Farrokh Nadim

Seismic triggering of submarine slides in soft cohesive soil deposits

Abstract The geological profile of many submerged slopes on the continental shelf consists of normally to lightly overconsolidated clays with depths ranging from a few meters to hundreds of meters. For these soils, earthquake loading can generate significant excess pore water pressures at depth, which can bring the slope to a state of instability during the event or at a later time as a result of pore pressure redistribution within the soil profile. Seismic triggering mechanisms of landslide initiation for these soils are analyzed with the use of a new simplified model for clays which predicts realistic variations of the stress–strain–strength relationships as well as pore pressure generation during dynamic loading in simple shear. The proposed model is implemented in a finite element program to analyze the seismic response of submarine slopes. These analyses provide an assessment of the critical depth and estimated displacements of the mobilized materials and thus are important components for the estimation of submarine landslide-induced tsunamis.

Statistical modelling of Europe-wide landslide susceptibility using limited landslide inventory data

In many regions, the absence of a landslide inventory hampers the production of susceptibility or hazard maps. Therefore, a method combining a procedure for sampling of landslide-affected and landslide-free grid cells from a limited landslide inventory and logistic regression modelling was tested for susceptibility mapping of slide- and flow-type landslides on a European scale. Landslide inventories were available for Norway, Campania (Italy), and the Barcelonnette Basin (France), and from each inventory, a random subsample was extracted. In addition, a landslide dataset was produced from the analysis of Google Earth images in combination with the extraction of landslide locations reported in scientific publications. Attention was paid to have a representative distribution of landslides over Europe. In total, the landslide-affected sample contained 1,340 landslides. Then a procedure to select landslide-free grid cells was designed taking account of the incompleteness of the landslide inventory and the high proportion of flat areas in Europe. Using stepwise logistic regression, a model including slope gradient, standard deviation of slope gradient, lithology, soil, and land cover type was calibrated. The classified susceptibility map produced from the model was then validated by visual comparison with national landslide inventory or susceptibility maps available from literature. A quantitative validation was only possible for Norway, Spain, and two regions in Italy. The first results are promising and suggest that, with regard to preparedness for and response to landslide disasters, the method can be used for urgently required landslide susceptibility mapping in regions where currently only sparse landslide inventory data are available.

Slope reliability analysis accounting for spatial variation

A practical and transparent procedure is described for implementing a generalized limit equilibrium method via cell-object-oriented constrained optimization in the spreadsheet platform. The formulation allows switching among the Spencer, Bishop simplified and wedge methods on the same template by specifying different side-force inclination and different constraints of optimization. Search for the critical circular or non-circular slip surface is possible. The deterministic procedure is extended probabilistically by implementing the first-order reliability method via constrained optimization of the equivalent dispersion ellipsoid in the original space of the random variables. This procedure is illustrated for an embankment on soft ground, and for a clay slope in southern Norway, both involving spatially correlated soil properties. The effects of autocorrelation distance on the results of reliability analysis are studied. Shear strength anisotropy is modelled via user-created simple function codes in the programming environment of the spreadsheet. The meaning of probability of failure is discussed.

Landslide Risk Assessment and Mitigation Strategy

Each year, natural disasters cause countless deaths and formidable damage to infrastructure and the environment. In 2004–2005, more than 200 000 people lost their lives in natural disasters. Material damage was estimated at USD 300 billion. Many lives could have been saved if more had been known about forecasting and mitigation. The need to improve the ability to deal with the hazards and risks was accentuated by increased sliding and flooding in many regions around the world in recent years, concern for their disastrous consequences on mankind, infrastructure and material property and the catastrophic Indian Ocean tsunami in December 2004.

Stochastic design of an early warning system

Early warning systems (EWS) are monitoring devices designed to avoid or to mitigate the impact posed by a threat. Since EWS are time-sensitive or stochastic, it is necessary to develop a design methodology that defines the integration of the participating monitoring information sources, the identification of potential warning thresholds, and the assessment of the associated risk within an explicit causal analysis. This paper proposes a framework for a stochastic design of an early warning system, introducing a risk measure as the reference variable that encapsulates the different effects retrieved by the monitoring instruments. Within a decision-making framework the risk measure serves as the index for defining the system warning thresholds. A Bayesian approach is proposed as a suitable tool for integrating and updating the joint states of information and the warning levels as the information flows through the warning system.

