Nov 24, 2020

Dynamic Neural Network For Predicting Creep Of Structural Masonry An Application Of Artificial Intelligence Techniques

dynamic neural network for predicting creep of structural masonry an application of artificial intelligence techniques

This study investigates the potential use of Dynamic Neural Network (DNN) for predicting creep of structural masonry. The main motive of use DNN is that DNN could memorize the sequential or time-varying patterns while training process. Thus, DNN becomes more capable of capturing the time-dependent of creep deformation than the static networks. The results showed that the developed DNN models ...

(PDF) Creep Predicting Model in Masonry Structure ...

Artificial neural networks for predicting creep with an example application to structural masonry . Article (PDF Available) in Canadian Journal of Civil Engineering 30(3):523-532 · June 2003 with ...

Creep Predicting Model in Masonry Structure Utilizing ...

Dynamic Neural Network for Predicting Creep of Structural Masonry: An Application of Artificial Intelligence Techniques: Abed, Mustafa Mohammed: 9783846588208: Books - Amazon.ca

(PDF) Neural network modelling of creep in masonry

Feedforward artificial neural networks (ANN) are investigated as a modelling technique for predicting creep. Experimental data for creep of structural masonry are used to develop the networks. Changes in network architecture are examined to produce prediction models. Fifteen networks are developed and analysed statistically. Creep models with accuracy in the range ± 15% are attainable using ...

Neural network modelling of creep in masonry | Proceedings ...

Construction Press, London, UK. Reda Taha MM, Noureldin A, El-Sheimy N and Shrive NG (2003) Artificial neural networks to predict creep with an example application to structural masonry. Canadian ...

Dynamic versus static artificial neural network model for ...

Artificial neural networks have proven successful in many instances where conventional mathematical modeling techniques were not as accurate or capable. Here, the potential use of ANNs in predicting creep is examined. A new ANN model is applied to the prediction of creep of structural masonry. The ANN developed is able to predict the creep performance with an excellent level of accuracy ...

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Neural Computing & Applications is an international journal which publishes original research and other information in the field of practical applications of neural computing and related techniques such as genetic algorithms, fuzzy logic and neuro-fuzzy systems. All items relevant to building practical systems are within its scope, including but not limited to:

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NEURAL NETWORK APPLICATION OVERVIEW IN PREDICTION OF PROPERTIES OF CEMENT-BASED MORTAR AND CONCRETE . N.K. Lee1), H. Souri2) and *H.K. Lee3) 1), 2), 3) Department of Civil Engineering, KAIST, Daejeon 305-600, Korea . 3) haengki@kaist.ac.kr. ABSTRACT . Neural networks have recently been broadly used in civil engineering applications due to their versatile capability as a simulator for the ...

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I-H. Yang, M-S. Yeo, K-W. KimApplication of artificial neural network to predict the optimal start time for heating system in building Energy Conversion and Management, 44 (2003), pp. 2791-2809 Google Scholar

Application of Neural Network to Improve Dynamic Branch ...

Artificial neural network (ANN) approaches combine the complexity of some techniques from statistics with the objective of machine learning that is imitating human intelligence. In this chapter the potential of different ANN approaches for the prediction of sliding friction and wear properties of polyphenylene-based polymer composites used as bulk materials or coatings in sliding wear ...

Engineering Applications of Artificial Intelligence ...

Regularization techniques for Neural Networks. Yash Upadhyay . Follow. Mar 14, 2019 · 9 min read. Source. In our last post, we learned about feedforward neural networks and how to design them. In this post, we will learn how to tackle one of the most central problems that arise in the domain of machine learning, that is how to make our algorithm to find a perfect fit not only to the training ...

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This paper explores the knowledge of linguistic structure learned by large artificial neural networks, trained via self-supervision, whereby the model simply tries to predict a masked word in a given context. Human language communication is via sequences of words, but language understanding requires constructing rich hierarchical structures that are never observed explicitly.

Artificial Intelligence in Medicine | Machine Learning | IBM

CiteScore: 4.58 ℹ CiteScore: 2019: 4.580 CiteScore measures the average citations received per document published in this title. CiteScore values are based on citation counts in a given year (e.g. 2015) to documents published in three previous calendar years (e.g. 2012 – 14), divided by the number of documents in these three previous years (e.g. 2012 – 14).

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Understanding how artificial intelligence (AI) and machine learning (ML) can benefit your business may seem like a daunting task. But there is a myriad of applications for these technologies that ...

Research Articles | Challenge Journal of Structural Mechanics

The objective of this study is to evaluate the performance of the artificial neural network (ANN) approach for predicting interlayer conditions and layer modulus of a multi-layered flexible pavement structure. To achieve this goal, two ANN based back-calculation models were proposed to predict the interlayer conditions and layer modulus of the pavement structure. The corresponding database ...

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In this study, the whole creep behaviour of polypropylene for all stresses were obtained with 10% accuracy errors by artificial neural networks trained using existing experimental test results of the materials for a particular working range. The artificial neural network model was trained with traditional as well as heuristic based methods. It is demonstrated that heuristically trained ANN ...

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Neural Network to Instantly Predict the Tertiary Structure of the Protein. Another powerful breakthrough in healthcare made by AI. ProteinNet neural network is able to predict the structure of a protein in milliseconds. Well, predicting the structure of a protein from its sequence is a central biochemistry problem. Convolutional neural networks ...

Neural network modeling of time-dependent creep ...

Artificial intelligent techniques such as fuzzy expert systems, Bayesian networks, artificial neural networks, and hybrid intelligent systems were used in different clinical settings in health care. In 2016, the biggest chunk of investments in AI research were in healthcare applications compared with other sectors. AI in medicine can be dichotomized into two subtypes: Virtual and physical. The ...

AI problems and promises | McKinsey

Artificial Neural Networks (ANNs) have been used in various domains for modeling and prediction with high accuracy due to its ability to learn and adapt. This thesis concentrates on designing an ANN prediction engine to predict the thermal profile of the cores and Network-on-Chip elements of the chip. This thermal profile of the chip is then ...

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Artificial neural networks are relatively crude electronic networks of neurons based on the neural structure of the brain. They process records one at a time, and learn by comparing their prediction of the record (largely arbitrary) with the known actual record. The errors from the initial prediction of the first record is fed back to the network and used to modify the network's algorithm for ...

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For Industrial Intelligence, PSI relies on solutions that combine the reliability and robustness of industrial process knowledge with the entire spectrum of artificial intelligence (AI) methods.. The stability of the solutions is ensured by the industrially proven PSI software technology and the PSI framework.

Prediction of airblast loads in complex environments using ...

Dynamic Neural Network for Predicting Creep of Structural Masonry: An Application of Artificial Intelligent Techniques LAMBERT Academic Publishing - Germany 2012. Creep Predicting Model in Masonry Structure Utilizing Dynamic Neural Network Journal of Computer Science 2010. Prediction of Time-Dependent Creep Deformations in Masonry Structures Using Neural Network Regional Engineering ...


Dynamic Neural Network For Predicting Creep Of Structural Masonry An Application Of Artificial Intelligence Techniques



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Dynamic Neural Network For Predicting Creep Of Structural Masonry An Application Of Artificial Intelligence Techniques