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Showing 5 results for Artificial Neural Networks

Misaghi F., Mohammadi K., Mousavizadeh M.h.,
Volume 1, Issue 1 (9-2003)
Abstract

In the present paper, ANN is used to predict the tidal level fluctuations, which is an important parameter in maritime areas. A time lagged recurrent network (TLRN) was used to train the ANN model. In this kind of networks, the problem is representation of the information in time instead of the information among the input patterns, as in the regular ANN models. Two sets of data were used to test the proposed model. San Francisco Bay tidal levels were used to test the performance of the model as a predictive tool. The second set of data was collected in Gouatr Bay in southeast of Iran. This data set was used to show the ability of the ANN model in predicting and completing of data in a station, which has a short period of records. Different model structures were used and compared with each other. In addition, an ARMA model was used to simulate time series data to compare the results with the ANN forecasts. Results proved that ANN can be used effectively in this field and satisfactory accuracy was found for the two examples. Based on this study, an operational real time environment could be achieved when using a trained forecasting neural network.
Mohammad T. Dastorani, Nigel G. Wright,
Volume 2, Issue 3 (9-2004)
Abstract

In this study, an artificial neural networks (ANN) was used to optimise the results obtained from a hydrodynamic model of river flow prediction. The study area is Reynolds Creek Experimental Watershed in southwest Idaho, USA. First a hydrodynamic model was constructed to predict flow at the outlet using time series data from upstream gauging sites as boundary conditions. The model, then was replaced with an ANN model using the same inputs. Finally a hybrid model was employed in which the error of the hydrodynamic model is predicted using an ANN model to optimise the outputs. Simulations were carried out for two different conditions (with and without data from a recently suspended gauging site) to evaluate the effect of this suspension in hydrodynamic, ANN and the hybrid model. Using ANN in this way, the error produced by the hydrodynamic model was predicted and thereby, the results of the model were improved.
F. Rezaie Moghaddam, Sh. Afandizadeh, M. Ziyadi,
Volume 9, Issue 1 (3-2011)
Abstract

In spite of significant advances in highways safety, a lot of crashes in high severities still occur in highways. Investigation of influential factors on crashes enables engineers to carry out calculations in order to reduce crash severity. Therefore, this paper deals with the models to illustrate the simultaneous influence of human factors, road, vehicle, weather conditions and traffic features including traffic volume and flow speed on the crash severity in urban highways. This study uses a series of artificial neural networks to model and estimate crash severity and to identify significant crash-related factors in urban highways. Applying artificial neural networks in engineering science has been proved in recent years. It is capable to predict and present desired results in spite of limited data sets, which is the remarkable feature of the artificial neural networks models. Obtained results illustrate that the variables such as highway width, head-on collision, type of vehicle at fault, ignoring lateral clearance, following distance, inability to control the vehicle, violating the permissible velocity and deviation to left by drivers are most significant factors that increase crash severity in urban highways.


H. Shahnazari, M. A. Shahin, M. A. Tutunchian,
Volume 12, Issue 1 (1-2014)
Abstract

Due to the heterogeneous nature of granular soils and the involvement of many effective parameters in the geotechnical behavior of soil-foundation systems, the accurate prediction of shallow foundation settlements on cohesionless soils is a complex engineering problem. In this study, three new evolutionary-based techniques, including evolutionary polynomial regression (EPR), classical genetic programming (GP), and gene expression programming (GEP), are utilized to obtain more accurate predictive settlement models. The models are developed using a large databank of standard penetration test (SPT)-based case histories. The values obtained from the new models are compared with those of the most precise models that have been previously proposed by researchers. The results show that the new EPR and GP-based models are able to predict the foundation settlement on cohesionless soils under the described conditions with R2 values higher than 87%. The artificial neural networks (ANNs) and genetic programming (GP)-based models obtained from the literature, have R2 values of about 85% and 83%, respectively which are higher than 80% for the GEP-based model. A subsequent comprehensive parametric study is further carried out to evaluate the sensitivity of the foundation settlement to the effective input parameters. The comparison results prove that the new EPR and GP-based models are the most accurate models. In this study, the feasibility of the EPR, GP and GEP approaches in finding solutions for highly nonlinear problems such as settlement of shallow foundations on granular soils is also clearly illustrated. The developed models are quite simple and straightforward and can be used reliably for routine design practice.
Masoud Ahmadi , Hosein Naderpour , Ali Kheyroddin ,
Volume 15, Issue 2 (3-2017)
Abstract

Concrete filled steel tube is constructed using various tube shapes to obtain most efficient properties of concrete core and steel tube. The compressive strength of concrete is considerably increased by the lateral confined steel tube in circular concrete filled steel tube (CCFT). The aim of this study was to present an integrated approach for predicting the steel-confined compressive strength of concrete in CCFT columns under axial loading based on large number of experimental data using artificial neural networks. Neural networks process information in a similar way the human brain does. Neural networks learn by example. The main parameters investigated in this study include the compressive strength of unconfined concrete (f'c), outer diameter (D) and length (L) of column, wall thickness (t) and tensile yield stress (fy) of steel tube. Subsequently, using the reliable network, empirical equations are developed for the confinement effect. The results of proposed model are compared with recently existing model on the basis of the experimental results. The findings demonstrate the precision and applicability of the empirical approach to determine capacity of CCFT columns.



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