Predicting Shear Strength of Reinforced Concrete Deep Beams by Artificial Neural NetworksArtificial Neural Networks

• Main Authors: Yun-Hsin chen, 陳芸岫
• Other Authors: H.H.Pam
• Format: Others
• Language: zh-TW
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/78312968339225888120

碩士 === 國立高雄應用科技大學 === 土木工程與防災科技研究所 === 97 === Shear strength is one of the major concrete mechanical properties that are indispensably used in different building and bridge design codes. However, the nonlinear behavior of concrete under shear force is very complicated and modeling its behavior is a hard task. Thus, it would be of interest to develop new methods that are easier, convenient, and more accurate than the existing methods in light of the availability of more experimental data and recent advance in the area of data analysis techniques. In this study, a database on shear failure of reinforced concrete deep beams with rectangular section subjected to shear force was retrieved from the existing literature for analysis instead of the practical and experimental data. Multilayer perceptrons networks (MLPN) were developed sequentially and the ultimate shear strength of each beams was determined from the MLPN model. Besides, the MLPN model’s predictions were also compared with those obtained using empirical equations (i.e. Strut-Tie Model and Soft Strut-Tie Model). It was found that the MLPN models could infer solutions from the data presented to them, capturing quite subtle relationships. In other words, the MLPN models give reasonable predictions of the ultimate shear strength of RC deep beams. The results also show that the MLPN models provide better accuracy than the existing parametric models.