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2009 |
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Transaction on Civil Engineering |
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Transaction on Mechanical Engineering |
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Transactions on Chemistry and Chemical Engineering |
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Transaction on Computer Science & Engineering and Electrical Engineering |
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Transaction on Industrial Engineering |
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Transaction on Nanotechnology |
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Volume 16, Issue 1, 2009
Transaction on Civil Engineering
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Predicting Density and Compressive Strength
of Concrete Cement Paste Containing Silica
Fume Using Articial Neural Networks
M.H. Afshar (PhD.)
E. Rasa [PhD.]
H. Ketabchi [PhD.]
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Abstract: Articial Neural Networks (ANNs) have recently been introduced as an ecient articial
intelligence modeling technique for applications involving a large number of variables, especially with
highly nonlinear and complex interactions among input/output variables in a system without any prior
knowledge about the nature of these interactions. Various types of ANN models are developed and used
for dierent problems. In this paper, an articial neural network of the feed-forward back-propagation
type has been applied for the prediction of density and compressive strength properties of the cement paste
portion of concrete mixtures. The mechanical properties of concrete are highly in
uenced by the density
and compressive strength of concrete cement paste. Due to the complex non-linear eect of silica fume on
concrete cement paste, the ANN model is used to predict density and compressive strength parameters. The
density and compressive strength of concrete cement paste are aected by several parameters, viz, watercementitious
materials ratio, silica fume unit contents, percentage of super-plasticizer, curing, cement
type, etc. The 28-day compressive strength and Saturated Surface Dry (SSD) density values are considered
as the aim of the prediction. A total of 600 specimens were selected. The system was trained and validated
using 350 training pairs chosen randomly from the data set and tested using the remaining 250 pairs.
Results indicate that the density and compressive strength of concrete cement paste can be predicted much
more accurately using the ANN method compared to existing conventional methods, such as traditional
regression analysis, statistical methods, etc.
Keywords: Cement paste |
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Identication of Inelastic Shear Frames
Using the Prandtl-Ishlinskii Model
A. Joghataie (PhD.)
M. Farrokh [PhD.]
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Abstract: In this paper, a new method is proposed for identication of inelastic shear frame structures
with hesteresis, using data collected on their dynamic response. It uses the Prandtl-Ishlinskii rate
independent model for hysteresis, which was originally used in the eld of plasticity and ferromagnetism.
The proposed identication method is capable of identifying the mass, damping and restoring force of
a frame structure, which can be used in forming the equations of motion of the frame. By solving the
equations of motion, the dynamic response is predicted. The method is based on the combined use of
Quadratic Programming (QP) and Genetic Algorithms (GA). First, assuming a set of Prandtl-Ishlinskii
constants, the QP is used to nd the best frame parameters that can be used in its equations of motion to
predict its dynamic response with the minimum of error compared to the real data collected on its dynamic
response, while the GA is used to nd the best Prandtl-Ishlinskii constants for more reduction in error.
The method has been applied to dierent frames with bilinear nonlinearity where the results show the high
capability of the method. Two examples, a Single and a Multi Degree Of Freedom (SDOF and MDOF)
frame, are included in the paper.
Keywords: Prandtl-Ishlinskii model |
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On the Distribution of Velocity
in a V-Shaped Channel
M.A. Mohammadi (PhD.)
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Abstract: Several series of measurements were conducted to explore the hydraulic characteristics
of a V-shaped bottom channel by using low & high-speed velocity propellers for point-wise velocity
measurements. Also, in order to understand the eect of cross sectional channel shape on the distribution
of depth-averaged velocity in the experimental channel, cases with dierent
ow rates were examined.
Using SURFER software, the contour plots of 2D isovels were drawn as interpolation among averaged
depths and velocities, obtained from superposing the various prole sections. It was observed that isovels
are parallel to the channel boundary in a region close to the bed, and almost symmetric about the centerline,
with some deviations. The variation of point velocities in each slice considered along a spanwise direction,
in order to study the depthwise velocity prole distributions, is shown. The lateral variations of depthaveraged
velocities indicate that the velocity distributions are almost symmetrical about the cross sectional
centerline, except for some
ow cases, in which there are slight deviations, despite the fact that the
ow
condition was uniform for all cases. It was found that the widely used log-law for the vertical prole of
velocity does not appropriately model the velocity distribution in this particular channel shape. Considering
the results obtained for the span- and depth-wise velocity distributions, especially the distortion of the
isovels and the location of maximum velocity, there are strong evidences of secondary currents that are
present in this channel cross section.
