Journal of Soft Computing in Civil Engineering 1-2 (2017) 70-88
Contents lists available at SCCE
Journal of Soft Computing in Civil Engineering
Journal homepage: www.jsoftcivil.com
Artificial Neural Networks for Construction Management: A
Review
P.S. Kulkarni 1* , S.N. Londhe 2 , M.C. Deo 3
1. Associate Professor, Vishwakarma Institute of Information Technology, Pune, India
2. Professor, Vishwakarma Institute of Information Technology, Pune, India
3. Professor, Indian Institute of Technology, Mumbai, India
Corresponding author:
https://doi.org/10.22115/SCCE.2017.49580
ARTICLE INFO ABSTRACT
Article history:
Received: 12 July 2017 Construction Management (CM) has to deal with a variety of
Revised: 23 August 2017 uncertainties related to Time, Cost, Quality, and Safety, to
Accepted: 23 August 2017 name a few. Such uncertainties make the entire construction
process highly unpredictable. It, therefore, falls under the
Keywords: purview of artificial neural networks (ANNs) in which the
Construction management; given hazy information can be effectively interpreted in
Artificial neural networks; order to arrive at meaningful conclusions. This paper reviews
Training algorithm; the application of ANNs in construction activities related to
Sensitivity analysis. the prediction of costs, risk, and safety, tender bids, as well
as labor and equipment productivity. The review suggests
that the ANN’s had been highly beneficial in correctly
interpreting inadequate input information. It was seen that
most of the investigators used the feed forward back
propagation type of the network; however, if a single ANN
architecture was found to be insufficient, then hybrid
modeling in association with other machine learning tools
such as genetic programming and support vector machines
were much useful. It was however clear that the authenticity
of data and experience of the modeler are important in
obtaining good results.
1. Introduction
The construction industry is highly competitive and faces challenges in the areas of costs of
projects, delays in construction activities, labor productivity, disputes, tenders, bidding prices,
How to cite this article: Kulkarni PS, Londhe SN, Deo MC. Artificial neural networks for construction management: a review. J
Soft Comput Civ Eng 2017;1(2):70–88. https://doi.org/10.22115/scce.2017.49580.
2588-2872/ © 2017 The Authors. Published by Pouyan Press.
This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
, P.S. Kulkarni et al./ Journal of Soft Computing in Civil Engineering 1-2 (2017) 70-88 71
safety aspects, the rate of materials, maintenance costs, risk analysis, etc. which are highly
complicated in nature. To deal with these challenges, Artificial Intelligence (AI) techniques like
fuzzy logic, case-based reasoning, probabilistic methods for uncertain reasoning, classifiers and
learning methods, Artificial Neural Networks (ANN), Genetic Algorithms and hybrid techniques
are widely used in the field of Construction Management (CM). In the last two decades of the
twentieth century, there was a surge in publications dealing with Artificial Intelligent techniques
and especially ANN in various aspects of CM. In 2001, Adeli and Yeh provided a comprehensive
review of such applications made before the turn of the century [1]. The current work presents a
review of about 70 papers published in the area of CM. The objective of the paper is to highlight
the applications of ANN in the following fields of CM: Cost, Productivity, Risk Analysis, Safety,
Duration, Dispute, Unit rate and Hybrid Models. Further critical review of the findings will help
the readers to focus on important areas for potential use and development of ANN in the said
areas of CM. The future scope will facilitate continued research efforts. The paper is further
synthesized as follows: Initially, a brief introduction on ANN is presented and is followed by the
assessment of their recent applications in the areas of Cost, Productivity, Risk Analysis, Safety,
Duration, Dispute, Unit rate and Hybrid Models. Discussion and critical review are done in the
preceding section followed by author’s comments on the findings and future scope.
2. Artificial neural network
ANN is a soft computing tool, mimicking the ability of human mind to employ modes of
reasoning and pattern recognition effectively. ANN as a concept was existing for a long time;
however, its application in civil engineering started in the late 1980’s primarily in construction
activities [1]. ANN’s were found to learn from the relationships between input and output
provided through training data and could generalize the output, making it suitable for non-linear
problems where judgment, experience, and surrounding conditions are the key features. ANNs
typically comprise of 3 layers viz. the input layer with input neurons, hidden layer(s) with hidden
neurons and output layers with output neurons (figure 1).
