Factors affecting the durability of selected weak and clay-bearing rocks from Turkey, with particular emphasis on the influence of the number of drying and wetting cycles

Weathering can induce a rapid change of rock material from initial rock-like properties to soil-like properties. The resistance of a rock to short-term weathering is described through a durability parameter called the slake durability index. As durability is an important engineering parameter, particularly for weak and clay-bearing rocks, it was assessed by a number of tests. The main purpose of this study is to assess the influence of the number of drying and wetting cycles and controls of mineralogical composition and strength on durability. For this purpose, 141 samples of different types of weak and clay-bearing rocks were selected from different parts of Turkey, and relationships between the above-mentioned rock characteristics were statistically investigated. The samples were subjected to multiple-cycle slake durability testing, X-ray diffraction (XRD) analysis and uniaxial compression testing. In addition, to assess the influence of mineralogical composition on durability, the mineral contents of the original material and the material passing from the drum of the slake durability apparatus after each cycle were also determined by XRD. The results indicate that the type and amount of clay minerals are the main factors influencing the variations of the slake durability index in all samples. The durability of the clay-bearing rocks studied correlates best with the amount of expandable clay minerals. A strong relationship between the uniaxial compressive strength and the fourth-cycle slake durability index is found only for the marls among the rock types studied. Assessment of gradation results of the spoil pile materials consisting of clay-bearing rocks also reveals that the increase in percentage of fines in old piles is indicative of material degradation, as is evident by multiple-cycle slaking. It is emphasized that two-cycle conventional slake durability testing did not appear to offer an acceptable indication of the durability of weak and clay-bearing rocks when compared with multiple-cyclic wetting and drying. Comments on the performance of the test are made that aim to make the testing process and interpretation of the results more reliable.     

An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps

The main purpose of this study is to highlight the conceptual differences of produced susceptibility models by applying different sampling strategies: from all landslide area with depletion and accumulation zones and from a zone which almost represents pre-failure conditions. Variations on accuracy and precision values of the models constructed considering different algorithms were also investigated. For this purpose, two most popular techniques, logistic regression analysis and back-propagation artificial neural networks were taken into account. The town Ispir and its close vicinity (Northeastern part of Turkey), suffered from landsliding for many years was selected as the application site of this study. As a result, it is revealed that the back-propagation artificial neural network algorithms overreact to the samplings in which the presence (1) data were taken from the landslide masses. When the generalization capacities of the models are taken into consideration, these reactions cause imprecise results, even though the area under curve (AUC) values are very high (0.915 < AUC < 0.949). On the other hand, the susceptibility maps, based on the samplings in which the presence (1) data were taken from a zone which almost represents pre-failure conditions constitute more realistic susceptibility evaluations. However, considering the spatial texture of the final susceptibility values, the maps produced using the outputs of the back-propagation artificial neural networks could be interpreted as highly optimistic, while of those generated using the resultant probabilities of the logistic regression equations might be evaluated as pessimistic. Consequently, it is evident that, there are still some needs for further investigations with more realistic validations and data to find out the appropriate accuracy and precision levels in such kind of landslide susceptibility studies.