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International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 06 | June 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1540
Artificial Neural Network:Overview
Aamer Shaikh1, Prof. B.A. konnur2
1P.G. Student, Government College of Engineering, Karad, Maharshtra, India.
2Professer, Civil Engineering Department, Govt. College of Engineering, Karad, Maharashtra, India
---------------------------------------------------------------------***----------------------------------------------------------------------
Abstract - Artificial neural network is tool of Artificial
intelligence which is recently most popular research area in
the field construction field as it doesn’trequiresthetraditional
programming based on the mathematical formula etc. In this
paper introduction to neural network and the application of
neural network in construction industries are Discussed.
Neural network is mimic of human brain system, in ANN
perceptron is a identical element with neuron in brain. ANN is
mostly used to mimic the functions of brain such as storage,
think and recall Based on this function ANN is used in
construction industries to solve the regression, classification
problems.
Key Words: ANN, Neural Networks, Deep Learning,
Neural Networks, Artificial Intelligence.
1. INTRODUCTION
An artificial neural network belongs to artificial
intelligence (AI) data modelling tool that can store and
represent complex input / output relationships. Researchin
artificial network grown up with the idea to simulate the
functioning of human brain.Scientist are trying to make the
modelling tool which can learn store, think and retrieve the
data or complex relationship like human brain Artificial
neural networks are particularly useful for modelling
underlying patterns in data through a learning process. The
knowledge of a neural network isstored withinthestrengths
of connection between neurons knownas‘synaptic weights’.
The main advantage of neural networks is ability to
represent both linear and non-linear relationships and their
ability to learn these relationships. ANNs can be used for
many tasks such as pattern recognition, function
approximation, optimization, forecasting,data retrieval,and
automatic control. As many of the managerial works in
construction industry is based on past experience of the
manager which he cannot justify providing a fix rule or
mathematical formula. Artificial neural networksaregetting
too much popularity in construction application because it
does not require a mathematical rule or fix programme to
perform the task likr traditional computing models.
1.1 Historical developments of Neural Network
Widrow and Hoff (1960) developed a
mathematical method which adapts the Weights, a gradient
search method was utilized with assumption that a desired
answer existed, which was based on minimizingthesquared
error. Pao (1989) developed a technique to improve the
initial representation of the data to the neuronal network he
introduced functional links to linear input.. The functional
links area try to find simple mathematical correlations
between input and output, such As a periodicityortermsofa
higher network order. Functional linksareveryimportant in
preprocessing of the data for the neural network. A
functional link is sometimes called conditioning the
entrance. There is a parallel between adaptive and neuronal
control networks. A functional link in itssimplestformcould
restrict the entry of neuronal network. If the input of the
neuronal network is poorly conditioned, the functional link
makes the entry more usable by the neural network. Calvin
et al. (1993) He developed a method called graylayer.Agray
layer uses the neuronal output of network to incorporate a
priori information of the system. Hung and Adeli (1993)
presented a Mathematical model for resource planning
considering Characteristics of project scheduling which are
ignored in previous investigations, including precedence
relationships, multiple crew strategies and cost-time
compensation. Previous resource scheduling formulations
have traditionally focused on minimizing the durationofthe
project. Raja R.A. Issa (1995) tried to predict the
construction material prices using ANN buthewasunable to
find the suitable input parameter to predicttheconstruction
material prices. Mohammad and Haykin (1995) formulated
the problem of allocating the available annual budget
optimally to Projects of rehabilitation and replacement of
bridges between a seriesofalternativessuchasOptimization
problem using the Hopfield network. I-Cheng Yeh (1995)
used annealed neural network forplanningconstruction site
layouts. adeli (1998) presenteda regularizedartificial neural
network to estimate the constructioncostofthehighwaysby
training the network with past examples. Wilmot(2005)
used artificial neural network model to predict the highway
construction cost using previous trends.Arazi (2011) has
developed a model using ANN to predict the cost of labour
that has also Taking into account the subjective factors.
Factors that influence the productivity of The work on the
site identified through the literaturereviewincludesthelack
of material and Equipment, inadequate drawing, weather,
project location, incompetent site. Supervision, change
orders, absenteeism, etc. Among these, the most common
factors present have been identified during the data
collection. Mostafa A. Abo-Hashema (2013) used artificial
network model to predict the asphalt concrete temperature
in pavement with air temperature as a inputparameter.Naik
(2015) implemented the artificial neural network tool to
optimize the construction cost and duration of the project.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 06 | June 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1541
2. MODEL REPRESENTATION OF NETWORKS
Artificial neural network consist of number of neurons
connected with strings called synaptic weights. A neuron is
fundamental operation unit of neural network which
processes the information .Neuron is a junction at which all
the inputs gets linearly summed.
