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

10.36227/techrxiv.12678371 ◽

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

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. 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%.

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

10.36227/techrxiv.12678371.v1 ◽

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

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. 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%.

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ANN Application in the Field of Structural Concrete

Encyclopedia of Artificial Intelligence ◽

10.4018/978-1-59904-849-9.ch018 ◽

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.

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Learning of N-layers neural network

Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis ◽

10.11118/actaun200553060075 ◽

2005 ◽

Vol 53 (6) ◽

pp. 75-84 ◽

Cited By ~ 3

Author(s):

Vladimír Konečný ◽

Anděla Matiášová ◽

Ivana Rábová

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

Learning Process ◽

Process Management ◽

Error Function ◽

Back Propagation ◽

Neural Net ◽

Learning Factor ◽

The Stability ◽

Quality Decision

In the last decade we can observe increasing number of applications based on the Artificial Intelligence that are designed to solve problems from different areas of human activity. The reason why there is so much interest in these technologies is that the classical way of solutions does not exist or these technologies are not suitable because of their robustness. They are often used in applications like Business Intelligence that enable to obtain useful information for high-quality decision-making and to increase competitive advantage.One of the most widespread tools for the Artificial Intelligence are the artificial neural networks. Their high advantage is relative simplicity and the possibility of self-learning based on set of pattern situations.For the learning phase is the most commonly used algorithm back-propagation error (BPE). The base of BPE is the method minima of error function representing the sum of squared errors on outputs of neural net, for all patterns of the learning set. However, while performing BPE and in the first usage, we can find out that it is necessary to complete the handling of the learning factor by suitable method. The stability of the learning process and the rate of convergence depend on the selected method. In the article there are derived two functions: one function for the learning process management by the relative great error function value and the second function when the value of error function approximates to global minimum.The aim of the article is to introduce the BPE algorithm in compact matrix form for multilayer neural networks, the derivation of the learning factor handling method and the presentation of the results.

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Evolutionary Integrated Heuristic with Gudermannian Neural Networks for Second Kind of Lane–Emden Nonlinear Singular Models

Applied Sciences ◽

10.3390/app11114725 ◽

2021 ◽

Vol 11 (11) ◽

pp. 4725

Author(s):

Kashif Nisar ◽

Zulqurnain Sabir ◽

Muhammad Asif Zahoor Raja ◽

Ag. Asri Ag. Ibrahim ◽

Joel J. P. C. Rodrigues ◽

...

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

Active Set ◽

Active Set Method ◽

Hybrid Heuristics ◽

Hidden Layer ◽

The Stability ◽

Intelligence Approach ◽

Artificial Intelligence Approach ◽

Search Capability

In this work, a new heuristic computing design is presented with an artificial intelligence approach to exploit the models with feed-forward (FF) Gudermannian neural networks (GNN) accomplished with global search capability of genetic algorithms (GA) combined with local convergence aptitude of active-set method (ASM), i.e., FF-GNN-GAASM to solve the second kind of Lane–Emden nonlinear singular models (LE-NSM). The proposed method based on the computing intelligent Gudermannian kernel is incorporated with the hidden layer configuration of FF-GNN models of differential operatives of the LE-NSM, which are arbitrarily associated with presenting an error-based objective function that is used to optimize by the hybrid heuristics of GAASM. Three LE-NSM-based examples are numerically solved to authenticate the effectiveness, accurateness, and efficiency of the suggested FF-GNN-GAASM. The reliability of the scheme via statistical valuations is verified in order to authenticate the stability, accuracy, and convergence.

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Using neural networks and artificial intelligence as a modern method of glaucoma diagnosis

Modern technologies in ophtalmology ◽

10.25276/2312-4911-2020-3-27-28 ◽

2020 ◽

pp. 27-28

Author(s):

A.B. Movsisyan ◽

◽

A.V. Kuroyedov ◽

G.A. Ostapenko ◽

S.V. Podvigin ◽

...

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

Modern Method ◽

Glaucoma Diagnosis

Актуальность. Определяется увеличением заболеваемости глаукомой во всем мире как одной из основных причин снижения зрения и поздней постановкой диагноза при имеющихся выраженных изменений со стороны органа зрения. Цель. Повысить эффективность диагностики глаукомы на основании оценки диска зрительного нерва и перипапиллярной сетчатки нейросетью и искусственным интеллектом. Материал и методы. Для обучения нейронной сети были выделены четыре диагноза: первый – «норма», второй – начальная глаукома, третий – развитая стадия глаукомы, четвертый – глаукома далеко зашедшей стадии. Классификация производилась на основе снимков глазного дна: область диска зрительного нерва и перипапиллярной сетчатки. В результате классификации входные данные разбивались на два класса «норма» и «глаукома». Для целей обучения и оценки качества обучения, множество данных было разбито на два подмножества: тренировочное и тестовое. В тренировочное подмножество были включены 8193 снимка с глаукомными изменениями диска зрительного нерва и «норма» (пациенты без глаукомы). Стадии заболевания были верифицированы согласно действующей классификации первичной открытоугольной глаукомы 3 (тремя) экспертами со стажем работы от 5 до 25 лет. В тестовое подмножество были включены 407 снимков, из них 199 – «норма», 208 – с начальной, развитой и далекозашедшей стадиями глаукомы. Для решения задачи классификации на «норма»/«глаукома» была выбрана архитектура нейронной сети, состоящая из пяти сверточных слоев. Результаты. Чувствительность тестирования дисков зрительных нервов с помощью нейронной сети составила 0,91, специфичность – 0,93. Анализ полученных результатов работы показал эффективность разработанной нейронной сети и ее преимущество перед имеющимися методами диагностики глаукомы. Выводы. Использование нейросетей и искусственного интеллекта является современным, эффективным и перспективным методом диагностики глаукомы.

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