Neural Networks in Structural Engineering: Some Recent Results and Prospects for Applications

2009 ◽  
pp. 479-515 ◽  
Author(s):  
Z. Waszczyszyn
Keyword(s):  
Neural Networks ◽  
Structural Engineering

ANN Application in the Field of Structural Concrete

2011 ◽  
pp. 118-124
Author(s):  
Juan L. Pérez ◽  
Mª Isabel Martínez ◽  
Manuel F. Herrador
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
High Resistance ◽  
Structural Engineering ◽  
Construction Material ◽  
Fabrication Process ◽  
Structural Concrete ◽  
Cost Efficient ◽  
Ann Application

Artificial Intelligence (AI) mechanisms are more and more frequently applied to all sorts of civil engineering problems. New methods and algorithms which allow civil engineers to use these techniques in a different way on diverse problems are available or being made available. One AI techniques stands out over the rest: Artificial Neural Networks (ANN). Their most remarkable traits are their ability to learn, the possibility of generalization and their tolerance towards mistakes. These characteristics make their use viable and cost-efficient in any field in general, and in Structural Engineering in particular. The most extended construction material nowadays is concrete, mainly because of its high resistance and its adaptability to formwork during its fabrication process. Along this chapter we will find different applications of ANNs to structural concrete.

Soft Computing Techniques in Probabilistic Seismic Analysis of Structures

2007 ◽  
pp. 248-269
Author(s):  
Nikos Lagaros ◽  
Yiannis Tsompanakis ◽  
Michalis Fragiadakis ◽  
Manolis Papadrakakis
Keyword(s):  
Neural Networks ◽  
Soft Computing ◽  
Earthquake Engineering ◽  
Structural Engineering ◽  
Seismic Analysis ◽  
Limit State ◽  
Computing Methods ◽  
Earthquake Resistant Design ◽  
Computationally Intensive ◽  
Using Data

Earthquake-resistant design of structures using probabilistic analysis is an emerging field in structural engineering. The objective of this chapter is to investigate the efficiency of soft computing methods when incorporated into the solution of computationally intensive earthquake engineering problems. Two methodologies are proposed in this work where limit-state probabilities of exceedance for real world structures are determined. Neural networks based metamodels are used in order to replace a large number of time-consuming structural analyses required for the calculation of a limit-state probability. The Rprop algorithm is employed for the training of the neural networks; using data obtained from appropriately selected structural analyses.

Applications of Backpropagation and Conjugate Gradient Neural Networks in Structural Engineering

2021 ◽  
pp. 285-304
Author(s):  
S. Varadharajan ◽  
Kirthanashri S. Vasanthan ◽  
Shwetambara Verma ◽  
Priyanka Singh
Keyword(s):  
Neural Networks ◽  
Conjugate Gradient ◽  
Structural Engineering

scholarly journals Analytical Prediction of Steel Grid-Shell Stability and Dynamic Behaviors Using Neural Networks – Part 1

2020 ◽  
Author(s):  
Abambres M ◽  
Cabello A
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Structural Engineering ◽  
Analytical Prediction ◽  
Target Variable ◽  
Uniform Loading ◽  
Single Target ◽  
Grid Shell ◽  
Data Points ◽  
The Stability

<p>Artificial Intelligence is a cutting-edge technology expanding very quickly into every industry. It has made its way into structural engineering and it has shown its benefits in predicting structural performance as well as saving modelling and experimenting time. This paper is the first one (out of three) of a broader research where artificial intelligence was applied to the stability and dynamic analyzes of steel grid-shells. In that study, three Artificial Neural Networks (ANN) with 8 inputs were independently designed for the prediction of a single target variable, namely: (i) the critical buckling factor for uniform loading (i.e. over the entire roof), (ii) the critical buckling factor for uniform loading over half of the roof, and (iii) the fundamental frequency of the structure. This paper addresses target variable (i). The ANN simulations were based on 1098-point datasets obtained via thorough finite element analyzes.</p> <p>The proposed ANN for the prediction of the critical buckling factor in steel grid-shells under uniform loading yields mean and maximum errors of 1.1% and 16.3%, respectively, for all 1098 data points. Only in 10.6% of those examples (points), the prediction error exceeds 3%. </p>

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