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A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength

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Abstract

Despite the extensive use of mortars materials in constructions over the last decades, there is not yet a reliable and robust method, available in the literature, which can estimate its strength based on its mix parameters. This limitation is due to the highly nonlinear relation between the mortar’s compressive strength and the mixed components. In this paper, the application of artificial intelligence techniques toward the prediction of the compressive strength of cement-based mortar materials with or without metakaolin has been investigated. Specifically, surrogate models (such as artificial neural network, ANN and adaptive neuro-fuzzy inference system, ANFIS models) have been developed to the prediction of the compressive strength of mortars trained using experimental data available in the literature. The comparison of the derived results with the experimental findings demonstrates the ability of both ANN and ANFIS models to approximate the compressive strength of mortars in a reliable and robust manner. Although ANFIS was able to obtain higher performance prediction to estimate the compressive strength of mortars compared to ANN model, it was found through the verification process of some other additional data, the ANFIS model has overfitted the data. Therefore, the developed ANN model has been introduced as the best predictive technique for solving problem of the compressive strength of mortars. Furthermore, using the optimum developed model an ambitious attempt to reveal the nature of mortar materials has been made.

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Abbreviations

ANFIS:

Adoptive neuro-fuzzy inference system

ANNs:

Artificial neural networks

AI:

Artificial intelligence

AS:

Age of the specimen

BNNs:

Biological neural networks

BPNNs:

Back-propagation neural networks

B/S:

Binder to sand ratio

CS:

Compressive strength

DNNs:

Deep neural networks

FIS:

Fuzzy inference system

logsig:

Log-sigmoid transfer function

MDA:

Maximum diameter of aggregate

MK/B:

Metakaolin percentage in relation to total binder

ML:

Machine learning

PSO:

Particle swarm optimization

purelin:

Linear transfer function

SVM:

Support vector machine

SP:

Superplasticizer in relation to the total binder

Tansig:

Hyperbolic tangent sigmoid transfer function

W/B:

Water-to-binder ratio (W/B)

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Acknowledgements

The authors would like to thank Dr. Liborio Cavaleri, Prof. of Structural Engineering and Seismic Design at Dipartimento di Ingegneria Civile, Ambientale, Aerospaziale, dei Materiali, University of Palermo, Italy and Dr. Binh Thai Pham, Prof. at University of Transport Technology, Hanoi, Vietnam, for their valuable comments and discussions. The authors would also like to express his acknowledgement to graduate students Maria Douvika, Chrysoula Karamani, Athanasia Skentou and Ioanna Zoumpoulaki for their assistance on the computational implementation of the ANN models.

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  1. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Danial Jahed Armaghani

  2. Computational Mechanics Laboratory, School of Pedagogical and Technological Education, 14121, Heraklion, Athens, Greece

    Panagiotis G. Asteris

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Armaghani, D.J., Asteris, P.G. A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength. Neural Comput & Applic 33, 4501–4532 (2021). https://doi.org/10.1007/s00521-020-05244-4

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