The demand for lighter and more efficient structures has led to a search not only of lighter materials, but also suitable and efficient joining techniques. Adhesive bonding offers significant advantages, such as weight reduction and high strength, so there is a growing interest in its application and development. However, the certification and standardization of bonded joints and their inspection and monitoring is still lacking. Particularly concerning is the development of cracks in the adhesive layer, which may grow under the applied loads, potentially leading to failure. The inspection and monitoring of such damage is particularly challenging due to the multi-material nature of adhesive joints, and because the bondline is generally not accessible for inspection.
To address these challenges, an experimental campaign was conducted, focused on the development and assessment of suitable non-destructive and structural health monitoring techniques for monitoring crack growth in adhesively bonded joints. Cracked Lap Shear specimens were subjected to tensile fatigue loading, which induces a mixed mode loading condition in the adhesive layer, representative of typical in-service loading conditions. The effectiveness and accuracy of several monitoring methods – namely phased array ultrasonic testing, backface strain monitoring using distributed optical fibre sensors, and acoustic emission – were evaluated and compared against digital image correlation and visual crack length measurements.

Emerging needs for accurate, efficient, and scalable structural inspections demand advanced tools that transcend traditional, labor-intensive methods. While fundamental to assessing building integrity, manual visual inspections suffer from inconsistency, high labor costs, and delays that can compromise safety and hinder timely decision-making. This study introduces a novel mobile application that integrates augmented reality and machine learning to revolutionize indoor structural inspection workflows. The system leverages Apple’s ARKit and RoomPlan APIs to generate real-time 3D spatial reconstructions and perform indoor localization with high fidelity. Simultaneously, the YOLOv8 deep learning model detects common structural defects, such as surface cracks and spalling, in real time using live camera input. These components are seamlessly integrated within an interactive AR interface, allowing inspectors to document defects by raycasting them into a shared 3D coordinate system and associating them with floor plan geometry. Field validations are conducted across indoor environments to evaluate the system’s accuracy, usability, and performance under realistic conditions. Results show significant gains, including a 30–50% reduction in inspection time, enhanced spatial precision, and streamlined data management. All inspection data, including annotated images, floor plans, and spatial coordinates, are automatically consolidated into a single exportable dataset. This integrated solution demonstrates a promising direction for scalable, technician-friendly inspection tools that fuse real-time spatial computing with intelligent defect recognition.

This presentation motivates the need for advanced in situ non-destructive evaluation for understanding the health of dynamic systems real-time during dynamic experimentation. Recent advancements in structural dynamic test allow close replication of field environments during laboratory test. This leads to increased stresses, strains, and increased likelihood of failure during laboratory qualification testing. As dynamic environment testing strives to replicate high-input environments the ability to monitor the health and provide diagnostics of the hardware-under-test has become increasingly important. This talk previews a broad set of enhancements to laboratory dynamic environmental testing including the use of multiple-input, multiple-output experiments and dynamic substructuring to update results. These enhancements set the stage for a call for enhanced diagnostics which provide real-time condition monitoring on systems across multiple length-and time-scales. These systems have inherent complexities including irregular geometry and nonclassical nonlinearities like bolted joints and contact interfaces that must be understood for the design of effective nondestructive evaluation (NDE) frameworks. Pump-probe methods and other related vibroacoustic modulation techniques are well-positioned to provide information useful for insitu condition monitoring of systems subjected to vibratory environments. Challenges and opportunities involved in applying these methods to real-world systems are discussed, including augmentation of dynamic environmental testing efforts, characterization of multiple defect and system degradation modalities in the presence of inherent nonlinearity and geometric complexity, and service life estimation.

*This abstract describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the summary do not necessarily represent the views of the U.S. Department of Energy or the United States Government. Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Corrosion of concrete components is ubiquitous in the civil infrastructure, from reinforced concrete bridges to buildings and power production infrastructure. The corrosion of embedded reinforcing bars leads to subsurface damage that propagates below the surface and manifests in spalling and loss of structural reliability. This presentation will describe a new approach for imaging subsurface damage in concrete components using a time-lapse thermography approach. Traditional methods of assessment such as sounding, sonic methods (e.g. impact echo), or Ground Penetrating Radar (GPR) can be intrusive, require traffic control or special access to implement, or have been shown to be ineffective in quantifying damage. Conventional Infrared thermography (IRT) can be utilized to detect subsurface damage with minimal access or traffic control, but has limitations due to varying environmental conditions that affect its reliability. A new approach known as Infrared Ultra-Time Domain imaging (IR-UTD) for imaging subsurface damage in concrete components has been recently developed. This new technological approach mitigates the environmental effects on measurements by combining long-term measurements (24 – 48 hrs) with advanced processing algorithms. Quantitative assessment of damage in concrete members can be achieved without access to the surface being assessed. Case study results will be presented that demonstrate the application of this new imaging technology on bridge decks, soffits and concrete cooling towers.