Iran University of Science and Technology

About: Iran University of Science and 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 12917 authors who have published 24965 publications receiving 372013 citations. The organization is also known as: Governmental Technical Institute & Advanced Art College.

Railway passenger train delay prediction via neural network model

Abstract: SUMMARY The aim of this paper is to present an artificial neural network model with high accuracy to predict the delay of passenger trains in Iranian Railways. In the proposed model, we use three different methods to define inputs including normalized real number, binary coding, and binary set encoding inputs. One of the great challenges of using neural network is how to design a superior network for a specific task. To find an appropriate architecture, three different strategies called quick method, dynamic method, and multiple method are investigated. To prevent the proposed model from overfitting in modeling, according to cross validation, we divide existing passenger train delays data set into three subsets called training set, validation set, and testing set. To evaluate the proposed model, we compare the results of three different data input methods and three different architectures with each other and with some common prediction methods such as decision tree and multinomial logistic regression. For comparing different neural networks, we consider training time and accuracy of neural networks on test data set and network size. In addition, for comparing neural networks with other well-known prediction methods, we consider training time and the accuracy of neural network on test data sets. To make a fair comparison among all models, we sketch a time-accuracy graph. The results revealed that the proposed model has higher accuracy. Copyright © 2012 John Wiley & Sons, Ltd.

Optimum design of steel frames using Cuckoo Search algorithm with Lévy flights

Abstract: SUMMARY In the last two decades, many researchers have implemented various kinds of meta-heuristic algorithms in order to overcome the complex nature of the optimum design of structures. In this paper, the optimum design of two-dimensional steel frames for discrete variables based on the Cuckoo Search (CS) algorithm is developed. The CS is one of the recently developed population-based algorithms inspired by the behavior of some cuckoo species in combination with the Levy flight behavior of some birds and insects. The design algorithm is supposed to obtain minimum weight frame through suitable selection of sections from a standard set of steel sections such as the American Institute of Steel Construction (AISC) wide-flange (W) shapes. Strength constraints of AISC load and resistance factor design specification and displacement constraints are imposed on frames. In order to demonstrate the effectiveness and robustness of the CS, low-weight design and performance comparisons are made between the CS and other algorithms for some benchmark frames. Copyright © 2011 John Wiley & Sons, Ltd.

Prediction of blast-produced ground vibration using particle swarm optimization

Abstract: Blasting operation is an inseparable operation of the rock fragmentation process in the surface mines and tunneling projects. Ground vibration is one of the most undesirable effects induced by blasting operation which can cause damage to the surrounding residents and structures. So, the ability to make precise predictions of ground vibration is very important to reduce the environmental side effects caused by mine blasting. The aim of this paper is to develop a simple, accurate, and applicable model based on particle swarm optimization (PSO) approach for predicting the ground vibration induced by blasting operations in Shur River dam region, Iran. In this regard, two forms of PSO models, linear and power, were developed. For this work, a database including 80 data sets was collected, and the values of the maximum charge weight used per delay (W), distance between blast-point and monitoring station (D) and peak particle velocity (PPV) were measured. To develop the PSO models, PPV was used as output parameter, while W and D were used as input parameters. To check the performance of the proposed PSO models, multiple linear regression (MLR) model and United States Bureau of Mines (USBM) equation were also developed. Accuracy of models established was evaluated using statistical criteria, i.e., coefficient of correlation (R2) and root mean square error (RMSE), variance absolute relative error (VARE) and Nash & Sutcliffe (NS). Finally, it was found that the PSO power form provided more accurate predictions in comparison with PSO linear form, MLR and USBM models.