Application of the ANFIS model in deflection prediction of concrete deep beam

Mohammadhassani, Mohammad; Nezamabadi‐pour, Hossein; Mo, Kim Hung; Jameel, Mohammed; Hakim, S J S; Zargar, Majid

doi:10.12989/sem.2013.45.3.323

Cited by 29 publications

(10 citation statements)

References 20 publications

(21 reference statements)

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“…ANNs are usually employed when the relationship between the input and output is complicated or if the application of another available method takes a large amount of computational time and if the effort is very expensive. It requires suitable input parameters, good data selection for training and suitable computational algorithms, so that it is able to learn complicated relationships between inputs and outputs with a high precision (Mohammadhassani et al 2013a, 2013b, Hakim et al 2011, Nam et al 2009.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Structural damage detection of steel bridge girder using artificial neural networks and finite element models

Hakim¹,

Razak²

2013

Steel & Composite structures

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Damage in structures often leads to failure. Thus it is very important to monitor structures for the occurrence of damage. When damage happens in a structure the consequence is a change in its modal parameters such as natural frequencies and mode shapes. Artificial Neural Networks (ANNs) are inspired by human biological neurons and have been applied for damage identification with varied success. Natural frequencies of a structure have a strong effect on damage and are applied as effective input parameters used to train the ANN in this study. The applicability of ANNs as a powerful tool for predicting the severity of damage in a model steel girder bridge is examined in this study. The data required for the ANNs which are in the form of natural frequencies were obtained from numerical modal analysis. By incorporating the training data, ANNs are capable of producing outputs in terms of damage severity using the first five natural frequencies. It has been demonstrated that an ANN trained only with natural frequency data can determine the severity of damage with a 6.8% error. The results shows that ANNs trained with numerically obtained samples have a strong potential for structural damage identification.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Structural damage detection of steel bridge girder using artificial neural networks and finite element models

Hakim¹,

Razak²

2013

Steel & Composite structures

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“…Prediction using ANFIS method [4]. Multiphysics COMSOL method on validation of concrete deflection analysis [5], strengthening of concrete beams using basalt FRP bars [6], and research using the continuous beam method is preferred because of the ease of economic side, contraction joints and redistribution moment [7], as well as research that performs performance evaluation of reinforced concrete structures with fuzzy logic approach [8].…”

Section: Introductionmentioning

confidence: 99%

Deflection prediction of cantilever concrete beam using adaptive fuzzy inference system

Jepriani¹,

Ibayasid²,

Wibawa³

et al. 2018

IJET

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The cantilever concrete beam is a beam made of concrete that is only supported or clamped at one end and the other end free or without the pedestal. The measure to which a structural member gets deviated from the initial position is called deflection. All the internal forces of cantilever beam serve to hold all the external forces due to the load so that the structure remains balanced. While the beam gets deflected under the loads, bending occurs in the same plane due to which stresses are developed. From the moment balance formula after integrated and solved with required boundary conditions, we get the downward deflection of beam. Fuzzy logic provides an inference structure that enables appropriate human reasoning capabilities. FIS (Fuzzy Inference System) is a system that processes the mapping formulation of a given input to produce an output using Fuzzy Logic. By using randomized data for all the variables of deflection formula within their respective ranges as training data, the FIS will be able to predict deflection of cantilever concrete beam after going through the training process adaptively.

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“…have been extensively used in the field of structural engineering for prediction of the parameters without any rigorous analysis and experiments [11][12][13][14][15]. Many researchers have proposed the closed form expressions using the weight matrices and activated function of the trained neural network.…”

Section: Introductionmentioning

confidence: 99%

Rapid prediction of moments in highrise composite frames considering cracking and time-effects

Pendharkar

Patel

Chaudhary

et al. 2016

Period. Polytech. Civil Eng.

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Application of the ANFIS model in deflection prediction of concrete deep beam

Cited by 29 publications

References 20 publications

Structural damage detection of steel bridge girder using artificial neural networks and finite element models

Structural damage detection of steel bridge girder using artificial neural networks and finite element models

Deflection prediction of cantilever concrete beam using adaptive fuzzy inference system

Rapid prediction of moments in highrise composite frames considering cracking and time-effects

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