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Model calibration is usually based on trial-and-error procedures that, in turn, rely on expert judgment or previously acquired experiences for similar phenomena. Efficient and reliable procedures for model calibration of the propagation stage of landslides are still needed. This paper addresses this issue by proposing an inverse analysis procedure and applying it to the case history of a short run-out landslide triggered by a rising perched water table after a heavy rainfall. It focuses on the key role played by the field observations used to set up the inverse analysis, and evaluating the reliability of the numerical simulations. It also investigates the effect of different types of optimization parameters on the inverse analysis results, referring to a mixed-phase model or to a two-phase model for the propagating soil. Several sets of observations are used; all of them refer to the soil deposit thickness at the end of propagation, but differ in both location and number of the adopted values. The numerical analysis of the case history is performed through the academic “GeoFlow_SPH” model, and model calibration by inverse analysis is conducted using the “UCODE” software. The results obtained are discussed with the aim to provide practical criteria to identify the minimum amount of information required for a satisfactory model calibration. [ABSTRACT FROM AUTHOR]

The paper deals with the citywide landslide early warning system “Alerta-Rio” operated by the municipality of Rio de Janeiro (Brasil). The city is divided, for warning purposes, into four alert zones. Two different alert sets co-exist within Alerta-Rio: rainfall warnings and landslide warnings. The landslide warning levels are four and they are related to an expected spatial density of landslides. The presented study focuses on the evaluation of the efficiency of the correlation models and rainfall thresholds employed by Alerta-Rio to issue the landslide warnings. The analyses refer to the four-year time frame 2010–2013; they are conducted considering the spatial and temporal distribution of rainfall, landslides and alert phases. An alert phase is herein defined as the time when the landslide warning level is associated to the possibility of diffuse or widespread landsliding within the alert zone. The performance of the system is evaluated by looking at the errors generated by the landslide alerts, computed as a function of the temporal overlap between alert phases and landslide events. Landslides are grouped in events so as to explicitly consider the extent of the landslide disasters in relation to the alerts issued. The discussion on the results of the performed analyses mainly focuses on the following issues: false and missed alerts; duration of alerts; zoning for early-warning purposes. [ABSTRACT FROM AUTHOR]

Abstract: A procedure is proposed to produce landslide distribution zoning maps to be considered preparatory to susceptibility, hazard and risk zoning maps, based on 1) the results from a statistical multivariate analysis of a landslide inventory, which must be available for only a portion of the territory to be zoned, and 2) the use of appropriately defined terrain mapping units. The units are divided into terrain computational units (TCUs) and terrain zoning units (TZUs), whose size is related to the scale of zoning. The procedure comprises three phases: calibration, validation and prediction. The purpose of the prediction phase is the application of a calibrated and validated statistical model in a territory, previously recognized as viable on the basis of ‘a-priori applicability maps,’ for which no information is available regarding the distribution of landslides or where the information provided by the landslides inventory is unreliable or heterogeneous. The proposed procedure is applied to two case studies in southern Italy for the analysis and zoning of slow-moving landslides at 1:25,000 and 1:100,000 scales, respectively. The first case study illustrates the applicability of the procedure. The aim of the second case study is to address the part of the procedure related to the evaluation of the computational maps at the end of the calibration and validation phases. [Copyright &y& Elsevier]

A study evaluating the benefits of using inverse analysis techniques to select the appropriate parameters to optimize when calibrating a soil constitutive model is presented. The factors that affect proper calibration are discussed with reference to the optimization of the elasto-plastic Hardening-Soil model for four layers of Chicago glacial clays. The models are initially calibrated using results from triaxial compression tests performed on specimens from four clay layers and subsequently re-calibrated using inclinometer data that recorded the displacements of a supported excavation in these clays. Finite element simulations of both the triaxial tests and the supported excavation are performed. A parameter optimization algorithm is used to fit the computed results and observed data, expressed in the form of stress–strain curves and inclinometer readings, respectively. A procedure is presented which uses the results of sensitivity analyses conducted on the soil model parameters for the identification of the relevant and uncorrelated parameters to calibrate. In both cases the inverse analysis methodology effectively calibrates the soil parameters considered, which numerically converge to realistic values that minimize the errors between computed responses and experimental observations. [Copyright &y& Elsevier]