2020-11-28T03:39:28Z
http://scientiairanica.sharif.edu/?_action=export&rf=summon&issue=261
Scientia Iranica
Scientia Iranica
1026-3098
1026-3098
2009
16
1
Predicting Density and Compressive Strength of Concrete Cement Paste Containing Silica Fume Using Articial Neural Networks
H.
Ketabchi
M.H.
Afshar
E.
Rasa
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.
Cement paste
compressive strength
Density
Neural network
Silica Fume
2009
02
01
http://scientiairanica.sharif.edu/article_3173_454f1a607cb905002607a3539b12f4a3.pdf
Scientia Iranica
Scientia Iranica
1026-3098
1026-3098
2009
16
1
Application of Particle Swarm Optimization to Optimal Design of Cascade Stilling Basins
M.H.
Afshar
M.
Daraeikhah
S.H.
Meraji
Abstract. This paper employs the Particle Swarm Optimization (PSO) method to solve the problem
of the optimal design of cascade stilling basins. PSO is a relatively recent heuristic search method whose
mechanism is inspired by the swarming or collaborative behavior of biological populations. The objective
of this research is to minimize the total construction cost of cascade stilling basins, which is a function
of height of the falls and length of stilling basins, while fullling the hydraulic and topographical criteria.
To illustrate the application of PSO, a benchmark design is taken from the work of Vittal and Porey [1]
on a cascade stilling basin for the Tehri Dam, India.
particle swarm optimization
Cascade stilling basins
Global optimization
2009
02
01
http://scientiairanica.sharif.edu/article_3174_9830801b80419218ee5b7d41a382182b.pdf
Scientia Iranica
Scientia Iranica
1026-3098
1026-3098
2009
16
1
The Eect of Geosynthetic Reinforcement on the Damage Propagation Rate of Asphalt Pavements
K.
Fakhri
A.K.
Darban
H.R.A.
Hosseini
Abstract. There are several approaches for modeling the fatigue life and damage of asphalt pavements,
such as stress-strain and damage mechanics. In this research, a simple mechanistic approach is used to
explain the destruction of asphalt pavements. For asphalt reinforcement, two types of geosynthetic were
used in the aireld at Imam Khomeini airport, Tehran. Non-reinforced, reinforced with a geogrid and
geotextile specimens with dimensions of 5063381 mm were obtained from the asphalt slab eld section.
Fatigue tests of this study have been conducted with a four point beam test and a fatigue load with a half-sin
wave at a frequency of 10 cycle/sec (no rest period), has been used. The results indicated that specimens
reinforced with geosynthetics exhibit a higher initial stiness module and lower crack propagation rate
than non-reinforced specimens.
asphalt pavement
failure
Geosynthetics
geogrid
geotextile
Beam fatigue test crack propagation
2009
02
01
http://scientiairanica.sharif.edu/article_3175_6bbb7246fbae43e7706fda4340e4e526.pdf
Scientia Iranica
Scientia Iranica
1026-3098
1026-3098
2009
16
1
An Improved Non-Linear Physical Modeling Method for Brace Elements
A.
Davaran
M.
Adelzadeh
Abstract. In this paper, the cyclic nonlinear behavior of a brace element has been modeled. A brace
element is modeled as two elastic beam-column segments, which are connected to each other via a plastic
hinge. The far ends of the element are hinged. By a suitable combination of the isotropic and kinematical
hardening rules of plasticity, the nonlinear axial force-displacement relation for a beam element has been
derived. So, the strain hardening, strain softening, tangential modulus of elasticity and Bauschinger
eects are taken into account. This model shows good agreement with experimental results that have been
reported in other research works.
Bracing
Nonlinear
Work hardening
2009
02
01
http://scientiairanica.sharif.edu/article_3176_2a55c2aecdd2026d2fb489a1456449ea.pdf
Scientia Iranica
Scientia Iranica
1026-3098
1026-3098
2009
16
1
Identication of Inelastic Shear Frames Using the Prandtl-Ishlinskii Model
A.
Joghataie
M.
Farrokh
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.
Prandtl-Ishlinskii model
Identication
Inelastic behavior
Structural dynamic
earthquake
2009
02
01
http://scientiairanica.sharif.edu/article_3177_e67cc52b82415f66f828f551156f99ad.pdf
Scientia Iranica
Scientia Iranica
1026-3098
1026-3098
2009
16
1
On the Distribution of Velocity in a V-Shaped Channel
M.A.
