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- 주제어 : ABC-ANN (검색결과 2 건)
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An ABC-BP-ANN Algorithm for Semi-active Control for Magnetorheological Damper
Qiang Xu,Jianyun Chen,Xiaopeng Liu,Jing Li,Chenyang Yuan 대한토목학회 2017 KSCE Journal of Civil Engineering Vol.21 No.6
The Magnetorheological (MR) damper is one of the most popular semi-active devices, which uses MR fluids to produce controllable dampers. In this work, the Back-propagation (BP) Artificial Neural Network (ANN) optimized by the Artificial Bee Colony (ABC) algorithm (ABC-BP-ANN) is proposed to obtain the required voltage for semi-active control of MR damper simulated by Spencer model. It is found that the control-forces of MR damper are close to the results of active control algorithms such as the conventional Linear Quadratic Regulator (LQR) control algorithm. The initial weights and the thresholds of BP-ANN are regarded as solutions; the training errors of BP-ANN are used for the cost function and ABC algorithm is used to optimize the initial weights and the thresholds of BP-ANN. The proposed model uses the Spencer model to calculate the train samples to train proposed ABC-BP-ANN model. The proposed ABC-BP-ANN model predicts the voltage based on the results of control-force calculated by LQR model. Several numerical examples are used to verify the proposed model. The results show that the control-forces of MR damper calculated by proposed model are close to those calculated by LQR algorithm. The proposed ABC-BP-ANN inversion algorithm for obtaining the voltage for MR damper has greater computational efficiency and higher accuracy than the conventional BP-ANN algorithm.
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Evaluating the bond strength of FRP in concrete samples using machine learning methods
Juncheng Gao,Mohammadreza Koopialipoor,Danial Jahed Armaghani,Aria Ghabussi,Shahrizan Baharom,Armin Morasaei,Ali Shariati,Majid Khorami,Jian Zhou 국제구조공학회 2020 Smart Structures and Systems, An International Jou Vol.26 No.4
In recent years, the use of Fiber Reinforced Polymers (FRPs) as one of the most common ways to increase the strength of concrete samples, has been introduced. Evaluation of the final strength of these specimens is performed with different experimental methods. In this research, due to the variety of models, the low accuracy and impact of different parameters, the use of new intelligence methods is considered. Therefore, using artificial intelligent-based models, a new solution for evaluating the bond strength of FRP is presented in this paper. 150 experimental samples were collected from previous studies, and then two new hybrid models of Imperialist Competitive Algorithm (ICA)-Artificial Neural Network (ANN) and Artificial Bee Colony (ABC)-ANN were developed. These models were evaluated using different performance indices and then, a comparison was made between the developed models. The results showed that the ICA-ANN model's ability to predict the bond strength of FRP is higher than the ABC-ANN model. Finally, to demonstrate the capabilities of this new model, a comparison was made between the five experimental models and the results were presented for all data. This comparison showed that the new model could offer better performance. It is concluded that the proposed hybrid models can be utilized in the field of this study as a suitable substitute for empirical models.
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