Search for: All records

This article presents a study of seismically-induced failure of massive steep rock slopes. A dynamic implementation of the bonded particle model (BPM) for rock is used to simulate the dynamic response and initiation of fracture in the slopes. Observation of forces that develop within the model in response to wave transmission and dynamic excitation provides insight into the fundamental mechanisms at work in seismically induced rock slope failure. Five distinct mechanisms of failure initiation are identified using non-destructive simulations and confirmed with destructive simulations. Three distinct modes of rock mass movement enabled by the failure mechanisms are identified. The predominant co-seismic failure mode was a shallow, highly-disrupted cliff collapse. Cliff collapse is initiated by relatively low levels of shaking. Shallow failures are also triggered at higher levels of shaking prior to the initiation of deeper, more coherent failures in the same seismic event. The results of the numerical study agree with qualitative historical surveys of seismically-induced rock slope failure trends and provide insight into the mechanisms behind observed co-seismic rock slope behavior. The frequently observed shallow failures are triggered by high compression stresses near the cliff toe combined with shallow subhorizontal ruptures behind the cliff face. These mechanisms are not well-captured by simplified analysis methods which may lead to underprediction of shallow co-seismic events. Deeper failure surfaces from stronger shaking create a base-isolation effect, slowing further disruption in the failure mass. Slope dynamic response and damage accumulation were shown to be interdependent and complex, emphasizing the importance of further research into the interaction between rock mass strength, slope geometry, structure, and ground motion characteristics. 

This study investigates seismically-induced failure mechanisms in massive rock slopes using the bonded particle model. The data from this study can be used to track seismically-induced stresses in steep slope geometries leading up to failure initiation. The data can also be used to study the propagation of damage initiated by these failure mechanisms and track the development of mass movement enabled by the seismically-induced damage. The bonded particle model data includes the motion time-histories of an array of monitoring particles in the slope, the stress tensors of representative volume elements throughout loading, and the full model geometry, which can be used to reproduce the discrete element model. 

Street View Images (SVI) are a common source of valuable data for researchers. Researchers have used SVI data for estimating pedestrian volumes, demographic surveillance, and to better understand built and natural environments in cityscapes. However, the most common source of publicly available SVI data is Google Street View. Google Street View images are collected infrequently, making temporal analysis challenging, especially in low population density areas. Our main contribution is the development of an open-source data pipeline for processing 360-degree video recorded from a car-mounted camera. The video data is used to generate SVIs, which then can be used as an input for longitudinal analysis. We demonstrate the use of the pipeline by collecting an SVI dataset over a 38-month longitudinal survey of Seattle, WA, USA during the COVID-19 pandemic. The output of our pipeline is validated through statistical analyses of pedestrian traffic in the images. We confirm known results in the literature and provide new insights into outdoor pedestrian traffic patterns. This study demonstrates the feasibility and value of collecting and using SVI for research purposes beyond what is possible with currently available SVI data. Our methods and dataset represent a first of its kind longitudinal collection and application of SVI data for research purposes. Limitations and future improvements to the data pipeline and case study are also discussed. 

