Material properties of the Menderes Massif Marbles from SW Turkey

Marbles are extensively quarried at four different stratigraphical levels from Permo-Carbonifereous to Paleogene in the southern flank of the Menderes Massif in SW Turkey. These marbles differ in color, texture and pattern depending on their stratigraphical levels and are well known in the international trade as the Mugla Black (Permo-Carbonifereous), Mugla White (Cretaceous), Milas Lemon, Lilac, Aubergine, Pearl, Veined and White (Triassic) and Aegean Bordeaux (Paleogene) marbles. The mineralogical, chemical, physical and mechanical properties of the representative marbles samples obtained from the quarries working in four major metamorphic carbonate horizons in the cover successions of the Menderes Massif's southern flank in SW Turkey are determined and the results of over 1700 tests carried out on the selected marble samples are presented. The mean test values of the physical and mechanical tests are in general, found to be above the threshold acceptance values suggested by the American and Turkish Standards for the use of marbles as a building stone and in the same order as the properties of Italian (Carrara) and Greek marbles reported in the literature. Additionally, the mean test values of the marbles have given high correlations with one another and the relations obtained between the index test results determined by simple techniques requiring minimal sample preparation effort and the mean values of the more elaborate engineering tests results are presented as tables and graphs for wider use.     

Geological parameters affecting the marble production in the quarries along the southern flank of the Menderes Massif, in SW Turkey

The Mugla province is one of the major marble producing regions located in the southern flank of the Menderes Massif in SW Turkey. The Menderes Massif is a regionally metamorphosed massif with an old Pan-African core and cover successions from the Permo–Carboniferous to Paleocene. There are four major metamorphic carbonate horizons in the cover successions exploited for the marble production. These horizons are located within the Permo–Carboniferous, Triassic, Upper Cretaceous and Paleocene successions along the southern flank of the Menderes Massif. Here the world wide known marbles with names such as the Mugla Black, the Milas White, Veined, Pearl, Aubergine, Lilac and Lemony, the Mugla White and the Aegean Bordeaux are found.

Detailed geological studies were carried out in selected marble quarries representing the different stratigraphic levels to determine the geological parameters affecting the marble production in the southern flank of the Menderes Massif in SW Turkey. The geological parameters such as bedding, joints, schist interlayers and mica filled joints affecting the block production from the marble beds are considered to be primary features. The presence of dolomite bands and lenses, abnormal sized calcite crystals and emery minerals which affect the slab and the production qualities and appearances are considered to be secondary geological parameters. The primary and secondary geological parameters affecting the marble productions at different stratigraphical levels in SW Turkey, are determined and the practical aspects of these findings are discussed.     

Prediction of ground vibrations resulting from the blasting operations in an open-pit mine by adaptive neuro-fuzzy inference system

The aim of this study is to predict the peak particle velocity (PPV) values from both presently constructed simple regression model and fuzzy-based model. For this purpose, vibrations induced by bench blasting operations were measured in an open-pit mine operated by the most important magnesite producing company (MAS) in Turkey. After gathering the ordered pairs of distance and PPV values, the site-specific parameters were determined using traditional regression method. Also, an attempt has been made to investigate the applicability of a relatively new soft computing method called as the adaptive neuro-fuzzy inference system (ANFIS) to predict PPV. To achieve this objective, data obtained from the blasting measurements were evaluated by constructing an ANFIS-based prediction model. The distance from the blasting site to the monitoring stations and the charge weight per delay were selected as the input parameters of the constructed model, the output parameter being the PPV. Valid for the site, the PPV prediction capability of the constructed ANFIS-based model has proved to be successful in terms of statistical performance indices such as variance account for (VAF), root mean square error (RMSE), standard error of estimation, and correlation between predicted and measured PPV values. Also, using these statistical performance indices, a prediction performance comparison has been made between the presently constructed ANFIS-based model and the classical regression-based prediction method, which has been widely used in the literature. Although the prediction performance of the regression-based model was high, the comparison has indicated that the proposed ANFIS-based model exhibited better prediction performance than the classical regression-based model.     

Processing of earthquake catalog data of Western Turkey with artificial neural networks and adaptive neuro<Emphasis>-</Emphasis>fuzzy inference system

Turkey is one of several countries frequently facing significant earthquakes because of its geological and tectonic position on earth. Especially, graben systems of Western Turkey occur as a result of seismically quite active tensional tectonics. The prediction of earthquakes has been one of the most important subjects concerning scientists for a long time. Although different methods have already been developed for this task, there is currently no reliable technique for finding the exact time and location of an earthquake epicenter. Recently artificial intelligence (AI) methods have been used for earthquake studies in addition to their successful application in a broad spectrum of data intensive applications from stock market prediction to process control. In this study, earthquake data from one part of Western Turkey (37–39.30° N latitude and 26°–29.30° E longitude) were obtained from 1975 to 2009 with a magnitude greater than M ≥ 3. To test the performance of AI in time series, the monthly earthquake frequencies of Western Turkey were calculated using catalog data from the region and then the obtained data set was evaluated with two neural networks namely as the multilayer perceptron neural networks (MLPNNs) and radial basis function neural networks (RBFNNs) and adaptive neuro-fuzzy inference system (ANFIS). The results show that for monthly earthquake frequency data prediction, the proposed RBFNN provides higher correlation coefficients with real data and smaller error values.     

