Seismic fragility denotes the probabilities of a system exceeding some prescribed damage levels under a range of seismic intensities. Classical seismic fragility studies in slope engineering usually construct fragility functions by making some assumptions for fragility curve shape, and always neglect spatial variability of soil materials. In this study, an assumption-free method on the basis of probability density evolution theory (PDET) is proposed for seismic fragility assessment of slopes. The random input earthquakes and spatially-variable soil parameters in slope are simultaneously quantified. By the proposed method, assumption-free fragility curves of a slope are established without limiting the fragility curve shape. The obtained fragility results are also compared with those from two classic parametric fragility methods (linear regression and maximum likelihood estimation) and Monte Carlo simulation. The results demonstrate that the proposed assumption-free method has potential to gives more rigorous and accurate fragility results than classical parametric fragility analysis methods. With the proposed method, more reliable fragility results can be obtained for slope seismic risk assessment.

The horizontal displacement of monopile under cyclic loading is subject to uncertainty due to variations in metocean conditions and soil parameters at offshore wind farms. However, the current design for cyclically loaded monopiles relies on the p-y method recommended by API and DNV, which does not accurately capture the horizontal displacement of the monopiles. In this study, finite element simulations are performed using ABAQUS, where the soil is modeled with the Einav-Randolph model to account for soil softening effects. The impact of parameter uncertainties, such as soil stiffness, undrained shear strength, and the pile-soil friction coefficient, on the reliability index of the monopile's horizontal displacement for different length diameter (L/D) ratios is investigated. A case study is provided to assess the horizontal displacement reliability of a monopile under cyclic loading. The results show that the horizontal displacement reliability index decreases as the coefficient of variation (COV) of the random variables, the correlation coefficient, and the monopile's L/D ratio increase. Conversely, the reliability index increases with an increase in the allowable horizontal displacement. The horizontal displacement reliability index is most sensitive to soil stiffness, followed by undrained shear strength and pile-soil friction coefficient. The findings of this study offer valuable insights into how parameter uncertainties influence the horizontal displacement of monopiles under cyclic loading.

Cement mixing techniques are widely used to improve the mechanical properties of weak soils in geotechnical engineering. However, due to the influence of various factors such as material properties, mixing conditions, and curing conditions, cement-mixed soil exhibits pronounced spatial variability which is greater than that of natural soil deposits, introducing additional uncertainty into the measurement and evaluation of its unconfined compressive strength. The purpose of this study is to propose a novel framework that integrates image analysis with Bayesian approach to evaluate the unconfined compressive strength of cement-mixed soil. A portable scanner is used to capture high-quality digital images of cement-mixed soil specimens. Mixing Index (MI) is defined to effectively evaluate mixing quality of specimens. An equation describing the relationship between water cement ratio (W/C) and unconfined compressive strength (qu) is introduced to estimate the strength of uniform specimens. To estimate the strength of non-uniform specimens, the equation is developed by integrating MI with the strength of uniform specimens. The coefficients of equations are obtained using Bayesian approach and Markov Chain Monte Carlo (MCMC) method, which effectively estimating the strength of both uniform and non-uniform specimens, with coefficients of determination (R2) of 0.9858 and 0.8745, respectively. For each specimen, a distribution of estimated strength can be obtained rather than a single fixed estimate, providing a more comprehensive understanding of the variability in strength. Bayesian approach robustly quantifies uncertainties, while image analysis serves as a convenient and non-destructive method for strength evaluation, providing accurate method for optimizing the mechanical properties of cement-mixed soil.

Current practice to model the occurrence of submarine landslides is based on methods that assess the potential of site-specific failures, all with the objective of providing elements to identify and quantify regional features associated to geohazards, before a project development takes place. Also, survey data to estimate parameters required to model submarine landslides show typically limited availability, mainly because of the cost associated to offshore surveying campaigns. In this paper, a probabilistic calibration approach is introduced using Bayesian statistical inference to maximize the use of available site investigation data, and to best estimate the occurrence of a marine landslide. For this purpose, a landslide model thought for its simplicity is used to illustrate the applicability and potential of the calibration methodology. The aim is to introduce a systematic approach to produce prior probability distributions of the model parameters, based on an actual integrated marine site investigation including geological, geophysical, and geomatics data, to then compare it with a posterior probability distribution of the same model parameters, but estimated after collecting in situ soil samples and testing them in the laboratory to produce the corresponding soil strength properties. This comparison allows to explore (a) the influence of the number of in situ samples, (b) the influence of a landslide factor of safety, and (c) the influence of the soil heterogeneity, into the likelihood of the occurrence of a marine landslide. The model parameters that are considered for calibration include the initial state of the submerged and saturated soil unit weight, the thickness of the soils' unit layers, the pseudo-static seismic coefficient, and the slope angle, while the soil undrained shear strength is considered as the reference parameter to conduct the calibration (i.e., to compare model predictions vs. actual observations). Results show the potential of the proposed methodology to produce landslide geohazard maps, which are needed for the planning and design of marine infrastructure.

Estimating the spatial distribution of hydromechanical properties in the investigated subsoil by defining an Engineering Geological Model (EGM) is crucial in urban planning, geotechnical designing and mining activities. The EGM is always affected by (i) the spatial variability of the measured properties of soils and rocks, (ii) the uncertainties related to measurement and spatial estimation, as well as (iii) the propagated uncertainty related to the analytical formulation of the transformation equation. The latter is highly impactful on the overall uncertainty when design/target variables cannot be measured directly (e.g., in the case of piezocone Cone Penetration Test-CPTu measurements). This paper focuses on assessing the Propagated Uncertainty (PU) when defining 3D EGMs of three CPTu-derived design/target variables: the undrained shear resistance (su), the friction angle ((p'), and the hydraulic conductivity (k). We applied the Sequential Gaussian Co-Simulation method (SGCS) to the measured profiles of tip (qc) and shaft resistance (fs), and the pore pressure (u2), measured through CPTus in a portion of Bologna district (Italy). First, we calculated 1000 realizations of the measured variables using SGCS; then, we used the available transformation equations to obtain the same number of realizations of su, (p', and k. The results showed that PU is larger when the transformation equation used to obtain the design/target variable is very complex and dependent on more than one input variable, such as in the case of k. Instead, linear (i.e., for su) or logarithmic (i.e., for (p') transformation functions do not contribute to the overall uncertainty of results considerably.

Despite significant advances in laboratory testing in recent decades, geotechnical designs that incorporate data from in-situ testing remain predominant worldwide. One of the most commonly employed techniques for correlating soil mechanical properties is the standard penetration test. However, while this test provides valuable information for identifying soil strata and offering general descriptions of soil characteristics, its correlation with shear strength parameters has several limitations that are often overlooked. In this article, we aim to i) present a critical literature review concerning the applicability of correlations between the undrained shear strength of fine-grained soils and standard penetration test data; ii) estimate the uncertainties associated with the adoption of these empirical correlations, which are frequently disregarded in engineering practice; iii) present simulation results from typical slope stability analyses, taking into account the uncertainties associated with the estimation of the undrained shear strength. The findings of our study suggest that geotechnical engineers should exercise caution when using such simplified equations, as they often lead to underestimations or overestimations of the stability of geotechnical structures.

For offshore platforms installed in seismically active regions, maintaining the safety of operations is an important concern. Therefore, the reliability of these structures, under earthquake ground motions, should be evaluated accurately. In this study, reliability methods are applied to determine the probability of failure of jacket platforms against extreme level earthquake (ELE), considering uncertainties in ground motions and the properties of the structure and soil. They are verified by two variance reduction Monte Carlo sampling methods to find the most efficient method in terms of both accuracy and calculation time. During the ELE event, also called strength level earthquake, structural members and foundation components are permitted to sustain localised and limited nonlinear behaviour, so a force-based criterion is utilized for the limit-state function. The results indicate that all reliability methods, except for FOSM, provide a good approximation of the probability of failure. Also, Point-fitting SORM is the most efficient method.

