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

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-2 ◽  
Author(s):  
Milos Knezevic ◽  
Meri Cvetkovska ◽  
Tomáš Hanák ◽  
Luis Braganca ◽  
Andrej Soltesz
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Civil Engineering ◽  
Fuzzy Neural Networks ◽  
Engineering Problems ◽  
Fuzzy Neural ◽  
Artificial Neural

Artificial Neural Networks (ANNs) and Solution of Civil Engineering Problems

2020 ◽  
pp. 13-38
Author(s):  
Melda Yucel ◽  
Sinan Melih Nigdeli ◽  
Gebrail Bekdaş
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Structural Properties ◽  
Structural Design ◽  
Civil Engineering ◽  
Back Propagation ◽  
Training Algorithm ◽  
Design Problems ◽  
Engineering Problems ◽  
Artificial Neural

This chapter reveals the advantages of artificial neural networks (ANNs) by means of prediction success and effects on solutions for various problems. With this aim, initially, multilayer ANNs and their structural properties are explained. Then, feed-forward ANNs and a type of training algorithm called back-propagation, which was benefited for these type networks, are presented. Different structural design problems from civil engineering are optimized, and handled intended for obtaining prediction results thanks to usage of ANNs.

On-Line Prediction Model of Ultrasonic Polishing Surface Roughness

2010 ◽  
Vol 455 ◽  
pp. 539-543
Author(s):  
Ming Zhang ◽  
X.Q. Yang ◽  
Bo Zhao
Keyword(s):  
Neural Networks ◽  
Surface Roughness ◽  
Fuzzy Logic ◽  
Artificial Neural Networks ◽  
Prediction Model ◽  
Fuzzy Neural Networks ◽  
Fuzzy Logic Systems ◽  
Identification Method ◽  
Fuzzy Neural ◽  
On Line

In order to solve the difficulty of on-line measuring the surface roughness of workpiece under ultrasonic polishing, the artificial neural networks and fuzzy logic systems are introduced into the on-line prediction model of surface roughness. The surface roughness identification method based on fuzzy-neural networks is put forward and used to the process of plane polishing. In the end, the on-line prediction model of surface roughness is established. The actual ultrasonic polishing experiments show that the accuracy of this prediction model is up to 96.58%, which further evidence the feasibility of the on-line prediction model.

scholarly journals Importance of artificial neural networks in civil engineering: a systematic review of the literature

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Frank Jesús Valderrama Purizaca ◽  
Daniel Armando Chávez Barturen ◽  
Sócrates Pedro Muñoz Pérez ◽  
Victor A. Tuesta-Monteza ◽  
Heber Ivan Mejía-Cabrera
Keyword(s):  
Systematic Review ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Civil Engineering ◽  
Seismic Analysis ◽  
Ann Model ◽  
Review Of The Literature ◽  
Real Estate Valuation ◽  
Relevant Role ◽  
Artificial Neural

Artificial neural networks (ANN) have a relevant role nowadays; several areas apply this technique due to the advantages they have to solve complex problems with many constraints compared to traditional methods, which are becoming outdated. Very little is known about this technique and its application in different branches of Civil Engineering. For this reason, the present research aims to conduct a systematic review of the literature to identify the use of this technique and to determine the results of the application of ANN models in civil engineering. A total of 41 scientific articles were included, distributed as follows: 6 in Scopus, 1 in ScienceDirect, 23 in ProQuest, 7 in Google Scholar, 2 in DialNet, 2 in SciELO. It was found that ANNs are used to predict or forecast variables associated with the fields of study in civil engineering; 8 applications of ANN were found for concrete properties, 11 for soil properties, 5 for seismic analysis, 9 for hydraulics, 7 for real estate valuation and 1 for bridge design. Likewise, it was found that the multilayer Perceptron is the most used ANN model, achieving an average R2 of 0.99, which shows advantages to solve problems with precision, in shorter times, with missing data in the data sets, as well as the reduction of the error factor.

