Complexity

Artificial Neural Networks and Fuzzy Neural Networks for Solving Civil Engineering Problems


Status
Published

Lead Editor

1University of Podgorica, Podgorica, Montenegro

2Ss. Cyril and Methodius University, Skopje, Macedonia

3Brno University of Technology, Brno, Czech Republic

4University of Minho, Guimaraes, Portugal

5Slovak University of Technology in Bratislava, Bratislava, Slovakia


Artificial Neural Networks and Fuzzy Neural Networks for Solving Civil Engineering Problems

Description

Based on the life cycle engineering aspects, as prediction, design, assessment, maintenance, and management of structures and according to performance based approach, civil engineering structures have to fulfill essential requirements for resilience, sustainability, and safety from possible risks, as earthquakes, fires, floods, extreme winds, and explosions.

Analysis of the performance indicators, which are of a great importance for the structural behavior and for fulfillment of the above-mentioned requirements, is impossible without conducting complex mathematical calculations.

Artificial Neural Networks and Fuzzy Neural Networks are a typical example of a modern interdisciplinary field which gives the basic knowledge principles that could be used for solving many different and complex engineering problems which could not be solved otherwise (using traditional modeling and statistical methods). Neural Networks are capable of collecting, memorizing, analyzing, and processing large amount of data gained from some experiments or numerical analyses. Because of that, Neural Networks are often better calculation and prediction methods compared to some of the classical and traditional calculation methods. They are excellent in predicting data and they can be used for creating prognostic models that could solve various engineering problems and tasks. Trained Neural Network serves as an analytical tool for 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 precise. These positive effects completely justify their application, as prognostic models, in engineering researches.

The use of the neural-network-based approach, as an unconventional approach for solving complex civil engineering problems, has a huge significance in the modernization of the construction design processes. Worldwide studies show that Artificial Neural Networks and Fuzzy Neural Networks can be successfully used as prognostic models in different engineering fields, especially in those cases where some prior (numerical or experimental) analyses were already made.

The objective of this special issue is to highlight the importance of more deep investigations on possibilities of using Artificial Neural Networks and Fuzzy Neural Networks as effective and powerful tools for solving engineering problems. High quality research papers with original results, as well as review articles which are closely related to the topic of the special issue are welcome.

Potential topics include but are not limited to the following:

  • Theory and application of Fuzzy Neural Networks for solving civil engineering problems
  • Theory and application of Artificial Neural Networks for solving civil engineering problems
  • Theory and application of Neural Networks for multicriterion optimization of structures
  • Neural Networks in theory of structures
  • Neural Networks as prediction models based on experimental investigations
  • Neural Networks as prediction models based on numerical investigations
  • Application of Neural Networks for predicting the structural behavior for different load conditions
  • Application of Neural Networks for defining material properties
  • Application of Neural Networks for estimating the value of real estate

Articles

  • Special Issue
  • - Volume 2018
  • - Article ID 8149650
  • - Editorial

Artificial Neural Networks and Fuzzy Neural Networks for Solving Civil Engineering Problems

Milos Knezevic | Meri Cvetkovska | ... | Andrej Soltesz
  • Special Issue
  • - Volume 2018
  • - Article ID 8204568
  • - Research Article

Determination of Fire Resistance of Eccentrically Loaded Reinforced Concrete Columns Using Fuzzy Neural Networks

Marijana Lazarevska | Ana Trombeva Gavriloska | ... | Meri Cvetkovska
  • Special Issue
  • - Volume 2018
  • - Article ID 5160417
  • - Research Article

Urban Road Infrastructure Maintenance Planning with Application of Neural Networks

Ivan Marović | Ivica Androjić | ... | Tomáš Hanák
  • Special Issue
  • - Volume 2018
  • - Article ID 7952434
  • - Research Article

ANN Based Approach for Estimation of Construction Costs of Sports Fields

Michał Juszczyk | Agnieszka Leśniak | Krzysztof Zima
  • Special Issue
  • - Volume 2018
  • - Article ID 1472957
  • - Research Article

Assessment of the Real Estate Market Value in the European Market by Artificial Neural Networks Application

Jasmina Ćetković | Slobodan Lakić | ... | Mladen Gogić
  • Special Issue
  • - Volume 2017
  • - Article ID 3418145
  • - Research Article

Development of ANN Model for Wind Speed Prediction as a Support for Early Warning System

Ivan Marović | Ivana Sušanj | Nevenka Ožanić
  • Special Issue
  • - Volume 2017
  • - Article ID 2450370
  • - Research Article

Estimation of Costs and Durations of Construction of Urban Roads Using ANN and SVM

Igor Peško | Vladimir Mučenski | ... | Milena Krklješ
Complexity
Publishing Collaboration
More info
Wiley Hindawi logo
 Journal metrics
Acceptance rate43%
Submission to final decision64 days
Acceptance to publication35 days
CiteScore3.200
Impact Factor2.462
 Submit