E. Çevik

Bio: E. Çevik is an academic researcher. The author has contributed to research in topics: Ultimate tensile strength & Pipeline transport. The author has an hindex of 1, co-authored 1 publications receiving 286 citations.

GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline, Hendek (Turkey)

Abstract: A segment of natural gas pipeline was damaged due to landsliding near Hendek. Re-routing of the pipeline is planned, but it requires the preparation of a landslide susceptibility map. In this study, the statistical index (Wi) and weighting factor (Wf) methods have been used with GIS to prepare a landslide susceptibility map of the problematic segment of the pipeline. For this purpose, thematic layers including landslide inventory, lithology, slope, aspect, elevation, land use/land cover, distance to stream, and drainage density were used. In the study area, landslides occur in the unconsolidated to semi-consolidated clayey unit and regolith. The Wf method gives better results than the Wi method. Lithology is found to be the most important aspect in the study area. Based on the findings obtained in this study, the unconsolidated to semi-consolidated clayey unit and alluvium should be avoided during re-routing. Agricultural activities should not be allowed in the close vicinity of the pipeline.

Reference material development process for tensile test method

Abstract: Öz Reference materials (RMs) are one of the basic materials used by the laboratories to prove the reliability of their test results. In this research, preliminary studies have been carried out to develop RM to use in tensile tests according to ISO 6892-1. For this purpose, bone shape flat specimens were prepared from EN 10130:2006 quality 1 mm thick sheet steel related to the given standard. Homogeneity tests and inter-laboratory comparison tests were performed for calculation of measurement uncertainty and value assignment of proposed RM. Results were compared with commercial certified reference materials (CRMs). It was found that relative uncertainty values of the proposed RM were coherent with commercial CRMs. Therefore, these produced RM can be used in quality control studies for laboratories, also by the production route proposed in this study, every laboratory can be produced RM related to their needs.

GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): Comparisons of results and confirmations

Abstract: The Ardesen area is prone to landslides because of the climate conditions, geologic, and geomorphologic characteristics of the region. As in previous years, in 2001 due to heavy rainfall there were many landslides resulting in damage and human casualties. There is still a great danger of further landslides in the region. Therefore, it is vitally important to prepare a landslide susceptibility map of the region. In this study, the analytical hierarchy process (AHP), the statistical index (Wi), and weighting factor (Wf) methods were used to produce and later compare three susceptibility maps. For this purpose, thematic layers including landslide inventory, lithology-weathering, slope, aspect, land cover, distance to stream, drainage density, and distance to road were used. In this study area, 98% of landslides occurred in highly or completely weathered units. Lithology-weathering, land cover, and slope data layers were found to be important factors in the study area. To confirm the practicality of the three susceptibility maps were compared with a landslide activity map containing 16 active landslide zones. The outcome was that the active landslide zones do not completely fit into the high and very high susceptibility classes. But 81.3% of these landslide zones fall into the high and very high susceptibility zones of the AHP method while this is 62.5% in the case of Wi method, and 68.8% with the Wf method. In spite of the results obtained in this study, the development of a susceptibility map is usually determined by the needs and available resources. The results showed that the AHP method gave a more realistic picture of the actual distribution of landslide susceptibility, than the Wi and Wf methods.

A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey

Abstract: Over the last few decades, many researchers have produced landslide susceptibility maps using different techniques including the probability method (frequency ratio), the analytical hierarchy process (AHP), bivariate, multivariate, logistics regression, fuzzy logic and artificial neural network In addition, a number of parameters such as lithology, slope, aspect, land cover, elevation, distance to stream, drainage density, distance to lineament, seismicity, and distance to road are recommended to analyze the mechanism of landslides. The data quality is a very important issue in landslide studies, and more accurate results will be achieved if the data is adequate, appropriate and drawn from a wide range of parameters. The aim of this study was to evaluate the susceptibility of the occurrence of landslides in Trabzon province, situated in north east Turkey. This was achieved using the following five methods the frequency ratio model, AHP, the statistical index (Wi), weighting factor (Wf) methods, and the logistics regression model, incorporating a Geographical Information System (GIS) and remote sensing techniques. In Trabzon province there has been an increasing occurrence of landslides triggered by rainfall. These landslides have resulted in death, significant injury, damage to property and local infrastructure and threat of further landslides continues. In order to reduce the effects of this phenomenon, it is necessary to scientifically assess the area susceptible to landslide. To achieve this, landslide susceptible areas were mapped the landslide occurrence parameters were analyzed using five different methods. The results of the five analyses were confirmed using the landslide activity map containing 50 active landslide zones. Then the methods giving more accurate results were determined. The validation process showed that the Wf method is better in prediction than the frequency ratio model, AHP, the statistical index (Wi), and logistics regression model.