Potential of adaptive neuro fuzzy inference system for evaluating the factors affecting steel-concrete composite beam's shear strength

Safa, Maryam; Shariati, Mahdi; Ibrahim, Zainah; Toghroli, Ali; Baharom, Shahrizan; Nor, Norazman M.; Petković, Dalibor

doi:10.12989/scs.2016.21.3.679

Cited by 181 publications

(28 citation statements)

References 23 publications

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“…Machine learning is a bionics-based problem solution scheme which mimics the functionality of human brain [1][2][3][4][5][6][7][8][9][10][11][12][13]. It is usually constituted by a series of mathematic operations and transformations which are implemented by computer programs coupled with modern electronic computers.…”

Section: Introductionmentioning

confidence: 99%

Hybrid machine learning hydrological model for flood forecast purpose

Kan¹,

Liang²,

Yu³

et al. 2020

Open Geosciences

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Machine learning-based data-driven models have achieved great success since their invention. Nowadays, the artificial neural network (ANN)-based machine learning methods have made great progress than ever before, such as the deep learning and reinforcement learning, etc. In this study, we coupled the ANN with the K-nearest neighbor method to propose a novel hybrid machine learning (HML) hydrological model for flood forecast purpose. The advantage of the proposed model over traditional neural network models is that it can predict discharge continuously without accuracy loss owed to its specially designed model structure. In order to overcome the local minimum issue of the traditional neural network training, a genetic algorithm and Levenberg–Marquardt-based multi-objective training method was also proposed. Real-world applications of the HML hydrological model indicated its satisfactory performance and reliable stability, which enlightened the possibility of further applications of the HML hydrological model in flood forecast problems.

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Section: Introductionmentioning

confidence: 99%

Hybrid machine learning hydrological model for flood forecast purpose

Kan¹,

Liang²,

Yu³

et al. 2020

Open Geosciences

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“…Moreover, although a beam is designed as a full composite section, a perfect composite action without any slip could not be obtained due to the deformation of shear studs. The effect of slip on the deflection of a SCCBB with partial shear connections may be significant, and the deflection calculated by ignoring this interface slip would underestimate the deflection compared to the experimental measurements (Ding et al, 2016;Liu et al, 2016;Nie et al, 2004Nie et al, , 2007Safa et al, 2016). At the same time, the negligence of the shear deformation of the concrete slab and steel box beam such as Euler-Bernoulli beams (i.e.…”

Section: Introductionmentioning

confidence: 98%

Natural vibration analysis of steel–concrete composite box beam using improved finite beam element method

Jiang

Lai

Zhou

et al. 2017

Advances in Structural Engineering

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Based on Hamilton's principle and a cubic Hermite polynomial shape function, first the improved steel-concrete composite box beam element stiffness and mass matrixes with a few numbers of degree were derived by considering many influencing factors such as interface slip, shear lag, shear deformation, and rotational inertia, and then the finite beam element method program was established. The natural vibration frequencies of many steel-concrete composite box beam calculation samples with different spans, degrees of shear connection, and boundary conditions were calculated. The analysis results show that the finite beam element method calculation results are consistent with ANSYS' calculation, thus demonstrating the rationality and validity of the improved steel-concrete composite box beam element stiffness and mass matrixes proposed in this study. Moreover, some meaningful conclusions to engineering design were drawn as follows: The effect of shear lag increases with the order of steel-concrete composite box beam's natural vibration frequency and degree of shear connection. Shear deformation also increases with the order of steel-concrete composite box beam's natural vibration frequency; thus, the shear deformation corresponding to the steel-concrete composite box beam's highorder natural vibration frequency cannot be ignored. The interface slip of steel-concrete composite box beam's high-order natural vibration frequency is negligible; however, the interface slip of steel-concrete composite box beam's low-order natural vibration frequency cannot be ignored.

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“…Because of that, scientists in this field have developed Neural Network (NN) and ANFIS algorithms for various applications [5]. In literature, these algorithms are used for various disciplines such as structures [6,7], energy [8], fluids etc. [9].…”

Section: Introductionmentioning

confidence: 99%

Comparison of self-tuned Neuro-Fuzzy controllers on 2 DOF helicopter: an application

Öztürk

Özkol

2021

SN Appl. Sci.

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In this paper, a new approach for Neuro-Fuzzy Controller (NFC) has been presented and compared to previously defined NFCs given in open literature. The proposed controller is based on an on-line Adaptive Neuro-Fuzzy Inference System (ANFIS) and meticulous analysis through simulations is performed to show its robustness. The performance of Neuro-Fuzzy Controllers (NFC) depends on controller inputs. To show the difference and superiority of the proposed controller, many studies in the open literature are examined and compared. Therefore, the advantages and disadvantages of the Neuro-Fuzzy controller are outlined and an optimum Neuro-Fuzzy controller is structured and presented. To test our developed controller for a nonlinear problem, having coupling effects, a 2 DOF helicopter model is chosen. Also to show the robustness, the controller performance which is applied to a 2 DOF helicopter is investigated and compared with other Neuro-Fuzzy controller structures. To better show NFC performance, NFC control results were compared with LQR+I. It is observed that besides being on-line adaptive for all systems, the controller developed has many priorities such as noiseless, strong stability, and better response time.

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Potential of adaptive neuro fuzzy inference system for evaluating the factors affecting steel-concrete composite beam's shear strength

Cited by 181 publications

References 23 publications

Hybrid machine learning hydrological model for flood forecast purpose

Hybrid machine learning hydrological model for flood forecast purpose

Natural vibration analysis of steel–concrete composite box beam using improved finite beam element method

Comparison of self-tuned Neuro-Fuzzy controllers on 2 DOF helicopter: an application

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