High-performance concrete compressive strength prediction using Genetic Weighted Pyramid Operation Tree (GWPOT)

• GWPOT is introduced to predict the compressive strength of HPC.
• To demonstrate the GWPOT application, a total 1030 experiment data are provided.
• Model accuracy was compared against other AI models.
• GWPOT model produces less error than competing models.
• GWPOT is able to generate explicit input–output formulas whilst the others not.

This study uses the Genetic Weighted Pyramid Operation Tree (GWPOT) to build a model to solve the problem of predicting high-performance concrete compressive strength. GWPOT is a new improvement of the genetic operation tree that consists of the Genetic Algorithm, Weighted Operation Structure, and Pyramid Operation Tree. The developed model obtained better results in benchmark tests against several widely used artificial intelligence (AI) models, including the Artificial Neural Network (ANN), Support Vector Machine (SVM), and Evolutionary Support Vector Machine Inference Model (ESIM). Further, unlike competitor models that use “black-box” techniques, the proposed GWPOT model generates explicit formulas, which provide important advantages in practical application.