Prediction of ultrasonic pulse velocity for enhanced peat bricks using adaptive neuro-fuzzy methodology

Motamedi S 1 , Roy C 2 , Shamshirband S 3 , Hashim R 4 , Petković D 5 , Song KI 6

Affiliations 

  • 1 Department of Civil Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia; Institute of Ocean and Earth Sciences, University of Malaya, 50603 Kuala Lumpur, Malaysia
  • 2 Department of Civil Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
  • 3 Department of Computer System and Information Technology, Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia. Electronic address: [email protected]
  • 4 Department of Civil Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia; Institute of Ocean and Earth Sciences, University of Malaya, 50603 Kuala Lumpur, Malaysia. Electronic address: [email protected]
  • 5 University of Niš, Faculty of Mechanical Engineering, Department for Mechatronics and Control, Aleksandra Medvedeva 14, 18000 Niš, Serbia
  • 6 Department of Civil Engineering, Inha University, 100 Inha-ro, Nam-gu, Incheon 402-751, South Korea
Ultrasonics, 2015 Aug;61:103-13.
PMID: 25957464 DOI: 10.1016/j.ultras.2015.04.002

Abstract

Ultrasonic pulse velocity is affected by defects in material structure. This study applied soft computing techniques to predict the ultrasonic pulse velocity for various peats and cement content mixtures for several curing periods. First, this investigation constructed a process to simulate the ultrasonic pulse velocity with adaptive neuro-fuzzy inference system. Then, an ANFIS network with neurons was developed. The input and output layers consisted of four and one neurons, respectively. The four inputs were cement, peat, sand content (%) and curing period (days). The simulation results showed efficient performance of the proposed system. The ANFIS and experimental results were compared through the coefficient of determination and root-mean-square error. In conclusion, use of ANFIS network enhances prediction and generation of strength. The simulation results confirmed the effectiveness of the suggested strategies.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

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