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1. Sensor-aided camera calibration for three dimensional digital image correlation measurements

   https://doi.org/10.1117/12.2657163  

   Bottalico, Fabio; Valente, Nicholas A.; Niezrecki, Christopher; Jerath, Kshitij; Luo, Yan; Sabato, Alessandro (April 2023, Health Monitoring of Structural and Biological Systems XVII)

   Fromme, Paul; Su, Zhongqing (Ed.)

   Stereovision systems can extract full-field three-dimensional (3D) displacements of structures by processing the images collected with two synchronized cameras. To obtain accurate measurements, the cameras must be calibrated to account for lens distortion (i.e., intrinsic parameters) and compute the cameras’ relative position and orientation (i.e., extrinsic parameters). Traditionally, calibration is performed by taking photos of a calibration object (e.g., a checkerboard) with the two cameras. Because the calibration object must be similar in size to the targeted structure, measurements on large-scale structures are highly impractical. This research proposes a multi-sensor board with three inertial measurement units and a laser distance meter to compute the extrinsic parameters of a stereovision system and streamline the calibration procedure. In this paper, the performances of the proposed sensor-based calibration are compared with the accuracy of the traditional image-based calibration procedure. Laboratory experiments show that cameras calibrated with the multi-sensor board measure displacements with 95% accuracy compared to displacements obtained from cameras calibrated with the traditional procedure. The results of this study indicate that the sensor-based approach can increase the applicability of 3D digital image correlation measurements to large-scale structures while reducing the time and complexity of the calibration. 

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2. Angular Velocity Estimation using Non-coplanar Accelerometer Array

   https://doi.org/10.1109/JSEN.2021.3107470  

   Maynard, Michael; Vikas, Vishesh (August 2021, IEEE Sensors Journal)

   null (Ed.)

   Over the last few decades, Gyro-Free Inertial Measurement Units (GF-IMUs) have been extensively researched to overcome the limitations of gyroscopes. This research presents a Non-coplanar Accelerometer Array (NAA) for estimating angular velocity with non-specific geometric arrangement of four or more triaxial accelerometers with non-coplanarity constraint. The presented proof of non-coplanar spacial arrangement also provides insights into propagation of the sensor noise and construction of the noise covariance matrices. The system noise depends on the singular values of the relative displacement matrix (between the sensors). A dynamical system model with uncorrelated process and measurement noise is proposed where the accelerometer readings are used simultaneously as process and measurement inputs. The angular velocity is estimated using an Extended Kalman Filter (EKF) that discretizes and linearizes the continuous-discrete time dynamical system. The simulations are performed on a Cube-NAA (Cu-NAA) comprising four accelerometers placed at different vertices of a cube.They analyze the estimation error for static and dynamic movement as the distance between the accelerometers (four accelerometers in cube-orientation) is varied. Here, the system noise is observed to decrease inversely with the length of the cube edge as the arrangement is kept identical. Consequently, the simulation results indicate asymptotic decrease in the standard error of estimation with edge length. The experiments are conducted on a Cu-NAA with five reflective optical markers. The reflective markers are visually tracked using Vicon® to construct the ground truth angular velocity. This unique experimental setup, apart from providing three degrees of rotational freedom of movement, also allows for three degrees of spacial translation (linear acceleration of the Cu-NAA in space). The simulation and experimental results indicate better performance of the proposed EKF as compared to one with correlated process and measurement noises. 

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3. Observations of mean and wave orbital flows in the ocean’s upper centimetres

   https://doi.org/10.1017/jfm.2019.1019  

   Laxague, Nathan J.; Zappa, Christopher J. (March 2020, Journal of Fluid Mechanics)