Evaluation of ground vibration effect of blasting operations in a magnesite mine

Ground vibrations induced by blasting are one of the fundamental problems in the mining industry and may cause severe damage to structures and plants nearby. Therefore, a vibration control study plays an important role in the minimization of environmental effects of blasting in mines. This paper presents the results of ground vibration measurement induced by bench blasting at Magnesite Incorporated Company (MAS) open pit mine in Turkey. The scope of this study is to predict peak particle velocity and to determine the slope of the attenuation curve for this site. For this purpose, the blasting parameters of 43 shots were carefully recorded and the ground vibration components were measured for each event. After carrying out statistical analysis, the site specific parameters were determined to predict the peak particle velocity. In the light of this analysis, the prediction graphics of maximum charge weight per delay versus distance for different damage criteria was proposed to be able to perform controlled blasting in order not to damage to the nearby structures, especially to the plant where rotary and shaft kilns have been established.     

First results on determination of cosmogenic 36Cl in limestone from the Yenicekale Complex in the Hittite capital of Hattusha (Turkey)

We have measured ³⁶Cl in three rock surfaces of the Yenicekale building complex in Hattusha (Bo?azköy, Turkey). Hattusha was the capital of Hittite Empire which lasted from about 1650/1600 to 1200 BC. At Yenicekale, Hittite masons flattened the summit of an outcropping limestone knoll to form an artificial platform as the foundation for a building. Next they built a circuit wall along the lateral precipices of the flattened bedrock platform. We took one sample from the limestone bedrock platform and two samples from limestone building blocks of the circuit wall for cosmogenic ³⁶Cl analysis. Calculated exposure ages are 20 ± 1 ka for the sample from the bedrock platform and 24 ± 1 ka and 52 ± 2 ka for the circuit wall blocks. These exposure ages are significantly older than the age expected based on the estimated time of construction between 3.2 ka and 3.7 ka. We conclude that the sampled surfaces contain significant inherited cosmogenic ³⁶Cl. We cannot directly determine exposure ages for the building complex based on these three samples. On the other hand we may use the measured concentrations to determine how much of the rock was removed from the platform during flattening. To this end we modeled the variation of ³⁶Cl production with depth at Yenicekale using the results from the bedrock sample. We conclude that the Hittite masons removed only around 3 m from top of the limestone block. This means that the volume of rock removed from the bedrock platform is significantly less than the volume in the circuit wall atop the platform. They did not gain enough rock from this flattening to make the building. In agreement with this, the first results of our detailed microfacies analysis indicate that many of the building blocks are not of the same facies as the underlying limestone and must have been quarried elsewhere. Although we were not able to exposure date the Yenicekale complex due to the presence of inherited ³⁶Cl, our data suggest that Hittite masons excavated (most of) the building stones not at Yenicekale, but in quarries outside of Hattusha and then transported them to the construction site. These quarries have not yet been identified.     

Vibration characteristics of a suspension bridge under traffic and no traffic conditions

This paper presents the variations of vibration under different traffic conditions on the Fatih Sultan Mehmet suspension bridge in Istanbul, Turkey.The main intention is to determine the vibration amplifications under heavy-traffic as opposed to no-traffic conditions. This is the first study in this particular area that has been performed on this bridge, over which an average of 200,000 cars pass daily. Two full-scale ambient vibration surveys were carried out on two different days to determine the response of the bridge to diverse traffic conditions. Initial measurements were taken as the bridge experienced heavy stress conditions caused by rush-hour traffic. Secondary measurements were recorded after closing the bridge to traffic. The data were analyzed to gauge the vibration effects of heavy-traffic conditions on the bridge and to determine the effects of different traffic conditions on the free vibration characteristics of the bridge. The analyses were performed utilizing different amplification methods. Results show that there are important differences in the amplifications of the vibration amplitudes. Especially heavy-traffic on the bridge causes the vibration response of the bridge to be intensified in comparison to no-traffic conditions. Additionally, predominant frequencies are shifted as a direct result of traffic load acting on the bridge. Even more importantly and is probably analogous for all long-span bridges, is the fact that any movement causing vibration on the bridge is carried and amplified along its length. These significant amplifications indicate the important effect of varying traffic loads and how the bridge responds to the diverse movements it experiences. Copyright © 2011 John Wiley & Sons, Ltd.