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On 6 February 2023, two major earthquakes occurred in the East Anatolian Fault Zone (EAFZ) of Türkiye. The EAFZ forms the east border of the Anatolian Plate. The magnitude 7.7 and 7.5 Kahramanmaraş – Türkiye Earthquakes that struck southern Türkiye and resulted in common destruction in 11 provinces in the region. Total 6 main fault segments of the EAFZ ruptured during the earthquake sequence on 6 February 2023, and approximately 400 km surface rupture occurred. The life losses are reported as over 50000, and approximately 300,000 buildings were collapsed or severely damaged. In addition to damages on the buildings and the infrastructures, liquefactions, landslides, rockfalls, and rock avalanches were also observed during the earthquake sequence. The purpose of this study is to present a general overview on the 6 February 2023 Earthquake sequence.

The geomorphological community typically assesses the landslide susceptibility at the catchment or larger scales through spatial predictive models. However, the spatial information is conveyed only through the geographical distribution of the covariates. Spatial dependence, which allows capturing similarities at neighboring sites that are not directly explained by covariate information, is typically not accounted for in the landslides literature, whilst such spatial models have become commonplace in the geostatistical literature. Here we explain step by step how to rigorously model and predict activations of debris flow based on an adequate statistical model by using the R-INLA library from the statistical software R in the context of a real multiple landslide event. This chapter follows the analysis of Lombardo, Opitz, and Huser with a few modifications; it is written in a tutorial style to provide the geomorphological community with a hands-on guide to replicate similar analyses in R. While our focus here is on implementation and computing, more details about the underlying statistical theory, modeling, and estimation can be found in the original work by Lombardo, Opitz, and Huser Our modeling approach deviates fundamentally from the commonly used regression models fitted to binary presence/absence data. Specifically, we use a Bayesian hierarchical Cox point process model to describe landslide counts at high resolution (i.e., at the pixel level), while capturing spatial dependence through a latent spatial effect defined at lower resolution over slope units. Our point process modeling approach allows us to derive the distribution of aggregated landslide counts for any areas of interest. Crucially, the latent spatial effect represents the unexplained but spatially structured component in the data when the linear or nonlinear effects of covariates are removed. Thus, in the case of sparse rain gauge or seismic networks, we suggest using the latent spatial effect to uncover the trigger distribution over space. In particular, for landslides triggered by extreme precipitation, the meteorological stress can play a dominant role with respect to the covariates that are typically introduced in predictive models; therefore, accounting for the trigger in modeling may dramatically improve the performance of landslide prediction.

Understanding rock material characterizations and solving relevant problems are quite difficult tasks because of their complex behavior, which sometimes cannot be identified without intelligent, numerical, and analytical approaches. Because of that, some prediction techniques, like artificial neural networks (ANN) and nonlinear regression techniques, can be utilized to solve those problems. The purpose of this study is to examine the effects of the cycling integer of slake durability index test on intact rock behavior and estimate some rock properties, such as uniaxial compressive strength (UCS) and modulus of elasticity (E) from known rock index parameters using ANN and various regression techniques. Further, new performance index (PI) and degree of consistency (Cd) are introduced to examine the accuracy of generated models. For these purposes, intact rock dataset is established by performing rock tests including uniaxial compressive strength, modulus of elasticity, Schmidt hammer, effective porosity, dry unit weight, p-wave velocity, and slake durability index tests on selected carbonate rocks. Afterward, the models are developed using ANN and nonlinear regression techniques. The concluding remark given is that four-cycle slake durability index (I d4 ) provides more accurate results to evaluate material characterization of carbonate rocks, and it is one of the reliable input variables to estimate UCS and E of carbonate rocks; introduced performance indices, both PI and Cd, may be accepted as good indicators to assess the accuracy of the complex models, and further, the ANN models have more prediction capability than the regression techniques to estimate relevant rock properties. © 2011 John Wiley & Sons, Ltd.

Sampling strategies which have very significant role on examining data characteristics (i.e. imbalanced, small, exhaustive) have been discussed in the literature for the last couple decades. In this study, the sampling problem encountered on small and continuous data sets is examined. Sampling with measured data by employing k-fold cross validation, and sampling with synthetic data generated by fuzzy c-means clustering are applied, and then the performances of genetic programming (GP) and adaptive neuro fuzzy inference system (ANFIS) on these data sets are discussed. Concluding remarks are that when the experimental results are considered, fuzzy c-means based synthetic sampling is more successful than k-fold cross validation while modeling small and continous data sets with ANFIS and GP, so it can be proposed for these type of data sets. Additionally, ANFIS shows slightly better performance than GP when sytnthetic data is employed, but GP is less sensitive to data set and produces ouputs that are narrower range than ANFIS's outputs while k-fold cross validation is employed. © 2012 - IOS Press and the authors. All rights reserved.

