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EditorialArtificial Neural Networks and Fuzzy Neural Networks
for SolvingCivil Engineering Problems
Milos Knezevic ,1 Meri Cvetkovska ,2 Tomáš Hanák ,3 Luis
Braganca ,4
and Andrej Soltesz5
1University of Podgorica, Faculty of Civil Engineering,
Podgorica, Montenegro2Ss. Cyril and Methodius University, Faculty
of Civil Engineering, Skopje, Macedonia3Brno University of
Technology, Faculty of Civil Engineering, Institute of Structural
Economics and Management,Brno, Czech Republic4Director of the
Building Physics & Construction Technology Laboratory, Civil
Engineering Department University of Minho,Guimaraes,
Portugal5Slovak University of Technology in Bratislava, Department
of Hydraulic Engineering, Bratislava, Slovakia
Correspondence should be addressed to Milos Knezevic;
[email protected]
Received 2 August 2018; Accepted 2 August 2018; Published 8
October 2018
Copyright © 2018 Milos Knezevic et al. This is an open access
article distributed under the Creative Commons Attribution
License,which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly
cited.
Based on the live cycle engineering aspects, such as
prediction,design, assessment, maintenance, and management of
struc-tures, and according to performance-based approach,
civilengineering structures have to fulfill essential
requirementsfor resilience, sustainability, and safety from
possible risks, suchas earthquakes, fires, floods, extreme winds,
and explosions.
The analysis of the performance indicators, which are ofgreat
importance for the structural behavior and for the fulfill-ment of
the above-mentioned requirements, is impossiblewithout conducting
complex mathematical calculations. Arti-ficial neural networks and
Fuzzy neural networks are typicalexamples of a modern
interdisciplinary field which gives thebasic knowledge principles
that could be used for solvingmany different and complex
engineering problems whichcould not be solved otherwise (using
traditional modelingand statistical methods). Neural networks are
capable of col-lecting, memorizing, analyzing, and processing a
large numberof data gained from some experiments or numerical
analyses.Because of that, neural networks are often better
calculationand prediction methods compared to some of the
classicaland traditional calculation methods. They are excellent in
pre-dicting data, and they can be used for creating
prognosticmodels that could solve various engineering problems
andtasks. A trained neural network serves as an analytical toolfor
qualified prognoses of the results, for any input data which
have not been included in the learning process of the
network.Their usage is reasonably simple and easy, yet correct and
pre-cise. These positive effects completely justify their
application,as prognostic models, in engineering researches.
The objective of this special issue was to highlight
thepossibilities of using artificial neural networks and
fuzzyneural networks as effective and powerful tools for
solvingengineering problems. From 12 submissions, 6 papers
arepublished. Each paper was reviewed by at least two reviewersand
revised according to review comments. The papers cov-ered a wide
range of topics, such as assessment of the realestate market value;
estimation of costs and duration of con-struction works as well as
maintenance costs; and predictionof natural disasters, such as wind
and fire, and prediction ofdamages to property and the
environment.
I. Marovic et al.’s paper presents an application of arti-ficial
neural networks (ANN) in the predicting process ofwind speed and
its implementation in early warning sys-tems (EWS) as a decision
support tool. The ANN predic-tion model was developed on the basis
of the input dataobtained by the local meteorological station. The
predic-tion model was validated and evaluated by visual andcommon
calculation approaches after which it was foundout that it is
applicable and gives very good wind speedpredictions. The developed
model is implemented in the
HindawiComplexityVolume 2018, Article ID 8149650, 2
pageshttps://doi.org/10.1155/2018/8149650
http://orcid.org/0000-0002-4952-9699http://orcid.org/0000-0002-9155-6420http://orcid.org/0000-0002-7820-6848http://orcid.org/0000-0003-4246-8157https://creativecommons.org/licenses/by/4.0/https://creativecommons.org/licenses/by/4.0/https://doi.org/10.1155/2018/8149650
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EWS as a decision support for the improvement of theexisting
“procedure plan in a case of the emergency causedby stormy wind or
hurricane, snow and occurrence of theice on the University of
Rijeka campus.”