Neural network can be classified based on connection
structures:
A) Feed forward Network B) Feedback network
Feed forward network is classified furtherbasedontheir
number of layers of neurons as
a) Single layer linear models (Traditional models)
b) Multilayer perceptron models (MLP)
In ANN neurons are arranged in groups called layers as
in Traditional linear models only single layer of neuron
exist.Hence they are unable to perform well when it deals
with the nonlinear data(Naik 2015). The most commonly
neural network model is the multilayer perceptron (MLP).
As the name suggest it contain more than one layers of
neurons including input layer, output layer and hidden
layers. This type of neural network comes under supervised
network as it requires a input –output historical data in
order to train the network. The objective of this type of
network is to create a model that correctly maps theinput to
the output using historical data sothatthemodel canthenbe
used to predict the output when the desired output is
unknown. A structural arrangement of an MLP is shown in
Figure 1.
Fig -1:.Multiplayer perceptron (MLP with two hidden
layer.
In neural network the input data parameters given to
network are linearly summed and get multiplied with the
synaptic weights at each layer each layer is associated with
the transfer function which process data. Most commonly
used transfer function is hyperbolic tangent. Out pur from
input layers get transferred to hidden layer again here it get
multiplied with the synaptic weights and get transferred to
output layer through transfer function , hidden layers are
most useful to deal with the non linear type problems.
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3. CHARACTERISTICS OF ANN
Salient Characteristics of ANN are as follows.
 ANN is particularly suited for pattern recognition
task.
 They produce fast responses, irrespective of their
requirement of large computer timeforlearning.This
is due to the parallel structure of neural networks
 They could extract classification (clustering)
characteristics from a large number of input
examples, as in the case of unsupervised learning. If,
for example, a large number of field data are
collected from a constructionsite,a suitablenetwork
can identify the different clusters (groupsorclasses)
that characterize the whole population.
 They have distributed memory; the connection
weights are the memory units of the network. The
value of the weights represent the current state of
knowledge of the network. A unit of knowledge,
representedfor exampleby aninput/desired-output
pair, is distributed across all the weighted
connections of the network.
4. LEARNING OF ANN
The MLP and many other neural networks learn
using an algorithm called backward propagation. With
backward propagation, the input data is repeatedly
presented to the neural network.With eachpresentation,the
output of the neural network is compared with the desired
output and an error is calculated.
Fig -2:. Learning of a neural network
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 06 | June 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1542
This error feeds back (spreads again) to the neural
network and is used to adjust the weights so that the error
decreases with each iteration and the neural model gets
closer and closer to the desired output. Figure 2 shows the
process of learning of neural network techniques.
There two types of learning
A) Supervised Learning B)Unsupervised learning
In supervised learning data is trained along with the
desired output and in unsupervised neural network target
ouput values are not trained. Supervised learning gives the
maximum efficiency as the error in supervised learning are
less as compared to unsupervised learning.
5. POTENTIAL AREAS OF NERAL NETWORK
APPLICATION IN CONSTRUCTION.
1. Estimation and classification. Forexample,a pattern
representing a project environment couldassociate
an estimated productivity factor or select oneofthe
existing productivity classes, representingdifferent
performance levels of a certain trade. As another
example, a project performance pattern could
associate estimated values for the schedule and the
cost indices. Probability and percentage of cost
overruns could also be estimated.
2. Selection between alternatives. For example, a
pattern representing the soil conditions of a
construction site could be associated with an
approximate value for the bearingcapacity,degrees
of suitability of different types of foundation,
appropriate dewatering methodology, and so on.
Other examples include formwork and equipment
selection.
3. Diagnostic problems such as those encountered in
building and facility defects and the needed
establishment of causation in construction dispute
management and their respective claim analyses.
4. Dynamic modelling. For example, construction
projects with varying performance levelsmeasured
in the different reporting periods could indicate a
projection of relative time and/or cost overruns.
Another example is modelling during periods of
rapid fluctuations in inflation or escalation rates.
These data could be used to giveanindicationabout
the market condition so that proper bidding
decisions could be made.
5. Optimization tasks. For example, applications
regarding the optimization of construction activity
and resource usage could be experimented.