Artificial fuzzy neural networks in civil engineering

1996 ◽  
Vol 61 (2) ◽  
pp. 291-302 ◽  
Author(s):  
S. Rajasekaran ◽  
M.F. Febin ◽  
J.V. Ramasamy
Keyword(s):  
Neural Networks ◽  
Civil Engineering ◽  
Fuzzy Neural Networks ◽  
Fuzzy Neural

Applications of Artificial Neural Networks in Bridge Engineering

2011 ◽  
Vol 243-249 ◽  
pp. 1984-1987
Author(s):  
Xing Wei ◽  
Jun Li
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Life Prediction ◽  
Bridge Engineering ◽  
General View ◽  
Bridge Health Monitoring ◽  
Engineering Problems ◽  
Artificial Neural ◽  
Prediction Reliability ◽  
Model Slope

Artificial neural networks (ANNs) have been widely applied to many bridge engineering problems and have demonstrated some degree of success. A review of the literature reveals that ANNs have been used successfully in member capacity prediction, reliability analysis, optimal design of structural systems, fatigue life prediction, construction control, material constitutive model , slope stability, bridge health monitoring. The objective of this paper is to provide a general view of some ANNs applications for solving some types of bridge engineering problems. A brief introduction to ANNs is given. Problems such as what is a neural network, how it works and what kind of advantages it has are discussed. After this, several applications in bridge engineering are presented.

scholarly journals Artificial Neural Networks in Pavement Engineering: A Recent Review

2021 ◽  
Author(s):  
Abhishek Kurian ◽  
Elvin Sunildutt
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Civil Engineering ◽  
Developed Countries ◽  
Pavement Management ◽  
Economic Management ◽  
Learning Techniques ◽  
Pavement Engineering ◽  
Artificial Neural

The application of Artificial Neural Networks (ANN) in civil engineering has increased drastically in the past few years. ANN tools are nowadays used commonly in developed countries over various fields of civil engineering like geotechnical, structural, traffic, pavement engineering etc. This paper deals with the review of recent advancements and utilization of ANNs in pavement engineering. The review will focus on pavement performance prediction, maintenance strategies, distress intensity detection through deep learning techniques, pavement condition index prediction etc. The use of ANNs in pavement management systems are expected to furnish a systematic schedule and economic management strategies in the field of pavement engineering. The use of ANNs combined with deep learning techniques help to address complex problems in pavement engineering and pave the way to a sustainable future.

scholarly journals Using of Artificial Neural Networks in Support System of Forensic Building-Technical Expertise

2020 ◽  
Vol 9 (4) ◽  
pp. 3162-3168 ◽  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Technical Condition ◽  
Fuzzy Neural Networks ◽  
Impact Factors ◽  
Technical Expertise ◽  
Structural Elements ◽  
Resonance Theory ◽  
Artificial Neural ◽  
The Impact

The article contains an analysis of the order of forensic building-technical expertise and expert research to determine the reasons for the deterioration of the technical condition of the structural elements of buildings. The conditions for forming expert conclusions about the possible correlation between the appearance of negative changes in the technical condition of the structural elements that have become the subject of forensic building-technical expertise and the various factors of influence of the environment are investigated. In doing so, the focus is on the impact factors associated with carrying out renovation work in adjacent premises. In addition, issues related to the fuzzy uncertainty of the different nature of the expert researches are highlighted. Some of these problems are proposed to be solved by the using of artificial neural networks in the fuzzy subsystem of the system of support of forensic building-technical expertise. It is shown that a considerable part of the materials of forensic building-technical expertise and expert research is represented by photographs of injuries. Fixation of damaged structures is reflected in the plans of premises and schemes of placement of structures in the buildings. The graphic information of the research materials is accompanied by textual information, the processing of which requires the use of models and methods of fuzzy mathematics. The fragment of the knowledge base is provided, which contains information on the geometric parameters of damage to building structures and an example of a fuzzy rule that reflects an expert conclusion. The expediency of using fuzzy neural networks of adaptive resonance theory of the Cascade ARTMAP category is substantiated. Cascade ARTMAP memory card schematic is shown.

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