Smart structures health monitoring using artificial neural network

This paper presents a non-model based technique to detect and locate structural damage with the use of artificial neural networks. This method utilizes high frequency structural excitation (typically greater than 30 kHz) through a surface-bonded piezoelectric sensor/actuator to detect changes in structural point impedance due to the presence of damage. Two sets of artificial neural networks were developed in order to detect, locate and characterize structural damage by examining changes in the measured impedance curves. A simulation beam model was developed to verify the proposed method. An experiment was successfully performed in detecting damage on a 4-bay structure with bolted-joints, where the bolts were progressively released.

Development of a voltammetric sensor for chromium(VI) determination in wastewater sample

Screen-printed carbon electrode (SPCE) modified with poly-L-histidine film can be successfully applied for chromium(VI) determination based on its pre-concentration. Optimum adherence and stability of the POIY-L-histidine film was obtained by direct addition of PH solution 1% (w/v) on the electrode surface, followed by heating at 80 degrees C during 5 min. Linear response range, sensitivity and limit of detection were 0. 1-150 [mu mol L-1, 4. 13 LA mu mol L` and 0.046 mu mol L-1. The repeatability of the proposed sensor, evaluated in terms of relative standard deviation, was measured as 3.2% for 10 experiments in 40 mu mol L-1 using the same electrode and 4.0% using screen-printed electrode as disposable sensor, respectively. The voltammetric sensor was applied to determination of Cr(VI) and indirect determination of Cr(III) in wastewater samples previously treated by a leather dyeing industry and the average recovery for these samples was around 97%. (C) 2006 Elsevier B.V. All rights reserved.

Structural health evaluation by optimization techinique and artificial neural network

This paper presents two different approaches to detect, locate, and characterize structural damage. Both techniques utilize electrical impedance in a first stage to locate the damaged area. In the second stage, to quantify the damage severity, one can use neural network, or optimization technique. The electrical impedance-based, which utilizes the electromechanical coupling property of piezoelectric materials, has shown engineering feasibility in a variety of practical field applications. Relying on high frequency structural excitations, this technique is very sensitive to minor structural changes in the near field of the piezoelectric sensors, and therefore, it is able to detect the damage in its early stage. Optimization approaches must be used for the case where a good condensed model is known, while neural network can be also used to estimate the nature of damage without prior knowledge of the model of the structure. The paper concludes with an experimental example in a welded cubic aluminum structure, in order to verify the performance of these two proposed methodologies.