Abstract

A new damage feature index is presented for the structural health monitoring based on Hilbert-Huang transform (HHT). The energy marginal spectrum of the dynamic signal is used to construct damage characteristic parameter, which can reflect the signal energy variation and benefit the structural damage detection. A sinusoidal wave with frequency change and a composite plate vibration experiment with pre-defined damage are designed to verify the effectiveness of characteristic parameter in damage detection. Results obtained from simulation and test show that the extracted non-model-based damage feature index is available and sensitive in damage detection of time-varying system.

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Guided wave-based Structural Health Monitoring (SHM) tools utilize the guided wave responses to interrogate damage in structures. This research demonstrates the use of various objective functions in single (mono) objective and multi-objective genetic algorithms for damage identification in isotropic 1D structures. The time domain spectral element method and a deep-learning-based surrogate is utilized for simulating wave propagation in an isotropic cracked rod. The genetic algorithms employ results ('numerical experiment') obtained from the spectral element model and the deep-learning-based surrogate to determine the optimized crack locations and crack depths as output parameters. The obtained optimized parameters from genetic algorithms are compared in terms of errors for various objective functions.

Abstract

During their life-cycle, civil infrastructures are continuously prone to significant functionality losses, primarily due to material's degradation and exposure to several natural hazards. Following these concerns, many researchers have attempted to develop reliable monitoring strategies, as integration to visual inspections, to efficiently ensure bridge maintenance and early-stage damage detection. In this framework, recent improvements in sensor technologies and data science have stimulated the use of Machine Learning (ML) algorithms for Structural Health Monitoring (SHM). Among unsupervised learning techniques, the potential of autoencoder networks has been attracting notable interest in the context of anomaly detection. In this light, the present paper proposes two different autoencoder-based damage detection techniques, focused on the Multi-Layer Perceptron (MLP) and the Convolutional Autoencoder (CAE) networks, respectively. During the training, the selected ML models learn how reconstructing raw acceleration sequences acquired from sound conditions. Unknown data, including both healthy and damaged bridge responses, are afterwards used to test the implemented networks and to detect damage occurrence. To this aim, a specific index of reconstruction loss is selected as a damage sensitive feature with the aim to quantify the errors between the original and reconstructed sequences. The performance exhibited by the two approaches is compared and evaluated by application to the Z24 benchmark bridge. Results demonstrate the effectiveness of the proposed methodology to perform feature classification and real time damage detection at the level of macro-sequences as new sensor data is collected, resulting suitable for continuous assessment of full-scale monitored bridges.

Abstract

Fiber-reinforced laminated composite materials are widely used in aircraft, modern vehicles and light-weight structures. With their high elastic modulus, high strength, and capability of being tailored for a several applications, these materials offer definite advantages compared to more traditional structural materials. However, their behavior under impact is of concern since those events may occur during manufacturing, normal operation or maintenance. The situation is critical for impacts that induce significant internal damage undetectable by visual inspection, which causes large reductions on the strength and stability of the structure. New standardized tests to assess the impact tolerance (ASTM D7136 and D7137) lose some of its appeal by its destructive nature as well as costs involved. Non-destructive techniques such as ultrasonic phased array or experimental modal analysis can be complementary in the evaluation of damage and structural integrity. The results of both destructive and non-destructive experiments carried out on forty-eight composite specimens are presented. A good correlation between incident impact energy and delaminated area as well as variation of eigenfrequencies is noticed. This study does not only provide a better understanding of the impact phenomenon but can also help in design and implementation of new test procedures for structural assessment.