Artificial Neural Network Aided Prediction of Frame Displacements under Seismic Load
| Publication Date: |
2018-11-09
|
|---|---|
| Publisher: |
Institute of Physics (IOP)
|
| Print ISSN: |
1757-8981
|
| Electronic ISSN: |
1757-899X
|
| Topics: |
Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
|
| Published by: |
| _version_ | 1836399083028217856 |
|---|---|
| autor | H S Moon, T M Kim, M K Kim and Y M Lim |
| beschreibung | The paper explored the usefulness of Artificial neural network (ANN) in predicting the frame displacements under seismic load. The acceleration that is relatively easy to measure is used as the input value and the displacements that can be used to intuitively judge the condition of structures is used as the output value. The methodology utilized the universal function approximation ability of ANN for defining the relations between two data. For training of ANN, learning data consisting of acceleration and displacements are calculated from a verified finite element model under various seismic loads. The performance of the trained ANN was evaluated by comparing the displacements from ANN and FEM for seismic loads not used for training. The study showed that the ANN trained by various seismic loads can predicts the displacements from the acceleration for the new seismic loads. The trained ANN can be used for predicting the displacements of various buildings exposed to seismic loads... |
| citation_standardnr | 6355172 |
| datenlieferant | ipn_articles |
| feed_id | 123476 |
| feed_publisher | Institute of Physics (IOP) |
| feed_publisher_url | http://www.iop.org/ |
| insertion_date | 2018-11-09 |
| journaleissn | 1757-899X |
| journalissn | 1757-8981 |
| publikationsjahr_anzeige | 2018 |
| publikationsjahr_facette | 2018 |
| publikationsjahr_intervall | 7984:2015-2019 |
| publikationsjahr_sort | 2018 |
| publisher | Institute of Physics (IOP) |
| quelle | IOP Conference Series: Materials Science and Engineering |
| relation | http://iopscience.iop.org/1757-899X/431/12/122011 |
| search_space | articles |
| shingle_author_1 | H S Moon, T M Kim, M K Kim and Y M Lim |
| shingle_author_2 | H S Moon, T M Kim, M K Kim and Y M Lim |
| shingle_author_3 | H S Moon, T M Kim, M K Kim and Y M Lim |
| shingle_author_4 | H S Moon, T M Kim, M K Kim and Y M Lim |
| shingle_catch_all_1 | Artificial Neural Network Aided Prediction of Frame Displacements under Seismic Load The paper explored the usefulness of Artificial neural network (ANN) in predicting the frame displacements under seismic load. The acceleration that is relatively easy to measure is used as the input value and the displacements that can be used to intuitively judge the condition of structures is used as the output value. The methodology utilized the universal function approximation ability of ANN for defining the relations between two data. For training of ANN, learning data consisting of acceleration and displacements are calculated from a verified finite element model under various seismic loads. The performance of the trained ANN was evaluated by comparing the displacements from ANN and FEM for seismic loads not used for training. The study showed that the ANN trained by various seismic loads can predicts the displacements from the acceleration for the new seismic loads. The trained ANN can be used for predicting the displacements of various buildings exposed to seismic loads... H S Moon, T M Kim, M K Kim and Y M Lim Institute of Physics (IOP) 1757-8981 17578981 1757-899X 1757899X |
| shingle_catch_all_2 | Artificial Neural Network Aided Prediction of Frame Displacements under Seismic Load The paper explored the usefulness of Artificial neural network (ANN) in predicting the frame displacements under seismic load. The acceleration that is relatively easy to measure is used as the input value and the displacements that can be used to intuitively judge the condition of structures is used as the output value. The methodology utilized the universal function approximation ability of ANN for defining the relations between two data. For training of ANN, learning data consisting of acceleration and displacements are calculated from a verified finite element model under various seismic loads. The performance of the trained ANN was evaluated by comparing the displacements from ANN and FEM for seismic loads not used for training. The study showed that the ANN trained by various seismic loads can predicts the displacements from the acceleration for the new seismic loads. The trained ANN can be used for predicting the displacements of various buildings exposed to seismic loads... H S Moon, T M Kim, M K Kim and Y M Lim Institute of Physics (IOP) 1757-8981 17578981 1757-899X 1757899X |
| shingle_catch_all_3 | Artificial Neural Network Aided Prediction of Frame Displacements under Seismic Load The paper explored the usefulness of Artificial neural network (ANN) in predicting the frame displacements under seismic load. The acceleration that is relatively easy to measure is used as the input value and the displacements that can be used to intuitively judge the condition of structures is used as the output value. The methodology utilized the universal function approximation ability of ANN for defining the relations between two data. For training of ANN, learning data consisting of acceleration and displacements are calculated from a verified finite element model under various seismic loads. The performance of the trained ANN was evaluated by comparing the displacements from ANN and FEM for seismic loads not used for training. The study showed that the ANN trained by various seismic loads can predicts the displacements from the acceleration for the new seismic loads. The trained ANN can be used for predicting the displacements of various buildings exposed to seismic loads... H S Moon, T M Kim, M K Kim and Y M Lim Institute of Physics (IOP) 1757-8981 17578981 1757-899X 1757899X |
| shingle_catch_all_4 | Artificial Neural Network Aided Prediction of Frame Displacements under Seismic Load The paper explored the usefulness of Artificial neural network (ANN) in predicting the frame displacements under seismic load. The acceleration that is relatively easy to measure is used as the input value and the displacements that can be used to intuitively judge the condition of structures is used as the output value. The methodology utilized the universal function approximation ability of ANN for defining the relations between two data. For training of ANN, learning data consisting of acceleration and displacements are calculated from a verified finite element model under various seismic loads. The performance of the trained ANN was evaluated by comparing the displacements from ANN and FEM for seismic loads not used for training. The study showed that the ANN trained by various seismic loads can predicts the displacements from the acceleration for the new seismic loads. The trained ANN can be used for predicting the displacements of various buildings exposed to seismic loads... H S Moon, T M Kim, M K Kim and Y M Lim Institute of Physics (IOP) 1757-8981 17578981 1757-899X 1757899X |
| shingle_title_1 | Artificial Neural Network Aided Prediction of Frame Displacements under Seismic Load |
| shingle_title_2 | Artificial Neural Network Aided Prediction of Frame Displacements under Seismic Load |
| shingle_title_3 | Artificial Neural Network Aided Prediction of Frame Displacements under Seismic Load |
| shingle_title_4 | Artificial Neural Network Aided Prediction of Frame Displacements under Seismic Load |
| timestamp | 2025-06-30T23:37:19.863Z |
| titel | Artificial Neural Network Aided Prediction of Frame Displacements under Seismic Load |
| titel_suche | Artificial Neural Network Aided Prediction of Frame Displacements under Seismic Load |
| topic | ZL |
| uid | ipn_articles_6355172 |