Structure from Motion (SfM) is a photogrammetry technique with diverse applications, such as surveying, mapping, and inspection. It facilitates remote assessment of large systems by converting visible spectrum images into threedimensional (3D) point clouds. Recent advancements have extended SfM to employ infrared (IR) images, enabling the detection of issues such as water infiltration and sub-surface defects that can cause energy loss in a building. Combining IR-based SfM with unmanned aerial vehicle (UAV) technologies yields high-definition 3D point clouds that can be used in a virtual reality (VR) environment. This study showcases the application of the SfM-IR-UAV method to create 3D virtual models of selected buildings in the University of Massachusetts Lowell’s campus to assess energy loss. The 3D virtual models are made accessible via a VR platform to develop a remote inspection and maintenance tool. The VR platform also holds the capabilities to mark abnormalities in the structure, which can later be used for informing renovation or repair. The proposed approach simplifies remote assessment, reducing costs and operational risks. While this research focuses on energy audits, its outcomes extend to diverse domains. Further development holds the potential to expedite nondestructive evaluation and enhance structural health monitoring in civil and mechanical engineering, utilizing the 3D point cloud thermal model within a VR environment.
Full-field data provides a comprehensive understanding of the behavior of a system or structure, which is particularly crucial when identifying local damages. This damage may exhibit complex and subtle effects that could be overlooked with sparse measurements. Recent advancements in machine learning, such as Autoencoders (AE), have enabled the reconstruction of full-field data using sparse measurements. However, a study assessing the accuracy of AE in reconstructing full-field data concerning measurement locations, data sparsity, and noise density is still lacking in the context of Nondestructive Evaluation (NDE). To address these gaps, this study adopts a parametric approach to evaluate the effectiveness of an LSTM-based AE model in terms of measurement locations, data sparsity, and noise density. The two sets of data (i.e., configuration #1 and #2) were generated using a finite element method for a 2D metallic plate cooling. The configuration #1 data were then used to train the LSTM-based AE and the model’s full field reconstruction performance was validated on sparse measurements of configuration #2 using the Average Reconstruction Error (ARE) as a testing parameter. The result shows, there was no significant impact of different measurement locations on ARE. Whereas ARE increased with increase in data sparsity and noise density. This research presents a parametric study with potential applications in full-field reconstruction, not limited to thermal data. It can be extended to other applications, such as strain, displacement, and velocity, in scenarios where the targeted system undergoes temporal evolution.
KEYWORDS: Machine learning, Education and training, Video, Signal attenuation, Data modeling, Structural health monitoring, Dynamical systems, Vibration, Interference (communication), Video processing
Recent machine learning (ML) algorithms have resulted in new paradigms for extracting spatio-temporal characteristics (STCs), such as frequency spectra of critical infrastructures and performing structural health monitoring. However, the accuracy of the STCs extracted using ML is affected by any noise in the data used to train and test the ML algorithms. While noise reduction methods have been successfully proposed, they are application-specific, and none consider the dynamics of the targeted system. Hence, a novel framework named time-inferred autoencoder (TIA) is proposed. The TIA is based on a long, short-term memory (LSTM) neural network to learn the dynamics of the system and a maximum correntropy loss function for noise removal. The robustness of the TIA is validated by collecting a video of an undamaged beam for training the framework and learning the structure’s STCs. Later, the capability of the trained TIA to adapt to changes in the system’s dynamics and reconstruct the STCs is validated by recording noise-corrupted videos of a beam in three different damaged configurations. Results of laboratory tests showed that the TIA reconstructs the natural frequencies of the structure with an error of less than 1%. If further developed, the proposed framework can be used as a structural dynamics tool given its robustness and capability to adapt to changes and noise in the system.
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