Comparison Study of Soft Computing Approaches for Estimation of the Non-Ductile RC Joint Shear Strength

Journal of Soft Computing in Civil Engineering

Mirrashid, Masoomeh¹ 

关键词: ANFIS;    RC joint;    Shear strength;    Soft Computing;    Neural Networks;    Non-Ductile;   

DOI  :  10.22115/scce.2017.46318

学科分类：工程和技术（综合）

来源: Pouyan Press

【 摘 要 】

Today, retrofitting of the old structures is important. For this purpose, determination of capacities for these buildings, which mostly are non-ductile is a very useful tool. In this context, non-ductile RC joint in concrete structures, as one of the most important elements in these buildings are considered and the shear capacity, especially for retrofitting goals can be very beneficial. In this paper, three famous soft computing methods including artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS) and also group method of data handling (GMDH) were used to estimating the shear capacity for this type of RC joints. A set of experimental data which were a failure in joint are collected and first, the effective parameters were identified. Based on these parameters, predictive models are presented in detail and compare with each other. The results showed that the considered soft computing techniques are very good capabilities to determine the shear capacity.

【 授权许可】

CC BY