The data sets, model formulations, and results from four new fault displacement models (FDMs) developed through the Fault Displacement Hazard Initiative (FDHI) Project are summarized and compared with each other and previously published FDMs. The models were developed using the new FDHI Database and provide predictions for principal or aggregate surface fault displacement, where aggregate is the combined displacement across principal and distributed ruptures. Different definitions of displacement are used among the models, and the differences should be considered when comparing model predictions or using multiple models in a logic tree. All new models are applicable between M 6.0 and 8.0, although some are also applicable to lower or higher magnitudes. Two models were developed for all styles of faulting, while the other two only apply to a single style. Quantitative comparisons are provided for a range of scenarios defined by style of faulting, magnitude, and normalized along-strike location. Average displacement predictions in the new models are within a factor of about 1.5 for most magnitudes and styles of faulting. Upper tail (i.e. 99th percentile) predictions in the new models are within a factor of about 2.5 in most cases. Compared to previously published models, average displacements in the new models are about 40% higher for M ∼7, whereas upper tail predictions are up to six times lower. Key features of the new models include the use of a large, high-quality empirical database and improved modeling of the magnitude scaling and aleatory variability. Together, these lead to upper tail predictions that are in reasonable agreement with empirical observations of maximum displacement for large magnitudes, which supports the use of the FDHI FDMs in probabilistic fault displacement hazard analysis (PFDHA) at long return periods.

This paper presents new empirical models for estimating displacements on surface-fault ruptures for reverse faults within a hazard forecasting framework. These models include revised relationships between earthquake magnitude and maximum or average displacement and revised distributions of normalized principal displacement as a function of location along fault strike. This work has been in conjunction with the Fault Displacement Hazard Initiative (FDHI) and utilizes the recently released database that contains surface-fault displacement and rupture trace data from global reverse and reverse-oblique mechanism earthquakes.

The 6 February 2023 Türkiye earthquakes and the accompanying aftershocks were a once-in-a-century catastrophe that has greatly impacted Türkiye and Syria. The repercussions of these events will have a lasting effect on the entire region. This article documents the geotechnical and geological observations performed by GEER (Geotechnical Extreme Events Reconnaissance) immediately following the events. Observations of ground damage, including surface fault rupture, liquefaction and lateral spreading, landslides and rock falls, and foundation failure of buildings, dams, and other civil infrastructure, are described herein. This article summarizes the key findings that were originally reported in the joint GEER-EERI (Earthquake Engineering Research Institute) reconnaissance report. The goal of these reconnaissance efforts is to document perishable data and disseminate it widely so that lessons can be learned from these events.

We investigate the influence of earthquake source characteristics and geological site parameters on fault scarp morphologies for thrust and reverse fault earthquakes using geomechanical models. A total of 3434 distinct element method (DEM) model experiments were performed to evaluate the impact of the sediment depth, density, homogeneous and heterogeneous sediment strengths, fault dip, and the thickness of unruptured sediment above the fault tip on the resultant coseismic ground surface deformation for a thrust or reverse fault earthquake. A machine learning model based on computer vision (CV) was applied to obtain measurements of ground surface deformation characteristics (scarp height, uplift, deformation zone width, and scarp dip) from a total of 346,834 DEM model stages taken every 0.05 m of slip. The DEM dataset exhibits a broad range of scarp behaviors, generating monoclinal, pressure ridge, and simple scarps—each of which can be modified by hanging wall collapse. The parameters that had the most influence on surface rupture patterns are fault displacement, fault dip, sediment depth, and sediment strength. The DEM results comprehensively describe the range of historic surface rupture observations in the Fault Displacement Hazards Initiative (FDHI) dataset with improved relationships obtained by incorporating additional information about the earthquake size, fault geometry, and surface deformation style. We suggest that this DEM dataset can be used to supplement field data and help forecast patterns of ground surface deformation in future earthquakes given specific anticipated source and site characteristics.

