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Summary: Damage diagnosis in the structural field (mechanical, civil, aerospace, etc.) is a topic of active development and research. In recent years, considerable enhancements in this field have been achieved mainly due to advances in sensor technologies, the evolution of signal processing algorithms, and the increase of computational power. As one of the main consequences, the amount of data recorded from the sensorial equipment has steadily grown in quantity and complexity. In addition to that, these data are almost always significantly affected by many factors, which are not only related to the presence of damages but, for instance, also to the environmental and operative conditions under which the structural system is working. In order to handle these challenges, in the last few years, new deep learning models have been proposed, based on deep and heterogeneous architectures, able to deal with big data, also containing intricate diagnostic features that are difficult to be extracted. With this aim, this paper proposes a new vibration‐based structural diagnosis tool that exploits the power of convolutional neural networks (CNNs) to extract subtle damage‐related features from complex transmissibility function (TF) spectra even in presence of potentially confounding temperature variations. The diagnostic algorithm stems from the coupling of a CNN with an unsupervised anomaly detection algorithm based on autoencoders (AEs) to neutralize the effects of temperature variations and increase the damage diagnosis accuracy. The proposed approach is demonstrated with reference to a simple, but realistic, numerical case study of a structural beam subjected to temperature changes. [ABSTRACT FROM AUTHOR]

Structural Health Monitoring (SHM) has been researched for a long time and continues to be an active area of research. Initial work on SHM involved identification of hand-crafted features and predictive models relied on statistical methods. The recent improvements in computing capabilities, coupled with better integration of sensor data, has led to the emergence of more effective techniques in terms of scalability and predictive power. Machine learning offers a solution through automatic feature extraction algorithms, and scalable and noise robust models. Convolutional Neural Networks (CNN) have been used as state-of-art classifiers for images as well as for text. This paper proposes the use of the monitored structure's transmissibility functions for the structure under observation, which can be fed into a novel composite architecture consisting of Deep CNN followed by multivariate linear regressors to detect, localize, and quantify the damage extent in a system. The proposed method was tested on the Los Alamos' Eight degree-of-freedom (DOF) structure, and the Structural Beam Data from Laboratory of Mechanical Vibrations and Rotor Dynamics, University of Chile. This study on damage localization and quantification can be leveraged to comment on the safety and soundness of the structure under inspection and can help in making more informed inferences. It is expected that, in general, this will lead to extended structure life, which not only improves the resource utilization in terms of structure maintenance and its longevity but also decreases the carbon footprint and capital expenditure. [ABSTRACT FROM AUTHOR]

Nowadays, structural health monitoring is the area of a great interest of continuing research aiming at establishing a reliable condition monitoring strategy of civil engineering infrastructure. Such finding will allow moving from the schedule-based inspection policy to condition-based policy. This study is dedicated to identify the damage in reinforced concrete beams by using only frequency domain vibration measurements. In the meantime, statistical pattern recognition model was tested through the course of this research. A transmissibility-based damage detection and classification system was proposed. Subsequently, the measure distance of the spectra envelope (COSH) was suggested as classification tool. The proposed method was examined on datasets from numerical beam model and experimental measurements from 2.0m reinforced concrete beam. For the two models, the results of the proposed approach proved effective and managed to detect various levels of defects, and classify the defects according to their size. Having processed response signals to detect and classify state conditions, the devised approach is relevant to use in embedded online structural health monitoring.

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Abstract: This paper deals with the use of transmissibility functions for damage localization. The first part is dedicated to a critical review of the state-of-the-art highlighting the major difficulties when using transmissibility functions for damage detection and localization. In the second part, an analytical study is presented for non dispersive systems such as chain-like mass-spring systems. The link between the transmissibility function and the mechanical properties of four subsystems defined by the boundary conditions, the position of the excitation and the two measurement locations used for the computation of the transmissibility functions is derived. This result is used to discuss the situations in which damage localization is likely to work. The last section discusses the extension of these results to more general dispersive systems such as beams or plates. [Copyright &y& Elsevier]

One of the current challenges in structural health monitoring (SHM) is to take the most advantage of large amounts of data to deliver accurate damage measurements and predictions. Deep Learning methods tackle these problems by finding complex relations hidden in the data available. Amongst these, Capsule Neural Networks (CapsNets) have recently been developed, achieving promising results in benchmark Deep Learning problems. In this paper, Capsule Networks are expanded to locate and to quantify structural damage. The proposed approach is evaluated in two case studies: a system with springs and masses that simulate a structure, and a beam with different damage scenarios. For both case studies, training and validation sets are created using Finite Element (FE) models and calibrated with experimental data, which is also used for testing. The main contributions of this study are: A novel CapsNets-based method for dual classification–regression task in SHM, analysis of both routing algorithms (dynamic routing and Expectation–Maximization routing) in the context of SHM, and analysis of generalization between FE models and real-life experiments. The results show that the proposed Capsule Networks with dynamic routing achieve better results than Convolutional Neural Networks (CNN), especially when it comes to false positive values. • Novel Capsule Neural Networks for structural damage assessment • Capsule Neural Networks for dual purpose goals: Damage localization and quantification. • Exploration of dynamic routing and EM routing for structural damage assessment. [ABSTRACT FROM AUTHOR]