Quantification of vulnerability to natural hazards

Risk is recognised in both the social sciences and natural sciences as some combination of hazard and vulnerability, and often includes exposure and coping capacity. How these four components are defined, measured and evaluated differs greatly between the two disciplines, especially in the case of vulnerability and coping capacity: in natural sciences methods are generally quantitative, but consideration of vulnerability is limited and consideration of coping capacity is non-existent; in social sciences vulnerability and coping capacity are considered in broader detail, but because of the resulting complexity qualitative methods are favoured. Risk analysis in the physical sciences can benefit from introduction of simplified and more quantitative adaptations of approaches in the social sciences. Realisation of these benefits, however, requires clear and consistent understanding of vulnerability and coping capacity. The required understanding is provided here through explicit definition of critical terms related to the conceptualisation of risk and through integration of these into a detailed conceptual risk model. Aspects related to vulnerability and coping capacity not typically addressed in the natural sciences are emphasised and discussed with consideration of developments in the social sciences. The context provided by the risk model and by the highlighted aspects forms the basis for a semi-quantitative conceptual framework that holistically represents vulnerability and coping capacity. Differentiation of components by theme and unit of quantification facilitates more realistic quantification of individual components. Functionality of the framework is demonstrated through its application to a real world scenario. Finally, based on the logical organisation of the framework, research priorities relating to vulnerability and coping capacity are identified.

Tools and Strategies for Dealing with Uncertainty in Geotechnics

Working with uncertainty is an essential aspect of engineering—the larger the uncertainty and the closer to critical, the greater the need for evaluating its effect(s) on the results. To characterize the uncertainties in soil and/or rock properties, the engineer needs to combine, in addition to actual data, knowledge about the quality of the data, knowledge on the geology and, most importantly, engineering judgment. Once the uncertainty in input parameters and model(s) for solving a particular problem are quantified, the engineer has a variety of tools at his disposal to evaluate the uncertainty in the output. The most common practical tools are Monte Carlo simulation techniques, first-order, second moment (FOSM) approach, first- and second-order reliability methods (FORM and SORM), and event tree analysis. Each has its advantages and shortcomings. The more complicated methods often provide more useful information about the possible outcomes of a problem. These methods are described and their applications are demonstrated through example problems. Many geotechnical problems involve several possible failure modes, which may or may not be correlated. These problems should be treated as systems. Component reliability vs. system reliability are discussed, and example calculations are presented.

Modeling cyclic behavior of lightly overconsolidated clays in simple shear

Abstract Assessment of seismic performance and estimation of permanent displacements for submerged slopes require the accurate description of the soils stress–strain-strength relationship under irregular cyclic loading. The geological profile of submerged slopes on the continental shelf typically consists of normally to lightly overconsolidated clays with depths ranging from a few meters to a few hundred meters and very low slope angles. This paper describes the formulation of a simplified effective-stress-based model, which is able to capture the key aspects of the cyclic behavior of normally consolidated clays. The proposed constitutive law incorporates anisotropic hardening and bounding surface principles to allow the user to simulate different shear strain and stress reversal histories as well as provide realistic descriptions of the accumulation of plastic shear strains and excess pore pressures during successive loading cycles.

On probability analysis in snow avalanche hazard zoning

Abstract The reduced societal acceptance of living in regions exposed to snow avalanches, and the increased economic consequences when houses are located within a hazard zone, highlight the uncertainty concerning avalanche run-out prediction. The limitations of today’s zoning procedures are especially pronounced in potential avalanche terrain where there are few observations of snow avalanches, where old buildings are present in the potential run-out zone, and where the local climate does not favour severe snow accumulation. This paper combines a mechanical probabilistic model for avalanche release with a statistical/topographical model for avalanche run-out distance to obtain the unconditional probability of extreme run-out distance. For the mechanical model, a first-order reliability method (FORM) and Monte Carlo simulations are compared. The interpretation of the statistical/topographical model either as an extreme value model or as a single value model is discussed. Furthermore, both a classical approach where the probability of an avalanche occurring is a constant, and a Bayesian approach with stochastic probability, are compared. Finally, example applications in hazard zoning are presented, with emphasis on how the influence of historical observations, local climate, etc., on run-out distance can be quantified in statistical terms and how a specified certainty level can be found from constructing confidence intervals for, for example, the most likely largest run-out distance during various time intervals.

Landslide-triggering rainfall thresholds: a conceptual framework

Abstract Rainfall-triggered landslides pose a major threat to Central America and other landslide-prone regions. The development of new tools to understand and characterize landslide-triggering rainfall is a key aspect in the context of hazard assessment and risk mitigation strategies. The purpose of this study is to present a new conceptual framework in which a connection is established between rainfall thresholds and landslide susceptibility, and to suggest three potential applications of this framework: (1) an empirical estimation of the probability of landslide-triggering rainfall; (2) a threshold-based time-trend analysis of rainfall series; (3) a susceptibility-based early warning system. The applications are illustrated with cases and data from tropical climates, with particular emphasis on cases with climatological and geological settings comparable with those in Central America.