Keywords: V-shaped bottom channelUniform
owVelocity distributionDepth-averaged velocity;
Boundary shear stress. |
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Reliability Analysis of Bridge
Structures for Earthquake Excitations
S. Pourzeynali (PhD.)
A. Hosseinnezhad [PhD.]
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Abstract: In this paper, a numerical approach to the reliability analysis of prestressed reinforced
concrete long span bridges is presented. A bridge is modeled by nite element software and the analysis
is performed in time domain by considering the bridge material nonlinearity. The considered random
variables are: Specic strength of concrete, yield stress of steel bars, yield stress of prestressed bars,
all sectional dimensions, structural damping ratio, eective depth of steel bars and the magnitude and
PGA of earthquake. In this study, the reliability of a bridge structure is evaluated under earthquake
excitations. For this purpose, the First-Order Second-Moment (FOSM) method is used. In this method,
the mean value and standard deviation of the random variables are considered for evaluating structural
reliability. The proposed procedure is applied to evaluate the reliability of an existing prestressed arch
concrete bridge located in Bandar-e-Anzali in Iran. Bandar-e-Anzali is a very high-risk earthquake zone.
The results of the study show that the structural damping ratio, magnitude and PGA of earthquakes have
a signicant eect on the variation of reliability in the structure, while variations in the dimensions of
the structure have little eect on the reliability index.
Keywords: Structural reliabilityNon-linear analysisArch bridgePrestressed concrete structures. |
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Effect of Asphalt Content on the
Marshall Stability of Asphalt Concrete
Using Artificial Neural Networks
M. Saffarzadeh (PhD.)
A. Heidaripanah [PhD.]
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Abstract: The Marshall Stability of asphalt concrete is one of the most important parameters in
mix design and quality control. This property depends on many factors such as gradation, percentage of
crushed aggregates, asphalt content and construction quality. In this research, the variation of Marshall
Stability with asphalt content is simulated using Articial Neural Networks (ANNs) with a Levenberg-
Marquardt Back Propagation (LMBP) training algorithm. The percentage of crushed aggregatesthe
percentage passing through sieve numbers 200, 50, 30, 8, 4 and 1/2 inch, and the percentage of asphalt
content are considered as network inputs and Marshall Stability as the network output. In the rst stage,
the maximum generalization ability of each network with a specied number of neurons in the hidden layer
is determined. Comparing these maximum values reveals that the network with 8 neurons in the hidden
layer has the maximum generalization ability. In the second stage, the variation of Marshall Stability
with asphalt content is simulated by applying a sensitivity analysis to the network with the maximum
generalization ability. This simulation is in good agreement with theory.
Keywords: Marshall Stability |
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Application of a Maintenance Management
Model for Iranian Railways Based on the Markov
Chain and Probabilistic Dynamic Programming
Y. Shafahi (PhD.)
R. Hakhamaneshi [PhD.]
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Abstract: Railway managers have a strong economic incentive to minimize track maintenance costs,
while maintaining safety standards and providing adequate service levels to train operators. The objective
of this study is to apply a procedure for making optimal maintenance decisions in Iranian Railways. This
study consists of two parts. First, a cumulative damage model, based on a Markov process, is applied to
model the deterioration of the track. For this reason, tracks are categorized into six classes, so that those
tracks with similar trac loads and geographical location are collected into one class. The track survey
data from 215 blocks (4,228 km) of the ten divisions of the Iranian Railway system, during 2002-2004,
is used to identify the transition matrix. Secondly, probabilistic dynamic programming is used to nd the
optimal repair for each possible track state in the planning horizon. This approach allows an optimal
maintenance decision to be determined for the track at any point in time within the planning horizon.
Keywords: Maintenance management |
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