This study aims to establish an objective analytical framework for determining the number of boreholes that are essential for addressing soil slope design challenges in diverse geological/geotechnical settings. This study utilizes the covariance matrix decomposition method and a two-directional one-dimensional Markovian covariance function to create a two-dimensional random field. A Monte Carlo simulation is used to assess the statistical response based on the generated random fields. A random limit equilibrium method (RLEM) code in MATLAB (version R2023a) is developed using circular slip surfaces equipped with a chaotic particle swarm optimization technique for the reliability analysis of soil slopes. Additionally, the strength reduction method based on the finite difference/finite-element (FE) techniques is adopted to compare the reliability analysis results, such as the probability of failure (Pf). A new programming strategy is adopted to simulate the spatial variability in the FE soil slope model and calculate the factor of safety using a gradient of the maximum slope displacement. Bayesian updating is applied to adjust the conditional probabilities of decision variables and the component reliability. The strategic deployment of boreholes at the toe, middle, and top of the slope results in a significant reduction in the estimated Pf according to the RLEM, the random finite difference method (RFDM), and random FEM (RFEM) analyses. However, employing subsequent boreholes does not proportionally decrease the Pf. The influence of the horizontal autocorrelation distance (ACD) on the Pf is explored, showing that as the horizontal ACD increases from 10 to 20 m, the estimated Pf for the three boreholes decreases to 19% and 13% in the RFEM and RLEM, respectively. This reduction becomes less pronounced, dropping to 4% and 1.3%, respectively, when the ACD increases to 30 m.

The 6 February 2023 Kahramanmara & scedil;-T & uuml;rkiye earthquake sequence (M7.8 and M7.6) presents an exceptional opportunity to investigate both the effects of local soil conditions on damage patterns under strong shaking conditions and the performance of building foundations in areas that experienced ground failure. The significant ground failure and structural damage in Ad & imath;yaman-G & ouml;lba & scedil;& imath; triggered an intensive series of detailed reconnaissance and field surveys. This article aims to present the resulting database of observations on ground failures, building, and foundation performances. The field reconnaissance of ground failures and their effects on building performances involved aerial and walk-down surveys, including high-quality photographs taken across the town. In addition, data on building damage statistics compiled by the Ministry of Environment, Urbanization, and Climate Change were accessed and analyzed. The subsurface characteristics of the town were characterized using available data from pre-earthquake site investigation campaigns employed for town planning purposes. It is concluded that the ground failures in the town primarily resulted from soil liquefaction and cyclic softening. Most of the poor building and foundation performances and ground failures were documented in the northern part of Atat & uuml;rk Boulevard, closer to the lake of G & ouml;lba & scedil;& imath;, where soil site characteristics were unfavorable. This revealed once again the significant effects of local soil site conditions, particularly soil liquefaction, on the intensified ground failures, foundation, and structural damage levels.

The earthquake sequence that occurred on 6 February 2023 in T & uuml;rkiye, Kahramanmara & scedil;, had a significant impact on 140 dams, most of which are located within a distance of 50 km from surface projection of the fault rupture. These dams experienced moderate to high levels of seismic intensity, with peak ground acceleration (PGA) estimated to vary between 0.1 and 1.3 g during the Pazarc & imath;k earthquake and 0.15 to 0.45 g during the Elbistan earthquake, depending on their proximity to the fault rupture. Although all dams were able to maintain water-retaining capabilities, some of them suffered from moderate to large permanent deformations. As part of the emergency response measures, the water levels at two of these dams, namely Sultansuyu and Ar & imath;kl & imath;ka & scedil;, were lowered in a controlled manner. Following the earthquakes, a comprehensive survey of all hydraulic structures within the influence zone was conducted, and the findings are represented in this study. These findings revealed that earthfill and rockfill dams sustained more significant damage compared with concrete dams, particularly in areas close to the fault rupture, where the shaking intensity was most pronounced. The amount of permanent displacements was observed to consistently increase with the height of the dam's transverse section.

