Explainable machine learning and ensemble models for predicting fresh properties of self consolidating concrete.

ABSTRACT: Accurately predicting the fresh properties of self-consolidating concrete (SCC) is critical for enhancing construction efficiency and ensuring robust performance in complex and highly reinforced structures. Traditional experimental testing is time-consuming, costly, and prone to human error. In this study, over 2500 experimental data points were initially collected from 176 published studies to develop a comprehensive and reliable dataset. After rigorous data cleaning and filtering 348 SCC mix designs with complete rheological information were selected for model development. After rigorous data cleaning and filtering the dataset divided into 85% for training and 15% for testing. Five state-of-the-art machine learning (ML) models Gene Expression Programming (GEP), Deep Neural Networks (DNN)

SUBMITTER: Khan M 

PROVIDER: S-EPMC12660996 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature