Prediction of uniaxial compressive strength of granitic rocks by various nonlinear tools and comparison of their performances

WOS: 000323533900014The main goal of this study is to develop some prediction models for the UCS of six different granitic rocks selected from Turkey. During the modeling stage of the study, various approaches such as multiple regression, Artificial Neural Network (ANN), and Adaptive Neuro Fuzzy Inference System (ANFIS) are applied to estimate UCS. Tensile strength (sigma(t)), block punch index (BPI), point load index (Is((50))) and P-wave velocity (V-p) are considered as the input parameters for the models. In the study, total 75 cases including all inputs and output are used. In accordance with the analyses employed in the study, and considering the inputs, three different models are constructed as tensile strength and P-wave velocity (Model 1), BPI and P-wave velocity (Model 2), Is((50)) and P-wave velocity (Model 3) to estimate UCS. Performance assessments show that ANFIS is the better predictive tool than the other methods employed, and Model 1 is the better model for the prediction of UCS. The results show that the models developed can be used as preliminary stages of rock engineering assessments because the models developed herein have high prediction performances. It is evident that such prediction studies provides not only some practical tools but also understanding of the controlling index parameters of UCS of rocks. (C) 2013 Elsevier Ltd. All rights reserved.Hacettepe University Scientific Research Unit, Ankara, Turkey [08D07602 002]This study was supported by the Hacettepe University Scientific Research Unit, Ankara, Turkey with Project no. 08D07602 002. Also, the authors are grateful to the reviewers for their constructive comments

Simultaneous pickup and delivery model suggestion for personnel transportation in COVID-19 pandemic conditions

The impact of COVID-19 on the transportation costs of a large-scale company has been examined. Before the pandemic, shift personnel were transported to the factory by shuttles, and after a quick shift change, other shift personnel were transported back to their homes. However, with the implementation of laws mandating the reduction of shuttle seat capacities, transportation costs have risen significantly. To address this issue, a new simultaneous pickup and delivery model is proposed as an alternative to the separate transportation of shift workers. The results of this study indicate that the proposed model provides a substantial advantage in terms of both the number of vehicles used and the total distance traveled, leading to a significant reduction in costs. This research underscores the importance of effective operations research practices for the profitability of companies, particularly in extraordinary circumstances such as the COVID-19 pandemic

Landform effect on rockfall and hazard mapping in Cappadocia (Turkey)

WOS: 000288802500012The Cappadocia region has unique geomorphological features resulting from differential erosional processes which make it very attractive to tourists. Besides the fairy chimneys for which the area is best known, there are also impressive buttes and mesas. Buttes and mesas are formed in regions having flat-lying strata in which the uppermost levels are composed of well-cemented limestones and granular ignimbrites, whereas the lower parts and slopes consist of low-durability tuff and ignimbrites. This durability difference results in serious rockfall events. This study involves two-dimensional rockfall analyses in and near the Avanos, Zelve, and Cavusini areas, where volcano-sedimentary units of Neogene age outcrop, to provide a rockfall hazard map in which areas of tourism activity are also considered