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Estimation of compressive strength of stirrup‐confined circular columns using artificial neural networks
In concrete structures design, the compressive strength of circular concrete columns confined by spiral stirrups is an important mechanical property in evaluating the performance of concrete structures. However, evaluating the compressive strength of confined concrete columns is rich in challenge due to the complex mechanics between the concrete and the transverse reinforcements. The objective of this paper is to establish an artificial neural network (ANN) model to evaluate the compressive strength of concrete columns confined by transverse reinforcements. The model proposed in this study is suitable for both normal‐strength and high‐strength concrete columns, covering concrete strengths were in the range of 19.1–151 MPa. Three main influential parameters, including the tensile yield strength and the volumetric ratio of the transverse reinforcements, as well as the concrete strength, were applied as input variables to the model. The ANN model was trained and tested by a reliable database consisting of 240 data sets obtained from authors and published literature. The proposed ANN model used to predict the compressive strength of circular concrete columns confined by spiral stirrup had high applicability and reliability compared with existing analytical models.
Estimation of compressive strength of stirrup‐confined circular columns using artificial neural networks
In concrete structures design, the compressive strength of circular concrete columns confined by spiral stirrups is an important mechanical property in evaluating the performance of concrete structures. However, evaluating the compressive strength of confined concrete columns is rich in challenge due to the complex mechanics between the concrete and the transverse reinforcements. The objective of this paper is to establish an artificial neural network (ANN) model to evaluate the compressive strength of concrete columns confined by transverse reinforcements. The model proposed in this study is suitable for both normal‐strength and high‐strength concrete columns, covering concrete strengths were in the range of 19.1–151 MPa. Three main influential parameters, including the tensile yield strength and the volumetric ratio of the transverse reinforcements, as well as the concrete strength, were applied as input variables to the model. The ANN model was trained and tested by a reliable database consisting of 240 data sets obtained from authors and published literature. The proposed ANN model used to predict the compressive strength of circular concrete columns confined by spiral stirrup had high applicability and reliability compared with existing analytical models.
Estimation of compressive strength of stirrup‐confined circular columns using artificial neural networks
Chang, Wei (Autor:in) / Zheng, Wenzhong (Autor:in)
Structural Concrete ; 20 ; 1328-1339
01.08.2019
12 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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