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1. Convolutional Neural Network Approach for Robust Structural Damage Detection and Localization

   https://doi.org/10.1061/%28ASCE%29CP.1943-5487.0000820  

   Gulgec, Nur Sila; Takáč, Martin; Pakzad, Shamim N. (May 2019, Journal of computing in civil engineering)

   Damage diagnosis has been a challenging inverse problem in structural health monitoring. The main difficulty is characterizing the unknown relation between the measurements and damage patterns (i.e., damage indicator selection). Such damage indicators would ideally be able to identify the existence, location, and severity of damage. Therefore, this procedure requires complex data processing algorithms and dense sensor arrays, which brings computational intensity with it. To address this limitation, this paper introduces convolutional neural network (CNN), which is one of the major breakthroughs in image recognition, to the damage detection and localization problem. The CNN technique has the ability to discover abstract features and complex classifier boundaries that are able to distinguish various attributes of the problem. In this paper, a CNN topology was designed to classify simulated damaged and healthy cases and localize the damage when it exists. The performance of the proposed technique was evaluated through the finite-element simulations of undamaged and damaged structural connections. Samples were trained by using strain distributions as a consequence of various loads with several different crack scenarios. Completely new damage setups were introduced to the model during the testing process. Based on the findings of the proposed study, the damage diagnosis and localization were achieved with high accuracy, robustness, and computational efficiency. 

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2. DEEP CASCADED NEURAL NETWORKS FOR AUTOMATIC DETECTION OF STRUCTURAL DAMAGE AND CRACKS FROM IMAGES

   https://doi.org/10.5194/isprs-annals-V-2-2020-411-2020  

   Bai, Y.; Zha, B.; Sezen, H.; Yilmaz, A. (January 2020, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences)

   Abstract. In this paper, two different convolutional neural networks (CNNs) are applied on images for automated structural damage detection (SDD) in earthquake damaged structures and cracking localization (e.g., detection of cracks, their widths and distributions) at various scales, such as pixel level, object level, and structural level. The proposed method has two main steps: 1) diagnosis, and 2) localization of cracking or other damage. At first a residual CNN with transfer learning is employed to classify the damage in the structures and structural components. This step performs damage detection using two public datasets. The second step uses another CNN with U-Net structure to locate the cracking on low resolution images. The implementations using public and self-collected datasets show promising performance for a problem that had remained a challenge in the structure engineering field for a long time and indicate that the proposed approach can perform detection and localization of structural damage with an acceptable accuracy. 

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3. Deep Neural Network-Based Guided Wave Damage Localization

   Khurjekar, Ishan D.; Harley, Joel B. (December 2019, Proc. of the Review of Nondestructive Evaluation)

   Damage detection and localization remain challenging research areas in structural health monitoring. Guided wave-based methods that utilize signal processing tools (e.g., matched field processing and delay-and-sum localization) have enjoyed success in damage detection. To locate damage, such techniques rely on a model of wave propagation through materials. Measured data is then compared with these models to determine the origin of a wave. As a result, the analytical model and actual data may have a mismatch due to environmental variations or a lack of knowledge about the material. Deep neural networks are a class of machine learning algorithms that learn a non-linear functional mapping. The paper presents a deep neural network-based approach to damage localization. We use simulated data to assess the performance of localization frameworks under varying levels of noise and other uncertainty in our ultrasonic signals. 

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4. Wireless Ultrasonic Phased Arrays for On-Demand Wall Thickness Measurement and Damage Detection

   https://doi.org/10.1115/SMASIS2025-167971  

   Bo, Luyu; Li, Jiali; Li, Teng; Cai, Bowen; Wang, Zhide; Liu, Hongye; Moomaw, Andrew; Tian, Zhenhua (September 2025, American Society of Mechanical Engineers)

   Abstract Ultrasonics structural health monitoring (SHM) is widely recognized as an effective technique that enables early damage detection in large-scale structures and helps prevent potential catastrophic failures. Ultrasonic phased array technology has gained prominence in SHM due to its ability to inspect a large area with high spatial resolution. However, conventional systems often rely on physical wired sensor networks, limiting their deployment for hard-to-access regions. In this study, we present a wireless ultrasonic phased array system capable of dual-mode operation for both wall thickness measurement and structural damage detection. The system integrates wireless power transfer (WPT) modules and customized matching circuits, enabling efficient and flexible deployment. Proof-of-concept experiments demonstrate successful wall thickness evaluation and accurate defect localization in metallic structures using both delay-and-sum (DAS) and minimum variance (MV) imaging methods, with the MV algorithm offering improved imaging resolution. Future work will focus on advancing real-time monitoring through machine learning, enabling 3D imaging, and extending system applicability to anisotropic composite materials. 

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5. Integration of structural health monitoring in shape memory alloy reinforced self-healing matrix composites using piezoelectric transducers

   https://doi.org/10.1177/14759217251345863  

   Alzoubi, Jomah; Srivastava, Vaibhav; Bellah, Masum; Salowitz, Nathan; Rohatgi, Pradeep (July 2025, Structural Health Monitoring)

   Integrating self-healing materials with structural health monitoring (SHM) represents a significant advancement in materials science and engineering. In applications such as aerospace, self-healing composites with SHM can detect and repair damage autonomously, enhancing safety and reducing maintenance time, which significantly improves structural durability by maintaining integrity and extending material service life. This research employs an experimental approach to validate the self-healing process of self-healing metal matrix composites reinforced with shape memory alloy fibers, by utilizing lead zirconate titanate (PZT) piezoelectric transducers mounted on the surface of the composite. A three-point bending test is used to induce damage in the metal matrix composites by applying a load at its midpoint while supporting it at both ends; then, self-healing is used to return the specimen to its original state. The ultrasonic signals from the PZT sensor were compared at three stages: pristine state, during bending (i.e., damage), and after the healing process, to evaluate the effectiveness of the healing process and health monitoring technique adopted for this study. Real-time monitoring using digital image correlation, laser profilometry, and mechanical testing was used to validate the damaged/healed signal state—demonstrated by improved recovered signal amplitude, reduced scatter energy, a notable decrease in damage indices root-mean-square deviation, normalized scatter energy, and stabilized wave propagation. The successful self-healing composite design and health monitoring results confirm the restoration of the specimen, which regained 84% of its initial shape deformation at 135 °C for 30 min (i.e., during the first step of healing). Complete shape restoration was achieved by raising the temperature to 150 °C for 90 min (during the second step of healing), recovering approximately 96% of the original flexural strength after healing. 

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