1. Web-based Class Project on Rock Mechanics Report prepared as part of course CEE 544: Rock Mechanics Winter 2015 Semester Instructor: Professor Dimitrios Zekkos Department of Civil and Environmental Engineering University of Michigan Coseismic Landslide Hazard Modeling Methodologies Prepared by: Jennifer Von Voigtlander With the Support of:

2. More Information More detailed technical information on this project can be found at: http://www.geoengineer.org/education/web-based-class-projects/rock- mechanics

3. Coseismic Landslide Hazard Modeling Methodologies Jennifer Von Voigtlander

4. Coseismic landslides are the phenomena resulting from an earthquake that produced ground accelerations that exceed the threshold necessary to initiate slope failure (Newmark, 1965) Detrimental economic and human loss (Keefer, 1994) Massive volumes of rock and soil are displaced (Keefer, 1994) Methodology and Analysis Conclusions Introduction Yuxia, 2008

5. Landslide hazard modeling (e.g. Keefer et al., 2002; Dia et al., 2011) – Spatial and temporal domains – Probabilistic or Deterministic – Involves multiple variable incorporation Past slope failures Environmental conditions Geologic and geotechnical parameters Other phenomena associated with an area Importance of landslide hazard modeling (e.g. Brenning, 2005; Nowicki et al., 2014) – Aids in land-use development and emergency planning – Aids disaster relief teams in recovery and reconstruction following a seismic event Methodology and Analysis Conclusions Introduction

6. Common Methods – Statistical techniques: preferable, minimizes subjectivity in weightage assignment procedure, producing more objective and reproducible results (Kangungo et al., 2009) Logistic regression Artificial neural networks (ANN) Discriminant analysis Support vector machines Bootstrap-aggregated classification trees – Physical techniques: based on simple mechanical laws instead of complete landslide inventories and data (Pardeshi et al., 2013) Newmark Sliding block Methodology and Analysis Conclusions Introduction

7. Review – Statistical techniques Logistic Regression – Nowicki et al., 2014 Artificial Neural Networks (ANN) – Li et al., 2012 – Physical techniques Capacity-Demand – Romeo et al., 2007 Methodology and Analysis Conclusions Introduction Romeo et al., 2007

8. Most commonly used statistical prediction technique (Nowicki et al., 2014) Lower rate of conditional error compared to other modeling techniques (Brenning, 2005) High generalization capability (Pardeshi et al., 2013) Methodology and Analysis Logistic Regression Conclusions Introduction

9. Defined by formula coefficients and linear combination of predictor variables  models the presence or absence of a landslide in a particular area (Brenning, 2005) Limitations: – Linear nature of covariant relationships  fewer variables may be accounted for in the model (Pardeshi et al., 2013) Methodology and Analysis Logistic Regression Conclusions Introduction

10. Nowicki et al., 2014 Failure may be due to other factors this model neglected – Previous slope failures or environmental contributions Separate regression models may be needed for more accurate landslide prediction Stronger correlation with PGA and slope rather than friction and compound topography index (CTI) Methodology and Analysis Logistic Regression Conclusions Introduction

11. Non-linear bonds between three or more layers of nodes (Lillesand et al., 2008; Paradeshi et al., 2013) – Connections are based on weighting of inputs – More variable incorporation Adds hidden layers allowing for more complex problems – Allows of more complex relationships between variables to be accounted for – Predicts classification schemes with increased accuracy Inputs (Lillesand et al., 2008) – Spectral bands from remote sensing images – Textural features – Other inputs obtained from a Digital Elevation Model (DEM) slope, aspect, and elevation Methodology and Analysis Artificial Neural Network Conclusions Introduction

12. Methodology and Analysis Artificial Neural Network Conclusions Introduction Lillisand et al., 2008

13. Limitations (Lillesand et al., 2008; Paradeshi et al., 2013) – Increased variable incorporation restricts generalization capability of the model & increases training time – Variable relationships heavily dependent on supplied training data – May not reach an absolute minimum error – Not guaranteed to find the most ideal solutions Methodology and Analysis Artificial Neural Network Conclusions Introduction

14. Li et al., 2012 Analyzed landslide susceptibility when induced by rainfall and seismic activity Results: – No conclusive relationship between landslide hazard and lithology or catchment area – Slope aspect and elevation contribute to landslide susceptibility Methodology and Analysis Artificial Neural Network Conclusions Introduction

15. Ideal to use in areas of incomplete landslide inventories (Pardeshi et al., 2013) Simple way to predict coseismic displacement in a specified shaking scenario Determines the probability that a specified amount of seismic shaking will be exceeded, resulting in displacement (Romeo et al., 2000) Methodology and Analysis Capacity-Demand Analysis Conclusions Introduction

16. Mechanical laws are utilized to understand the effects of seismic activity and its relation to slope stability (Romeo et al., 2000) Relies on Newmark (1965) sliding block model Methodology and Analysis Capacity-Demand Analysis Conclusions Introduction

17. Methodology and Analysis Capacity-Demand Analysis Conclusions Introduction Romeo et al., 2007 Where D C is the critical displacement for an intensity, Y, exceeded at a mean annual rate, P(.)