Sampling strategies which have very significant role on examining data characteristics (i.e. imbalanced, small, exhaustive) have been discussed in the literature for the last couple decades. In this study, the sampling problem encountered on small and continuous data sets is examined. Sampling with measured data by employing k-fold cross validation, and sampling with synthetic data generated by fuzzy c-means clustering are applied, and then the performances of genetic programming (GP) and adaptive neuro fuzzy inference system (ANFIS) on these data sets are discussed. Concluding remarks are that when the experimental results are considered, fuzzy c-means based synthetic sampling is more successful than k-fold cross validation while modeling small and continous data sets with ANFIS and GP, so it can be proposed for these type of data sets. Additionally, ANFIS shows slightly better performance than GP when sytnthetic data is employed, but GP is less sensitive to data set and produces ouputs that are narrower range than ANFIS's outputs while k-fold cross validation is employed.

Understanding rock material characterizations and solving relevant problems are quite difficult tasks because of their complex behavior, which sometimes cannot be identified without intelligent, numerical, and analytical approaches. Because of that, some prediction techniques, like artificial neural networks (ANN) and nonlinear regression techniques, can be utilized to solve those problems. The purpose of this study is to examine the effects of the cycling integer of slake durability index test on intact rock behavior and estimate some rock properties, such as uniaxial compressive strength (UCS) and modulus of elasticity (E) from known rock index parameters using ANN and various regression techniques. Further, new performance index (PI) and degree of consistency (Cd) are introduced to examine the accuracy of generated models. For these purposes, intact rock dataset is established by performing rock tests including uniaxial compressive strength, modulus of elasticity, Schmidt hammer, effective porosity, dry unit weight, p-wave velocity, and slake durability index tests on selected carbonate rocks. Afterward, the models are developed using ANN and nonlinear regression techniques. The concluding remark given is that four-cycle slake durability index (Id4) provides more accurate results to evaluate material characterization of carbonate rocks, and it is one of the reliable input variables to estimate UCS and E of carbonate rocks; introduced performance indices, both PI and Cd, may be accepted as good indicators to assess the accuracy of the complex models, and further, the ANN models have more prediction capability than the regression techniques to estimate relevant rock properties. Copyright (c) 2011 John Wiley & Sons, Ltd.

Brittleness is one of the most crucial rock features for underground excavation and design considerations in rock mass. Direct standard testing method for measuring rock brittleness, the combination of rock properties rather than only one rock parameter have not available yet. Therefore, it is indirectly calculated as a function of some rock properties such as rock strength by using various ratios and prediction tools. The aim of this study is to estimate the rock brittleness by constructing fuzzy inference system and nonlinear regression analysis. For this purpose, a dataset established by utilizing the relevant laboratory rock tests (i.e., punch penetration, uniaxial compressive strength, Brazilian tensile strength and unit weight of rock) at the Earth Mechanics Institute of Colorado School of Mines in the USA on the rock samples assembled from 48 tunnels projects throughout the world. Running the established models, the performance values such as RMSE, VAF, absolute error and coefficient of cross-correlation were computed for developed models. The VAF and RMSE indices were calculated as 89.8% and 2.97 for the nonlinear multiple regression model and 83.1% and 3.82 for fuzzy model, respectively. As a result, these indices revealed that the prediction performance of the nonlinear multiple regression model is higher than that of the fuzzy inference system model. However, it is concluded that both constructed models exhibited a high performance according to the obtained prediction values. © 2009 Elsevier Ltd. All rights reserved.

