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Akbar Salemi,Reza Mikaeil,Sina Shaffiee Haghshenas 대한토목학회 2018 KSCE JOURNAL OF CIVIL ENGINEERING Vol.22 No.5
In this study, it is aimed to investigate the behavior of the concrete lining of circular shallow tunnels in sedimentary urban areasunder seismic loads using integration of numerical and metaheuristic techniques. The Tabriz Urban Railway (TUR) Tunnel is used asa case study in this investigation. The seismic and geotechnical characteristics of the area were studied, and seismic analysis wascarried out using a finite difference code (i.e., FLAC2D) and genetic algorithm. In the first step, final induced loads on lining due toDesign Base Level (DBL), Maximum Credible Level (MCL) and static loads were determined using FLAC2D software. Then, eightparts of lining were classified using genetic algorithm based on axial force, bending moment and shear force for two types ofearthquake loads. The results of classification were verified by the safety factors of the studied parts of the lining. By comparing theseresults, it can be concluded that the genetic algorithm can be reliably used to classify and evaluate the safety of lining based on staticand dynamic loads.
S. Najmedin Almasi,Raheb Bagherpour,Reza Mikaeil,Yilmaz Ozcelik 한국자원공학회 2017 Geosystem engineering Vol.20 No.6
Predicting the sawability of dimension stone is one of the most important factors in the optimized design and cost estimation of quarrying. This paper aims to predict the cutting rate of diamond wire saw (DWS) as main performance criteria. For this purpose, a classification system for ranking the sawability of hard dimension stone based on the toughness, abrasiveness, and hardness of rock was initially developed, and a Hard Dimension Stone Sawability index (HDSSi) was defined. Then, by means of multiple curvilinear regression analysis, the data were analyzed and the relationship between the cutting rate with the HDSSi, and pullback amperage was obtained with a high correlation coefficient (.846) in data training, and .801 in data test. Validation of the model was carried out by considering the t-test, F-test, and the coefficient of determination. During this research, varieties of 11 types of hard rock were cut in a laboratory using a DWS and a fully instrumented cutting platform at different pullbacks. The results show that the cutting rate of hard dimension stones with a DWS can be successfully predicted using the developed model.