   Sophisticated measurements of fluid velocity near to an undulating air–water boundary have traditionally been confined to the laboratory setting. Developments in camera technology and the opening of novel modes of analysis have allowed for sensitive measurements of the current profile in the ocean’s uppermost layer. Taking advantage of the Research Platform R/P FLIP as a ‘laboratory at sea’, here we present first-of-their-kind thermal and polarimetric camera-based observations of wave orbital velocities and mean shear flows in the upper centimetres of the ocean surface layer. Measurements reveal a well-defined logarithmic layer as seen in laboratory measurements and described by classical surface layer theory; however, substantial spread of observations is found at low levels of wind forcing, where the Stokes drift of swell may have a substantial impact on the near-surface current profile. A novel application of short time window Fourier transforms allows for the estimation of near-surface wave orbital velocity magnitudes. These are found to be in general agreement with the prescriptions of linear wave theory, although observations diverge from theory at high levels of wind forcing where the interface is subject to surface wave breaking. Finally, the surface gravity wave phase-coherent short wave growth is presented and discussed in the context of hydrodynamic wave and airflow modulation. 

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4. Integrating Multi-Sensing Technology for Preemptive Detection of Highway Slope Instability

   Salunke, R; Biswas, R; Khan, S; La_Cour, I (March 2025, American Society of Civil Engineers)

   Highway slopes are susceptible to various geohazards, including landslides, rockfalls, and soil creep, necessitating early detection to minimize disruptions, prevent collisions, and ensure road safety. Conventional methods, such as visual inspections and periodic surveys, may overlook subtle changes or fail to provide timely alerts. This research aims to enhance slope movement and instability detection by leveraging advanced remote-sensing technologies such as interferometric synthetic aperture radar (InSAR), light detection and ranging (LiDAR), and uncrewed aerial vehicles (UAV). The primary objective is to develop an integrated approach combining multiple data sources to detect and predict highway slope movement effectively. InSAR offers surface deformation measurements over time, capturing nuanced slope movements, while LiDAR and UAVs provide high-resolution elevation information, including slope angles, curvature, and vegetation cover. This study explores methods to integrate these complementary data sets to validate the slope movement detection from InSAR. The research involves establishing a baseline ground motion scenario using historical open-access Sentinel-1 satellite data spanning 10 years (2018􀀐2024) for the central Mississippi region, characterized by expansive clay prone to volume changes, then comparing the ground motions with those observed from near-surface remote sensing. The baseline ground motion scenario is compared with ground truthing from near-surface remote sensing surveys conducted by LiDAR and UAV photogrammetry. The point cloud and imagery obtained from LiDAR and UAVs facilitated cross-verification and validation of the InSAR ground displacements. This study provides a comprehensive and innovative methodology for monitoring highway infrastructure using InSAR and near-surface remote sensing techniques such as LiDAR and UAV. Continuous ground motion analysis provides immediate feedback on slope performance, helping to prevent potential failures. LiDAR change detection allows for detailed evaluation of highway slopes and precise identification of potential failure locations. Integrating remote sensing techniques into geotechnical asset management programs is crucial for proactively assessing risks and enhancing highway safety and resilience. Future studies will use this data set to create finite-element-based numerical models, aiding in developing surrogate models for highway embankments based on observed InSAR ground motion patterns. This study will also serve as a foundation for future machine-learning classification models for detecting vulnerable geo-infrastructure assets. 

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5. LEAP-UCD-2017 Centrifuge Test at University of California, Davis

   https://doi.org/10.1007/978-3-030-22818-7_13  

   Carey, Trevor J.; Stone, Nicholas; Bonab, Masoud H.; Kutter, Bruce L. (January 2020, Model Tests and Numerical Simulations of Liquefaction and Lateral Spreading)

   Three centrifuge experiments were performed at the University of California, Davis, for LEAP-UCD-2017. LEAP is a collaborative effort to assess repeatability of centrifuge test results and to provide data for the validation of numerical models used to predict the effects of liquefaction. The model configuration used the same geometry as the LEAP-GWU-2015 exercise: a submerged slope of Ottawa F-65 sand inclined at 5 degrees in a rigid container. This paper focuses on presenting results from the two destructive ground motions from each of the three centrifuge models. The effect of each destructive ground motion is evaluated by accelerometer recordings, pore pressure response, and lateral deformation of the soil surface. New techniques were implemented for measuring liquefaction-induced lateral deformations using five GoPro cameras and GEO-PIV software. The methods for measuring the achieved density of the as-built model are also discussed. 

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