Search for: All records

Geotechnical data are increasingly utilized to aid investigations of coastal erosion and the development of coastal morphological models; however, measurement techniques are still challenged by environmental conditions and accessibility in coastal areas, and particularly, by nearshore conditions. These challenges are exacerbated for Arctic coastal environments. This article reviews existing and emerging data collection methods in the context of geotechnical investigations of Arctic coastal erosion and nearshore change. Specifically, the use of cone penetration testing (CPT), which can provide key data for the mapping of soil and ice layers as well as for the assessment of slope and block failures, and the use of free-fall penetrometers (FFPs) for rapid mapping of seabed surface conditions, are discussed. Because of limitations in the spatial coverage and number of available in situ point measurements by penetrometers, data fusion with geophysical and remotely sensed data is considered. Offshore and nearshore, the combination of acoustic surveying with geotechnical testing can optimize large-scale seabed characterization, while onshore most recent developments in satellite-based and unmanned-aerial-vehicle-based data collection offer new opportunities to enhance spatial coverage and collect information on bathymetry and topography, amongst others. Emphasis is given to easily deployable and rugged techniques and strategies that can offer near-term opportunities to fill current gaps in data availability. This review suggests that data fusion of geotechnical in situ testing, using CPT to provide soil information at deeper depths and even in the presence of ice and using FFPs to offer rapid and large-coverage geotechnical testing of surface sediments (i.e., in the upper tens of centimeters to meters of sediment depth), combined with acoustic seabed surveying and emerging remote sensing tools, has the potential to provide essential data to improve the prediction of Arctic coastal erosion, particularly where climate-driven changes in soil conditions may bias the use of historic observations of erosion for future prediction. 

In June 2022, the southern part of Montana and the northern part of Yellowstone National Park experienced flooding along multiple watersheds, including the Yellowstone River. The flooding resulted from heavy snowmelt between June 10-13th, leading to record levels of river water elevation in most of the main tributaries to the Yellowstone River. Substantial damage occurred to residential, commercial, and transport infrastructure, however, no fatalities were reported. Estimated damages accumulate to approximately U.S. $29 million. The GEER reconnaissance effort, conducted between June 30th – July 4th, recorded geotechnical, geo-structural, and geomorphological observations of failures as well as successful mitigation of flood damage. In addition to traditional terrestrial photography and aerial imagery, the team collected (Light Detection Ranging (LIDAR) scanning, Structure for Motion (SfM) imagery, and Multispectral Imagery to establish point cloud models for case history analyses and post-reconnaissance failure analyses. 

Unmanned aerial vehicles (UAV) enable detailed historical preservation of large-scale infrastructure and contribute to cultural heritage preservation, improved maintenance, public relations, and development planning. Aerial and terrestrial photo data coupled with high accuracy GPS create hyper-realistic mesh and texture models, high resolution point clouds, orthophotos, and digital elevation models (DEMs) that preserve a snapshot of history. A case study is presented of the development of a hyper-realistic 3D model that spans the complex 1.7 km2 area of the Brigham Young University campus in Provo, Utah, USA and includes over 75 significant structures. The model leverages photos obtained during the historic COVID-19 pandemic during a mandatory and rare campus closure and details a large scale modeling workflow and best practice data acquisition and processing techniques. The model utilizes 80,384 images and high accuracy GPS surveying points to create a 1.65 trillion-pixel textured structure-from-motion (SfM) model with an average ground sampling distance (GSD) near structures of 0.5 cm and maximum of 4 cm. Separate model segments (31) taken from data gathered between April and August 2020 are combined into one cohesive final model with an average absolute error of 3.3 cm and a full model absolute error of <1 cm (relative accuracies from 0.25 cm to 1.03 cm). Optimized and automated UAV techniques complement the data acquisition of the large-scale model, and opportunities are explored to archive as-is building and campus information to enable historical building preservation, facility maintenance, campus planning, public outreach, 3D-printed miniatures, and the possibility of education through virtual reality (VR) and augmented reality (AR) tours. 

Unsupervised machine learning algorithms (clustering, genetic, and principal component analysis) automate Unmanned Aerial Vehicle (UAV) missions as well as the creation and refinement of iterative 3D photogrammetric models with a next best view (NBV) approach. The novel approach uses Structure-from-Motion (SfM) to achieve convergence to a specified orthomosaic resolution by identifying edges in the point cloud and planning cameras that “view” the holes identified by edges without requiring an initial model. This iterative UAV photogrammetric method successfully runs in various Microsoft AirSim environments. Simulated ground sampling distance (GSD) of models reaches as low as 3.4 cm per pixel, and generally, successive iterations improve resolution. Besides analogous application in simulated environments, a field study of a retired municipal water tank illustrates the practical application and advantages of automated UAV iterative inspection of infrastructure using 63 % fewer photographs than a comparable manual flight with analogous density point clouds obtaining a GSD of less than 3 cm per pixel. Each iteration qualitatively increases resolution according to a logarithmic regression, reduces holes in models, and adds details to model edges. 

