Artificial intelligence (AI) and Machine learning (ML) train machines to achieve a high level
of cognition and perform human-like analysis. Both AI and ML seemingly fit into our daily …

The question of silicate glass chemical durability is at the heart of many industrial and
environmental issues, with certain glasses, such as bioglasses, needing to transform rapidly …

The prediction of failure mode of columns is critical in deciding the operational and recovery
strategies of a bridge after a seismic event. This paper contributes to the critical need of …

Abstract Machine learning (ML) algorithms have been gradually used in predicting tunneling-
induced settlement, but there is no uniform process for establishing ML models and even …

This paper presents an ensemble machine learning (ML) model for prediction of modulus of
elasticity (MOE) of concrete formulated using recycled concrete aggregate (RCA), in relation …

This paper summarizes the vast body of literature (over 200 documents) related to
vitrification of the low-activity waste (LAW) fraction of the Hanford tank wastes. Details are …

The global demand for data storage and processing is increasing exponentially. To deal
with this challenge, massive efforts have been devoted to the development of advanced …

Predicting the tunneling-induced maximum ground surface settlement is a complex problem
since the settlement depends on plenty of intrinsic and extrinsic factors. This study …

We provide a review of machine learning (ML) tools for material discovery and sophisticated
applications of different ML strategies. Although there have been a few published reviews on …

The design of new glasses is often plagued by poorly efficient Edisonian “trial-and-error”
discovery approaches. As an alternative route, the Materials Genome Initiative has largely …