Erratum to: `Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia` [Expert Systems with Applications 38 (2011) 8208`8219]

This 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.

Landslide susceptibility mapping using support vector machine and GIS at the Golestan province, Iran

The main goal of this study is to produce landslide susceptibility map using GIS-based support vector machine (SVM) at Kalaleh Township area of the Golestan province, Iran. In this paper, six different types of kernel classifiers such as linear, polynomial degree of 2, polynomial degree of 3, polynomial degree of 4, radial basis function (RBF) and sigmoid were used for landslide susceptibility mapping. At the first stage of the study, landslide locations were identified by aerial photographs and field surveys, and a total of 82 landslide locations were extracted from various sources. Of this, 75% of the landslides (61 landslide locations) are used as training dataset and the rest was used as (21 landslide locations) the validation dataset. Fourteen input data layers were employed as landslide conditioning factors in the landslide susceptibility modelling. These factors are slope degree, slope aspect, altitude, plan curvature, profile curvature, tangential curvature, surface area ratio (SAR), lithology, land use, distance from faults, distance from rivers, distance from roads, topographic wetness index (TWI) and stream power index (SPI). Using these conditioning factors, landslide susceptibility indices were calculated using support vector machine by employing six types of kernel function classifiers. Subsequently, the results were plotted in ArcGIS and six landslide susceptibility maps were produced. Then, using the success rate and the prediction rate methods, the validation process was performed by comparing the existing landslide data with the six landslide susceptibility maps. The validation results showed that success rates for six types of kernel models varied from 79% to 87%. Similarly, results of prediction rates showed that RBF (85%) and polynomial degree of 3 (83%) models performed slightly better than other types of kernel (polynomial degree of 2 = 78%, sigmoid = 78%, polynomial degree of 4 = 78%, and linear = 77%) models. Based on our results, the differences in the rates (success and prediction) of the six models are not really significant. So, the produced susceptibility maps will be useful for general land-use planning

A fuzzy triangular chart to predict the uniaxial compressive strength of the Ankara agglomerates from their petrographic composition

High-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

Assessment of rate of penetration of a tunnel boring machine in the longest railway tunnel of Turkey

AbstractOne of the most important issues in tunnels to be constructed with tunnel boring machines (TBMs) is to predict the excavation time. Excavation time directly affects tunnel costs and feasibility. For this reason, studies on the prediction of TBM performance have always been interesting for tunnel engineers. Therefore, the purpose of the study is to develop models to predict the rate of penetration (ROP) of TBMs. In accordance with the purpose of the study, a new database including 5334 cases is obtained from the longest railway tunnel of Turkey. Each case includes uniaxial compressive strength, Cerchar Abrasivity Index, α angle, weathering degree and water conditions as input or independent variables. Two multiple regression models and two ANN models are developed in the study. The performances of the ANN models are considerably better than those of the multiple regression equations. Before deep tunnel construction in a metamorphic rock medium, the ANN models developed in the study are reliable and can be used. In contrast, the performances of the multiple regression equations are promising, but they predict lower ROP values than the measured ROP values. Consequently, the prediction models for ROP are open to development depending on the new data and new prediction algorithms.</jats:p

A fuzzy model to predict the uniaxial compressive strength and the modulus of elasticity of a problematic rock

Although the uniaxial compressive strength and modulus of elasticity of intact rocks are highly important parameters for rock engineering and engineering geology projects, the necessary core samples cannot always be obtained from weak, highly fractured, thinly bedded, or block-in-matrix rocks. For this reason, the predictive models are often employed for the indirect estimation of mechanical parameters. However, to obtain the realistic values is very important for a predictive model. In this study, some predictive models using regression analysis and fuzzy inference system have been developed for the greywackes cropping out in the city of Ankara and its close vicinity. For this purpose, a series of rock mechanics tests were applied and the relevant intact rock parameters were obtained. Following the tests, descriptive statistical studies on the parameters, regression analyses and construction of fuzzy inference system studies were carried out. While meaningful relationships were not obtained from the simple regression analyses, both multiple regression analyses and the fuzzy inference system exhibited good predictive performance. In addition to the coefficient of correlation, the values account for (VAF) and the root mean square error indices were also calculated to check the prediction performance of the obtained models. The VAF and root mean square error indices were calculated as 41.49% and 15.62 for the uniaxial compressive strengths obtained from the multiple regression model; 64.02% and 8.85 for the modulus of elasticity values obtained from the multiple regression model; 81.24% and 13.06 for uniaxial compressive strengths obtained from the fuzzy inference system; and 78.64% and 6.87 for the modulus of elasticity values obtained from the fuzzy inference system. As a result, these indices revealed that the prediction performances of the fuzzy model are higher than those of multiple regression equations. (C) 2004 Elsevier Ltd. All rights reserved

An Assessment on Permeability and Grout Take of Limestone: A Case Study at Mut Dam, Karaman, Turkey

The main purposes of the present study are to evaluate pilot grouting and to develop regression equations for prediction of grout intake. There are no permeability problems with the sandstone-siltstone-claystone alternations and basement clayey limestone at the dam site. Karstic limestone block is permeable due to karstification and heavy discontinuities. For the purpose of the study, Q system, geological strength index (GSI), secondary permeability index (SPI), joint spacing (JSP), joint apertures (Ap), Lugeon (Lu), and the permeability coefficient (k) were determined. Karstic limestone block rock mass properties correlated with grouting material amount. A series of simple and multiple nonlinear regression analyses was performed between grout take material amount (Gt) and average values of these rock mass properties. Significant determination coefficients were determined. Prediction capacity of the empirical equations were also examined with root mean square error (RMSE), values account for (VAF), mean absolute percentage error (MAPE), and prediction error evaluations. Considering simple regression analyses, the equation derived with Gt-SPI gives the best performance. The best prediction is determined with the equation derived with rock quality designation values (RQD), SPI, and joint aperture as input parameters with the multiple nonlinear regression analysis, in addition to this, other empirical equations also provide acceptable results.</jats:p

The modified block punch index test

The block punch index test, requiring only a small cylindrical disc sample, has been developed during the last decade and provides a practical index in assessing the strength of intact rock. In this study, earlier developments in the block punch index test are reviewed and a method for sample size correction is described. Analysis of the results, obtained from 23 different rocks, reveals that size correction in the block punch index test is indispensable, A strong correlation was found between the block punch index and the uniaxial compressive strength and strength anisotropy. Utilization of the block punch index as an input parameter in rock mass classification was also suggested