Input Layer Hidden Layer Output Layer
Neuron Weight Bias Neuron
Fig. 1. Basic ANN architecture
, 72 P.S. Kulkarni et al./ Journal of Soft Computing in Civil Engineering 1-2 (2017) 70-88
Each neuron in the input layer is connected to each neuron in the hidden layer, and each neuron
in a hidden layer is connected to each neuron in the output layer. The number of hidden layers
and the number of neurons in each hidden layer can be one or more than one. The number of
input neurons, hidden neurons, and output neurons constitutes the network architecture. Before
its application the network is trained, i.e., the connection weights and bias values are fixed, with
the help of a mathematical optimization algorithm and using part of the data set until a very low
value of the error is attained. The network is then tested with an unseen data set to judge the
accuracy of the developed model. The network is trained using various training algorithms which
aim at minimizing the error between the observed and network predicted values. The networks
are classified according to the passage of the flow of information either in the forward direction
(feed forward) or reverse or lateral directions (recurrent network). Generally, three-layer feed-
forward or recurrent networks are found to be sufficient in civil engineering practices. Other
types of networks include the counter-propagation ANN, Hamming's network and the radial
basis function network. For details, readers are referred to [2–6].
3. Applications
Since the late 1980’s several investigators have applied ANN in civil engineering to carry out a
variety of tasks such as prediction, optimization, system modeling and classification [7].
Applications can be seen in areas of i) construction costs ii) productivity iii) risk analysis and
safety and iv) project duration, disputes and unit rates which are dealt with herein.
3.1. Cost
ANNs as a tool is used to estimate the costs of school buildings [8] residential projects,
apartment projects [9,10], cost of structural systems of reinforced concrete skeleton buildings in
early stage [11–13], costs of overall building projects [14,15], cost for highway [16,17], tunnels
[18], general overheads [19], cost of deviation in reconstruction projects was predicted through a
single quantifiable measure, the cost performance index [20]. Cost estimation of continuing care
retirement community projects was done by developing regression and neural network models
[21]. In 2013, Naik and Kumar utilized ANN for optimizing project cost with data of 512 houses
in India [22]. Minli and Shanshan in 2012 used ANN to estimate the tender offer price based on
environmental factors, business factors and project factors [23]. ANN was used for estimating
the optimal contingency for an owner’s funding of transportation construction projects that can
achieve solutions that are closer to the optimum than existing tools [24], modeling of
construction project management effectiveness in terms of construction cost variation [25],
predicting maintenance cost of construction equipment [26], pre-estimating models to predict the
final cost of highway projects constructed by the New Jersey Department of Transportation [27],
contingency costs for road maintenance activities [28] and project cost along with schedule
success prediction models [29]. ANN is also used as a tool to predict the cost premium of green
buildings based on LEED categories [30]. In 2003, Apanaviciene and Juodis modeled cost
variation and carried out a sensitivity analysis to reduce the input variables from 27 to 12 [25].
For maintenance cost forecasting of selected equipment groups, General Regression Neural
Network (GRNN) models were developed and further compared in terms of complexity,
Contents lists available at SCCE
Journal of Soft Computing in Civil Engineering
Journal homepage: www.jsoftcivil.com
Artificial Neural Networks for Construction Management: A
Review
P.S. Kulkarni 1* , S.N. Londhe 2 , M.C. Deo 3
1. Associate Professor, Vishwakarma Institute of Information Technology, Pune, India
2. Professor, Vishwakarma Institute of Information Technology, Pune, India
3. Professor, Indian Institute of Technology, Mumbai, India
Corresponding author:
https://doi.org/10.22115/SCCE.2017.49580
ARTICLE INFO ABSTRACT
Article history:
Received: 12 July 2017 Construction Management (CM) has to deal with a variety of
Revised: 23 August 2017 uncertainties related to Time, Cost, Quality, and Safety, to
Accepted: 23 August 2017 name a few. Such uncertainties make the entire construction
process highly unpredictable. It, therefore, falls under the
Keywords: purview of artificial neural networks (ANNs) in which the
Construction management; given hazy information can be effectively interpreted in
Artificial neural networks; order to arrive at meaningful conclusions. This paper reviews
Training algorithm; the application of ANNs in construction activities related to
Sensitivity analysis. the prediction of costs, risk, and safety, tender bids, as well
as labor and equipment productivity. The review suggests
that the ANN’s had been highly beneficial in correctly
interpreting inadequate input information. It was seen that
most of the investigators used the feed forward back
propagation type of the network; however, if a single ANN
architecture was found to be insufficient, then hybrid
modeling in association with other machine learning tools
such as genetic programming and support vector machines
were much useful. It was however clear that the authenticity
of data and experience of the modeler are important in
obtaining good results.
1. Introduction
The construction industry is highly competitive and faces challenges in the areas of costs of
projects, delays in construction activities, labor productivity, disputes, tenders, bidding prices,
How to cite this article: Kulkarni PS, Londhe SN, Deo MC. Artificial neural networks for construction management: a review. J
Soft Comput Civ Eng 2017;1(2):70–88. https://doi.org/10.22115/scce.2017.49580.
2588-2872/ © 2017 The Authors. Published by Pouyan Press.
