices, Signal to noise ratio, Structural health monitoring, Earthquakes, Damage detection, Finite element methods, Interference (communication), Complex systems
The detection of structural damages, either on-line or almost on-line, based on vibration data measured from sensors,
is essential for the structural health monitoring system. The problem is quite challenging, in particular when the external
excitations are not completely measured and when the structural system is complex. In practical applications, external
excitations (inputs), such as seismic excitations, wind loads, traffic loads, etc., may not be measured or may not be
measurable, and the structure may not always be shear-beam type which can be easily represented as spring-mass
system. In this paper, a newly proposed damage detection method, referred to as the adaptive quadratic sum-squares
error with unknown inputs (AQSSE-UI), is used for the detection of structural damages of a plane steel truss with finite
element model. In this approach, external excitations and some structural responses may not be measured. Analytical
recursive solution for the proposed AQSSE-UI method will be presented. The accuracy and effectiveness of the
proposed approach will be demonstrated by numerical simulations where the structure is excited by different external
loads. The simulation results indicate that the proposed approach is a viable damage detection technique capable of: (i)
identifying structural parameters, (ii) tracking the changes of parameters leading to the detection of structural damages,
and (iii) identifying the unknown external excitations.
An early detection of structural damages is critical for the decision making of repair and replacement maintenance in
order to guarantee a specified structural reliability. Consequently, the structural damage detection, based on vibration
data measured from the structural health monitoring (SHM) system, has received considerable attention recently. The
traditional time-domain analysis techniques, such as the least square estimation (LSE) method and the extended Kalman
filter (EKF) approach, require that all the external excitations (inputs) be available, which may not be the case for some
SHM systems. Recently, these two approaches have been extended to cover the general case where some of the external
excitations (inputs) are not measured, referred to as the LSE with unknown inputs (LSE-UI) and the EKF with unknown
inputs (EKF-UI). Also, new analysis methods, referred to as the sequential non-linear least-square estimation with
unknown inputs and unknown outputs (SNLSE-UI-UO) and the quadratic sum-square error with unknown inputs
(QSSE-UI), have been proposed for the damage tracking of structures when some of the acceleration responses are not
measured and the external excitations are not available. In this paper, these newly proposed analysis methods will be
compared in terms of accuracy, convergence and efficiency, for damage identification of structures based on
experimental data obtained through a series of experimental tests using a small-scale 3-story building model with white
noise excitation. The capability of the LSE-UI, EKF-UI, SNLSE-UI-UO and QSSE-UI approaches in tracking the
structural damages will be demonstrated.
Damage identification of structures is an important task of a health monitoring system. The ability to detect damages
on-line or almost on-line will ensure the reliability and safety of structures. Analysis methodologies for structural
damage identification based on measured vibration data have received considerable attention, including the least-square
estimation (LSE), extended Kalman filter (EKF), etc. Recently, new analysis methods, referred to as the sequential non-linear
least-square estimation (SNLSE) and quadratic sum-squares error (QSSE), have been proposed for the damage
tracking of structures. In this paper, these newly proposed analysis methods will be compared with the LSE and EKF
approaches, in terms of accuracy, convergence and efficiency, for damage identification of structures based on
experimental data. A series of experimental tests using a small-scale 3-story building model have been conducted. In
these experimental tests, white noise excitations were applied to the model, and different damage scenarios were
simulated and tested. Here, the capability of the adaptive LSE, EKF, SNLSE and QSSE approaches in tracking the
structural damage are demonstrated using experimental data. The tracking results for the stiffness of all stories, based on
each approach, are compared with the stiffness predicted by the finite-element method. The advantages and drawbacks
for each damage tracking approach will be evaluated in terms of the accuracy, efficiency and practicality.
KEYWORDS: Sensors, Matrices, Damage detection, Structural health monitoring, Chemical elements, Signal to noise ratio, Solids, Information operations, Earthquakes, Velocity measurements
A challenging problem in structural damage detection based on vibration data is the requirement of a large number
of sensors and the numerical difficulty in obtaining reasonably accurate results when the system is large. To address
this issue, the substructure identification approach may be used. Due to practical limitations, the response data are not
available at all degrees of freedom of the structure and the external excitations may not be measured (or available). In
this paper, an adaptive damage tracking technique, referred to as the sequential nonlinear least-square estimation with
unknown inputs and unknown outputs (SNLSE-UI-UO) along with the sub-structure approach will be used to identify
damages at critical locations (hot spots) of the complex structure. In our approach, only a limited number of response
data are needed and the external excitations may not be measured, thus significantly reducing the number of sensors
required and computational efforts. The accuracy of the proposed approach is illustrated using a long-span truss with
finite-element formulation. Simulation results demonstrate that the proposed approach is capable of tracking the local
damages and it is suitable for local structural health monitoring.
KEYWORDS: Surface conduction electron emitter displays, Sensors, Error analysis, Solids, Finite element methods, Chemical elements, Data analysis, Structural health monitoring, Motion models, Aerospace engineering
The detection of structural damage is an important objective of structural health monitoring systems. Analysis
techniques for the damage detection of structures, based on vibration data measured from sensors, have been studied
without experimental verifications. In this paper, a newly proposed data analysis method for structural damage
identifications, referred to as the adaptive quadratic sum squares error (AQSSE), will be verified experimentally. A
series of experimental tests using a scaled 3-story building model have been conducted recently. In the experimental
tests, white noise excitations were applied to the top floor of the model, and different damage scenarios were simulated
and tested. These experimental data will be used to verify the capability of the AQSSE approach in tracking the
structural damage. The tracking results for the stiffness of all stories, based on the AQSSE approach, are compared with
the stiffness predicted by the finite-element method. Experimental results demonstrate that the AQSSE approach is
capable of tracking the structural damage with reasonable accuracy.
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