6. Real-time applications suchasthoseassociatedwith
time-dependent changes (e.g. material costs,
inflation rate, etc.).
7. CONCLUSION
Artificial Neural network is a deep learning tool in
branch of artificial intelligence which efficiently mimic
the brain functioning .using artificial modellingdifferent
models can be created to perform pattern recognition,
function approximation, optimization, forecasting, data
retrieval, and automatic control. Recently In
constructionindustryartificial neural networksareused
for predicting construction cost and optimizing the
construction cost and duration.
REFERENCES
[1] Osama Moselhi, Member, ASCE, Tarek Hegazy, Member,
ASCE, and Paul Fazio3 Member, ASCE, “Neural networks
as tools in construction”−Journal of Construction
Engineering and Management, Vol. 117, No. 4,
December, 1991. ©ASCE, ISSN 0733-9364/91/0004-
0606.
[2] M. Gopal Naik1 and D. Rupesh Kumar2, “Construction
Project Cost and Duration Optimization Using Artificial
Neural Network”−AEI 2015
[3] Mostafa A. Abo-Hashema, “Modeling Pavement
Temperature Prediction using Artificial
NeuralNetworks”− Airfield and Highway Pavement
2013: Sustainable and Efficient Pavements © ASCE
2013.
[4] Mostafa A. Abo-Hashema, “Modeling Pavement
Temperature Prediction using Artificial
NeuralNetworks”− Airfield and Highway Pavement
2013: Sustainable and Efficient Pavements © ASCE
2013.
[5] Hoijat Adelil and Mingyang Wu, “Regularization neural
network for construction cost estimation”− Journalof
Construction Engineering and Management, Vol. 124,
No. I, February, 1998. ©ASCE, ISSN 0733-
9364/98/0001-0018-0024. Paper No. 15205.
[6] Chester G. Wilmot and Bing Mei, “Neural Network
Modeling of Highway Construction Costs”−
10.1061/(ASCE)0733-9364(2005)131:7(765).
[7] Raja R.A. Issa, “Application of Artificial network to
predicting construction material prices”− Computing in
Civil and Building Engineering (2000) ©ASCE.
[8] I-Cheng Yeh, “Construction-site layout using Annealed
neural network” − Journal oj Computing in Civil
Engineering, Vol. 9, No.3, July, 1995. ©ASCE,ISSN 0887-
3801/95/0003-0201-0208Paper No.

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  • 1. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 06 | June 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1540 Artificial Neural Network:Overview Aamer Shaikh1, Prof. B.A. konnur2 1P.G. Student, Government College of Engineering, Karad, Maharshtra, India. 2Professer, Civil Engineering Department, Govt. College of Engineering, Karad, Maharashtra, India ---------------------------------------------------------------------***---------------------------------------------------------------------- Abstract - Artificial neural network is tool of Artificial intelligence which is recently most popular research area in the field construction field as it doesn’trequiresthetraditional programming based on the mathematical formula etc. In this paper introduction to neural network and the application of neural network in construction industries are Discussed. Neural network is mimic of human brain system, in ANN perceptron is a identical element with neuron in brain. ANN is mostly used to mimic the functions of brain such as storage, think and recall Based on this function ANN is used in construction industries to solve the regression, classification problems. Key Words: ANN, Neural Networks, Deep Learning, Neural Networks, Artificial Intelligence. 1. INTRODUCTION An artificial neural network belongs to artificial intelligence (AI) data modelling tool that can store and represent complex input / output relationships. Researchin artificial network grown up with the idea to simulate the functioning of human brain.Scientist are trying to make the modelling tool which can learn store, think and retrieve the data or complex relationship like human brain Artificial neural networks are particularly useful for modelling underlying patterns in data through a learning process. The knowledge of a neural network isstored withinthestrengths of connection between neurons knownas‘synaptic weights’. The main advantage of neural networks is ability to represent both linear and non-linear relationships and their ability to learn these relationships. ANNs can be used for many tasks such as pattern recognition, function approximation, optimization, forecasting,data retrieval,and automatic control. As many of the managerial works in construction industry is based on past experience of the manager which he cannot justify providing a fix rule or mathematical formula. Artificial neural networksaregetting too much popularity in construction application because it does not require a mathematical rule or fix programme to perform the task likr traditional computing models. 