Thesis (M.S.)--Oregon State University, 1997. / Typescript (photocopy). Includes bibliographical references (leaves 92-94). Also available on the World Wide Web.

The prediction of motion of slowly moving landslides, also referred to as creeping slopes, is important for the reduction of landslide hazards. Such continuous and slowly moving landslides do not represent the usual stability problems of geotechnical analysis because these slopes are neither still nor ruptured but they move. For proper modeling of the motion of landslides, it is essential to develop improved techniques that integrate appropriate modeling of geological materials involved, laboratory and field tests, and verifications using computational methods. This dissertation focusses attention on the development of such an appropriate model for the time-dependent behavior of creeping landslides. Based on field observations it is proposed that the phenomenon of creeping landslides can be considered as involving the motion of a large mass of soil over a parent (fixed) mass with pronounced shear deformations occuring in a thin layer between the moving mass and the parent mass. The thin layer is refered to as interface zone while the overlying mass is refered to as solid body. The generalized Hierarchical Single Surface (HiSS) series of plasticity models are adopted to characterize the solid body. The interface zone is modeled using the specialization of the HiSS models for conditions occuring in the thin layer. Time dependency is introduced in constitutive models by adopting Perzyna's elastoviscoplastic formulation. The parameters for the HiSS and interface models are determined from laboratory tests on soils obtained from an actual slowly moving landslide at Villarbeney in Switzerland. Triaxial tests along various stress paths and oedemeter tests are conducted for the solid body. New analytical solutions are derived for prediction of oedometer tests. A general procedure for determination of viscous parameters is developed and techniques to process raw creep test data are proposed. Novel and representative simple shear interface tests are conducted to find parameters for the interface model. Special techniques for experimental analysis have been developed. A modified interface model to simulate the observed phenomenon of only compaction under shear is proposed. The parameters for the constitutive models are verified by numerically backpredicting experimental tests. An existing finite element code has been modified to incorporate various aspects of the small strain elastoviscoplastic formulation. Field measurements in the form of inclinometer profiles at various borehole locations on Villarbeney landslide are available. These inclinometer profiles are predicted using the proposed model. A comparison of the field measurements and the results from finite element analysis shows that such a model can be successfully used for predicting the behavior of slowly moving landslides.

Earthquake-induced sliding displacements are commonly used to assess the seismic performance of slopes. These displacements represent the cumulative, downslope movement of a sliding block due to earthquake shaking. While the sliding block model is a simplified representation of the field conditions, the displacements predicted from this model have been shown to be a useful index of seismic performance of slopes. Current evaluation procedures that use sliding block displacements to evaluate the potential for slope instability typically are based on a deterministic approach or a pseudo-probabilistic approach, in which the variabilities in the expected ground motion and predicted displacement are either ignored or not treated rigorously. Thus, there is no concept of the actual hazard (i.e., the annual probability of exceedance) associated with the computed displacement. This dissertation focuses on quantifying the risk for earthquake-induced landslides. The basic approach involves a probabilistic framework for computing the annual rate of exceedance of different levels of sliding displacement for a slope such that a hazard curve for sliding displacement can be developed. The framework incorporates the uncertainties in the prediction of earthquake ground shaking, in the prediction of sliding displacement, and in the assessment of soil properties. The framework considers two procedures that will yield a displacement hazard curve: the scalar hazard approach that utilizes a single ground motion parameter and its associated hazard curve to compute permanent sliding displacements; and a vector hazard approach that predicts displacements based on two (or more) ground motion parameters and the correlation between these parameters. Current predictive models for sliding displacement provide the expected level of displacement as a function of the characteristics of the slope (e.g., geometry, strength, yield acceleration) and the characteristics of earthquake shaking (e.g., peak ground acceleration, peak ground velocity). However, current models contain significant aleatory variability such that the range of predicted displacements is large. To reduce the variability in the sliding displacement prediction and to provide models appropriate for the presented probabilistic framework, sliding displacement predictive equations are developed that utilize single and multiple ground motion parameters. The developed framework is implemented to the Mint Canyon 7.5-minute quadrangle in California to generate a map of earthquake-induced landslide hazard. Application of the probabilistic procedure to a 7-1/2 minute quadrangle of California is an important exercise to identify potential difficulties in California Geological Survey’s (CGS) current application for hazard mapping, and for the eventual adoption by CGS and USGS. / text

Thesis (M.A.)--University of Montana, 2008. / Title from title screen. Description based on contents viewed Aug. 19, 2008. Includes bibliographical references (p. 86-91).

Thesis (M.S.)--West Virginia University, 2008. / Title from document title page. Document formatted into pages; contains vii, 129 p. : ill. (some col.), col. maps. Includes abstract. Includes bibliographical references (p. 79-86).

