Adaptive Neuro-Fuzzy Inference System for Predicting Strength of High Performance Concrete

EasyChair Preprint 5922

Abstract

This study examines the performance of Adaptive Neuro-Fuzzy Inference System (ANFIS) for estimation of the compressive strength of High Performance Concrete (HPC) from given mix proportion. An ANFIS model merges advantages of both ANN and Fuzzy Logic. A total of 54 experimental data sets were used, where 36 datasets were used in training and 18 datasets were used for validating the model. Six input parameters include age of testing, water binder ratio, silica fumes, fine aggregate coarse aggregate and superplasticizer whereas compressive strength is the single output parameter. The experimental and obtained results were compared. The result illustrates that ANFIS model can be used as an alter-nate method for prediction of the compressive strength of High Performance Concrete.

Keyphrases: ANFIS model, High performance concrete, Training & Testing, compressive strength

@booklet{EasyChair:5922,
  author    = {Prasad Raju Meesaraganda and Nilarghya Sarkar and Nilanjan Tarafder},
  title     = {Adaptive Neuro-Fuzzy Inference System for Predicting Strength of High Performance Concrete},
  howpublished = {EasyChair Preprint 5922},
  year      = {EasyChair, 2021}}