Abstract Machine learning (ML) has become the most successful branch of artificial
intelligence (AI). It provides a unique opportunity to make structural engineering more …

Concrete, as the most widely used construction material, is inextricably connected with
human development. Despite conceptual and methodological progress in concrete science …

Abstract Machine learning (ML) techniques are often employed for the accurate prediction of
the compressive strength of concrete. Despite higher accuracy, previous ML models failed to …

This study aims to provide an efficient and accurate machine learning (ML) approach for
predicting the creep behavior of concrete. Three ensemble machine learning (EML) models …

Steel fiber-reinforced concrete (SFRC) has a performance superior to that of normal
concrete because of the addition of discontinuous fibers. The development of strengths …

The effect of raw materials on the compressive strength of concrete is a complex process,
especially in the case of ultra-high-performance concrete (UHPC), where a higher number of …

Symbolic regression (SR) is a machine learning-based regression method based on genetic
programming principles that integrates techniques and processes from heterogeneous …

In this study, a model for the estimation of the compressive strength of concretes
incorporating metakaolin is developed and parametrically evaluated, using soft computing …

Population growth, economic development, and rapid urbanization in many areas have led
to increased exposure and vulnerability of structural and infrastructure systems to hazards …

This study aims to apply machine learning methods to predict the compression strength of
self-compacting recycled aggregate concrete. To obtain this goal, the ensemble methods …