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SUMMARYThis paper presents the feasibility of using an impedance-based health monitoring technique in monitoring a critical civil facility. The objective of this research is to utilize the capability of the impedance method in identifying structural damage in those areas where a very quick condition monitoring is urgently needed, such as in a post-earthquake analysis of a pipeline system. The basic principle behind this technique is to utilize high-frequency structural excitation (typically greater than 30 kHz) through surface-bonded piezoelectric sensors=actuators to detect changes in structural point impedance due to the presence of damage. Real-time damage detection in pipes connected by bolted joints was investigated, and the capability of the impedance method in tracking and monitoring the integrity of the typical civil facility has been demonstrated. Data collected from the tests illustrates the capability of this technology to detect imminent damage under normal operating conditions and immediately after a natural disaster.

This paper presents an integrated methodology to detect and locate structural damage. Two different damage detection schemes are combined in this methodology, which involves utilizing the electromechanical coupling property of piezoelectric materials and tracking the changes in the frequency response function data, respectively. Physical changes in the structure cause changes in mechanical impedance. Due to the electromechanical coupling in piezoelectric materials, this change in structural mechanical impedance causes a change in the electrical impedance of the piezoelectric sensor. Hence, by monitoring the electrical impedance one can qualitatively determine when structural damage has occurred or is imminent. Based on the fact that damage produces local dynamic changes, this technique utilizes a high frequency structural excitation (typically greater than 30 kHz) through the surface-bonded piezoelectric sensor/actuators. As a second step, a newly developed model-based technique, using a wave propagation approach, has been used to quantitatively assess the state of structures. Direct frequency response function data, as opposed to modal data, are utilized to characterize the damage in the structures. A numerical example and an experimental investigation of one-dimensional structures are presented to illustrate the performance of this technique.

This paper presents a non-model based technique to detect, locate, and characterize structural damage by combining the impedance-based structural health monitoring technique with an artificial neural network. The impedance-based structural health monitoring technique, 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 (typically >30 kHz), this technique is very sensitive to minor structural changes in the near field of the piezoelectric sensors. In order to quantitatively assess the state of structures, multiple sets of artificial neural networks, which utilize measured electrical impedance signals for input patterns, were developed. By employing high frequency ranges and by incorporating neural network features, this technique is able to detect the damage in its early stage and to estimate the nature of damage without prior knowledge of the model of structures. The paper concludes with experimental examples, investigations on a massive quarter scale model of a steel bridge section and a space truss structure, in order to verify the performance of this proposed methodology.