119 research outputs found
Liquefaction severity map for Aksaray city center (Central Anatolia, Turkey)
No full textTurkey having a long history of large earthquakes have been subjected to progressive adjacent earthquakes. Starting in 1939, the North Anatolian Fault Zone (NAFZ) produced a sequence of major earthquakes, of which the Mw 7.4 earthquake that struck western Turkey on 17 August 1999. Following the Erzincan earthquake in 1992, the soil liquefaction has been crucial important in the agenda of Turkey. Soil liquefaction was also observed widely during the Marmara and the Düzce Earthquake in 1999 (Sönmez, 2003). Aksaray city center locates in the central part of Turkey and the Tuzgolu Fault Zone passes through near the city center. The fault zone has been generated to moderate magnitude earthquakes. The geology of the Aksaray province basin contains Quaternary alluvial deposits formed by gravel, sand, silt, and clay layers in different thickness. The Tuzgolu Fault Zone (TFZ) came into being after the sedimetation of alluvial deposits. Thus, the fault is younger from lithological units and it is active. In addition, the ground water level is very shallow, within approximately 3 m from the surface. In this study, the liquefaction potential of the Aksaray province is investigated by recent procedure suggested by Sonmez and Gokceoglu (2005). For this purpose, the liquefaction susceptibility map of the Aksaray city center for liquefaction is presented. In the analysis, the input parameters such as the depth of the upper and lower boundaries of soil layer, SPT-N values, fine content, clay content and the liquid limit were used for all layers within 20 m from the surface. As a result, the category of very high susceptibility liquefaction class was not observed for the earthquake scenario of Ms=5.2, 4.9% of the study area has high liquefaction susceptibility. The percentage of the moderately, low, and very low liquefied areas are 28.2%, 30.2%, and 36.3%, respectively. The rank of non-liquefied susceptibility area is less than 1%
Erratum to: `Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia` [Expert Systems with Applications 38 (2011) 8208`8219]
No full textThis note is to point out and correct an error in Sezer et al. (2011). İn the paper (Sezer et al. 2011), the authors mention “ANFIS model has not been used for landslide susceptibility mapping previously”. This statement must be corrected as “The ANFIS model has been applied in landslide susceptibility mapping previously by Pradhan, Sezer, Gokceoglu, and Buchroithner (2010) in a different study area namely Cameron Highlands, Malaysia.
Spatial distribution of coal quality parameters with respect to production requirements: an adaptive neuro-fuzzy application for the can coal field (Turkey)
No full textWOS: 000365598900005Determination of spatial distribution of coal quality parameters can ease management of the operations in coal mines. In this study, in order to provide guidance for the excavations, Can coal mine production map showing regions having suitable coal parameters as feed coals for a power plant and also for public sale was prepared using adaptive neuro-fuzzy inference system tool. Statistical relationships among calorific value, ash content and sulphur content were evaluated using the data obtained from boreholes opened in the mine between 2006 and 2009. According to the obtained production map, coals of Can mine are not suitable for public sale because of their high sulphur content and hence they should be blended with low sulphur coals to meet the requirements, before sale
Use of non-linear prediction tools to assess rock mass permeability using various discontinuity parameters
No full textWOS: 000349060500001Because of complex discontinuity patterns, it is almost impossible to determine the permeability of rock masses if no proper testing methodology is used. As available in the literature, many empirical approaches to estimate the permeability of a rock mass have been proposed. There is no publication, however, that uses regression analyses and ANFIS (Adaptive Neuro-Fuzzy Inference System) modeling to determine the rock mass permeability. The purpose of the study is to develop various ANFIS and multiple regression models to estimate the rock mass permeability. To this end, a dataset including 453 cases with Lugeon test results and corresponding RQD (Rock Quality Designation), spacing of discontinuities and SCR (Surface Condition Rating) properties is employed. The data were obtained from granite, diorite, volcanic breccia, andesite and agglomerate rock masses from various dam sites and a coal mine in Turkey. Whole data were randomly divided into two parts for training and testing. Two different models were developed to estimate the rock mass permeability. The inputs of the first model are RQD and SCR (Model 1), and the inputs of the second model are discontinuity spacing and SCR (Model 2). Simple regression analyses indicate that there is no statistically meaningful relationship between the Lugeon values with discontinuity spacing and SCR. There is a statistically meaningful relationship, however, between the Lugeon values and RQD. Non-linear multiple regression analyses were implemented for two independent variables and a dependent variable because of the non-linear relationships between the inputs and the output. ANFIS was employed as a second non-linear tool to construct prediction models. According to the performance assessments of the developed models, both of the models and all of the sets are successful. ANFIS is a more successful tool than NLMR. These results show that the models developed are reliable enough and, if there is no direct test result, these models can be used in engineering projects
An application of adaptive neuro fuzzy inference system for estimating the uniaxial compressive strength of certain granitic rocks from their mineral contents
