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.

Evaluating construction projects of hotels based on environmental sustainability with MCDM framework

Abstract: Environmental issues have got incredible attention among daily life activities. Sustainability penetrated in all society practices specially construction industry due to its substantial impact on the environment. Monitoring and controlling architectural project contains a decision problem with multi-varieties analysis. This study aimed to evaluate construction projects of hotels regarding environmental sustainability. To this end, a hybrid Multiple Criteria Decision Making (MCDM) model is proposed. Step‐wise Weight Assessment Ratio Analysis (SWARA) and Complex proportional assessment (COPRAS) compose a unified framework. A private construction project is supposed as a case study. The project is based on establishing a five star hotel in Tehran, Iran. In this research SWARA produces criteria weights and COPRAS will rank decision alternatives. This study can be a strategic route for other similar researches in other fields.

Rutting performance prediction of warm mix asphalt containing reclaimed asphalt pavements

Abstract: Nowadays, saving energy and recycling materials are becoming priorities in the road construction industry. This paper presents an experimental study to characterise permanent deformation of a warm mix asphalt (WMA) mixture containing reclaimed asphalt pavement (RAP). The WMA mixtures containing 0%, 15%, 30%, 50% and 60% of RAP were plant prepared. To assess the impact of RAP on rutting properties of hot mix asphalt, mixtures were tested using Marshall and dynamic creep tests. It was found that replacing up to 60% of the virgin aggregate with RAP improved rutting properties of the asphalt mixtures because RAP increased asphalt binder's viscosity as a main factor of rutting, especially at high temperatures. However, replacement of too much RAP in asphalt mixtures can increase moisture sensitivity of flexible pavements. Therefore, an indirect tensile strength test was conducted to evaluate moisture damage of the mixtures. The results showed that the minimum permissible tensile strength ratio (TSR) of 70% was...

Convolutional neural networks for histopathology image classification: Training vs. Using pre-trained networks

Abstract: We explore the problem of classification within a medical image data-set based on a feature vector extracted from the deepest layer of pre-trained Convolution Neural Networks. We have used feature vectors from several pre-trained structures, including networks with/without transfer learning to evaluate the performance of pre-trained deep features versus CNNs which have been trained by that specific dataset as well as the impact of transfer learning with a small number of samples. All experiments are done on Kimia Path24 dataset which consists of 27,055 histopathology training patches in 24 tissue texture classes along with 1,325 test patches for evaluation. The result shows that pre-trained networks are quite competitive against training from scratch. As well, fine-tuning does not seem to add any tangible improvement for VGG16 to justify additional training while we observed considerable improvement in retrieval and classification accuracy when we fine-tuned the Inception structure.