STRUCTURAL RESPONSE OBSERVER BASED ON ARTIFICIAL NEURAL NETWORK
Authors
Abstract:
Structural vibration control is one of the most important features in structural engineering. Real-time information about seismic resultant forces is required for deciding module of intelligent control systems. Evaluation of lateral forces during an earthquake is a complicated problem considering uncertainties of gravity loads amount and distribution and earthquake characteristics. An artificial neural network (ANN) has been trained in this article to estimate these forces. This ANN was trained on the results of time history analysis of a three-story building under 702 different loadings. Results of numerical examples verify that the trained ANN can predict the expected forces with negligible deviations.
similar resources
Structural Response Observer Based on Artificial Neural Network
Structural vibration control is one of the most important features in structural engineering. Real-time information about seismic resultant forces is required for deciding module of intelligent control systems. Evaluation of lateral forces during an earthquake is a complicated problem considering uncertainties of gravity loads amount and distribution and earthquake characteristics. An artificia...
full textNanofluid Thermal Conductivity Prediction Model Based on Artificial Neural Network
Heat transfer fluids have inherently low thermal conductivity that greatly limits the heat exchange efficiency. While the effectiveness of extending surfaces and redesigning heat exchange equipments to increase the heat transfer rate has reached a limit, many research activities have been carried out attempting to improve the thermal transport properties of the fluids by adding more thermally c...
full textPrediction of structural forces of segmental tunnel lining using FEM based artificial neural network
To judge about the performance of designed support system for tunnels, structural forces i.e. peak values of axial and shear forces and moments are critical parameters. So in this study, at first a complete database using finite element method was prepared. Then, a model of artificial neural network (ANN) using multi-layer perceptron was developed to estimate lining structural forces. Sensitivi...
full textINTERVAL ARTIFICIAL NEURAL NETWORK BASED RESPONSE OF UNCERTAIN SYSTEM SUBJECT TO EARTHQUAKE MOTIONS
Earthquakes are one of the most destructive natural phenomena which consist of rapid vibrations of rock near the earth’s surface. Because of their unpredictable occurrence and enormous capacity of destruction, they have brought fear to mankind since ancient times. Usually the earthquake acceleration is noted from the equipment in crisp or exact form. But in actual practice those data may not be...
full textDaily Pan Evaporation Estimation Using Artificial Neural Network-based Models
Accurate estimation of evaporation is important for design, planning and operation of water systems. In arid zones where water resources are scarce, the estimation of this loss becomes more interesting in the planning and management of irrigation practices. This paper investigates the ability of artificial neural networks (ANNs) technique to improve the accuracy of daily evaporation estimation....
full textSoft Sensing Based on Artificial Neural Network
Soft-sensing or inferential estimation has long been considered a potent tool to deiil with the conflict between small control interval itntl large sampling interval existing in a wide variety of industrial processes. To extend the soft-sensing from linear system to nonlinear case, we propose it nonlinear soft-sensor on the basis of multi-step predication using recurrent neural network and a no...
full textMy Resources
Journal title
volume 4 issue 1
pages 77- 91
publication date 2014-03
By following a journal you will be notified via email when a new issue of this journal is published.
Hosted on Doprax cloud platform doprax.com
copyright © 2015-2023