Search published articles


Showing 8 results for Neural Network

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.
S.n. Moghaddas Tafreshi, Gh. Tavakoli Mehrjardi, S.m. Moghaddas Tafreshi,
Volume 5, Issue 2 (6-2007)
Abstract

The safety of buried pipes under repeated load has been a challenging task in geotechnical engineering. In this paper artificial neural network and regression model for predicting the vertical deformation of high-density polyethylene (HDPE), small diameter flexible pipes buried in reinforced trenches, which were subjected to repeated loadings to simulate the heavy vehicle loads, are proposed. The experimental data from tests show that the vertical diametric strain (VDS) of pipe embedded in reinforced sand depends on relative density of sand, number of reinforced layers and height of embedment depth of pipe significantly. Therefore in this investigation, the value of VDS is related to above pointed parameters. A database of 72 experiments from laboratory tests were utilized to train, validate and test the developed neural network and regression model. The results show that the predicted of the vertical diametric strain (VDS) using the trained neural network and regression model are in good agreement with the experimental results but the predictions obtained from the neural network are better than regression model as the maximum percentage of error for training data is less than 1.56% and 27.4%, for neural network and regression model, respectively. Also the additional set of 24 data was used for validation of the model as 90% of predicted results have less than 7% and 21.5% error for neural network and regression model, respectively. A parametric study has been conducted using the trained neural network to study the important parameters on the vertical diametric strain.
Shahriar Afandizadeh, Jalil Kianfar,
Volume 7, Issue 1 (3-2009)
Abstract

This paper presents a hybrid approach to developing a short-term traffic flow prediction model. In this

approach a primary model is synthesized based on Neural Networks and then the model structure is optimized through

Genetic Algorithm. The proposed approach is applied to a rural highway, Ghazvin-Rasht Road in Iran. The obtained

results are acceptable and indicate that the proposed approach can improve model accuracy while reducing model

structure complexity. Minimum achieved prediction r2 is 0.73 and number of connection links at least reduced 20%

as a result of optimization.


M.h. Vahidnia, A.a. Alesheikh, A. Alimohammadi, F. Hosseinali,
Volume 7, Issue 3 (9-2009)
Abstract

Landslides are major natural hazards which not only result in the loss of human life but also cause economic burden on the society. Therefore, it is essential to develop suitable models to evaluate the susceptibility of slope failures and their zonations. This paper scientifically assesses various methods of landslide susceptibility zonation in GIS environment. A comparative study of Weights of Evidence (WOE), Analytical Hierarchy Process (AHP), Artificial Neural Network (ANN), and Generalized Linear Regression (GLR) procedures for landslide susceptibility zonation is presented. Controlling factors such as lithology, landuse, slope angle, slope aspect, curvature, distance to fault, and distance to drainage were considered as explanatory variables. Data of 151 sample points of observed landslides in Mazandaran Province, Iran, were used to train and test the approaches. Small scale maps (1:1,000,000) were used in this study. The estimated accuracy ranges from 80 to 88 percent. It is then inferred that the application of WOE in rating maps’ categories and ANN to weight effective factors result in the maximum accuracy.
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.


M. H. Baziar, A. Saeedi Azizkandi,
Volume 11, Issue 2 (11-2013)
Abstract

Due to its critical impact and significant destructive nature during and after seismic events, soil liquefaction and liquefactioninduced

lateral ground spreading have been increasingly important topics in the geotechnical earthquake engineering field

during the past four decades. The aim of this research is to develop an empirical model for the assessment of liquefaction-induced

lateral ground spreading. This study includes three main stages: compilation of liquefaction-induced lateral ground spreading

data from available earthquake case histories (the total number of 525 data points), detecting importance level of seismological,

topographical and geotechnical parameters for the resulted deformations, and proposing an empirical relation to predict

horizontal ground displacement in both ground slope and free face conditions. The statistical parameters and parametric study

presented for this model indicate the superiority of the current relation over the already introduced relations and its applicability

for engineers.


A. Kaveh, R. Ghaffarian,
Volume 13, Issue 1 (3-2015)
Abstract

The main aim of this paper is to find the optimum shape of arch dams subjected to multiple natural frequency constraints by using an efficient methodology. The optimization is carried out by charged system search algorithm and its enhanced version. Computing the natural frequencies by Finite Element Analysis (FEA) during the optimization process is time consuming. In order to reduce the computational burden, Back Propagation (BP) neural network is trained and utilized to predict the arch dam natural frequencies. It is demonstrated that the optimum design obtained by the Enhanced Charged System Search using the BP network is the best compared with the results of other algorithms. The numerical results show the computational advantageous of the proposed methodology.
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 (fchr('39')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.



Page 1 from 1     

© 2021 CC BY-NC 4.0 | International Journal of Civil Engineering

Designed & Developed by : Yektaweb