Remote sensing with unmanned aerial vehicles (UAVs) facilitates photogrammetry for environmental and infrastructural monitoring. Models are created with less computational cost by reducing the number of photos required. Optimal camera locations for reducing the number of photos needed for structure-from-motion (SfM) are determined through eight mathematical set-covering algorithms as constrained by solve time. The algorithms examined are: traditional greedy, reverse greedy, carousel greedy (CG), linear programming, particle swarm optimization, simulated annealing, genetic, and ant colony optimization. Coverage and solve time are investigated for these algorithms. CG is the best method for choosing optimal camera locations as it balances number of photos required and time required to calculate camera positions as shown through an analysis similar to a Pareto Front. CG obtains a statistically significant 3.2 fewer cameras per modeled area than base greedy algorithm while requiring just one additional order of magnitude of solve time. For comparison, linear programming is capable of fewer cameras than base greedy but takes at least three orders of magnitude longer to solve. A grid independence study serves as a sensitivity analysis of the CG algorithms α (iteration number) and β (percentage to be recalculated) parameters that adjust traditional greedy heuristics, and a case study at the Rock Canyon collection dike in Provo, UT, USA, compares the results of all eight algorithms and the uniqueness (in terms of percentage comparisons based on location/angle metadata and qualitative visual comparison) of each selected set. Though this specific study uses SfM, the principles could apply to other instruments such as multi-spectral cameras or aerial LiDAR. 

Seismic hazard in Mexico City governed by site effects. The M8.1 1985 subduction zone earthquake, which caused significant damage and loss of thousands of lives at 350 km epicentral distance, has become the quintessential example of the role that site effects can play in modifying the amplitude, frequency, and duration of ground shaking; and in aggravating the catastrophic consequences of earthquakes. We here present observations and analyses of the M7.1 September 19, 2017, event that—while triggered by an intraplate rupture at approximately half the epicentral distance of the 1985 event relative to Mexico City—caused severe structural damage to a few tens of buildings located in a relatively narrow zone between the hill and lake zones of the basin, known as the transition zone. We show that the M 7.1 mainshock exposed the vulnerabilities of the pre-1985 building code in the transition zone; but more importantly highlighted the improvement of the 1987 building code revision in terms of the performance of modern high-rise buildings that suffered catastrophic consequences during the 1985 Michoácan earthquake sequence. We next analyze several records collected at stations in the basin over the past 20 years. We highlight the importance of three-dimensional heterogeneity of the basin sediments, the coupling between hydrological setting and site response and their evolution with time, and the energy interaction between the deep basin edge and the shallow clay layers. Results presented are the collective effort of the GEER teams that were deployed to perform post-earthquake reconnaissance in the affected regions of the epicentral area and in Mexico City after the M 7.1 September 19, 2017, earthquake sequence. 

Small rotorcraft unmanned air vehicles (sUAVs) are valuable tools in solving geospatial inspection challenges. One area where this is being widely explored is disaster reconnaissance [1]. Using sUAVs to collect images provides engineers and government officials critical information about the conditions before and after a disaster [2]. This is accomplished by creating high- fidelity 3D models from the sUAV’s imagery. However, using an sUAV to perform inspections is a challenging task due to constraints on the vehicle’s flight time, computational power, and data storage capabilities [3]. The approach presented in this article illustrates a method for utilizing multiple sUAVs to inspect a disaster region and merge the separate data into a single high-resolution 3D model. 

This study presents a novel multi-scale view-planning algorithm for automated targeted inspection using unmanned aircraft systems (UAS). In industrial inspection, it is important to collect the most relevant data to keep processing demands, both human and computational, to a minimum. This study investigates the viability of automated targeted multi-scale image acquisition for Structure from Motion (SfM)-based infrastructure modeling. A traditional view-planning approach for SfM is extended to a multi-scale approach, planning for targeted regions of high, medium, and low priority. The unmanned aerial vehicle (UAV) can traverse the entire aerial space and facilitates collection of an optimized set of views, both close to and far away from areas of interest. The test case for field validation is the Tibble Fork Dam in Utah. Using the targeted multi-scale flight planning, a UAV automatically flies a tiered inspection using less than 25% of the number of photos needed to model the entire dam at high-priority level. This results in approximately 75% reduced flight time and model processing load, while still maintaining high model accuracy where needed. Models display stepped improvement in visual clarity and SfM reconstruction integrity by priority level, with the higher priority regions more accurately modeling smaller and finer features. A resolution map of the final tiered model is included. While this study focuses on multi-scale view planning for optical sensors, the methods potentially extend to other remote sensors, such as aerial LiDAR. 

The 2016 Central Italy earthquake sequence caused numerous landslides over a large area in the Central Apennines. As a result, the Geotechnical Extreme Events Reconnaissance Association (GEER) organized post-earthquake reconnaissance missions to collect perishable data. Given the challenging conditions following the earthquakes, the GEER team implemented a phased reconnaissance approach. This paper illustrates this approach and how it was used to document the largest and most impactful seismically induced landslides. This phased approach relied upon satellite-based interferometric damage proxy maps, preliminary published reports of observed landslides, digital imaging from small unmanned aerial vehicles (UAVs), traditional manual observations, and terrestrial laser scanning. Data collected from the reconnoitered sites were used to develop orthophotos and meshed three-dimensional digital surface models. These products can provide valuable information such as accurate measurements of landslide ground movements in complex topographic geometries or boulder runout distances from rock falls. The paper describes three significant landslide case histories developed and documented with the phased approach: Nera Valley, Village of Pescara del Tronto, and near the villages of Crognaleto and Cervaro.