Two-dimensional basin effects often play a relevant role in defining earthquake ground motion at sites with deep morphologies. The quantitative estimation of two-dimensional effects requires a ...numerical approach which is typically complex and computationally demanding. Due to modelling complexities, basin effects are not routinely accounted for and have not received significant attention in seismic design codes. One-dimensional seismic response can be modeled with greater ease but does not allow their confident investigation. The use of simplified approaches which allow the estimation of two-dimensional effects from one-dimensional analyses is thus warranted. Past studies have defined and investigated aggravation factors, which can be applied as multiplicative coefficients to one-dimensional ground response outputs to account for two-dimensional basin effects. This paper proposes a novel framework for estimating basin effects by assigning aggravation factors for a vast range of symmetric basins overlain by a single-layer, homogeneous soil deposit and for varying basin sizes, dynamic geotechnical properties, and seismic inputs. In the study, aggravation factors are defined with reference to pseudo-acceleration response spectrum (5% of critical damping) and are calibrated statistically using a large set of dimensionless parameters from the outputs of an extensive set of one-dimensional and two-dimensional numerical analyses. The calibration process is described along with notable observations regarding the spatial variability of the magnitude of two-dimensional aggravation along the surface of the deposit. The proposed method is amenable to current design code formats involving tabulated partial factors. In principle, aggravation factors obtained in this study can be applied to 1D response spectra developed either from the provisions of the design codes themselves or from site-specific ground response analyses. A discussion focusing on the validation of the proposed approach using experimental and empirical measurements is provided. An example application attests to the simplicity of use of the method.
•1D ground response analyses often underestimate site effects in basins.•2D effects are due to geometric and geotechnical properties of basins.•2D effects can be parameterized through aggravation factors.•2D aggravation factors are spatially variable.•2D aggravation factors can be calibrated statistically in design code format.•2D aggravation factors can be applied to 1D response analysis or code-based spectra.
Failure envelopes written directly in terms of the applied vertical, horizontal and moment loads are finding increased popularity in the design of shallow foundations. However, these are ...deterministically derived and assume a spatially homogeneous soil without any variation in properties. The spatially variability of soil strength under a strip footing is accounted for in the results of this paper through the Random Finite Element Method, which combines spatial variability analysis, finite element simulation and Monte Carlo simulation. A multitude of combined loading failure envelopes are derived from finite element analysis of a soil domain with randomly generated and spatially varying values. This has allowed the size and shape of the failure envelopes to be defined for distinct levels of their probability of occurrence. These provide guidance for the reliable design of strip footings on clays in heterogeneous soil conditions, and have particular application offshore where horizontal and moment loads can be proportionally large.
Each pile in an offshore wind farm may be designed solely on the results of a single cone penetration test (CPT) positioned at its center or based on sample statistics calculated from multiple ...investigated locations in the same geotechnical unit. This study investigates quantitatively the effects of spatial variability of soil on the efficacy of the two approaches to achieve a reliable design. Random field theory is used to generate sets of cone tip resistance profiles with different characteristics to represent different seabed conditions and piles are ‘designed’ using the above two approaches and the partial factor method to withstand a given vertical drained design load distribution. The effects of the inaccuracies in prediction of pile capacity from the CPT data are quantified in terms of the scatter in calculated reliability (or probability of failure). Outputs of the parametric statistical analysis demonstrate that the ability of the two CPT-based approaches to produce a reliable foundation design is dependent on the ratio between the horizontal scale of fluctuation and the pile diameter, and the overall variability (coefficient of variation) of the seabed properties. Modifications to deterministic, partial factor design approaches are suggested to account for these uncertainties to ensure target reliabilities are achieved.
Factors which contribute to the vulnerability of physical elements such as road infrastructures to a natural hazard such as a flood event are pervaded by uncertainty due to the complexity of the ...hazard, of the vulnerable infrastructure and of their physical interaction. In the context of risk management efforts, it is conceptually correct to explicitly address this uncertainty and to parameterize the criticality of the vulnerable element and, consequently, an explicit target degree of conservatism and reliability in risk assessment and mitigation strategies. This paper illustrates the results of the probabilistic characterization of the vulnerability of road infrastructures to flood events for two areas in South-Eastern Norway. Flood intensity and road vulnerability serve as inputs to an analytical model, which expresses the latter as a function of the former with respect to a user-set level of probability of exceedance. Deterministic and probabilistic vulnerability estimates are compared quantitatively, and the results are assessed and analyzed critically.
