Adaptive Neuro Fuzzy Interface System (Anfis)

Shear failures exhibit a brittle nature, often resulting in catastrophic collapse without sufficient advance warning or the capacity to redistribute internal stresses. Consequently, shear failures pose a greater risk and require more attention from structural engineers. It is crucial to incorporate preventive measures in structural design to avoid abrupt shear failures. The work presented in this article attempts to predict the shear strength of reinforced concrete beams as a complex structural engineering problem without the need for extra computational resources by employing the capabilities of Artificial Intelligence (AI) techniques. In recent decades, significant amounts of research have been done on the AI methods such as artificial neural networks (ANNs), fuzzy logic and genetic algorithms to predict the shear strength of RC beams. In this research, adaptive neuro-fuzzy inference system (ANFIS) and ANNs are developed to predict the shear capacity of RC beams. The required data in the form of major factors affecting the shear capacity of RC beams lacking stirrups are compressive strength of concrete, beam depth, effective width, shear span-to-depth ratio, proportion of longitudinal steel and the yield strength of the reinforced longitudinal steel have been considered in this study. Also, in the context of this investigation, a comparison was conducted between the techniques of ANNs and ANFIS. The outcomes demonstrated that both methods exhibited favourable predictive capabilities. Nevertheless, the ANFIS architecture proposed, which incorporates a hybrid learning algorithm, outperformed the multilayer feedforward ANN that utilizes the backpropagation algorithm. The findings indicated that ANFIS is a suitable technique for predicting intricate relationships between input and output parameters, thus making it a valuable tool in predicting the shear strength of RC beams.

The dynamic behavior of structures can be investigated using concepts of complete (exact) and incomplete (distorted) similitudes.The incompleteness is much more of interest since the complete similitudes are difficult to be achieved and the experiments are often executed using distorted models as test articles. In this work, beams in similitude have been investigated using machine learning to establish degrees of correlation between similar systems, without invoking governing equations and/or solution schemes. Machine learning is based on algorithms that derive models from sample inputs providing datadriven predictions. The absence of an explicit algorithm, being the process totally data-driven, confers to the approach a high versatility which allows its application even in the vibroacoustic research fields and problems. In view to validate the machine learning predictions, numerical investigation of beams in similitude has been performed. The good predictions obtained with machine learning highlight the potentialities of these algorithms and open the way to analyses with more complex structures.

Shear failures exhibit a brittle nature, often resulting in catastrophic collapse without sufficient advance warning or the capacity to redistribute internal stresses. Consequently, shear failures pose a greater risk and require more attention from structural engineers. It is crucial to incorporate preventive measures in structural design to avoid abrupt shear failures. The work presented in this article attempts to predict the shear strength of reinforced concrete beams as a complex structural engineering problem without the need for extra computational resources by employing the capabilities of Artificial Intelligence (AI) techniques. In recent decades, significant amounts of research have been done on the AI methods such as artificial neural networks (ANNs), fuzzy logic and genetic algorithms to predict the shear strength of RC beams. In this research, adaptive neuro-fuzzy inference system (ANFIS) and ANNs are developed to predict the shear capacity of RC beams. The required data in the form of major factors affecting the shear capacity of RC beams lacking stirrups are compressive strength of concrete, beam depth, effective width, shear span-to-depth ratio, proportion of longitudinal steel and the yield strength of the reinforced longitudinal steel have been considered in this study. Also, in the context of this investigation, a comparison was conducted between the techniques of ANNs and ANFIS. The outcomes demonstrated that both methods exhibited favourable predictive capabilities. Nevertheless, the ANFIS architecture proposed, which incorporates a hybrid learning algorithm, outperformed the multilayer feedforward ANN that utilizes the backpropagation algorithm. The findings indicated that ANFIS is a suitable technique for predicting intricate relationships between input and output parameters, thus making it a valuable tool in predicting the shear strength of RC beams.

In this paper, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANNs) techniques are developed and applied to identify damage in a model steel girder bridge using dynamic parameters. The required data in the form of natural frequencies are obtained from experimental modal analysis. A comparative study is made using the ANNs and ANFIS techniques and results showed that both ANFIS and ANN present good predictions. However the proposed ANFIS architecture using hybrid learning algorithm was found to perform better than the multilayer feedforward ANN which learns using the backpropagation algorithm. This paper also highlights the concept of ANNs and ANFIS followed by the detail presentation of the experimental modal analysis for natural frequencies extraction.

Adaptive neural fuzzy inference system (ANFIS) is an intelligent neuro-fuzzy technique used for modeling and control of uncertain systems. In this paper, we proposed an ANFIS based modeling approach (called MLANFIS) where the number of data pairs employed for training was adjusted by application of clustering method. By employing this method, the number of data required for learning step and thereby its complexity were significantly reduced. The results obtained were compared with those obtained by using artificial neural networks (ANNs). Inputs to the first group were feed supply, fuel and machinery and the ones to second cluster were pullet, electricity and labor energies. Finally, the outputs of aforementioned networks were considered as inputs to ANFIS 3 network and predicted values of egg yield were derived. The coefficient of determination (R 2), root mean square error (RMSE) and mean absolute percentage error (MAPE) parameters of ANFIS 3 network were calculated as 0.92, 448.126, 0.014, respectively showing that ANFIS compared with ANNs with statistical parameters as 0.81, 751.96 and 0.019 respectively, can properly predict the egg yield of poultry farms. As a recommendation for future studies, ANFIS models with multi-layered structures can be developed to find the optimum number of layers.

Accurate and fast islanding detection of distributed generation is highly important for its successful operation
in distribution networks. Up to now, various islanding detection technique based on communication,
passive, active and hybrid methods have been proposed. However, each technique suffers from
certain demerits that cause inaccuracies in islanding detection. Computational intelligence based techniques,
due to their robustness and flexibility in dealing with complex nonlinear systems, is an option
that might solve this problem. This paper aims to provide a comprehensive review of computational
intelligence based techniques applied for islanding detection of distributed generation. Moreover, the
paper compares the accuracies of computational intelligence based techniques over existing techniques
to provide a handful of information for industries and utility researchers to determine the best method
for their respective system.

The power system blackout history of last two decades is presented.Conventional load shedding techniques, their types and limitations are presented.Applications of intelligent techniques in load shedding are presented.Intelligent techniques include ANN, fuzzy logic, ANFIS, genetic algorithm and PSO.The discussion and comparison between these techniques are provided.Recent blackouts around the world question the reliability of conventional and adaptive load shedding techniques in avoiding such power outages. To address this issue, reliable techniques are required to provide fast and accurate load shedding to prevent collapse in the power system. Computational intelligence techniques, due to their robustness and flexibility in dealing with complex non-linear systems, could be an option in addressing this problem. Computational intelligence includes techniques like artificial neural networks, genetic algorithms, fuzzy logic control, adaptive neuro-fuzzy inference system, and particle swarm optimization. Research in these techniques is being undertaken in order to discover means for more efficient and reliable load shedding. This paper provides an overview of these techniques as applied to load shedding in a power system. This paper also compares the advantages of computational intelligence techniques over conventional load shedding techniques. Finally, this paper discusses the limitation of computational intelligence techniques, which restricts their usage in load shedding in real time.