Timber Structural Design Based on Neural Networks Application and FE 3D Parametric Modelling

2009 ◽  
Author(s):  
A. Bjelanovic ◽  
V. Rajcic
Keyword(s):  
Neural Networks ◽  
Structural Design ◽  
Parametric Modelling

scholarly journals Applying Artificial Neural Networks In Construction

2020 ◽  
Vol 143 ◽  
pp. 01029
Author(s):  
Anna Doroshenko
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Networks ◽  
Construction Industry ◽  
Structural Design ◽  
Soil Mechanics ◽  
Construction Materials ◽  
Construction Engineering ◽  
Design And Optimization ◽  
Artificial Neural

Currently, artificial neural networks (ANN) are used to solve the following complex problems: pattern recognition, speech recognition, complex forecasts and others. The main applications of ANN are decision making, pattern recognition, optimization, forecasting, data analysis. This paper presents an overview of applications of ANN in construction industry, including energy efficiency and energy consumption, structural analysis, construction materials, smart city and BIM technologies, structural design and optimization, application forecasting, construction engineering and soil mechanics.

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 INVESTIGATIONS ON THE DESIGN PROCESS OF ELADIO DIESTE: 3D PARAMETRIC MODELLING OF MODERN LATIN AMERICAN ARCHITECTURAL HERITAGE

2019 ◽  
Vol XLII-2/W15 ◽  
pp. 775-782
Author(s):  
F. C. Melachos ◽  
W. Florio ◽  
F. Maietti ◽  
L. Rossato ◽  
M. Balzani
Keyword(s):  
Design Process ◽  
Latin American ◽  
Structural Design ◽  
Digital Fabrication ◽  
Shell Structures ◽  
Parametric Modelling ◽  
Architectural Heritage ◽  
Heritage Sites ◽  
Reinforced Masonry ◽  
Methodological Procedures

<p><strong>Abstract.</strong> The Uruguayan Engineer Eladio Dieste underwent a quest for thinness in the field of structural design which rendered his reinforced masonry thin-shell structures at a conspicuous position in Modern Latin American Architectural Heritage, so much so as to have Dieste’s work in Latin America and Europe included in an indicative list for UNESCO’s cultural heritage sites as of 2010. Nonetheless, the design process that led Dieste to such innovative structural typologies is yet to be fully academically explored. Thus, the objective of this paper is to examine the state-of-art regarding the intricate design process of Eladio Dieste’s gaussian vaults and shed some light on the existing gaps within this process by means of the 3D parametric modelling and digital fabrication of selected case studies. The adoption methodological procedures such as 3D parametrical modelling and digital fabrication allows for the establishment of important relationships between the design process and the resulting geometry of Eladio Dieste’s designs, as well as furthering registry of Dieste’s legacy for conservation purposes.</p>

scholarly journals Efficient Structural Design of a Prefab Concrete Connection by Using Artificial Neural Networks

2020 ◽  
Vol 12 (19) ◽  
pp. 8226
Author(s):  
Jorge Navarro-Rubio ◽  
Paloma Pineda ◽  
Roberto Navarro-Rubio
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Structural Design ◽  
Precast Concrete ◽  
Computing Time ◽  
Plate Thickness ◽  
Economic Benefits ◽  
Design Tool ◽  
Design Stage ◽  
Artificial Neural

In the built environment, one of the main concerns during the design stage is the selection of adequate structural materials and elements. A rational and sensible design of both materials and elements results not only in economic benefits and computing time reduction, but also in minimizing the environmental impact. Nowadays, Artificial Neural Networks (ANNs) are showing their potential as design tools. In this research, ANNs are used in order to foster the implementation of efficient tools to be used during the early stages of structural design. The proposed networks are applied to a dry precast concrete connection, which has been modelled by means of the Finite Element Method (FEM). The parameters are: strength of concrete and screws, diameter of screws, plate thickness, and the posttensioning load. The ANN input data are the parameters and nodal stresses obtained from the FEM models. A multilayer perceptron combined with a backpropagation algorithm is used in the ANN architecture, and a hyperbolic tangent function is applied as an activation function. Comparing the obtained predicted stresses to those of the FEM analyses, the difference is less than 9.16%. Those results validate their use as an efficient structural design tool. The main advantage of the proposed ANNs is that they can be easily and effectively adapted to different connection parameters. In addition, their use could be applied both in precast or cast in situ concrete connection design.

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