Data-driven prediction on critical mechanical properties of engineered cementitious composites based on machine learning.

ABSTRACT: The present study introduces a novel approach utilizing machine learning techniques to predict the crucial mechanical properties of engineered cementitious composites (ECCs), spanning from typical to exceptionally high strength levels. These properties, including compressive strength, flexural strength, tensile strength, and tensile strain capacity, can not only be predicted but also precisely estimated. The investigation encompassed a meticulous compilation and examination of 1532 datasets sourced from pertinent research. Four machine learning algorithms, linear regression (LR), K nearest neighbors (KNN), random forest (RF), and extreme gradient boosting (XGB), were used to establish the prediction model of ECC mechanical properties and determine the optimal model. The optimal model was u

SUBMITTER: Qing S 

PROVIDER: S-EPMC11222503 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature