ABSTRACT:A wavelet-based approach is proposed for structural damage detection and health monitoring. Characteristics of representative vibration signals under the wavelet transformation are examined. The method ology is then applied to simulation data generated from a simple structural model subjected to a harmonic excitation. The model consists of multiple breakable springs, some of which may suffer irreversible damage when the response exceeds a threshold value or the number of cycles of motion is accumulated beyond their fatigue life. In cases of either abrupt or accumulative damages, occurrence of damage and the moment when it occurs can be clearly determined in the details of the wavelet decomposition of these data. Similar results are observed for the real acceleration data of the seismic response recorded on the roof of a building during the 1971 San Fernando earthquake. Effects of noise intensity and damage severity are investigated and presented by a detectability map. Results show the great promise of the wavelet approach for damage detection and structural health monitoring.

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Shape memory alloys (SMAs) exhibit peculiar thermomechanical, thermoelectrical and thermochemical behaviors under mechanical, thermal, electrical and chemical conditions. Examples of these materials are Cu-based SMAs, NiTi SMAs, ferrous SMAs, shape memory ceramics and shape memory polymers. NiTi SMAs in particular, have unique thermomechanical behaviors such as shape memory effect and pseudoelasticity, which have made them attractive candidates for structural vibration control applications. Numerous studies have been conducted in modeling and applications of NiTi SMAs in structural vibration control. Several active, passive and hybrid energy absorption and vibration isolation devices have been developed utilizing NiTi SMAs. In this paper we present an overview of NiTi behaviors, modeling and applications as well as their limitations for structural vibration control and seismic isolation.

As intelligent sensing and sensor network systems have made progress and low-cost online structural health monitoring has become possible and widely implemented, large quantities of highly heterogeneous data can be acquired during the monitoring. This has resulted in exceeding the capacity of traditional data analytics techniques, especially in monitoring large-scale or critical civil structures. In particular, data storage has become a big challenge, hence, resulting in the emergence of data compression and reconstruction as a new area in structural health monitoring (SHM) of large infrastructure systems. SHM data generally include anomalies that can disturb structural analysis and assessment. The fundamental reasons for the abnormality of data are extremely complex. Therefore, reconstruction of the abnormal data is generally difficult and poses serious challenges to achieve high-accuracy after data has been compressed. Considering these significant challenges, in this paper, a novel deeplearning-enabled data compression and reconstruction framework is proposed that can be divided into two phases: (a) a one-dimensional Convolutional Neural Network (CNN) that extracts features directly from the input signals is designed to detect abnormal data with validated high accuracy; (b) a new SHM data compression and reconstruction method based on Autoencoder structure is further developed, which can recover the data with high-accuracy under such a low compression ratio. To validate the proposed approach, acceleration data from the SHM system of a long-span bridge in China are employed. In the abnormal data detection phase, the results show that the proposed method can detect anomaly with high accuracy. Subsequently, smaller reconstruction errors can be achieved even by using only 10% compression ratio for the normal data.