EXAMPLE FOR THE PREDICTION OF THE UNCONFINED COMPRESSIVE STRENGTH OF WEATHERED ROCKS USING THE WEATHERING INDICES BASED ON P-WAVE VELOCITY

The engineering behavior of rock materials depends not only on stress state and stress history but also on the physical and chemical change of the rock materials due to weathering. Changes in index properties such as dry density void ratio, clay content and seismic velocity caused by weathering processes is representing strength and elastic properties of rock materials. The changes caused by weathering processes in rock material may be mainly considered under the two topics; the mineralogical change and the physical change. Because the elastic wave velocity measurement is a non-destructive test, and this measurement is applied easily to repeat and be sensitive to the change of the related engineering properties. One of these parameters is Weathering State Index based on Mineralogical Change Parameter and Physical Change Parameter. Although the unconfined compressive strength of weathered rocks materials are highly important parameters for engineering geology projects, the necessary core samples cannot always be obtained from these rock materials.. For this reason, the predictive models are often employed for the indirect estimation of mechanical parameters

USING ELASTIC WAVE VELOCITY ON CLASSIFICATION WEATHERING ROCK MATERIALS AND PREDICTION OF ENGINEERING PROPERTIES IN KURTUN GRANODIORITE

A great number of landslides occurred in weathered granites outcropped the area in which Kurtun Dams with reservoirs and Gümüshane-Giresun highways pass trough. For this, weathering effect on the rock materials of the Kurtun granodiorite was investigated. In this study, both physical and mineralogical changes on the granitic materials due to weathering are described separately using P- wave velocity in rocks materials. Mineralogical Change Parameter and Physical Parameter defined based on P- wave velocity in rocks materials are applied on the samples from the selected weathering profiles, for the estimation of the effects of weathering on the physicomechanical properties of rock materials. The relative variation of mechanical properties and these indices display a statistically significant correlation. Besides, it is shown that P wave velocity in the solid parts of the samples. and Quantitative Weathering index originally defined Ceryan (1999a) as based on slake-durability index, Mineralogical Change Parameter and Physical Parameter can be used together to evaluate the effect of weathering on the mechanical behavior of rocks material from Kürtün granodiorite

Prediction of The Uniaxial Compressive Strength of Rocks Materials

This study briefly will review determining UCS including direct and indirect methods including regression model soft computing techniques such as fuzzy interface system (FIS), artifical neural network (ANN) and least sqeares support vector machine (LS-SVM). These has advantages and disadvantages of these methods were discussed in term predicting UCS of rock material. In addition, the applicability and capability of non-linear regression, FIS, ANN and LS-SVM SVM models for predicting the UCS of the magnatic rocks from east Pondite, NE Turkey were examined. In these soft computing methods, porosity and P-durability secon index defined based on P-wave velocity and slake durability were used as input parameters. According to results of the study, the performanc of LS-SVM models is the best among these soft computing methods suggested in this study

Machine learning models to estimate the elastic modulus of weathered magmatic rocks

In recent years, several soft computing models have been proposed to estimate the elastic modulus of magmatic rocks. However, there are lacks in models that consider the different weathering degrees in determining the elastic modulus of rocks. In the literature, mechanical properties are widely used as inputs in predictive models for weathered rocks; however, there are only a few models that use index properties representing the effect of weathering on magmatic rocks. In this study, support vector regression (SVR) Gaussian process regression (GPR), and artificial neural network (ANN) models were developed to predict the elastic modulus of magmatic rocks with different degrees of weathering. The inputs selected by the best subset regression approach were porosity, P-wave velocity, and slake durability index. Key performance indicators (KPIs) were computed to validate the accuracy of the developed models. In addition to KPIs, Taylor diagrams and regression error characteristic (REC) curves were used to assess the performance of the developed prediction models. In this study, considering the difficulties of expressing the error using only RMSE and MAE, a new performance index (PI), PIMAE, was proposed using normalized MAE instead of normalized RMSE. It was also indicated that PIRMSE and PIMAE should be used together in performance analysis. When considering the Taylor diagram, PIRMSE, and PIMAE, the GPR models performed best, and the SVR model performed the worst in both the training and test periods. Similarly, according to the REC curve in both periods, the performance of the SVR was the worst, while the performance of the ANN model was the best. The PIRMSE and PIMAE values of the GPR model for the test data were 1.3779 and 1.4142, respectively, and they were 1.2567 and 1.4139, respectively, for the ANN model. According to the computed response surfaces, an increase in the P-wave velocity, and a decrease in the porosity increased the elastic modulus. However, changes in slake durability index only had a minor effect on the elastic modulus

