in recent years, artificial neural networks (anns) have become one of the most promising tools in order to model complex hydrological processes such as the rainfall-runoff process. in many studies, anns have demonstrated superior results compared to alternative methods. anns are able to map underlying relationship between input and output data without prior understanding of the process under in...

Vibration based techniques of structural damage detection using model updating method, are computationally expensive for large-scale structures. In this study, after locating precisely the eventual damage of a structure using modal strain energy based index (MSEBI), To efficiently reduce the computational cost of model updating during the optimization process of damage severity detection, the M...

Free swelling index (FSI) is an important parameter for cokeability and combustion of coals. In this research, the effects of chemical properties of coals on the coal free swelling index were studied by artificial neural network methods. The artificial neural networks (ANNs) method was used for 200 datasets to estimate the free swelling index value. In this investigation, ten input parameters ...

porosity is one of the most important properties for comprehensive studies of hydrocarbon reservoirs. for determination of porosity in a rock, that is the ratio of volume of voids to the total volume of the rock, there are two conventional methods: in the first method, direct measurement of porosity is carried out by testing drilling cores. in the second method, porosity is determined indirectl...

1. Introduction Neural networks, more accurately called Artificial Neural Networks (ANNs), are computational models that consist of a number of simple processing units that communicate by sending signals to one another over a large number of weighted connections. They were originally developed from the inspiration of human brains. In human brains, a biological neuron collects signals from other...

In this paper, load-carrying capacity in steel shear wall (SSW) was estimated using artificial neural networks (ANNs). The SSW parameters including load-carrying capacity (as ANN’s target), plate thickness, thickness of stiffener, diagonal stiffener distance, horizontal stiffener distance and gravity load (as ANN’s inputs) are used in this paper to train the ANNs. 144 samples data of each of th...

Phytoplankton biomass within the Saginaw Bay ecosystem (Lake Huron, Michigan, USA) was characterized as a function of select physical/chemical indicators. The complexity and variability of ecological systems typically make it difficult to model the influences of anthropogenic stressors and/or natural disturbances. Here, Artificial Neural Networks (ANNs) were developed to model chlorophyll a con...

The IP+GMPLS over DWDM model has been considered a trend for the evolution of optical networks. However, a challenge that has been investigated in this model is how to achieve fast rerouting in case of DWDM failure. Artificial Neural Networks (ANNs) can be used to generate proactive intelligent agents, which are able to detect failure trends in optical network links early and to approximate opt...