This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
, P.S. Kulkarni et al./ Journal of Soft Computing in Civil Engineering 1-2 (2017) 70-88 71
safety aspects, the rate of materials, maintenance costs, risk analysis, etc. which are highly
complicated in nature. To deal with these challenges, Artificial Intelligence (AI) techniques like
fuzzy logic, case-based reasoning, probabilistic methods for uncertain reasoning, classifiers and
learning methods, Artificial Neural Networks (ANN), Genetic Algorithms and hybrid techniques
are widely used in the field of Construction Management (CM). In the last two decades of the
twentieth century, there was a surge in publications dealing with Artificial Intelligent techniques
and especially ANN in various aspects of CM. In 2001, Adeli and Yeh provided a comprehensive
review of such applications made before the turn of the century [1]. The current work presents a
review of about 70 papers published in the area of CM. The objective of the paper is to highlight
the applications of ANN in the following fields of CM: Cost, Productivity, Risk Analysis, Safety,
Duration, Dispute, Unit rate and Hybrid Models. Further critical review of the findings will help
the readers to focus on important areas for potential use and development of ANN in the said
areas of CM. The future scope will facilitate continued research efforts. The paper is further
synthesized as follows: Initially, a brief introduction on ANN is presented and is followed by the
assessment of their recent applications in the areas of Cost, Productivity, Risk Analysis, Safety,
Duration, Dispute, Unit rate and Hybrid Models. Discussion and critical review are done in the
preceding section followed by author’s comments on the findings and future scope.
2. Artificial neural network
ANN is a soft computing tool, mimicking the ability of human mind to employ modes of
reasoning and pattern recognition effectively. ANN as a concept was existing for a long time;
however, its application in civil engineering started in the late 1980’s primarily in construction
activities [1]. ANN’s were found to learn from the relationships between input and output
provided through training data and could generalize the output, making it suitable for non-linear
problems where judgment, experience, and surrounding conditions are the key features. ANNs
typically comprise of 3 layers viz. the input layer with input neurons, hidden layer(s) with hidden
neurons and output layers with output neurons (figure 1).
Input Layer Hidden Layer Output Layer
Neuron Weight Bias Neuron
Fig. 1. Basic ANN architecture
, 72 P.S. Kulkarni et al./ Journal of Soft Computing in Civil Engineering 1-2 (2017) 70-88
Each neuron in the input layer is connected to each neuron in the hidden layer, and each neuron
in a hidden layer is connected to each neuron in the output layer. The number of hidden layers
and the number of neurons in each hidden layer can be one or more than one. The number of
input neurons, hidden neurons, and output neurons constitutes the network architecture. Before
its application the network is trained, i.e., the connection weights and bias values are fixed, with
the help of a mathematical optimization algorithm and using part of the data set until a very low
value of the error is attained. The network is then tested with an unseen data set to judge the
accuracy of the developed model. The network is trained using various training algorithms which
aim at minimizing the error between the observed and network predicted values. The networks
are classified according to the passage of the flow of information either in the forward direction
(feed forward) or reverse or lateral directions (recurrent network). Generally, three-layer feed-
forward or recurrent networks are found to be sufficient in civil engineering practices. Other
types of networks include the counter-propagation ANN, Hamming's network and the radial
basis function network. For details, readers are referred to [2–6].
3. Applications
Since the late 1980’s several investigators have applied ANN in civil engineering to carry out a
variety of tasks such as prediction, optimization, system modeling and classification [7].
Applications can be seen in areas of i) construction costs ii) productivity iii) risk analysis and
safety and iv) project duration, disputes and unit rates which are dealt with herein.
3.1. Cost
ANNs as a tool is used to estimate the costs of school buildings [8] residential projects,
apartment projects [9,10], cost of structural systems of reinforced concrete skeleton buildings in
early stage [11–13], costs of overall building projects [14,15], cost for highway [16,17], tunnels
[18], general overheads [19], cost of deviation in reconstruction projects was predicted through a
single quantifiable measure, the cost performance index [20]. Cost estimation of continuing care
retirement community projects was done by developing regression and neural network models
[21]. In 2013, Naik and Kumar utilized ANN for optimizing project cost with data of 512 houses
in India [22]. Minli and Shanshan in 2012 used ANN to estimate the tender offer price based on
environmental factors, business factors and project factors [23]. ANN was used for estimating
the optimal contingency for an owner’s funding of transportation construction projects that can
achieve solutions that are closer to the optimum than existing tools [24], modeling of
construction project management effectiveness in terms of construction cost variation [25],
predicting maintenance cost of construction equipment [26], pre-estimating models to predict the
final cost of highway projects constructed by the New Jersey Department of Transportation [27],
contingency costs for road maintenance activities [28] and project cost along with schedule
success prediction models [29]. ANN is also used as a tool to predict the cost premium of green
buildings based on LEED categories [30]. In 2003, Apanaviciene and Juodis modeled cost
variation and carried out a sensitivity analysis to reduce the input variables from 27 to 12 [25].
For maintenance cost forecasting of selected equipment groups, General Regression Neural
Network (GRNN) models were developed and further compared in terms of complexity,
