A comparison of machine learning methods for predicting the compressive strength of field-placed concrete

Abstract

This study evaluates the efficacy of machine learning (ML) methods to predict the compressive strength of field-placed concrete. We employ both field- and laboratory-obtained data to train and test ML models of increasing complexity to determine the best-performing model specific to field-placed concrete. The ability of ML models trained on laboratory data to predict the compressive strength of field-placed concrete is evaluated and compared to those models trained exclusively on field-acquired data. Results substantiate that the random forest ML model trained on field-acquired data exhibits the best performance for predicting the compressive strength of field-placed concrete; the RMSE, MAE, and R2 values were 730 psi, 530 psi, and 0.51, respectively. We also show that hybridization of field- and laboratory-acquired data for training ML models is a promising method for reducing common over-prediction issues encountered by laboratory-trained models that are used in isolation to predict the compressive strength of field-placed concrete.