1.1 Historical developments of Neural Network Widrow and Hoff (1960) developed a mathematical method which adapts the Weights, a gradient search method was utilized with assumption that a desired answer existed, which was based on minimizingthesquared error. Pao (1989) developed a technique to improve the initial representation of the data to the neuronal network he introduced functional links to linear input.. The functional links area try to find simple mathematical correlations between input and output, such As a periodicityortermsofa higher network order. Functional linksareveryimportant in preprocessing of the data for the neural network. A functional link is sometimes called conditioning the entrance. There is a parallel between adaptive and neuronal control networks. A functional link in itssimplestformcould restrict the entry of neuronal network. If the input of the neuronal network is poorly conditioned, the functional link makes the entry more usable by the neural network. Calvin et al. (1993) He developed a method called graylayer.Agray layer uses the neuronal output of network to incorporate a priori information of the system. Hung and Adeli (1993) presented a Mathematical model for resource planning considering Characteristics of project scheduling which are ignored in previous investigations, including precedence relationships, multiple crew strategies and cost-time compensation. Previous resource scheduling formulations have traditionally focused on minimizing the durationofthe project. Raja R.A. Issa (1995) tried to predict the construction material prices using ANN buthewasunable to find the suitable input parameter to predicttheconstruction material prices. Mohammad and Haykin (1995) formulated the problem of allocating the available annual budget optimally to Projects of rehabilitation and replacement of bridges between a seriesofalternativessuchasOptimization problem using the Hopfield network. I-Cheng Yeh (1995) used annealed neural network forplanningconstruction site layouts. adeli (1998) presenteda regularizedartificial neural network to estimate the constructioncostofthehighwaysby training the network with past examples. Wilmot(2005) used artificial neural network model to predict the highway construction cost using previous trends.Arazi (2011) has developed a model using ANN to predict the cost of labour that has also Taking into account the subjective factors. Factors that influence the productivity of The work on the site identified through the literaturereviewincludesthelack of material and Equipment, inadequate drawing, weather, project location, incompetent site. Supervision, change orders, absenteeism, etc. Among these, the most common factors present have been identified during the data collection. Mostafa A. Abo-Hashema (2013) used artificial network model to predict the asphalt concrete temperature in pavement with air temperature as a inputparameter.Naik (2015) implemented the artificial neural network tool to optimize the construction cost and duration of the project.
  • 2. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 06 | June 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1541 2. MODEL REPRESENTATION OF NETWORKS Artificial neural network consist of number of neurons connected with strings called synaptic weights. A neuron is fundamental operation unit of neural network which processes the information .Neuron is a junction at which all the inputs gets linearly summed. Neural network can be classified based on connection structures: A) Feed forward Network B) Feedback network Feed forward network is classified furtherbasedontheir number of layers of neurons as a) Single layer linear models (Traditional models) b) Multilayer perceptron models (MLP) In ANN neurons are arranged in groups called layers as in Traditional linear models only single layer of neuron exist.Hence they are unable to perform well when it deals with the nonlinear data(Naik 2015). The most commonly neural network model is the multilayer perceptron (MLP). As the name suggest it contain more than one layers of neurons including input layer, output layer and hidden layers. This type of neural network comes under supervised network as it requires a input –output historical data in order to train the network. The objective of this type of network is to create a model that correctly maps theinput to the output using historical data sothatthemodel canthenbe used to predict the output when the desired output is unknown. A structural arrangement of an MLP is shown in Figure 1. Fig -1:.Multiplayer perceptron (MLP with two hidden layer. In neural network the input data parameters given to network are linearly summed and get multiplied with the synaptic weights at each layer each layer is associated with the transfer function which process data. Most commonly used transfer function is hyperbolic tangent. Out pur from input layers get transferred to hidden layer again here it get multiplied with the synaptic weights and get transferred to output layer through transfer function , hidden layers are most useful to deal with the non linear type problems. After the text edit has been completed, the paper is ready for the template. Duplicatethe template file by using the SaveAs command, andusethenamingconventionprescribedbyyour conference for the name of your paper. In this newly created file, highlight all of the contents and import your prepared text file. You are now ready to style your paper. 3. CHARACTERISTICS OF ANN Salient Characteristics of ANN are as follows.  ANN is particularly suited for pattern recognition task.  They produce fast responses, irrespective of their requirement of large computer timeforlearning.This is due to the parallel structure of neural networks  They could extract classification (clustering) characteristics from a large number of input examples, as in the case of unsupervised learning. If, for example, a large number of field data are collected from a constructionsite,a suitablenetwork can identify the different clusters (groupsorclasses) that characterize the whole population.  