Amirkabir University of Technology

About: Amirkabir University of Technology is a education organization based out in Tehran, Iran. It is known for research contribution in the topics: Nonlinear system & Finite element method. The organization has 15254 authors who have published 31165 publications receiving 487551 citations. The organization is also known as: Tehran Polytechnic & Tehran Polytechnic University.

Hot deformation behavior and microstructural evolution of a superaustenitic stainless steel

Abstract: Hot deformation behavior of the superaustenitic stainless steel type 1.4563 was investigated by conducting hot compression tests at the temperatures of 900–1050 °C and at strain rates in the range of 0.001–1 s −1 . The microstructural changes were then characterized using optical and scanning electron microscopy as well as energy dispersive X-ray (EDX) microanalyses. The results showed that hot deformation at low temperatures, i.e. 900–950 °C, and at low and medium strain rates, i.e. 0.001–0.1 s −1 , can lead to the formation of wormlike precipitates on grain boundaries resulting in the restriction or even inhibition of dynamic recrystallization. At higher strain rates or higher temperatures when respectively the time was too short or the driving force for dynamic precipitation was rather low, dynamic recrystallization occurred readily. Further, at low strain rates and high temperatures, where the occurrence of dynamic precipitation is difficult, there was no sign of particles. In this case, the interactions between solute atoms and mobile dislocations resulted in tiny serrations in the flow curves instead. The EDX analyses indicated that the chemical composition of the observed precipitates was (Cr, Fe, Mo) 23 C 6 .

An extended stepwise weight assessment ratio analysis (SWARA) method for improving criteria prioritization process

Abstract: The process of criteria prioritization and weighting is an important part of multiple attributes decision making. The most frequently applied multi-attribute decision-making weighting tools include analytical hierarchy process, stepwise weight assessment ratio analysis, factor relationship, and best---worst method. When policies are at the core of decision making, stepwise weight assessment ratio analysis method is the most efficient method for criteria evaluation. It involves two important steps: the first is to prioritize the criteria by consulting experts, while the second is the weighting process. This research seeks to extend stepwise weight assessment ratio analysis to improve the quality of the decision-making process by incorporating the reliability evaluation of experts' idea into the first step. Such a component is absent from the first step in all other similar models. Thus, an extended version of stepwise weight assessment ratio analysis can be applied for such evaluation. To test the applicability and performance of the proposed method, a numerical example from an earlier study was used. The proposed version can replace the classic version in future studies as an improved method in decision-making area.

Development of a new hybrid ANN for solving a geotechnical problem related to tunnel boring machine performance

Abstract: Prediction of tunnel boring machine (TBM) performance parameters can be caused to reduce the risks associated with tunneling projects. This study is aimed to introduce a new hybrid model namely Firefly algorithm (FA) combined by artificial neural network (ANN) for solving problems in the field of geotechnical engineering particularly for estimation of penetration rate (PR) of TBM. For this purpose, the results obtained from the field observations and laboratory tests were considered as model inputs to estimate PR of TBMs operated in a water transfer tunnel in Malaysia. Five rock mass and material properties (rock strength, tensile strength of rock, rock quality designation, rock mass rating and weathering zone) and two machine factors (trust force and revolution per minute) were used in the new model for predicting PA. FA algorithm was used to optimize weight and bias of ANN to obtain a higher level of accuracy. A series of hybrid FA-ANN models using the most influential parameters on FA were constructed to estimate PR. For comparison, a simple ANN model was built to predict PR of TBM. This ANN model was improved on the basis of new ways. By doing this, the best ANN model was chosen for comparison purposes. After implementing the best models for two methods, the data were divided into five separate categories. This will minimize the chance of randomness. Then the best models were applied for these new categories. The results demonstrated that new hybrid intelligent model is able to provide higher performance capacity for predicting. Based on the coefficient of determination 0.948 and 0.936 and 0.885 and 0.889 for training and testing datasets of FA-ANN and ANN models, respectively, it was found that the new hybrid model can be introduced as a superior model for solving geotechnical engineering problems.