Performance evaluation of the M8 algorithm to predict M7+ earthquakes in Turkey

The M8 algorithm is one of the most reliable intermediate-term middle-range earthquake prediction algorithms. The present study evaluates the ability of the M8 algorithm and its modified versions for predicting major events (M7+) in Turkey. Thirty different algorithms were developed by changing the radius of circle of investigation (CI) and the lower magnitude cutoff of the M8 algorithm. These modified algorithms were executed all over the territory of Turkey, and the results were evaluated using the error diagram. Each modified algorithm was executed for consecutive half-year intervals over a specified period of time. Subsequently, the seismic catalog was updated, and failures-to-predict ratio and the fraction of alarm were considered. Results showed that the location of areas of alarm change gradually over consecutive intervals, and no sudden changes can be observed. In addition, the annual changes of areas of alarm are not random and follow a pattern. This study also showed that the modified algorithm having a three to six annual average of events and a 393-km CI radius is an efficient algorithm for predicting the future seismic events in Turkey. This algorithm predicted six out of six target events, retrospectively, with a confidence level of 96.4 %. According to the obtained results, it will be possible to rely on this modified algorithm to predict near future earthquakes of Turkey. Furthermore, this study proves that it is possible to alter the M8 algorithm for being used in regional studies.     

Geostatistics of near-surface moisture in bare cultivated organic soils

The aim of this study was to characterise fine scale patterns of organic soil moisture content in the top 5 cm by means of semi-variogram modelling. Soil moisture content was observed along a transect on 2 occasions, early in the 1999 growing season to avoid any influences originating from vegetation and cultural practices. Soil moisture values were found to be normally distributed and were not significantly correlated with the soil organic matter content. Many similarities were depicted between the exponential semi-variograms characteristics of this study and another one in mineral soils, reported in the literature, except for the much higher sills associated with organic soils. Of particular interest were similar correlation lengths, indicating that a correlation range of the order of 100 m should be expected for mineral soils and for the level of moisture and organic matter contents found in this study.     

Stability analysis and optimization of the support system of an underground powerhouse cavern considering rock mass variability

Input parameters, such as rock mass strength parameters and deformation modulus, considered in the design of underground openings involve some uncertainty. The current uncertainty in these parameters is due to the inherent variability of these parameters. To quantify these parameters and design underground openings, the statistical methods must be utilized. In this research, a statistical method was used to define the GSI of rock mass (Geological Strength Index), block volume (V_b), and joint conditions (J_c). Using the GSI distribution function obtained from field data and intact rock strength characteristics, the statistical distribution functions of rock mass parameters were defined using the Monte Carlo method. The statistical analysis of the stability in Azad-pumped storage powerhouse cavern was carried out through the point estimate method. The appropriate support system was suggested according to the support pressure and the plastic zone around the cavern. This study showed the application of the statistical method, by combining the uncertainties of the intact rock strength and discontinuity parameters, in the assessment of the strength and deformability of rock masses and the support selection process in comparison with the deterministic methods.     

GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms

In this study, a novel approach of the landslide numerical risk factor(LNRF) bivariate model was used in ensemble with linear multivariate regression(LMR) and boosted regression tree(BRT) models, coupled with radar remote sensing data and geographic information system(GIS), for landslide susceptibility mapping(LSM) in the Gorganroud watershed, Iran. Fifteen topographic, hydrological, geological and environmental conditioning factors and a landslide inventory(70%, or 298 landslides) were used in mapping. Phased array-type L-band synthetic aperture radar data were used to extract topographic parameters. Coefficients of tolerance and variance inflation factor were used to determine the coherence among conditioning factors. Data for the landslide inventory map were obtained from various resources, such as Iranian Landslide Working Party(ILWP), Forestry, Rangeland and Watershed Organisation(FRWO), extensive field surveys, interpretation of aerial photos and satellite images, and radar data. Of the total data, 30% were used to validate LSMs, using area under the curve(AUC), frequency ratio(FR) and seed cell area index(SCAI).Normalised difference vegetation index, land use/land cover and slope degree in BRT model elevation, rainfall and distance from stream were found to be important factors and were given the highest weightage in modelling. Validation results using AUC showed that the ensemble LNRF-BRT and LNRFLMR models(AUC = 0.912(91.2%) and 0.907(90.7%), respectively) had high predictive accuracy than the LNRF model alone(AUC = 0.855(85.5%)). The FR and SCAI analyses showed that all models divided the parameter classes with high precision. Overall, our novel approach of combining multivariate and machine learning methods with bivariate models, radar remote sensing data and GIS proved to be a powerful tool for landslide susceptibility mapping.     

Seismic design of yielding structures on flexible foundations

This paper introduces a simple method to consider the effects of inertial soil–structure interaction (SSI) on the seismic demands of a yielding single‐degree‐of‐freedom structure. This involves idealizing the yielding soil–structure system as an effective substitute oscillator having a modified period, damping ratio, and ductility. A parametric study is conducted to obtain the ratio between the displacement ductility demand of a flexible‐base system and that of the corresponding fixed‐base system. It is shown that while additional foundation damping can reduce the overall response, the effects of SSI may also increase the ductility demand of some structures, mostly being ductile and having large structural aspect ratio, up to 15%. Finally, a design procedure is provided for incorporation of the SSI effects on structural response. Copyright © 2015 John Wiley & Sons, Ltd.     

Application of soft computing in predicting rock fragmentation to reduce environmental blasting side effects

In the blasting operation, risk of facing with undesirable environmental phenomena such as ground vibration, air blast, and flyrock is very high. Blasting pattern should properly be designed to achieve better fragmentation to guarantee the successfulness of the process. A good fragmentation means that the explosive energy has been applied in a right direction. However, many studies indicate that only 20–30 % of the available energy is actually utilized for rock fragmentation. Involvement of various effective parameters has made the problem complicated, advocating application of new approaches such as artificial intelligence-based techniques. In this paper, artificial neural network (ANN) method is used to predict rock fragmentation in the blasting operation of the Sungun copper mine, Iran. The predictive model is developed using eight and three input and output parameters, respectively. Trying various types of the networks, it was found that a trained model with back-propagation algorithm having architecture 8-15-8-3 is the optimum network. Also, performance comparison of the ANN modeling with that of the statistical method was confirmed robustness of the neural networks to predict rock fragmentation in the blasting operation. Finally, sensitivity analysis showed that the most influential parameters on fragmentation are powder factor, burden, and bench height.