ABSTRACT Earthquake-induced landslides can record information about the seismic shaking that generated them. In this study, we present new mapping, Light Detection and Ranging-derived roughness dating, and analysis of over 1000 deep-seated landslides from the Puget Lowlands of Washington, U.S.A., to probe the landscape for past Seattle fault earthquake information. With this new landslide inventory, we observe spatial and temporal evidence of landsliding related to the last major earthquake on the Seattle fault ∼1100 yr before present. We find spatial clusters of landslides that correlate with ground motions from recent 3D kinematic models of Seattle fault earthquakes. We also find temporal patterns in the landslide inventory that suggest earthquake-driven increases in landsliding. We compare the spatial and temporal landslide data with scenario-based ground motion models and find stronger evidence of the last major Seattle fault earthquake from this combined analysis than from spatial or temporal patterns alone. We also compare the landslide inventory with ground motions from different Seattle fault earthquake scenarios to determine the ground motion distributions that are most consistent with the landslide record. We find that earthquake scenarios that best match the clustering of ∼1100-year-old landslides produce the strongest shaking within a band that stretches from west to east across central Seattle as well as along the bluffs bordering the broader Puget Sound. Finally, we identify other landslide clusters (at 4.6–4.2 ka, 4.0–3.8 ka, 2.8–2.6 ka, and 2.2–2.0 ka) in the inventory which let us infer potential ground motions that may correspond to older Seattle fault earthquakes. Our method, which combines hindcasting of the surface response to the last major Seattle fault earthquake, using a roughness-aged landslide inventory with forecasts of modeled ground shaking from 3D seismic scenarios, showcases a powerful new approach to gleaning paleoseismic information from landscapes. 

A first foundational assessment is provided for disaster debris reconnaissance that includes identifying tools and techniques for reconnaissance activities, identifying challenges in field reconnaissance, and identifying and developing preliminary guidelines and standards based on advancements from a workshop held in 2022. In this workshop, reconnaissance activities were analyzed in twofold: in relation to post-disaster debris and waste materials and in relation to waste management infrastructure. A four-phase timeline was included to capture the full lifecycle of management activities ranging from collection to temporary storage to final management route: pre-disaster or pre-reconnaissance, post-disaster response (days/weeks), short-term recovery (weeks/months), and long-term recovery (months/years). For successful reconnaissance, objectives of field activities and data collection needs; data types and metrics; and measurement and determination methods need to be identified. A reconnaissance framework, represented using a 3x2x2x4 matrix, is proposed to incorporate data attributes (tools, challenges, guides), reconnaissance attributes (debris, infrastructure; factors, actions), and time attributes (pre-event, response, short-term, long-term). This framework supports field reconnaissance missions and protocols that are longitudinally based and focused on post-disaster waste material and infrastructure metrics that advance sustainable materials management practices. To properly frame and develop effective reconnaissance activities, actions for all data attributes (tools, challenges, guides) are proposed to integrate sustainability and resilience considerations. While existing metrics, tools, methods, standards, and protocols can be adapted for sustainable post-disaster materials management reconnaissance, development of new approaches are needed for addressing unique aspects of disaster debris management. 

Point cloud models of the Mayfield Clothing Mill, a tornado-damaged historic masonry building, are analyzed to understand any possible structural deviations. A point cloud alignment and deviation analysis workflow are described. Cloud2FEM, a point cloud to finite element model (FEM) conversion software, is utilized to generate a FEM for the Clothing Mill. The resulting FEM can be used for structural analysis purposes and be simulated under load conditions to study the structure’s response. 

Abstract In early December 2020, an atmospheric river (AR) and rain-on-snow (ROS) event impacted the Haines, Alaska area, resulting in record-breaking rainfall and snowmelt that caused flooding and dozens of mass movement events. We consider the AR—a one-in-500-year event—as the trigger for the devastating Beach Road Landslide (BRLS), which destroyed or damaged four residences and took the lives of two people. The BRLS started as a debris avalanche and transitioned into a debris flow, with a total approximate landslide volume of 187,100 m³. Geomorphic analysis using lidar data identified evidence of paleo-landslides and displaced masses of rock, one of which served as the source area for the BRLS. Significant structural features in the weak ultramafic bedrock defined the head scarp area and formed the failure plane. This study illustrates the importance of identifying pre-existing landslide features and source areas likely to produce future landslides. As an increase in ROS events is projected for Southeast Alaska with warmer and wetter winters, we recommend the development of an AR scale coupled with geological information for the region, to enhance warnings to residents in landslide-prone areas. 

Reconnaissance following Hurricane Ida. Wind damage to light structures, flooding, levee failures, coastal erosion. Field photos, Lidar, UAVs.