No full textWOS: 000311133600012The uniaxial compressive strength (UCS) of rocks is an important intact rock parameter, and it is commonly used for various engineering applications. This parameter is mainly controlled by the mineralogical and textural characteristics of rocks. In this study, a soft computing method, an adaptive neuro-fuzzy inference system (ANFIS), was employed to estimate UCS from the mineral contents of certain granitic rocks selected from Turkey; nonlinear multiple regression analysis was then employed to validate these estimations. Five nonlinear multiple regressions and ANFIS models were constructed with three inputs: quartz, orthoclase and plagioclase. To determine the optimal model, various performance indices (R, values account for and root mean square error) were determined, and the model obtained from dataset #3 was selected as the optimal model. The coefficients of correlation for the nonlinear multiple regression and ANFIS models were 0.87 and 0.91, respectively. Thus, both models yielded acceptable results, and the ANFIS is a suitable method for estimating the UCS of rocks. (C) 2012 Elsevier Ltd. All rights reserved
Prediction of uniaxial compressive strength of granitic rocks by various nonlinear tools and comparison of their performances
No full textWOS: 000323533900014The main goal of this study is to develop some prediction models for the UCS of six different granitic rocks selected from Turkey. During the modeling stage of the study, various approaches such as multiple regression, Artificial Neural Network (ANN), and Adaptive Neuro Fuzzy Inference System (ANFIS) are applied to estimate UCS. Tensile strength (sigma(t)), block punch index (BPI), point load index (Is((50))) and P-wave velocity (V-p) are considered as the input parameters for the models. In the study, total 75 cases including all inputs and output are used. In accordance with the analyses employed in the study, and considering the inputs, three different models are constructed as tensile strength and P-wave velocity (Model 1), BPI and P-wave velocity (Model 2), Is((50)) and P-wave velocity (Model 3) to estimate UCS. Performance assessments show that ANFIS is the better predictive tool than the other methods employed, and Model 1 is the better model for the prediction of UCS. The results show that the models developed can be used as preliminary stages of rock engineering assessments because the models developed herein have high prediction performances. It is evident that such prediction studies provides not only some practical tools but also understanding of the controlling index parameters of UCS of rocks. (C) 2013 Elsevier Ltd. All rights reserved.Hacettepe University Scientific Research Unit, Ankara, Turkey [08D07602 002]This study was supported by the Hacettepe University Scientific Research Unit, Ankara, Turkey with Project no. 08D07602 002. Also, the authors are grateful to the reviewers for their constructive comments
Effects of land-use changes on landslides in a landslide-prone area (Ardesen, Rize, NE Turkey)
No full textWOS: 000268776400019PubMed: 18780152Various natural hazards such as landslides, avalanches, floods and debris flows can result in enormous property damages and human casualties in Eastern Black Sea region of Turkey. Mountainous topographic character and high frequency of heavy rain are the main factors for landslide occurrence in Ardesen, Rize. For this reason, the main target of the present study is to evaluate the landslide hazards using a sequence of historical aerial photographs in Ardesen (Rize), Turkey, by Photogrammetry and Geographical Information System (GIS). Landslide locations in the study area were identified by interpretation of aerial photographs dated in 1973 and 2002, and by field surveys. In the study, the selected factors conditioning landslides are lithology, slope gradient, slope aspect, vegetation cover, land class, climate, rainfall and proximity to roads. These factors were considered as effective on the occurrence of landslides. The areas under landslide threat were analyzed and mapped considering the landslide conditioning factors. Some of the conditioning factors were investigated and estimated by employing visual interpretation of aerial photos and topographic data. The results showed that the slope, lithology, terrain roughness, proximity to roads, and the cover type played important roles on landslide occurrence. The results also showed that degree of landslides was affected by the number of houses constructed in the region. As a consequence, the method employed in the study provides important benefits for landslide hazard mitigation efforts, because a combination of both photogrammetric techniques and GIS is presented
Comment on “Comparative study of the deformation modulus of rock mass” by Panthee et al. (2018) in the Bulletin of Engineering Geology and the Environment
No full textA fuzzy triangular chart to predict the uniaxial compressive strength of the Ankara agglomerates from their petrographic composition
No full textHigh-quality core samples are necessary for the laboratory uniaxial compressive strength determinations. However, such core samples cannot always be obtained from weak, thinly bedded and block-in-matrix rocks, particularly from agglomerates and conglomerates. For this reason, the development of predictive models for the mechanical properties of rocks, mechanical indices or petrographical characteristics seems to be an attractive study area in rock engineering. Predictive models, generally, include simple and multivariate regression techniques, fuzzy logic and neural network approaches. In the present study, a fuzzy triangular chart for the prediction of uniaxial compressive strength of the Ankara agglomerates from their petrographical composition is suggested. A simple image classification method is used to determine the percentages of constituents of the agglomerate core samples. The Ankara agglomerates are mainly composed of tuff which is a cementing material, and pink and black andesite blocks ranging from few millimetres to about a meter. The classification chart developed in this study for the Ankara agglomerates includes 25 sub-triangle characterizing different petrographical composition expressed by if-then fuzzy rules. Based on the petrographical composition and uniaxial compressive strength values, a total of 15 membership function graphs were produced using if-then rules. Employing the membership functions and triangular petrographical composition chart, a fuzzy triangular chart for the prediction of uniaxial compressive strength of the agglomerates was obtained. To control performance of prediction capacity of the triangle, the variance accounts for (VAF) and the root mean square error (RMSE) indices were calculated as 96.76% and 9.37, respectively. It is noted that the fuzzy triangular chart exhibited a very high prediction capacity. (C) 2002 Elsevier Science B.V. All rights reserved
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