Mohammadi
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.
V-shaped bottom channel
Uniform ow
velocity distribution
Depth-averaged velocity
Boundary shear stress
2009
02
01
http://scientiairanica.sharif.edu/article_3178_765982161253aecedfcc8ec54c8320a5.pdf
Scientia Iranica
Scientia Iranica
1026-3098
1026-3098
2009
16
1
Element Free Galerkin Mesh-Less Method for Fully Coupled Analysis of a Consolidation Process
A.
Pak
M.N.
Oliaei
Abstract. A formulation of the Element Free Galerkin (EFG), one of the mesh-less methods, is
developed for solving coupled problems and its validity for application to soil-water problems is examined
through numerical analysis. The numerical approach is constructed to solve two governing partial
dierential equations of equilibrium and the continuity of pore water, simultaneously. Spatial variables
in a weak form, the displacement increment and excess pore water pressure increment, are discretized
using the same EFG shape functions. An incremental constrained Galerkin weak form is used to create
the discrete system equations and a fully implicit scheme is used to create the discretization of the time
domain. Implementation of essential boundary conditions is based on penalty method. Examples are
studied and the obtained results are compared with closed-form or nite element method solutions to
demonstrate the capability of the developed model. The results indicate that the EFG method is capable of
handling coupled problems in saturated porous media and can predict well, both soil deformation and the
variation of pore water pressure, over time.
Mesh-less
EFG
Penalty method
Soil-water coupled problem
Consolidation process
2009
02
01
http://scientiairanica.sharif.edu/article_3179_77f9b4a345d914bac2d86cb70bf69000.pdf
Scientia Iranica
Scientia Iranica
1026-3098
1026-3098
2009
16
1
Reliability Analysis of Bridge Structures for Earthquake Excitations
A.
Hosseinnezhad
S.
Pourzeynali
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.
Structural Reliability
Non-linear analysis
Arch bridge
Prestressed concrete structures
2009
02
01
http://scientiairanica.sharif.edu/article_3180_8f827f84f32651f6b2e4bf7148988475.pdf
Scientia Iranica
Scientia Iranica
1026-3098
1026-3098
2009
16
1
Effect of Asphalt Content on the Marshall Stability of Asphalt Concrete Using Artificial Neural Networks
M.
Saffarzadeh
A.
Heidaripanah
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 aggregates; the
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.
Marshall Stability
asphalt concrete
Backpropagation
sensitivity analysis
Mix design
2009
02
01
http://scientiairanica.sharif.edu/article_3181_be97356a0b8af729553d53dec2dced51.pdf
Scientia Iranica
Scientia Iranica
1026-3098
1026-3098
2009
16
1
Application of a Maintenance Management Model for Iranian Railways Based on the Markov Chain and Probabilistic Dynamic Programming
Y.
Shafahi
R.
Hakhamaneshi
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.
Maintenance management
Railways
Markov chain
Dynamic programming
2009
02
01
http://scientiairanica.sharif.edu/article_3182_130767cafcf651c287704fa787c6c3e0.pdf
Scientia Iranica
Scientia Iranica
1026-3098
1026-3098
2009
16
1
Displacement Based Intelligent Seismic Assessment of Existing Steel Buildings
M.
Tehranizadeh
M.
Safi
Abstract. Performance based seismic design usually requires nonlinear dynamic or static analyses
to assess the performance level of the structure under seismic action. To trace the exact performance
point of a structure, these analyses should sometimes be repeated several times over. Analysis iterations
mainly depend on the initial design and performance of the structure. So, a method that can present
an appropriate initial selection with minimum time and eort would be precious. Such a method would
also be very eective for seismic structural assessment. In this paper, an intelligent system has been
created for the estimation of plastic hinge distribution and lateral ductility distribution and, also, for the
assessment of existing steel structures, based on a direct displacement based design procedure. The method
has been applied to the steel braced frames with concentric bracing systems in low, medium and high rise
buildings. The designer can use this knowledge based system to obtain the performance level of existing
steel structures, according to proposed seismic code levels. Finally, the intelligent system has been veried
using nonlinear dynamic analysis.
Performance Assessment
Articial intelligence
Back propagation neural network
Nonlinear Analysis
2009
02
01
http://scientiairanica.sharif.edu/article_3183_9e35b9f0812fec41e0a0e0c5721908ec.pdf