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 is a combination of cement, sand, aggregate, and water. Cement manufacturing
causes the generation of various gases, mainly greenhouse gases like CO 2 in the …

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 …

Experimental studies using a substantial number of datasets can be avoided by employing
efficient methods to predict the mechanical properties of construction materials. The …

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 …

The significant requirement for natural resources, specifically as ingredients of cement, is
accelerating due to the considerable growth of the construction sector. Further, cement …

The demand for segment concrete increases rapidly with the expansion of urban rail transit
and underground space, which may lead to carbon emissions (CE). In the production of …

This study investigated the importance of applying explainable artificial intelligence (XAI) on
different machine learning (ML) models developed to predict the strength characteristics of …

The construction industry is among the riskiest industries around the world. Hence, the
preliminary studies exploring the consequences of occupational accidents have received …