An artificial neural network for predicting the ultimate bending moments in reinforced concrete beams with fiber-reinforced polymer strengthening

被引：0

作者：

Le Hoang T.T. ^{[1
]}

Masuya H. ^{[2
]}

Kurihashi Y. ^{[2
]}

Minh T.T. ^{[1
]}

机构：

[1] Department of Quality Assurance, Kien Giang College, Rạch Giá

[2] Faculty of Geosciences and Civil Engineering, Institute of Science and Engineering, Kanazawa University, Kanazawa

来源：

Asian Journal of Civil Engineering | 2023年 / 24卷 / 7期

关键词：

Artificial neural network; Fiber-reinforced polymer; Graphical user interface; Reinforced concrete beams; Ultimate bending moments;

D O I：

10.1007/s42107-023-00641-1

中图分类号：

学科分类号：

摘要：

A practical artificial neural network tool is proposed for predicting the ultimate bending moments of reinforced concrete beams strengthened by the techniques of externally bonded fiber-reinforced polymer and near-surface mounted fiber-reinforced polymer. Accordingly, the testing database of 131 specimens was gathered for use in developing the artificial neural network model. In this regard, the breadth and height of the beam section, the compression strength of the concrete, the ratio of material reinforcement, and the elastic modulus of fiber reinforced polymer were regarded as input variables, whereas the ultimate bending moment was regarded as an output variable. The performance of the proposed artificial neural network model was compared to the current design model of the American Concrete Institute guide. The comparative analysis demonstrated that the proposed model made more accurate predictions than the current model. Based on the proposed model, a graphical user interface was created to facilitate the prediction of the ultimate bending moments of reinforced concrete beams with fiber-reinforced polymer strengthening. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

引用

页码：2295 / 2305

页数：10

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