18. Methodology and Analysis Capacity-Demand Analysis Conclusions Introduction Romeo et al., 2007 Only valid in Northridge, California where it was calibrated, the equation is not valid on a global scale Model needs to be expended by means of further variable incorporation for proper risk assessment

19. Aims to finding the optimal prediction model – Produce no false-negatives and no false-positives – Predict on a global scale, spatially and temporally Assessment limited to regional scale – Susceptibility is affected by certain geomorphic or climatic variables particular to a specific area Spatial models lack incorporation of environmental conditions which induce rock-strength weakening and temporal models do not incorporate pre-event variables – Such as rainfall prior to a seismic event, or other tectonic and volcanic activity Methodology and Analysis Conclusions Introduction

20. Further improvement to hazard analysis is necessary in order to more correctly predict landslide hazard in space and time Methodology and Analysis Conclusions Introduction

21. Questions?

22. References Brenning, A., 2005. Spatial prediction models for landslide hazards: review, comparison and evaluation. Natural Hazards and Earth System Science, 5(6), 853-862. Dai, F.C., Xu, C., Yao, X., L, Tu, X.B., Gong, Q.M., 2011. Spatial distribution of landslides triggered by the 2008 Ms 8.0 Wenchuan earthquake, China. J. Asain Earth Sci. 40, 883-895. Hines, J. W., 1997. Fuzzy and Neural Approaches in Engineering: Wiley, New York, 210. Kanungo, D. P., Arora, M. K., Sarkar, S., and Gupta, R. P., 2006. A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Engineering Geology, 85(3), 347-366. Keefer, D. K., 1994. The importance of earthquake-induced landslides to long-term slope erosion and slope-failure hazards in seismically active regions: Geomorphology, v. 10, no. 1-4, p. 265- 284. Keefer, D. K., 2002. Investigating landslides caused by earthquakes- a historical review. Surv. Geophys. 23, 473-510. Li, G., West, J., Densmore, A. L., Jin, Z., Parker, R., Hilton, R., 2014. Seismic mountain building: Landslides associated with the 2008 Wenchuan earthquake in context of a generalized model for earthquake volume balance, G3, v. 15 Li, Y., Chen, G., Tang, C., Zhou, G., & Zheng, L., 2012. Rainfall and earthquake-induced landslide susceptibility assessment using GIS and Artificial Neural Network: Natural Hazards and Earth System Science, 12(8), 2719-2729. Lillesand, T. M., Kiefer, R. W., and Chipman, J. W., 2008. Remote sensing and image interpretation (No. Ed. 6). John Wiley & Sons Ltd. p. 606-609. Marano, K. D., Wald, D. J., and Allen, T. I., 2009. Global earthquake casualties due to secondary effects: a quantitative analysis for improving rapid loss analyses: Natural Hazards, v. 52, no. 2, p. 319-328. Newmark, N., 1965. Effect of earthquakes on dams and embankment. The Rankine lecture: Geotechnique, v. 15, no. 2. Nowicki, M. A., Wald, D. J., Hamburger, M. W., Hearne, M., and Thompson, E. M., 2014. Development of a globally applicable model for near real-time prediction of seismically induced landslides: Engineering Geology, v. 173, p. 54-65. Pardeshi, S. D., Autade, S. E., and Pardeshi, S. S., 2013. Landslide hazard assessment: recent trends and techniques. SpringerPlus, 2(1), 523. Romeo, R. W., Jibson, R. W.,and Pugliese, A., 2007. A methodology for assessing earthquake-induced landslide risk. In Proceedings of the 1 st North American Landslide Conference. p. 867-975. Romeo, R.W., Paciello, A., and Rinaldis, D., 2000. Seismic hazard maps of Italy including site effects: Soil Dynamics and Earthquake Engineering. Vol. 20, p. 85-92. Yuxia, J., 2008. Aerial view of badly stricken Yingxiu Town. Window of China. http://news.xinhuanet.com/english/2008- 05/15/content_8174986_4.htm (April, 19, 2015)