Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Damage Prediction for RC Slabs under Near-Field Blasts Using Artificial Neural Network
Spalling and cratering are common modes of failure in concrete structures subjected to blast load detonations. There are limited data and models available on the prediction of the spalling/cratering of reinforced concrete members under blast loads. Currently engineers rely on empirical curves to assess the level of damage represented by spalling and/or breach experienced by are inforced concrete (RC) slab under near-field detonations. In this paper a novel Artificial Neural Network (ANN) model capable of predicting the damage (spalling/cratering) size is developed and evaluated. The ANN model is trained and tested using data sets produced using the numerical hydrocode LS-DYNA. The numerical model, which was verified using field experiments, is reliable for detecting the highly dynamic response of the RCslab under severe blast loading. The ANN model is capable of predicting the damage size under different loading parameters. The predicted damage size showed good agreement with available extensive design charts used by the engineering community.
Damage Prediction for RC Slabs under Near-Field Blasts Using Artificial Neural Network
Spalling and cratering are common modes of failure in concrete structures subjected to blast load detonations. There are limited data and models available on the prediction of the spalling/cratering of reinforced concrete members under blast loads. Currently engineers rely on empirical curves to assess the level of damage represented by spalling and/or breach experienced by are inforced concrete (RC) slab under near-field detonations. In this paper a novel Artificial Neural Network (ANN) model capable of predicting the damage (spalling/cratering) size is developed and evaluated. The ANN model is trained and tested using data sets produced using the numerical hydrocode LS-DYNA. The numerical model, which was verified using field experiments, is reliable for detecting the highly dynamic response of the RCslab under severe blast loading. The ANN model is capable of predicting the damage size under different loading parameters. The predicted damage size showed good agreement with available extensive design charts used by the engineering community.
Damage Prediction for RC Slabs under Near-Field Blasts Using Artificial Neural Network
Ibrahim, Ahmed (Autor:in) / Salim, Hani (Autor:in) / Flood, Ian (Autor:in)
International Journal of Protective Structures ; 2 ; 315-332
01.09.2011
18 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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