A probabilistic cone penetration test (CPT) based liquefaction triggering procedure for granular soil is developed utilizing adaptive kernel density estimation (KDE). KDE with a fixed bandwidth has been applied for the computation of conditional probabilities for liquefied and non-liquefied datasets in prior studies; however, liquefaction data inherently have a combination of different distributions, so the use of a fixed bandwidth is a suboptimal solution. In this study, we presented a mathematical framework for the calculation of adaptive bandwidth through an iteration process. We validated the proposed adaptive KDE by comparing the result of conditional probability with the true density function for one-dimensional and two-dimensional problems. Then, the proposed adaptive KDE was applied for two-dimensional probabilistic liquefaction triggering using Bayes theory. The variables of tip resistance and cyclic stress ratio were considered as main predictors for two-dimensional classification. The proposed method's performance was evaluated using receiver operating characteristics (ROC) curves and the area under the curve (AUC) of the ROC. Training and testing data are selected randomly by a ratio of 80% and 20%. Three iterations were found as the best value satisfying the generality of conditional probability for liquefied and nonliquefied data and the performance of the Bayes classifier. An optimum classifier for this CPT database was found to be a threshold of 0.54 for the liquefaction probability. The results indicate that this estimator can effectively predict the liquefaction potential of CPT data, with an AUC above 0.88. The conclusion reached was that the variability in the probability of liquefaction calculated using the proposed method offers a better description of probabilities than the previous methods.

The earthquake sequence that occurred on 6 February 2023 in Turkiye caused significant damage to various infrastructures including geostructures such as dams. A total of 17 earth dams within a 200-km radius of the earthquake epicenter experienced varying degrees of damage, ranging from minor (∼2 cm) to major (up to ∼150 cm) deformations. As study of these reveals that the damaged dams are located within the closest distance to the fault of less than 30 km, with an average value of ∼12 km. This study specifically focuses on the seismic displacement analysis of the 17 damaged dams, utilizing the sliding block methods. The recorded motion data was analyzed using the kriging technique to estimate the spectral response at the dam sites. Moreover, the recorded ground motions were scaled to the resonant period of the dam site to estimate acceleration time history. The findings reveal that the rigid block analysis can provide an average estimation of seismic displacement with a relative error of less than 44%. The results of the damage analysis indicate that seven dams reached the ultimate limit state and two dams experienced the serviceability limit state. Moreover, the univariate and multivariate fragility functions are developed to estimate seismic probabilistic analysis of earth dams based on the observed data and the limit states. The results show that the selection of a single intensity measure (IM) and a combination of IMs can affect the predicted probability of failure. The findings provide an insight into the resilience assessment of dams and other geosystems during this strong earthquake.

This paper introduces a numerical investigation into the stability analysis of soil nailed slopes. It establishes a simplified framework within the limit equilibrium method, focusing on the physical distribution of tension force along nails. MATLAB (R) was adapted for the Analysis of Soil Nailed Walls (ASNW) based on Bishop's method, with major command loops optimized through 'array operations'. Employing K-fold cross-validation, five regression models predicted the maximum tensile force (Tmax), revealing Gaussian process regression (GPR) as the best model based on root mean square error and r-squared metrics. To capture the amount of uncertainty in the nail load model, we have used a clustering method for estimating the measured Tmax. Self Organizing Map (SOM) was employed for clustering the nail load dataset. We randomly generated Tmax between the measured Tmax obtained by SOM and the predicted Tmax gained by the GPR model for each soil nail. We apply these loads in ASNW program to compute the Factor of Safety, and finally estimate system failure probability for 35 different nail layouts. A parametric study with a uniform nail layout explores the impact of nail length on system failure probability. Results show diminishing significance of nail length on failure probability beyond a nominal length.