This paper develops new probabilistic correlations for assigning design values of effective friction angle (
) of clean to silty sands from cone penetration test (CPT) data. New transformation models ...are proposed using a high-quality database of undisturbed sands. The sand samples have been collected using special methods (freezing, gel samplers, Mazier tube) and tested in the laboratory triaxial compression mode. The CPT soundings have been performed using electronic piezocone penetrometers (
,
,
) advanced at the standard rate of 20 mm/s. Two CPT normalisation schemes (
,
) are investigated. Correlations are obtained through quantile regression, a statistical method which allows the assignment of regression functions which are directly and quantitatively associated with probabilities of exceedance. The derived relationships allow the geoengineer to estimate the effective stress friction angle at any desired level of conservatism. The paper concludes by demonstrating how to apply the results practically in geotechnical design.
The paper illustrates a method for scenario-based, quantitative estimation of physical vulnerability of the built environment to landslides. The rationale and main features of the procedure are ...presented in the context of quantitative risk estimation. Vulnerability is defined quantitatively as a function of landslide intensity and the susceptibility of vulnerable elements. Reference terminology is presented and discussed. Models for the quantification of intensity and susceptibility for some categories of elements at risk such as structures and persons are proposed. An example application is illustrated.
This paper is motivated by the Time Capsule Project (TCP) of the International Society for Soil Mechanics and Geotechnical Engineering (ISSMGE). The historical developments of geotechnical risk and ...reliability are reviewed for the past six decades. The key features distinguishing geotechnical and structural engineering are the natural origin of the ground and the lack of sufficient data to characterize the ground using the more familiar frequentist interpretation of probability. For the first feature, random field theory is applied to model spatial variability and the random finite element method or other methods are proposed for solving soil-structure interaction problems in spatially variable soil. For the second feature, compilation of databases is essential to serve as priors for Bayesian updating and more recently for Bayesian machine learning. There is a gradual evolution towards reliability-based design because probabilistic methods offer a pathway to address big data and implement data-centric geotechnics as one step towards digital transformation. Given the complexity of the natural ground (known unknowns can be large and there are unknown unknowns), engineering judgment remains important to bridge the gap between theory and practice. However, the role of engineering judgment needs to be updated as modern machine learning methods become more powerful.
This article focuses on the statistical characterisation and stochastic modelling of the load-displacement behaviour of shallow footings on cohesionless soils and on the probabilistic estimation of ...settlement for serviceability limit state design (LSD). The study relies on a field database of 30 full-scale footings subjected to vertical loading with cone penetration testing data available for each site. The performance of three load-displacement models in replicating field data is assessed comparatively through statistical analysis. Load-displacement uncertainty is subsequently modelled probabilistically to perform Monte Carlo Simulation (MCS)-based estimation of footing settlement using the best-performing power law model. The dependence among load-displacement model parameters is investigated and replicated using copula theory. Samples are generated to account for parametric uncertainties in model inputs. The simulation output samples of settlement are examined statistically in order to assess the relevance of parametric and load-displacement uncertainties in settlement estimation, as well as the importance of accounting for correlation between power law model parameters. A simple analytical model for the estimation of settlement at any target reliability level is obtained on the basis of the outputs of MCS. The model can be practically implemented in geotechnical LSD at serviceability limit states.
This paper presents recent innovations implemented in the LaRiMiT (Landslide Risk Mitigation Toolbox) webtool. These include an innovative methodology for utilising experts’ scoring of landslide risk ...mitigation measures directly within the decision support tool, and updating of the tool’s database over structural measures to include many nature-based solutions (NBS) for mitigating landslide risk. Landslides are a common and treacherous natural hazard, and due to the effect of climate change on the frequency and intensity of extreme weather events, climate-driven landslides are expected to become more frequent. This negative trend is a driving factor for developing decision-support solutions for risk mitigation, and the LaRiMiT toolbox and the expert scoring methodology presented in this article contribute to meeting these needs. Selecting suitable structural measures is complicated due to factors such as site-specific conditions, local knowledge and resources, socio-economic constraints, and environmental considerations. LaRiMiT uses user-input attributes of site-specific slope movements combined with expert scoring of the suitability of various mitigation solutions to identify and select appropriate mitigation measures from an extensive database of structural solutions. While the LaRiMiT database initially included only conventional (grey) solutions relying on traditional methods, it has been recently expanded to include nature-based solutions (NBS), which are sustainable techniques for managing erosion and mitigating shallow landslides using vegetation and the use of natural materials. NBS and conventional solutions can also be combined to provide hybrid solutions. The proposed methodology for implementing expert scoring is dynamic and iterative, consisting of statistical pooling of experts’ scores collected via online surveys, and consolidating these into an expert scoring utility embedded in the LaRiMiT web portal engine. An initial application of this methodology has been tested by surveying a set of landslides experts, mostly in Europe, and applying it to the structural measures database in the LaRiMiT tool. Although the number of experts in this initial application is limited, dynamic updating of the scoring allows the tool to continuously improve as additional experts contribute.
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