Machine learning models to estimate the elastic modulus of weathered magmatic rocks

In recent years, several soft computing models have been proposed to estimate the elastic modulus of magmatic rocks. However, there are lacks in models that consider the different weathering degrees in determining the elastic modulus of rocks. In the literature, mechanical properties are widely used as inputs in predictive models for weathered rocks; however, there are only a few models that use index properties representing the effect of weathering on magmatic rocks. In this study, support vector regression (SVR) Gaussian process regression (GPR), and artificial neural network (ANN) models were developed to predict the elastic modulus of magmatic rocks with different degrees of weathering. The inputs selected by the best subset regression approach were porosity, P-wave velocity, and slake durability index. Key performance indicators (KPIs) were computed to validate the accuracy of the developed models. In addition to KPIs, Taylor diagrams and regression error characteristic (REC) curves were used to assess the performance of the developed prediction models. In this study, considering the difficulties of expressing the error using only RMSE and MAE, a new performance index (PI), PIMAE, was proposed using normalized MAE instead of normalized RMSE. It was also indicated that PIRMSE and PIMAE should be used together in performance analysis. When considering the Taylor diagram, PIRMSE, and PIMAE, the GPR models performed best, and the SVR model performed the worst in both the training and test periods. Similarly, according to the REC curve in both periods, the performance of the SVR was the worst, while the performance of the ANN model was the best. The PIRMSE and PIMAE values of the GPR model for the test data were 1.3779 and 1.4142, respectively, and they were 1.2567 and 1.4139, respectively, for the ANN model. According to the computed response surfaces, an increase in the P-wave velocity, and a decrease in the porosity increased the elastic modulus. However, changes in slake durability index only had a minor effect on the elastic modulus

Trabzon-Taşönü Malzeme Ocağındaki Killerin Mühendislik Özellikleri

Doğu Karadeniz Bölgesinin en büyük çimento hammadde ocağı olan Taşönü Kireçtaşı Ocağında (Araklı-Trabzon) 2005-2006 tarihleri arasında üç ayrı düzlemsel yenilme gelişmiştir. Değişik fasiyeslerdeki kireçtaşlarından oluşan Kireçhane formasyonunda açılmış olan ocakta gelişen bu heyelanlar sonucunda malzeme alımı büyük miktarda azalmıştır. Heyelanların kayma düzlemleri kalınlığı 15-110 cm arasında değişen killi seviyelerdir. Bu nedenle söz konusu killerin jeomekanik ve jeofizik özellikleri incelenmiştir. Killi seviyelerden alınan örnekler yüksek plastisiteli kil (CH) grubuna girmektedir. Bu örneklerdeki kil minerallerinin yaklaşık % 85-90'ı montmorillonit ve % 10-15'i ise illitdir. Bu alandaki kil çeşitliliğini ortaya koymada yararlanılan hacim manyetik süseptibilite ölçüleri 129-163x10-6 cgs aralığında değişmektedir. Bu değerler kil içindeki ağır metal kirlilik oranları ile irdelenmiş ve özellikle demir oksit (% 3.6-6.8) oranlarının değişimine bağlı olduğu sonucuna varılmıştır. Bu çalışmada, killi seviyeler ve dolgu malzemesi için elde edilen indeks ve makaslama dayanım değerleri ocaktaki açılacak kazı şevlerinin duraylılığının araştırılmasında kullanılabilir

The Engineering Properties of Clays in Trabzon Taşönü Quarry

Taşönü limestone quarry located south of Araklı in Trabzon is the largest cement raw materials field in the Eastern Black Sea region. The quarry is run in the Kireçhane Formation composed of limestone with various facies. However, it has been encountered three separate planar failures occurred between 2005 and 2006. Due to the failures, the amount of raw material production from the quarry has been gradually decreased. These failures occurred on clayey layers varying between 15 and 110 centimeter thicknesses. Therefore, the relevant clayey layers were examined in terms of geochmechanical and geophysical properties. The samples collected from the clayey layers are classified as high plasticity clay (CH) in accordance with the Unified Soil Classification System. Clay samples consist of 85 to 90 % montmorillonite and 10 to 15 % illite. The measurement of volumetric magnetic susceptibility used in the diversity of clay varies in the range 129-163x10-6 cgs. These values have been analyzed with respect to the heavy metal pollution ratio of the clay and especially iron oxide depending on 3.6 to 6.8 % rates was the result. In this study, index and shearing strength values obtained from clayey level and filling material may be use for the investigation of the stability of quarry slopes during excavation