Shear Strength Prediction of RC Beams Using Adaptive Neuro-Fuzzy Inference System

Abstract

In complex engineering problems, there are some inexact conceptions or a lot of parameters that must be considered. Soft computing is an approach that is successfully applied to solve such problems. Determination of fuzzy rules for many problems has not been quite possible by an expert human. In this case, a neuro-fuzzy system that is a combination of neural network (for its ability to learn by datasets) and fuzzy system (for solving the drawback of the neural network) can enhance the performance of the system with several parameters or complex conditions. This paper shows the capability of a neurofuzzy system, namely Adaptive Neuro-Fuzzy Inference System (ANFIS), to predict the shear strength of Reinforced Concrete (RC) beams with steel stirrups. For this purpose, the collection of laboratory results published by di erent works of literature was used to train and test the proposed system. For this purpose, the Sub-Clustering (SC) approach was applied to generate ANFIS. The results indicated that the considered neuro-fuzzy system was able to predict the shear strength of the RC beams, which have been reinforced with steel stirrups.

Phương pháp dự đoán tải trọng ổn định đàn hồi của dầm thép H bản bụng khoét lỗ tròn dựa trên máy vec-tơ hỗ trợ

“…Ngoài ra, phương pháp này cũng được sử dụng trong nhiều nghiên cứu khác, ví dụ, trong [19], [20], hoặc Abambres và cộng sự [8], tác giả cũng đã sử dụng phương pháp ANN để nghiên cứu khả năng chịu tải của các dầm khoét lỗ chịu tác dụng của tải trọng phân bố đều với tám tham số đầu vào. Bên cạnh đó, hệ thống thần kinh mờ là ANFIS cũng là một thuật toán hiệu quả để dự đoán cường độ cắt của dầm [21]. Tuy nhiên, các nghiên cứu sử dụng thuật toán tiên tiến như máy vectơ hỗ trợ (Support Vector Machine -SVM) chưa được áp dụng trong phân tích ứng xử của kết cấu dầm khoét lỗ.…”

Moment capacity estimation of spirally reinforced concrete columns using ANFIS

“…ANFIS used a hybrid approach, which is a combination of backpropagation and least squares methods, to find its unknown parameters. This type of soft computing method is widely considered as a powerful system because it has the ability of both artificial neural networks and fuzzy systems simultaneously [7,[23][24][25][26][27][28][29][30][31].…”

Prediction about Effect of Span‐to‐Depth Ratio on Shear Capacity for FRP Bar Reinforced Concrete Beams without Web Reinforcement

“…Both ACI318R-19 and Eurocode 2 deem that the deep beams are designed with strut and tie models, which is different from the design model for slender beams. In recent years, Uday Naik has proved that the change of L/d has an effect on the shear capacity of steel bar reinforced concrete beams by the artificial neural network technology [17], which has been widely used in civil engineering because of its excellent performance in the development of accurate and reliable prediction models for the shear capacity of reinforced concrete beams without web reinforcement [18][19][20][21][22][23]. e previous experimental study indicated that although FRP bars and steel bars have the great differences in elastic modulus and some mechanical properties, FRP bar reinforced concrete beams and steel bar reinforced concrete beams are still similar in shear-carrying mechanism [1,9,24].…”