Multi-source precipitation products (MSPs) are critical for hydrologic modeling, but their spatial and temporal heterogeneity and uncertainty present challenges to simulation accuracy that need to be addressed urgently. This study assessed the impact of different precipitation data sources on hydrologic modeling in an arid basin. There were seven precipitation products and meteorological station interpolated data that were used to drive the hydrological model, and we evaluated their performance by fusing the six precipitation products through the dynamic bayesian averaging algorithm. Ultimately, the runoff simulation uncertainty was quantified based on the DREAM algorithm, and the information transfer entropy was used to quantify the differences in hydrologic simulation processes driven by different precipitation data. The results showed that CMFD and ERA5 weights were higher, and the DBMA fused precipitation annual mean value was about 309.83 mm with good simulation accuracy (RMSE of 1.46 and R-2 of 0.75). The simulation was satisfactory (NSE >0.80) after parameter calibration and data assimilation for all driving data, with CHIRPS and TRMM performed better in the common mode, and HRLT and CMFD performed excellently in the glacier mode. The DREAM algorithm indicated less uncertainty for DBMA, CHIRPS and HRLT data. The entropy of information transfer revealed that precipitation occupied a significant position in information transfer, especially affecting evapotranspiration and surface soil moisture. CMFD and TPS CMADS were highest in snow water equivalent information entropy, and CHIRPS and TPS CMADS were highest in evapotranspiration information entropy. CDR, CHIRPS, ERA5-Land and IDW STATION had the highest snow water equivalent information entropy, DBMA and CMORPH had the highest runoff information entropy, CHIRPS and TRMM had the highest soil moisture information entropy, whereas ERA5, HRLT, and TPS CMADS had the highest evapotranspiration information entropy in glacial mode. This study reveals significant differences between different precipitation data sources in hydrological modeling of arid basin, which is an important reference for future water resources management and climate change adaptation strategies.

Accurate estimation of soil properties is crucial for reliability-based design in engineering practices. Conventional empirical equations and prevalent data-driven models rarely consider uncertainty quantification in both measurement and modelling processes. This study tailors three uncertainty quantification methods including Bayesian learning, Markov chain Monte Carlo and ensemble learning into data-driven modelling, in which support vector regression is selected as the baseline algorithm. The compression index of clay is adopted as an example for model training and testing. In this context, Bayesian learning and Markov chain quantify uncertainty by considering the distribution of function and hyper-parameters, respectively, while different sampled data are employed to explore model uncertainty. These models are evaluated in terms of accuracy, reliability and cost-effectiveness and also compared with Gaussian process regression, etc. The results reveal that based on built-in structural risk minimization, sparse solution and uncertainty quantification, developed models can capture more accurate and reliable correlations from actual measured data over other methods. Their practicability and generalization ability are also verified on a new creep index database. The proposed probabilistic methods are also compiled into a user-friendly platform, showing a significant potential to enrich the data-driven modelling framework and be applied in other geotechnical properties.

Permafrost degradation is a growing direct impact of climate change. Detecting permafrost shrinkage, in terms of extension, depth reduction and active layer shift is fundamental to capture the magnitude of trends and address actions and warnings. Temperature profiles in permafrost allow direct understanding of the status of the frozen ground layer and its evolution in time. The Sommeiller Pass permafrost monitoring station, at about 3000 m of elevation, is the key site of the regional network installed in 2009 during the European Project PermaNET in the Piedmont Alps (NW Italy). The station consists of three vertical boreholes with different characteristics, equipped with a total of 36 thermistors distributed in three different chains. The collected raw data shows a degradation of the permafrost base at approximately 60 m of depth since 2014, corresponding to about 0.03 degrees C/yr. In order to verify and better quantify this potential degradation, three on-site sensor calibration campaigns were carried out to understand the reliability of these measurements. By repeating calibrations in different years, two key results have been achieved: the profiles have been corrected for errors and the re-calibration allowed to distinguish the effective change of permafrost temperatures during the years, from possible drifts of the sensors, which can be of the same order of magnitude of the investigated thermal change. The warming of permafrost base at a depth of similar to 60 m has been confirmed, with a rate of (4.2 +/- 0.5)center dot 10(-2) degrees C/yr. This paper reports the implementation and installation of the on-site metrology laboratory, the dedicated calibration procedure adopted, the calibration results and the resulting adjusted data, profiles and their evolution with time. It is intended as a further contribution to the ongoing studies and definition of best practices, to improve data traceability and comparability, as prescribed by the World Meteorological Organization Global Cryosphere Watch programme.