They have distributed memory; the connection weights are the memory units of the network. The value of the weights represent the current state of knowledge of the network. A unit of knowledge, representedfor exampleby aninput/desired-output pair, is distributed across all the weighted connections of the network. 4. LEARNING OF ANN The MLP and many other neural networks learn using an algorithm called backward propagation. With backward propagation, the input data is repeatedly presented to the neural network.With eachpresentation,the output of the neural network is compared with the desired output and an error is calculated. Fig -2:. Learning of a neural network
  • 3. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 06 | June 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1542 This error feeds back (spreads again) to the neural network and is used to adjust the weights so that the error decreases with each iteration and the neural model gets closer and closer to the desired output. Figure 2 shows the process of learning of neural network techniques. There two types of learning A) Supervised Learning B)Unsupervised learning In supervised learning data is trained along with the desired output and in unsupervised neural network target ouput values are not trained. Supervised learning gives the maximum efficiency as the error in supervised learning are less as compared to unsupervised learning. 5. POTENTIAL AREAS OF NERAL NETWORK APPLICATION IN CONSTRUCTION. 1. Estimation and classification. Forexample,a pattern representing a project environment couldassociate an estimated productivity factor or select oneofthe existing productivity classes, representingdifferent performance levels of a certain trade. As another example, a project performance pattern could associate estimated values for the schedule and the cost indices. Probability and percentage of cost overruns could also be estimated. 2. Selection between alternatives. For example, a pattern representing the soil conditions of a construction site could be associated with an approximate value for the bearingcapacity,degrees of suitability of different types of foundation, appropriate dewatering methodology, and so on. Other examples include formwork and equipment selection. 3. Diagnostic problems such as those encountered in building and facility defects and the needed establishment of causation in construction dispute management and their respective claim analyses. 4. Dynamic modelling. For example, construction projects with varying performance levelsmeasured in the different reporting periods could indicate a projection of relative time and/or cost overruns. Another example is modelling during periods of rapid fluctuations in inflation or escalation rates. These data could be used to giveanindicationabout the market condition so that proper bidding decisions could be made. 5. Optimization tasks. For example, applications regarding the optimization of construction activity and resource usage could be experimented. 6. Real-time applications suchasthoseassociatedwith time-dependent changes (e.g. material costs, inflation rate, etc.). 7. CONCLUSION Artificial Neural network is a deep learning tool in branch of artificial intelligence which efficiently mimic the brain functioning .using artificial modellingdifferent models can be created to perform pattern recognition, function approximation, optimization, forecasting, data retrieval, and automatic control. Recently In constructionindustryartificial neural networksareused for predicting construction cost and optimizing the construction cost and duration. REFERENCES [1] Osama Moselhi, Member, ASCE, Tarek Hegazy, Member, ASCE, and Paul Fazio3 Member, ASCE, “Neural networks as tools in construction”−Journal of Construction Engineering and Management, Vol. 117, No. 4, December, 1991. ©ASCE, ISSN 0733-9364/91/0004- 0606. [2] M. Gopal Naik1 and D. Rupesh Kumar2, “Construction Project Cost and Duration Optimization Using Artificial Neural Network”−AEI 2015 [3] Mostafa A. Abo-Hashema, “Modeling Pavement Temperature Prediction using Artificial NeuralNetworks”− Airfield and Highway Pavement 2013: Sustainable and Efficient Pavements © ASCE 2013. [4] Mostafa A. Abo-Hashema, “Modeling Pavement Temperature Prediction using Artificial NeuralNetworks”− Airfield and Highway Pavement 2013: Sustainable and Efficient Pavements © ASCE 2013. [5] Hoijat Adelil and Mingyang Wu, “Regularization neural network for construction cost estimation”− Journalof Construction Engineering and Management, Vol. 124, No. I, February, 1998. ©ASCE, ISSN 0733- 9364/98/0001-0018-0024. Paper No. 15205. [6] Chester G. Wilmot and Bing Mei, “Neural Network Modeling of Highway Construction Costs”− 10.1061/(ASCE)0733-9364(2005)131:7(765). [7] Raja R.A. Issa, “Application of Artificial network to predicting construction material prices”− Computing in Civil and Building Engineering (2000) ©ASCE. [8] I-Cheng Yeh, “Construction-site layout using Annealed neural network” − Journal oj Computing in Civil Engineering, Vol. 9, No.3, July, 1995. ©ASCE,ISSN 0887- 3801/95/0003-0201-0208Paper No.