Publications

In a companion paper, Pasala and Nagarajaiah analytically and experimentally validate the Adaptive Length Pendulum Smart Tuned Mass Damper (ALP-STMD) on a primary structure (2 story steel structure) whose frequencies are time invariant (Pasala and Nagarajaiah 2012). In this paper, the ALP-STMD effectiveness on a primary structure whose frequencies are time varying is studied experimentally. This study experimentally validates the ability of an ALP-STMD to adequately control a structural system in the presence of real time changes in primary stiffness that are detected by a real time observer based system identification. The experiments implement the newly developed Adaptive Length Pendulum Smart Tuned Mass Damper (ALP-STMD) which was first introduced and developed by Nagarajaiah (2009), Nagarajaiah and Pasala (2010) and Nagarajaiah et al. (2010). The ALP-STMD employs a mass pendulum of variable length which can be tuned in real time to the parameters of the system using sensor feedback. The tuning action is made possible by applying a current to a shape memory alloy wire changing the effective length that supports the damper mass assembly in real time. Once a stiffness change in the structural system is detected by an open loop observer, the ALP-STMD is re-tuned to the modified system parameters which successfully reduce the response of the primary system. Significant performance improvement is illustrated for the stiffness modified system, which undergoes the re-tuning adaptation, when compared to the stiffness modified system without adaptive re-tuning.

The seismic response of buildings and bridges can be improved by isolating the structure with a seismic sliding bearings. Specialized passive devices are commonly used to complement these bearings, to control the displacement of the structure during large magnitude events, and to restrict a build-up of drift. Active servo-hydraulic actuators are used herein to supplement the conventional sliding bearings creating a hybrid active-passive protective system. The control system is capable of minimizing the inertial forces in the structure, while providing higher damping ratios and better displacement control when necessary. This paper presents three control algorithms for the hybrid system. Two of these algorithms are verified experimentally, and the third is verified with an analytical model. The results show that the hybrid system is capable of significantly improving the seismic response of the structure.

A novel actuator failure detection algorithm is developed in this paper. An actuator failure is considered to occur when it produces an input to the structure that is different from the commanded input. In this paper, an error function, one for each actuator, is developed to monitor the working status of the examined actuator in real-time, regardless the status of other actuators. Non-zero signal profile in the error function indicates the time instant of failure of the examined actuator (for measurement noise free case). The coefficients of the error function are calculated directly from the healthy input data (from the examined actuator) and all outputs without having to identify the state-space model of the system. Thus the need to know the state-space model of the plant is bypassed in the presented direct approach. Experimental results from a NASA eight bay truss show that the direct method can successfully isolate and identify the failure of the examined actuator in real-time.

SUMMARY This paper presents a study on multi‐degree‐of‐freedom (MDOF) structures equipped with a negative stiffness amplifying damper (NSAD). The NSAD not only preserves the negative stiffness feature of negative stiffness devices (NSDs) but also achieves prominent damping magnification effect, substantially reducing a NSD's requirement for high additional damping, which is used to contain the increased displacements resulting from the reduction in overall stiffness of a system. The dynamic equations of MDOF systems with NSADs are described in state–space representation, and the effective damping and frequencies are parametrically studied. Then, a simple optimization method is proposed. A study of the 20‐storey benchmark building shows that NSADs are the most efficient of supplemental devices in reducing interstorey drifts compared with viscous dampers (VDs) and viscoelastic dampers (VEDs) with the same supplemental damping coefficient. For instance, when compared with that of VEDs, the maximal peak interstorey of NSADs can be further reduced by about 30%. In terms of reducing acceleration responses, NSADs perform much better than VDs and VEDs owing to their negative stiffness feature. Partially arranged, NSADs are best implemented at the storeys that have smaller interstorey drift responses. This is because the negative stiffness preserved by NSADs significantly reduces the interstorey drift of storeys without NSADs.

Radial Basis Function networks are extremely fast and require relatively small training data sets compared to other neural network methods such as back propagation. They are also less susceptible to problems with non-stationary inputs because of the behavior of the radial basis function hidden units. This makes RBF methods very attractive for realtime structural health monitoring (SHM) and damage detection. In this study, the aforementioned merits of the the RBF network will be exploited to perform realtime damage detection in buildings which are subjected to both sinusoidal and earthquake gound motion.

A new method based on a bank of ARMarkov observers is proposed in this study for determination of the extent of damage. The objective of this article is to present a new formulation using a predesigned set of ARMarkov observers to determine the extent of damage and track further changes in the stiffness of the damaged member. The primary advantages of the proposed formulation over the existing methods are: (1) ARMarkov observers are designed based on interaction matrix formulation so that knowledge about exact initial conditions is not necessary, and (2) noise statistics are not required a priori to design a bank of ARMarkov observers when compared to a bank of Kalman filters. The simulation results of several examples including a planar truss structure with progressive damage in a member are presented to highlight the capability of the proposed method. The proposed method works well in the case of either a full set or a limited number of available measurements. It is shown that sensitivity enhancing control (SEC) can be easily incorporated into the proposed method to enhance the sensitivity of structural damage. In case of noisy output measurements, it is shown that it is possible to distinguish between the errors due to structural damage and due to noise in output measurements.

Progress is reported in an emerging non-contact strain sensing technology based on optical properties of single-walled carbon nanotubes (SWCNTs). In this strain-sensing smart skin ("S4") method, nanotubes are dilutely embedded in a thin polymer film applied to a substrate of interest. Subsequent strain in the substrate is transferred to the nanotubes, causing systematic spectral shifts in their characteristic short-wave infrared fluorescence peaks. A small diode laser excites a spot on the coated surface, and the resulting emission is captured and spectrally analyzed to deduce local strain. To advance performance of the method, we prepare S4 films with structurally selected SWCNTs. These give less congested emission spectra that can be analyzed precisely. However, quenching interactions with the polymer host reduce SWCNT emission intensity by an order of magnitude. The instrumentation that captures SWCNT fluorescence has been made lighter and smaller for hand-held use or mounting onto a positioning mechanism that makes efficient automated strain scans of laboratory test specimens. Statistical analysis of large S4 data sets exposes uncertainties in measurements at single positions plus spatial variations in deduced baseline strain levels. Future refinements to S4 film formulation and processing should provide improved strain sensing performance suitable for industrial application.

Existing methods for structural health monitoring pose a formidable challenge to real time implementation due to the significantly large computational loads. The proposed algorithm is suitable for online applications because it maintains good pattern recognition capabilities while possessing a computationally compact network topology. This study employs the computational efficiency of single layer radial basis function (RBF) approximaters to create a subspace capable of isolating faults in multi-degree of freedom systems which involve coupled and uncoupled stiffness changes in real time. The RBF network transforms the displacement–time history of the varying plant into a decoupled output space which is then compared to a baseline healthy observer which undergoes the same decoupling transformation. The online comparison of the output of the time varying plant and the healthy observer in a decoupled subspace comprises the observer based error function. The error function is shown to not only detect the existence of faults, but also isolate these faults in real time in the presence of base excitation. The method is validated for systems that experience earthquake induced damage, as well as an experimental system using a semi-active independent variable stiffness device which is capable of varying system stiffness in real time. By simply observing the displacement–time history responses, the RBF augmented observer formulation is capable identifying changes in the stiffness at each degree of freedom.