The application of artificial neural networks as well
aseconometric models is characterized by specific advantagesand
disadvantages. Nevertheless, neural networks have beenimposed as a
real alternative to econometric methods andas a powerful tool for
assessment and forecasting, for exam-ple, in the field of
evaluating real estate. It is specially empha-sized that it is
possible to find estimated values instead ofexact values. The aim
of J. Cetkovic et al.’s research was toconstruct a prognostic model
of the real estate market valuein the EU countries depending on the
impact of macroeco-nomic indicators. Based on the available input
data—ma-croeconomic variables that influence the determination
ofreal estate prices, the authors sought to obtain fairly
correctoutput data—prices forecast in the real estate markets ofthe
observed countries.
Offer preparation has always been a specific part of abuilding
process which has a significant impact on companybusiness. Due to
the fact that income greatly depends onoffer’s precision and the
balance between planned costs, bothdirect and overheads, and wished
profit, it is necessary to pre-pare a precise offer within the
required time and availableresources which are always insufficient.
I. Peško et al.’s paperpresents research on precision that can be
achieved whileusing artificial intelligence for the estimation of
cost andduration in construction projects. Both artificial neural
net-works (ANNs) and support vector machines (SVM) wereanalyzed and
compared. Based on the investigation results,a conclusion was drawn
that a greater accuracy level in theestimation of costs and
duration of construction is achievedby using models that separately
estimate the costs and theduration. The reason for this lies
primarily in the differentinfluence of input parameters on the
estimation of costs incomparison with the estimation of duration of
the project.By integrating them into a single model, a compromise
interms of the significance of input data is made, resulting inthe
lower precision of estimation when it comes to ANNmodels. SVMmodels
feature a greater capacity of generaliza-tion, providing at the
same time greater accuracy of estima-tion, both for the estimation
of costs and duration ofprojects as well.
The same problem was treated by M. Juszczyk et al.Their research
was on the applicability of ANN for theestimation of construction
costs of sports fields. An appli-cability of multilayer perceptron
networks was confirmedby the results of the initial training of a
set of various arti-ficial neural networks. Moreover, one network
was tailoredfor mapping a relationship between the total cost of
con-struction works and the selected cost predictors whichare
characteristic for sports fields. Its prediction qualityand
accuracy were assessed positively. The research resultslegitimate
the proposed approach.
The maintenance planning within the urban road infra-structure
management is a complex problem from both themanagement and the
technoeconomic aspects. The focus ofI. Marovic et al.’s research
was on decision-making processes
related to the planning phase during the management ofurban road
infrastructure projects. The goal of thisresearch was to design and
develop an ANN model inorder to achieve a successful prediction of
road deteriora-tion as a tool for maintenance planning activities.
Such amodel was part of the proposed decision support conceptfor
urban road infrastructure management and a decisionsupport tool in
planning activities. The input data wereobtained from Circly 6.0
Pavement Design Software andused to determine the stress values. It
was found that itis possible and desirable to apply such a model in
thedecision support concept in order to improve urban
roadinfrastructure maintenance planning processes.
The fire resistance of civil engineering structures can
bedetermined based on the estimated fire resistance of
eachconstruction element (columns, beams, slabs, walls, etc.).As
fire resistance of structural elements directly affects
thefunctionality and safety of the whole structure, the
signifi-cance which new methods and computational tools have
onenabling a quick, easy, and simple prognosis of the same, isquite
clear. M. Lazarevska et al.’s paper considered the appli-cation of
fuzzy neural networks by creating prognosticmodels for determining
fire resistance of eccentrically loadedreinforced concrete columns.
Using the concept of the fuzzyneural networks and the results of
the performed numericalanalyses (as input parameters), the
prediction model fordefining the fire resistance of eccentrically
loaded RC col-umns incorporated in walls and exposed to standard
firefrom one side has been made. The numerical results wereused as
input data in order to create and train the fuzzy neu-ral network
so it can provide precise outputs for the fire resis-tance of
eccentrically loaded RC columns for any other inputdata (RC columns
with different dimensions of the cross-sec-tion, different
thickness of the protective concrete layer, dif-ferent percentage
of reinforcement and for different loads).
These papers represent an exciting, insightful observationinto
the state of the art as well as emerging future topics inthis
important interdisciplinary field. We hope that this spe-cial issue
would attract a major attention of the civil engi-neering’s
community.
We would like to express our appreciation to all theauthors and
reviewers who contributed to publishing thisspecial issue.
Conflicts of Interest
As guest editors, we declare that we do not have a
financialinterest regarding the publication of this special
issue.
Milos KnezevicMeri CvetkovskaTomáš HanákLuis BragancaAndrej
Soltesz
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