Brittleness is one of the most crucial rock features for underground excavation and design considerations in rock mass. Direct standard testing method for measuring rock brittleness, the combination of rock properties rather than only one rock parameter have not available yet. Therefore, it is indirectly calculated as a function of some rock properties such as rock strength by using various ratios and prediction tools. The aim of this study is to estimate the rock brittleness by constructing fuzzy inference system and nonlinear regression analysis. For this purpose, a dataset established by utilizing the relevant laboratory rock tests (i.e., punch penetration, uniaxial compressive strength, Brazilian tensile strength and unit weight of rock) at the Earth Mechanics Institute of Colorado School of Mines in the USA on the rock samples assembled from 48 tunnels projects throughout the world. Running the established models, the performance values such as RMSE, VAF, absolute error and coefficient of cross-correlation were computed for developed models. The VAF and RMSE indices were calculated as 89.8% and 2.97 for the nonlinear multiple regression model and 83.1% and 3.82 for fuzzy model, respectively. As a result, these indices revealed that the prediction performance of the nonlinear multiple regression model is higher than that of the fuzzy inference system model. However, it is concluded that both constructed models exhibited a high performance according to the obtained prediction values. (C) 2009 Elsevier Ltd. All rights reserved.

Predicting tunnel boring machine (TBM) performance is a crucial issue for the accomplishment of a mechanical tunnel project, excavating via full face tunneling machine. Many models and equations have previously been introduced to estimate TBM performance based on properties of both rock and machine employing various statistical analysis techniques. However, considering the nature of the problem, it is relatively difficult to estimate tunnel boring machine performance by linear prediction models. Artificial neural networks (ANNs) and non-linear multiple regression models have great potential for establishing such prediction models. The purpose of the present study is the construction of non-linear multivariable prediction models to estimate TBM performance as a function of rock properties. For this purpose, rock properties and machine data were collected from recently completed TBM tunnel project in the City of New York, USA and consequently the database was established to develop performance prediction models utilizing the ANN and the non-linear multiple regression methods. This paper presents the results of study into the application of the non-linear prediction approaches providing the acceptable precise performance estimations. (C) 2009 Elsevier Ltd. All rights reserved.

As a result of industrialization, throughout the world, cities have been growing rapidly for the last century. One typical example of these growing cities is Istanbul, the population of which is over 10 million. Due to rapid urbanization, new areas suitable for settlement and engineering structures are necessary. The Cekmece area located west of the Istanbul metropolitan area is studied, because the landslide activity is extensive in this area. The purpose of this study is to develop a model that can be used to characterize landslide susceptibility in map form using logistic regression analysis of an extensive landslide database. A database of landslide activity was constructed using both aerial-photography and field studies. About 19.2% of the selected study area is covered by deep-seated landslides. The landslides that occur in the area are primarily located in sandstones with interbedded permeable and impermeable layers such as claystone, siltstone and mudstone. About 31.95% of the total landslide area is located at this unit. To apply logistic regression analyses, a data matrix including 37 variables was constructed. The variables used in the forwards stepwise analyses are different measures of slope, aspect, elevation, stream power index (SPI), plan curvature, profile curvature, geology, geomorphology and relative permeability of lithological units. A total of 25 variables were identified as exerting strong influence on landslide occurrence, and included by the logistic regression equation. Wald statistics values indicate that lithology, SPI and slope are more important than the other parameters in the equation. Beta coefficients of the 25 variables included the logistic regression equation provide a model for landslide susceptibility in the Cekmece area. This model is used to generate a landslide susceptibility map that correctly classified 83.8% of the landslide-prone areas. © 2006 Springer-Verlag.

Recently, the Journal, Engineering Geology, celebrated its 50th anniversary. Engineering Geology (referred to hereinafter as "the Journal") was founded in 1965 with the inaugural issue published in August of that year. More than 3400 papers have been published in the Journal since then. To help celebrate the 50th anniversary of the Journal, a virtual special issue (VSI) that consists of thirty selected papers was published in 2015 (http://www.journals.elsevier.com/engineeringgeology/). These papers were selected by the two Chief Editors, Carlos Carranza-Torres and Charng Hsein Juang, with the assistance from the publisher, Kate Hibbert. The selection criteria included citations, the balance of the subjects, and contributions of individuals to the Journal. The preface of the VSIwaswritten by CharngHsein Juang with contributions from selected members of the Editorial Board of the Journal. This short communication, an enlarged version of the preface of the VSI, aims to summarize the history of the Journal, to discuss the future challenges faced by the engineering geology communities, and to provide new horizons for the young practitioners and researchers in this field.

