Towards the next generation of artificial neural networks for civil engineering

2008 ◽  
Vol 22 (1) ◽  
pp. 4-14 ◽  
Author(s):  
Ian Flood
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Civil Engineering ◽  
Next Generation ◽  
Artificial Neural

scholarly journals An artificial neural network identifies glyphosate-impacted brackish communities based on 16S rRNA amplicon MiSeq read counts

2019 ◽  
Author(s):  
René Janßen ◽  
Jakob Zabel ◽  
Uwe von Lukas ◽  
Matthias Labrenz
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Environmental Monitoring ◽  
Microbial Communities ◽  
List Type ◽  
Neural Net ◽  
Next Generation ◽  
Artificial Neural

AbstractArtificial neural networks can be trained on complex data sets to detect, predict, or model specific aspects. Aim of this study was to train an artificial neural network to support environmental monitoring efforts in case of a contamination event by detecting induced changes towards the microbial communities. The neural net was trained on taxonomic cluster count tables obtained via next-generation amplicon sequencing of water column samples originating from a lab microcosm incubation experiment conducted over 140 days to determine the effects of the herbicide glyphosate on succession within brackish-water microbial communities. Glyphosate-treated assemblages were classified correctly; a subsetting approach identified the clusters primarily responsible for this, permitting the reduction of input features. This study demonstrates the potential of artificial neural networks to predict indicator species in cases of glyphosate contamination. The results could empower the development of environmental monitoring strategies with applications limited to neither glyphosate nor amplicon sequence data.Highlight bullet pointsAn artificial neural net was able to identify glyphosate-affected microbial community assemblages based on next generation sequencing dataDecision-relevant taxonomic clusters can be identified by a stochastically subsetting approachJust a fraction of present clusters is needed for classificationFiltering of input data improves classification

On the predictability of next generation mobile network traffic using artificial neural networks

2013 ◽  
Vol 28 (8) ◽  
pp. 1484-1492 ◽  
Author(s):  
I. Loumiotis ◽  
E. Adamopoulou ◽  
K. Demestichas ◽  
T. Stamatiadi ◽  
M. Theologou
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Traffic ◽  
Mobile Network ◽  
Next Generation ◽  
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.

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.

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.

Hybrid System with Artificial Neural Networks and Evolutionary Computation in Civil Engineering

2006 ◽  
pp. 166-187 ◽  
Author(s):  
Juan R. Rabunal ◽  
Juan Puertas
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Evolutionary Computation ◽  
Real Time ◽  
Hybrid System ◽  
Civil Engineering ◽  
Alarm System ◽  
Artificial Neural

This chapter proposes an application of two techniques of artificial intelligence in a civil engineering area: the artificial neural networks (ANN) and the evolutionary computation (EC). In this chapter, it is shown how these two techniques can work together in order to solve a problem in hydrology. This problem consists on modeling the effect of rain on the runoff flow in a typical urban basin. The ultimate goal is to design a real-time alarm system for floods or subsidence warning in various types of urban basins. A case study is included as an example.

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