ANFIS-based prediction of moment capacity of reinforced concrete slabs exposed to fire

Bilgehan, Mahmut; Kurtoğlu, Ahmet Emin

doi:10.1007/s00521-015-1902-3

Cited by 10 publications

(3 citation statements)

References 35 publications

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“…Mashrei [21] developed an ANFIS model to predict the shear strength of concrete beams reinforced with fiber-reinforced polymer (FRP) bars. Bilgehan and Kurtoglu [22] applied ANFIS to predict the moment capacities of reinforced concrete (RC) slabs exposed to fire. Mansouri et al [23] investigated the ability of radial basis neural networks and ANFIS methods in the prediction of ultimate strength and strain of concrete cylinders confined with FRP sheets.…”

Section: Anfis: Literature Reviewmentioning

confidence: 99%

An Adaptive Neuro-Fuzzy Inference Model to Predict Punching Shear Strength of Flat Concrete Slabs

Mashrei

Mahdi

2019

Applied Sciences

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An adaptive neuro-fuzzy inference system (ANFIS)-based model was developed to predict the punching shear strength of flat concrete slabs without shear reinforcement. The model was developed using a database collected from 207 experiments available in the existing literature. Five key input parameters were used to build the model, which were slab effective depth, concrete strength, reinforcement ratio, yield tensile strength of reinforcement, and width of square loaded area. The output parameter of the model was punching shear strength. The results from the adaptive neural fuzzy inference model were compared to those from the simplified punching shear equations of ACI, BS-8110, Model Code 2010, Euro-Code 2, and also experimental results. The root mean square error (RMSE) and the correlation coefficient (R) were used as evaluation criteria. Parametric studies were presented using ANFIS to assess the effect of each input parameter on the punching shear strength and to compare ANFIS results to those from the equations proposed in commonly used codes. The results showed that the ANFIS model is simple and provided the most accurate predictions of the punching shear strength of two-way flat concrete slabs without shear reinforcement.

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Section: Anfis: Literature Reviewmentioning

confidence: 99%

An Adaptive Neuro-Fuzzy Inference Model to Predict Punching Shear Strength of Flat Concrete Slabs

Mashrei

Mahdi

2019

Applied Sciences

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“…The main advantage of ANN are pattern recognition and adaption to a changing environment, whereas, FIS has the advantage of incorporating human knowledge and expertise to deal with uncertainty and imprecision. As a hybrid modelling technique, adaptive neuro fuzzy inference system (ANFIS) has the advantages of both methods and is widely used in practical cases involving high uncertainty (Asrari, Shahriar, & Ataeepour, 2013;Bilgehan & Kurtoğlu, 2015;Fattahi, 2016;Basarir & Dincer, 2017;Basarir, Wesseloo, Karrech, Paternak, & Dyskin, 2017). A brief introduction to ANFIS modelling is given in Appendix A.…”

Section: Anfis Modellingmentioning

confidence: 99%

An adaptive neuro fuzzy inference system to model the uniaxial compressive strength of cemented hydraulic backfill

Başarır¹,

Han

Fourie³

et al. 2018

Min. miner. depos.

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The purpose of this paper is to develop the models for predicting the uniaxial compressive strength (UCS) of cemented hydraulic backfill (CHB), a widely used technique for filling underground voids created by mining operations as it provides the high strength required for safe and economical working environment and allows the use of waste rock from mining operations as well as tailings from mineral processing plants as ingredients.Methods. In this study, different modelling techniques such as conventional linear, nonlinear multiple regression and one of the evolving soft computing methods, adaptive neuro fuzzy inference system (ANFIS), were used for the prediction of UCS, the main criterion used to design backfill recipe.Findings. Statistical performance indices used to evaluate the efficiency of the developed models indicated that the ANFIS model can effectively be implemented for designing CHB with desired UCS. As proved by the performance indicators ANFIS model gives more compatible results with the expert opinion and current literature than conventional modelling techniques. Originality.In order to construct the models a very large database, containing more than 1600 UCS test results, was used. In addition to widely used conventional regression based modelling techniques, one of the evolving soft computing methods, ANFIS was employed. Numerical examples showing the implementation of constructed models were provided. Practical implementation.As proved by the statistical performance indicators, the developed models can be used for a reliable prediction of the UCS of CHB. However, more accurate results can be achieved by expanding the database and by constructing improved models using the algorithm presented in this paper.

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“…This process leads to setting more compatible parameters of a network [14][15][16][17][18]. Different intelligent models have been used to investigate various characteristics of concrete [19][20][21][22]. In the case of ANNs, Öztaş et al [23] successfully used this tool for predicting the slump and compressive strength of high strength concrete.…”

Section: Introductionmentioning

confidence: 99%

Application of Three Metaheuristic Techniques in Simulation of Concrete Slump

et al. 2019

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Slump is a workability-related characteristic of concrete mixture. This paper investigates the efficiency of a novel optimizer, namely ant lion optimization (ALO), for fine-tuning of a neural network (NN) in the field of concrete slump prediction. Two well-known optimization techniques, biogeography-based optimization (BBO) and grasshopper optimization algorithm (GOA), are also considered as benchmark models to be compared with ALO. Considering seven slump effective factors, namely cement, slag, water, fly ash, superplasticizer (SP), fine aggregate (FA), and coarse aggregate (CA), the mentioned algorithms are synthesized with a neural network to determine the best-fitted neural parameters. The most appropriate complexity of each ensemble is also found by a population-based sensitivity analysis. The findings revealed that the proposed ALO-NN model acquires a good approximation of concrete slump, regarding the calculated root mean square error (RMSE = 3.7788) and mean absolute error (MAE = 3.0286). It also outperformed both BBO-NN (RMSE = 4.1859 and MAE = 3.3465) and GOA-NN (RMSE = 4.9553 and MAE = 3.8576) ensembles.

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ANFIS-based prediction of moment capacity of reinforced concrete slabs exposed to fire

Cited by 10 publications

References 35 publications

An Adaptive Neuro-Fuzzy Inference Model to Predict Punching Shear Strength of Flat Concrete Slabs

An Adaptive Neuro-Fuzzy Inference Model to Predict Punching Shear Strength of Flat Concrete Slabs

An adaptive neuro fuzzy inference system to model the uniaxial compressive strength of cemented hydraulic backfill

Application of Three Metaheuristic Techniques in Simulation of Concrete Slump

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