Thesis (Ph. D.)--Oregon State University, 1997. / Typescript (photocopy). Includes bibliographical references (leaves 259-268). Also available on the World Wide Web.

Thesis (Ph. D.)--University of Canterbury, 2006. / Typescript (photocopy). Four maps in pocket. Includes bibliographical references (leaves 224-243). Also available via the World Wide Web.

Thesis (Ph. D.)--University of Texas at Austin, 2008. / Vita. Includes bibliographical references.

by Wong, Tak-yee Tammy. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1995. / Includes bibliographical references (leaves 170-178). / ABSTRACT --- p.i-iii / ACKNOWLEDGEMENTS --- p.iv-v / TABLE OF CONTENTS --- p.vi-x / LIST OF FIGURES --- p.xi-xii - / LIST OF PLATES --- p.xiii-ix / LIST OF TABLES --- p.x-xii / Chapter CHAPTER I: --- INTRODUCTION --- p.1 / Chapter 1.1 --- Introduction --- p.1 / Chapter 1.2 --- Research Questions --- p.5 / Chapter 1.3 --- Study Significance --- p.7 / Chapter 1.4 --- Organization of the Thesis --- p.8 / Chapter CHAPTER II: --- LITERATURE REVIEW --- p.10 / Chapter 2.1 --- Introduction --- p.10 / Chapter 2.2 --- Nature of Landslides --- p.10 / Chapter 2.2.1 --- Landslide Classification --- p.10 / Chapter 2.2.2 --- Morphometry of Landslides --- p.12 / Chapter 2.2.3 --- Factors Affecting Landslide Occurrence --- p.16 / Chapter 2.2.3.1 --- Gradient --- p.19 / Chapter 2.2.3.2 --- Slope Shape --- p.21 / Chapter 2.2.3.3 --- Aspect --- p.22 / Chapter 2.2.3.4 --- Vegetation --- p.24 / Chapter 2.2.3.5 --- Drainage --- p.26 / Chapter 2.2.3.6 --- Precipitation/Seismicity --- p.26 / Chapter 2.2.3.7 --- Lithology and Geological Influences --- p.28 / Chapter 2.2.3.8 --- Regolith --- p.29 / Chapter 2.2.3.8.1 --- Hydrological Properties of Soils --- p.29 / Chapter 2.2.3.8.2 --- Engineering Properties of Soils --- p.30 / Chapter 2.3 --- Data Sources for Landslide Studies --- p.31 / Chapter 2.3.1 --- Aerial Photo Interpretation (API) --- p.32 / Chapter 2.3.2 --- Remote Sensing --- p.34 / Chapter 2.3.3 --- Field Survey --- p.35 / Chapter 2.3.4 --- Subsurface Investigation --- p.36 / Chapter 2.4 --- Landslide Studies in Hong Kong --- p.36 / Chapter 2.5 --- Applications of GIS on Landslide Studies --- p.38 / Chapter 2.5.1 --- Major Data in GIS for Landslide Studies --- p.39 / Chapter 2.5.1.1 --- Triangulated Irregular Network (TIN) as a Representation of Surface --- p.39 / Chapter 2.5.2 --- Applications --- p.42 / Chapter 2.5.2.1 --- Inventory --- p.43 / Chapter 2.5.2.2 --- Landslide Hazard Assessment --- p.43 / Chapter 2.5.2.2.1 --- Statistical Modeling --- p.46 / Chapter 2.5.2.2.2 --- Physical Processes or Three- Dimensional Modeling --- p.50 / Chapter 2.6 --- Suggestions for Future Research Directions --- p.51 / Chapter CHAPTER III: --- STUDY AREA --- p.54 / Chapter 3.1 --- Location and Choice of Study Area --- p.54 / Chapter 3.2 --- Climatic Aspects --- p.56 / Chapter 3.3 --- Geological Aspects --- p.62 / Chapter 3.3.1 --- General Information of GASP V --- p.62 / Chapter 3.3.2 --- Rock Types Specific to the Two Sites Chosen --- p.63 / Chapter 3.3.2.1 --- Volcanic Units - Repulse Bay Formation --- p.65 / Chapter 3.3.2.2 --- Sedimentary Units - Port Island Formation (PI) --- p.65 / Chapter 3.4 --- Geomorphological Aspects --- p.66 / Chapter 3.4.1 --- General Information of GASP V --- p.66 / Chapter 3.5 --- Erosion and Stability --- p.67 / Chapter 3.6 --- Vegetation --- p.67 / Chapter 3.7 --- Summary --- p.70 / Chapter CHAPTER IV: --- DATABASE CONSTRUCTION AND MANIPULATION --- p.71 / Chapter 4.1 --- Data Collection --- p.73 / Chapter 4.1.1 --- Aerial Photo Interpretation (API) --- p.73 / Chapter 4.1.1.1 --- Landslip Inventory --- p.75 / Chapter 4.1.2 --- Field Techniques --- p.78 / Chapter 4.1.2.1 --- Slope Failure/Deposit Field Survey sheet --- p.78 / Chapter 4.1.2.2 --- Collection of Landslide Data --- p.79 / Chapter 4.1.3 --- Collection of Existing Data --- p.80 / Chapter 4.1.3.1 --- 1:5000 Topographic Maps --- p.80 / Chapter 4.1.3.2 --- Terrain Classification --- p.81 / Chapter 4.1.3.3 --- WWF Vegetation Database --- p.85 / Chapter 4.2 --- Data Input and Conversion --- p.86 / Chapter 4.2.1 --- Digitizing of Data --- p.87 / Chapter 4.2.1.1 --- Landslip Capture in Stereocord --- p.87 / Chapter 4.2.1.2 --- Data Conversion --- p.94 / Chapter 4.2.1.2.1 --- Topographic Maps - Scanning and Vectorization --- p.94 / Chapter 4.3 --- Data Editing --- p.94 / Chapter 4.3.1 --- Line Cleaning for Landslide Coverage --- p.96 / Chapter 4.3.2 --- Line Cleaning and Height Tagging for Topographic Map --- p.96 / Chapter 4.3.3 --- Editing on Terrain Classification Map --- p.97 / Chapter 4.4 --- Database Construction --- p.97 / Chapter 4.4.1 --- Data Base Design --- p.97 / Chapter 4.4.1.1 --- Graphical Data Base --- p.98 / Chapter 4.4.1.2 --- Attribute Data Base --- p.99 / Chapter 4.4.2 --- Creation of a Triangulated Irregular Network (TIN) --- p.104 / Chapter 4.5 --- Data Preparation and Pre-analysis Manipulation --- p.105 / Chapter 4.5.1 --- Extraction of Terrain Variables from TIN --- p.105 / Chapter 4.5.1.1 --- TIN'S Derived Variable - Elevation --- p.105 / Chapter 4.5.1.2 --- TIN'S Derived Variable - Gradient --- p.107 / Chapter 4.5.1.3 --- TIN'S Derived Variable - Orientation --- p.109 / Chapter 4.5.1.4 --- TIN's Derived Variable - Dimensions (surface distance) of Landslides --- p.109 / Chapter 4.5.1.5 --- Micro-DEM and Profile --- p.109 / Chapter 4.5.1.6 --- Weighting Method Adopted in Calculating the Gradient and Orientation of Primary Depletion Scar --- p.110 / Chapter 4.5.2 --- Data Preprocessing --- p.110 / Chapter 4.6 --- Summary --- p.114 / Chapter CHAPTER V: --- STATISTICAL ANALYSIS OF LANDSLIDE DISTRIBUTION --- p.115 / Chapter 5.1 --- Sampling --- p.116 / Chapter 5.1.1 --- Sampling Frame --- p.116 / Chapter 5.1.1.1 --- Simple Random Point Sampling --- p.117 / Chapter 5.1.1.2 --- Stratified Random Point Sampling --- p.117 / Chapter 5.2 --- Comparison of the Two Study Areas --- p.119 / Chapter 5.3 --- Statistical Analyses of Landslip Variables --- p.123 / Chapter 5.3.1 --- Gradient (TIN) and Elevation --- p.124 / Chapter 5.3.2 --- "Aspect, Geological Materials, Gradient, Terrain Component, Erosion & Instability, and Vegetation" --- p.126 / Chapter 5.3.2.1 --- Aspect --- p.127 / Chapter 5.3.2.2 --- Geological Materials --- p.130 / Chapter 5.3.2.3 --- Gradient --- p.132 / Chapter 5.3.2.4 --- Terrain Component --- p.137 / Chapter 5.3.2.5 --- Erosion and Instability --- p.140 / Chapter 5.3.2.6 --- WWF Vegetation --- p.140 / Chapter 5.3.3 --- Result of the Partial Model --- p.145 / Chapter 5.4 --- Logistic Regression Model --- p.147 / Chapter 5.4.1 --- Landslide Probability Mapping --- p.154 / Chapter 5.4.2 --- Testing the Model Output --- p.157 / Chapter 5.5 --- Summary --- p.161 / Chapter CHAPTER VI: --- CONCLUSIONS --- p.162 / Chapter 6.1 --- Summary of Findings --- p.162 / Chapter 6.2 --- Limitations of the Study --- p.163 / Chapter 6.3 --- Recommendations for Further Studies --- p.166 / BIBLOGRAPHY --- p.167 / APPENDICES / "APPENDIX I Draft 3.3 slope failure/deposit field survey sheet (King, 1994a)" / "APPENDIX II Landslide/deposit field description sheet (King, 1994b)" / "APPENDIX III Hourly rainfall (mm) record at N05 in September 26-27，1993 (Source: Special Projects Division, Geotechnical Engineering Office, Civil Engineering Department)" / "APPENDIX IV Hourly rainfall (mm) record at R23 in September 1993 (Source: Hydrometeorology Section, Royal Observatory, Hong Kong,1993)" / "APPENDIX V Hourly rainfall (mm) record at R31 in September 1993 (Source: Hydrometeorology Section, Royal Observatory, Hong Kong,1993)"