We seek to improve our understanding of the physical processes that control the style, distribution, and intensity of ground surface ruptures on thrust and reverse faults during large earthquakes. Our study combines insights from coseismic ground surface ruptures in historic earthquakes and patterns of deformation in analog sandbox fault experiments to inform the development of a suite of geomechanical models based on the distinct element method (DEM). We explore how model parameters related to fault geometry and sediment properties control ground deformation characteristics such as scarp height, width, dip, and patterns of secondary folding and fracturing. DEM is well suited to this investigation because it can effectively model the geologic processes of faulting at depth in cohesive rocks, as well as the granular mechanics of soil and sediment deformation in the shallow subsurface. Our results show that localized fault scarps are most prominent in cases with strong sediment on steeply dipping faults, whereas broader deformation is prominent in weaker sediment on shallowly dipping faults. Based on insights from 45 experiments, the key parameters that influence scarp morphology include the amount of accumulated slip on a fault, the fault dip, and the sediment strength. We propose a fault scarp classification system that describes the general patterns of surface deformation observed in natural settings and reproduced in our models, including monoclinal, pressure ridge, and simple scarps. Each fault scarp type is often modified by hanging-wall collapse. These results can help to guide both deterministic and probabilistic assessment in fault displacement hazard analysis.

Commonly used post-fire debris flow statistical triggering models consider predictor variables that account for; rainfall intensity, rainfall accumulation, area burned, burned intensity, geology, slope, and others. These models represent the physical process of debris flow initiation and subsequent failure by quantifying near-surface soil characteristics. Shear wave velocity as a proxy for sediment shear stiffness informs the likelihood of particle dislocation, contractive or dilative volume changes, and downslope displacement that result from flow-type failures. This broadly available variable common to other hazard predictions, such as liquefaction analysis, provides good coverage in the watersheds of interest for debris flow predictions. A logistic regression is used to compare the new variable against currently used variables for predictive post-fire debris flow triggering models. We find that the new variable produces slightly improved performance in prediction of triggering while better capturing the physics of flow-type failure. Additional suggestions are presented for utilizing statistical cross-validation methods to advance prediction performance and the utility of different variables for quick assessment of likelihood during post-fire rainfall events.

For longer than four decades, the current practice for liquefaction triggering engineering assessments have been dominated by case history-based deterministic and probabilistic models. The predictive model proposals have been constituted based on different sets of case histories concerning in-situ test indices, namely standard penetration test (SPT) N value, cone penetration test (CPT) q, and shear-wave velocity (Vs), etc. The present study uses the databases of Cetin et al., Moss et al., and Kayen et al. together to develop a unified liquefaction triggering predictive model within a probabilistic framework. The scope concentrates on the illustrative introduction of the proposed unified reliability-based framework along with the comparative presentation of model predictions. The unified model enables a joint assessment of liquefaction performance predictions at sites, where different in-situ test indices are used individually or jointly to characterize the soil resistance against liquefaction.

This study examines the details of creating and validating an empirical liquefaction model, using a worldwide cone penetration test (CPT) liquefaction database with the intent of incorporating the rigor found in predictive modeling in other fields and addressing shortcomings of existing models. Our study implements a logistic regression within a Bayesian measurement error framework to incorporate uncertainty in predictor variables and allow for a probabilistic interpretation of model parameters when making future predictions. The model is built using a hierarchal approach to account for intra-event correlation in loading variables and differences in event sample sizes. The model is tested using an independent set of recent case histories. We found that the Bayesian measurement error model considering two predictor variables, normalized CPT tip resistance and cyclic stress ratio decreased model uncertainty while maintaining predictive utility for new data. Hierarchical models revealed high model uncertainty potentially due to the database lacking in high loading non-liquefaction sites. Models considering friction ratio as a predictor variable performed worse than the two variable case and will require more data or informative priors to be adequately estimated. The framework developed is flexible and can be extended using different methods of predictor variable selection, model function forms, and validation processes.