Regression and artificial neural network models for strength properties of engineered cementitious composites
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This paper describes the development of regression and artificial neural network (ANN) models to determine the 28-day compressive and tensile strength of engineered cementitious composite (ECC) based on the mix design parameters. One hundred eighty ECC mixtures having variable mix designs were obtained from pervious experiments. Factors influencing the strengths were examined to determine the appropriate parameters for the ANN models. The optimized input parameters using training and development of ANN models were used to formulate the regression models. The ANN and regression models were tested with new sets of data for performance validation. Based on the good agreement and other statistical performance parameters, optimized ANN and regression models capable of predicting the strengths of ECC mixtures (using arbitrary mix design parameters) were developed and suggested for practical applications. ANN and regression models demonstrated excellent predictive ability showing predicted experimental strength ratio ranging between 0.95 and 1.00.
KeywordsEngineered cementitious composite Artificial neural network model Regression model Strength properties Training Validation
Authors acknowledge the financial support from Natural Science and Engineering Research Council (NSERC), Canada, for this project.
Compliance with ethical standards
Conflict of interest
There is no potential conflict of interest.
Human and animal rights statement
Research does not involve human participants and/or animals, and consent to submit has been received explicitly from all co-authors, as well as from the responsible authorities (tacitly or explicitly) at the institute/organization where the work has been carried out.
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