Landslides represent a serious hazard to people and property in the Pacific Northwest. Currently, the factors leading to sudden catastrophic failure vs. gradual slow creeping are not well understood. Utilizing high-resolution monitoring techniques at a sub-annual temporal scale can help researchers better understand the mechanics of mass wasting processes and possibly lead to better mitigation of their danger. This research used historical imagery analysis, precipitation data, aerial lidar analysis, Structure from Motion (SfM) photogrammetry, terrestrial laser scanning (TLS), and hydrologic measurements to monitor displacement of the Silt Creek Landslide in the western Cascade Mountain Range in Linn County, Oregon. This landslide complex is ~4 km long by ~400 m wide. The lower portion of the landslide reactivated following failure of an internal scarp in June 2014. Precipitation was measured on site and historical precipitation data was determined from a nearby SNOTEL site. Analysis of aerial lidar data found that the internal scarp failure deposited around 1.00x106 m3 of material over an area of 1.20x105 m2 at the uppermost portion of the reactivated slide. Aerial lidar analysis also found that displacement rates on the slide surface were as high as 3 m/yr during the 2015 water year, which was the year immediately following the failure. At the beginning of the 2016 water year, very low altitude aerial images were collected and used to produce point cloud data, via SfM, of a deformed gravel road which spans a portion of the reactivated slide. The SfM data were complimentary to the aerial and TLS scans. The SfM point cloud had an average point density of >7500 points per square meter. The resulting cloud was manipulated in 3D software to produce a model of the road prior to deformation. This was then compared to the original deformed model. Average displacement found in the deformed gravel road was 7.5 m over the 17 months between the scarp failure and the collection of the images, or ~3 m/yr. TLS point clouds were collected quarterly over the course of the 2016 water year at six locations along the eastern margin of the reactivated portion of the landslide. These 3D point cloud models of the landslide surface had an average density of 175 points per square meter. Scans were georeferenced to UTM coordinates and relative alignment of the scans was accomplished by first using the iterative closest point algorithm to align stable, off-slide terrain, and then applying the same rigid body translation to the entire scan. This was repeated for each scan at each location. Landmarks, such as tree trunks, were then manually selected at each location and their coordinates were recorded from the initial scan and each successive scan to measure displacement vectors. Average annual displacement for the 2016 water year ranged from a maximum of 0.92 m/yr in the uppermost studied area of the slide, to a low of 0.1 m/yr at the toe. Average standard deviation of the vectors of features on stable areas was 0.039 m, corresponding to a minimum detectable displacement of about ±4 cm. Displacement totals decreased with increasing distance downslope from the internal scarp failure. Additionally, displacement tended to increase with increasing distance laterally onto the slide body away from the right margin at all locations except the uppermost, where displacement rates were relatively uniform for all landmarks. Volumetric discharge measurements were collected for Silt Creek in 2016 using salt dilution gauging and found that discharge in the upslope portion of the study area was ~1 m3/s and increased to ~1.6 m3/s in the downslope portion. Landslide displacement rates were found to be much lower during the 2016 water year than during the 2015 water year, despite higher precipitation. This suggests that the over-all displacement trend was decoupled from precipitation values. Displacement rates at all locations on the slide decreased with each successive scan period with some portions of the landslide stopping by autumn of 2016, suggesting the study captured the slide as it returned to a state of stability. The spatial and temporal pattern of displacement is consistent with the interpretation that the landslide reactivation was a response to the undrained load applied by the internal scarp failure. This finding highlights the importance of detailed landslide monitoring to improve hazard estimation and quantification of landslide mechanics. This study provides new evidence that supports previous research showing that internal processes within landslide complexes can have feedback relationships, combines several existing 3D measurement tools to develop a detailed landslide monitoring methodology, uses a novel approach to landslide surface deformation measurements using SfM, and suggests that landslide initiation models which rely heavily on precipitation values may not account for other sources of landslide activation.