An artificial intelligence approach for concrete hardened property estimation

Authors

  • Tu Trung Nguyen Department of Civil, Construction, and Environmental Engineering, University of Alabama, Tuscaloosa, AL 35487, USA
  • Kien Dinh NDT Concrete LLC, 1082 Algoma St, Deltona, FL 32725, USA

DOI:

https://doi.org/10.31814/stce.nuce2020-14(2)-04

Abstract

An alternative method using Artificial Intelligence (AI) to predict the 28-day strength of concrete from its primary ingredients is presented in this research. A series of 424 data samples collected from a previous study were employed for developing, testing, and validation of Adaptive Neuro-Fuzzy Inference System (ANFIS) models. Seven mix parameters, namely Cement, Blast Furnace Slag, Fly Ash, Water, Superplasticizer, Coarse Aggregate, and Fine Aggregate were used as the inputs of the models while the output was the 28-day compressive strength of concrete. In the first step, different models with various input membership functions were explored and compared to obtain an optimal ANFIS model. In the second step, that model was utilized to predict the compressive strength value for each concrete sample, and to compare with those obtained from the compressive test in laboratory. The results showed that the selected ANFIS model can be used as a reliable tool for predicting the compressive strength of concrete with Root Mean Squared Error values of 5.97 MPa and 7.73 MPa, respectively, for the training and test sets. In addition, the sensitivity analysis results revealed that the accuracy of the proposed model improved with an increase in the number of input parameters/variables.

Keywords:

artificial intelligence; adaptive neuro-fuzzy inference system; concrete strength; sensitivity analysis.

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Published

2020-04-17

How to Cite

Nguyen, T. T., & Dinh, K. (2020). An artificial intelligence approach for concrete hardened property estimation. Journal of Science and Technology in Civil Engineering (JSTCE) - HUCE, 14(2), 40–52. https://doi.org/10.31814/stce.nuce2020-14(2)-04

Issue

Vol. 14 No. 2 (2020)

Section

Research Papers

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