[논문]Neuro-fuzzy based approach for estimation of concrete compressive strength

Xue, Xinhua; Zhou, Hongwei

doi:10.12989/cac.2018.21.6.697

Neuro-fuzzy based approach for estimation of concrete compressive strength

Computers & concrete, v.21 no.6, 2018년, pp.697 - 703

Xue, Xinhua (State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University) , Zhou, Hongwei (State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University)

Abstract ▼ AI-Helper

Compressive strength is one of the most important engineering properties of concrete, and testing of the compressive strength of concrete specimens is often costly and time consuming. In order to provide the time for concrete form removal, re-shoring to slab, project scheduling and quality control, it is necessary to predict the concrete strength based upon the early strength data. However, concrete compressive strength is affected by many factors, such as quality of raw materials, water cement ratio, ratio of fine aggregate to coarse aggregate, age of concrete, compaction of concrete, temperature, relative humidity and curing of concrete. The concrete compressive strength is a quite nonlinear function that changes depend on the materials used in the concrete and the time. This paper presents an adaptive neuro-fuzzy inference system (ANFIS) for the prediction of concrete compressive strength. The training of fuzzy system was performed by a hybrid method of gradient descent method and least squares algorithm, and the subtractive clustering algorithm (SCA) was utilized for optimizing the number of fuzzy rules. Experimental data on concrete compressive strength in the literature were used to validate and evaluate the performance of the proposed ANFIS model. Further, predictions from three models (the back propagation neural network model, the statistics model, and the ANFIS model) were compared with the experimental data. The results show that the proposed ANFIS model is a feasible, efficient, and accurate tool for predicting the concrete compressive strength.

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참고문헌 (21)

Ahmed, M.A. and Muhannad, I. (2018), "Predicting residual compressive strength of self-compacted concrete under various temperatures and relative humidity conditions by artificial neural networks", Comput. Conrete, 21(1), 47-54.
Ali, D. (2015), "Prediction of hybrid fibre-added concrete strength using artificial neural networks", Comput. Conrete, 15(4), 503-514.

상세보기
Bhoopal, R.S., Singh, R. and Sharma, P.K. (2012), "Adaptive neuro-fuzzy inference system for prediction of effective thermal conductivity of polymer-matrix composites", Model. Nume. Simul. Mater. Sci., 2, 43-50.
Bilgehan, M. (2011), "A comparative study for the concrete compressive strength estimation using neural network and neuro-fuzzy modeling approaches", Nondestruct. Test. Eva., 26(1), 35-55.

상세보기
Chiu, S.L. (1994), "Fuzzy model identification based on cluster estimation", J. Intell. Fuzzy Syst., 2(3), 267-278.

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Chopra, P., Sharma, R.K. and Kumar, M. (2016), "Prediction of compressive strength of concrete using artificial neural network and genetic programming", Adv. Mater. Sci. Eng., 1-10.
Djavareshkian, M.H. and Esmaeili, A. (2013), "Neuro-fuzzy based approach for estimation of Hydrofoil performance", Ocean Eng., 29, 1-8.
Faruqi, M.A., Agarwala, R., Sai, J. and Francisco, A. (2015), "Application of artificial intelligence to predict compressive strength of concrete from mix design parameters: a structural engineering application", J. Civil Eng. Res., 5(6), 158-161.
Gholamreza, A., Ehsan, J. and Zahra, K. (2016), "Predicting of compressive strength of recycled aggregate concrete by genetic programming", Comput. Conrete, 18(2), 155-163.

상세보기
Hola, J. and Schabowica, K. (2005), "Application of artificial neural network to determine concrete compressive strength based on non-destructive tests", J. Civil Eng. Manage., 11(1), 23-32.
Jang, J.S.R. (1993), "ANFIS: adaptive-network-based fuzzy inference system", IEEE T. Syst. Man Cyber., 23(3), 665-685.

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Khan, S.U., Ayub, T. and Rafeeqi, S.F.A. (2013), "Prediction of compressive strength of plain concrete confined with ferrocement using artificial neural network and comparison with existing mathematical models", Am. J. Civil Eng. Arch., 1(1), 7-14.
Lee, S.C. (2003), "Prediction of concrete strength using artificial neural networks", Eng. Struct., 25, 849-857.

상세보기
Mohammed, S., Steffen, G., Abdulkadir, C. and Joost, W. (2016), "Modelling fresh properties of self-compacting concrete using neural network technique", Comput. Conrete, 18(4), 903-921.

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Ni, H.G. and Wang, J.Z. (2000), "Prediction of compressive strength of concrete by neural networks", Cement Concrete Res., 30(8), 1245-1250.

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Nikoo, M., Moghadam, F.T. and Sadowski, L. (2015), "Prediction of concrete compressive strength by evolutionary artificial neural networks", Adv. Mater. Sci. Eng., 1-8.
Ramin, T., Hamid, R.S. and Mohsen, S. (2014), "The use of artificial neural networks in predicting ASR of concrete containing nano-silica", Comput. Conrete, 13(6), 739-748.

상세보기
Roshani, G.H., Feghhi, S.A.H. and Setayeshi, S. (2015), "Dual-modality and dual-energy gamma ray densitometry of petroleum products using an artificial neural network", Radiat. Meas., 82, 154-162.

상세보기
Takagi, T. and Sugeno, M. (1985), "Fuzzy identification of systems and its applications to modeling and control", IEEE T. Syst. Man Cyber., 15(1), 116-131.
Yeh, I.C. (1998), "Modeling of strength of high performance concrete using artificial neural networks", Cement Concrete Res., 28(12), 1797-1808.

상세보기
Zadeh, E.E., Feghhi, S.A.H., Roshani, G.H. and Rezaei, A. (2016), "Application of artificial neural network in precise prediction of cement elements percentages based on the neutron activation analysis", Eur. Phys. J. Plus., 131, 167.

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Neuro-fuzzy based approach for estimation of concrete compressive strength

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