WOS: 000323401000001, The Analytical Hierarchy Process (AHP) is a classic and powerful decision support tool. However, the conventional AHP has some disadvantages originating in the expert decision-making process. To minimize the disadvantages of the conventional AHP, a modified analytical hierarchy process (M-AHP), is suggested in this study. This study is conducted in three stages: (i) the theoretical background for the conventional AHP is introduced, (ii) essentials for the proposed M-AHP technique are given with an example solution for the evaluation of snow avalanche source susceptibility, and (iii) a computer code named M-AHP is presented. By applying the methodology suggested in this study, the consistency ratio value for the comparison matrix and the weight vector never exceeds 0.10. The M-AHP program is a complementary tool for natural hazard, natural resource, or nature preservation researchers who apply the M-AHP technique to their decision support problem. (C) 2013 Elsevier Ltd. All rights reserved.

WOS: 000323204700005, Particularly in the last decade, landslide susceptibility and hazard maps have been used for urban planning and site selection of infrastructures. Most of the procedures for preparing of landslide susceptibility maps need high-quality landslide inventory map. Although the rainfall and seismic activities are accepted as triggering factor for landslides, designation of the triggering factor for each landslide in the inventory is almost impossible when well-documented records are unavailable. Therefore, during preparation of landslide susceptibility map, whole landslide records in the inventory map are used together without classifying based on the triggering factors. Although seismic activity is accepted as a triggering factor, possible effect of the use of seismic activity on production of landslide susceptibility map was investigated in this study, and the subject is open to discussion. For this purpose, a series of stability analyses based on circular failure and infinite slope model were performed considering different pseudostatic conditions. The results of analyses show that gentle slopes have higher susceptibility to failure than steeper ones, even if their stability conditions (susceptibilities) are similar for static condition. The seismic forces acting on failure surfaces may not be sufficiently taken into consideration in the conventionally prepared landslide susceptibility maps. Employing the general decreasing trend in stability condition based on slope face angle and the seismic acceleration, a new procedure was introduced for preparing of the landslide susceptibility map for a scenario earthquake. The prediction performance of occurring landslides increased after the procedure was applied to the conventionally prepared landslide susceptibility map. According to the threshold independent spatial performance analyses of the proposed methodology and the produced landslide susceptibility maps, the area under ROC curve values were calculated as 0.801, 0.933, and 0.947 for the maps prepared by considering conventional method and scenario earthquakes having M (w) values of 5.5 and 7.5, respectively.

Sulphide-rich tailings constitute a major environmental concern due to the acid drainage (AD) production and release of potentially toxic elements (PTE). Therefore, the management of active mine dumps and the reclamation of inactive ones require to reduce the exposure of the possible environmental receptors to contamination. A common, critical feature of mine dumps is the heterogeneity of the tailing materials that may complicate the quantitative prediction of potential contamination. On the other hand, heterogeneity can be a key for a more rational and cost-effective remediation, given a good characterization of the dump materials. Moreover, mine wastes often contain valuable components and therefore a suitable solution is to make positive use of them. In fact, as mining and processing (comminution and concentration) constitute a major cost of the ore mineral cycle, the availability of partly-processed, although low-grade resources may represent an interesting opportunity of exploitation. The present work is a case-study for a quantitative flow-sheet-related approach to the aim of heterogeneous sulphide-rich tailing dumps management. We selected the former processing site of Reps, in the Mirdita District (northern Albania), where we characterized two tailing dumps and assessed the contamination. Moreover, we evaluated the concentration potential of commodities, namely pyrite and gold, from the tailing materials. We analysed the Reps tailing samples in order to evaluate mineralogy, grain size and bulk chemical composition, concentration of PTE, the presence and persistence of AD. The data set splits in two classes, based on the sulphide content. The reconstruction of the former processing flow-sheet allowed us to recognize this pattern as resulting from two distinct processing stages and to quantitatively improve the AD prediction results. We selected a shaking table concentration method for testing separation of pyrite and gold, by taking into consideration the need of reducing both costs and volumes. The effectiveness of the method was verified by monitoring both the feed and the output materials for grain size, geochemical and mineralogical parameters. The optimal grain size of the input samples was obtained through a closed-circuit regrinding and milling method. The concentrate output shows an enrichment in pyrite in all the samples. The concentrates constitute 16-37.4 wt% of the shaking table output, depending on the initial sulphide content of the sample. Au concentration data were gained by Activation Analysis on selected grain size classes, in order to verify the correlation of Au concentration and the particle size distribution. Preliminary data show that in our samples a valuable recovery can be achieved by sieving in order to separate the Au- rich (up to 580 ppb) fine fraction (