A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength
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ORIGINAL ARTICLE
A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materialscompressive strength Danial Jahed Armaghani1 • Panagiotis G. Asteris2 Received: 12 December 2019 / Accepted: 24 July 2020 Springer-Verlag London Ltd., part of Springer Nature 2020
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. Keywords Artificial neural networks Cement Compressive strength Metakaolin Mortar Artificial intelligence techniques Adaptive neuro-fuzzy inference system 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 MDA MK/B ML PSO purelin SVM SP Tansig W/B
Log-sigmoid transfer function Maximum diameter of aggregate Metakaolin percentage in relation to total binder Machine learning Particle swarm optimization Linear transfer function Support vector machine Superplasticizer in relation to the total binder Hyperbolic tangent sigmoid transfer function Water-to-binder ratio (W/B)
1 Introduction & Panagiotis G. Asteris [email protected]; [email protected] Danial Jahed Armaghani [email protected] 1
Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
2
Computational Mechanics Laboratory, School of Pedagogical and Technological Educ
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