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Evaluation of peak and residual conditions of actively confined concrete using neuro-fuzzy and neural computing techniques

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Abstract

This paper investigates the ability of four artificial intelligence techniques, including artificial neural network (ANN), radial basis neural network (RBNN), adaptive neuro-fuzzy inference system (ANFIS) with grid partitioning, and ANFIS with fuzzy c-means clustering, to predict the peak and residual conditions of actively confined concrete. A large experimental test database that consists of 377 axial compression test results of actively confined concrete specimens was assembled from the published literature, and it was used to train, test, and validate the four models proposed in this paper using the mentioned artificial intelligence techniques. The results show that all of the neural network and ANFIS models fit well with the experimental results, and they outperform the conventional models. Among the artificial intelligence models investigated, RBNN model is found to be the most accurate to predict the peak and residual conditions of actively confined concrete. The predictions of each proposed model are subsequently used to study the interdependence of critical parameters and their influence on the behavior of actively confined concrete.

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Authors and Affiliations

  1. Department of Civil Engineering, Birjand University of Technology, P.O. Box 97175-569, Birjand, Iran

    Iman Mansouri

  2. School of Civil, Environmental and Mining Engineering, University of Adelaide, Adelaide, SA, 5005, Australia

    Aliakbar Gholampour & Togay Ozbakkaloglu

  3. Department of Civil Engineering, Canik Basari University, Samsun, Turkey

    Ozgur Kisi

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  1. Iman Mansouri
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  2. Aliakbar Gholampour
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  3. Ozgur Kisi
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  4. Togay Ozbakkaloglu
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Correspondence to Togay Ozbakkaloglu.

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Mansouri, I., Gholampour, A., Kisi, O. et al. Evaluation of peak and residual conditions of actively confined concrete using neuro-fuzzy and neural computing techniques. Neural Comput & Applic 29, 873–888 (2018). https://doi.org/10.1007/s00521-016-2492-4

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  • DOI: https://doi.org/10.1007/s00521-016-2492-4

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