The use of smart dampers to optimally control the response of structures is on the increase. To maximize the potential use of such damper systems, their accurate modeling and assessment of their performance is of vital interest. In this study, the performance of a controllable fluid dashpot damper, in terms of damper forces, damper dynamic range and damping force hysteretic loops, respectively, is studied mathematically. The study employs a damper Bingham-Maxwell (BingMax) model whose mathematical formulation is developed using a Fourier series technique. The technique treats this one-dimensional Navier-Stokes's momentum equation as a linear superposition of initial-boundary value problems (IBVPs): boundary conditions, viscous term, constant Direct Current (DC) induced fluid plug and fluid inertial term. To hold the formulation applicable, the DC current level to the damper is supplied as discrete constants. The formulation and subsequent simulation are validated with experimental results of a commercially available magneto rheological (MR) dashpot damper (Lord model No's RD-1005-3) subjected to a sinusoidal stroke motion using a 'SCHENK' material testing machine in the Materials Laboratory at the University of Technology, Sydney.

With the discovery in plants of the proteinaceous forisome crystalloid (Knoblauch, et al. 2003), a novel, non-living, ATP-independent biological material became available to the designer of smart materials for advanced actuating and sensing. The in vitro studies of Knoblauch, et al. show that forisomes (2-4 micron wide and 10-40 micron long) can be repeatedly stimulated to contract and expand anisotropically by shifting either the ambient pH or the ambient calcium ion concentration. Because of their unique abilities to develop and reverse strains greater than 20% in time periods less than one second, forisomes have the potential to outperform current smart materials as advanced, biomimetic, multi-functional, smart sensors or actuators. Probing forisome material properties is an immediate need to lay the foundation for synthesizing forisomebased smart materials for health monitoring of structural integrity in civil infrastructure and for aerospace hardware. Microfluidics is a growing, vibrant technology with increasingly diverse applications. Here, we use microfluidics to study the surface interaction between forisome and substrate and the conformational dynamics of forisomes within a confined geometry to lay the foundation for forisome-based smart materials synthesis in controlled and repeatable environment.

Piezoelectric sensors have many applications in geotechnical engineering, especially in characterizing soils through measurement of wave velocities. Since mechanical properties of a material are closely associated with wave velocities, piezoelectric sensors provide a reliable and non-destructive method for the determination of soil properties. This paper presents results of recent research on measuring stiffness of a wide range of soils such as clay, sand, and gravel, characterizing anisotropic properties of soil induced by external loading, measuring stiffness of base and subgrade materials in the pavement, determining soil properties in a centrifuge model during the flight of a centrifuge, and understanding wave propagation in granular materials under micro-gravity environment using this technique.

The structural monitoring of multi-wire strands is of importance to prestressed concrete structures and cable-stayed or suspension bridges. This paper addresses the monitoring of strands by ultrasonic guided waves with emphasis on the signal processing and automatic defect classification. The detection of notch-like defects in the strands is based on the reflections of guided waves that are excited and detected by magnetostrictive ultrasonic transducers. The Discrete Wavelet Transform was used to extract damage-sensitive features from the detected signals and to construct a multi-dimensional Damage Index vector. The Damage Index vector was then fed to an Artificial Neural Network to provide the automatic classification of (a) the size of the notch and (b) the location of the notch from the receiving sensor. Following an optimization study of the network, it was determined that five damage-sensitive features provided the best defect classification performance with an overall success rate of 90.8%. It was thus demonstrated that the wavelet-based multidimensional analysis can provide excellent classification performance for notch-type defects in strands.

For health monitoring purpose usually the structure is instrumented with a large scale and multichannel measurement system. In case of highway bridges, operating vehicle could be utilized to reduce the number of measuring devices. First this paper presents a static damage detection algorithm of using operating vehicle load. The technique has been validated by finite element simulation and simple laboratory test. Next the paper presents an approach of using this technique to field application. Here operating vehicle load data has been used by instrumenting the bridge at single location. This approach gives an upper hand to other sophisticated global damage detection methods since it has the potential of reducing the measuring points and devices. It also avoids the application of artificial loading and interruption of any traffic flow.