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

Artificial Intelligence (AI), machine learning (ML), and deep learning (DL) are emerging
techniques capable of delivering elegant and affordable solutions which can surpass those …

While artificial intelligence (AI), and by extension machine learning (ML), continues to be
adopted in parallel engineering disciplines, the integration of AI/ML into the structural …

Fire is a chaotic and extreme phenomenon. While the past few years have witnessed the
success of integrating machine intelligence (MI) to tackle equally complex problems in …

This paper presents the development of systematic machine learning (ML) approach to
enable explainable and rapid assessment of fire resistance and fire-induced spalling of …

This paper reviews the fire problem in critical transportation infrastructures such as bridges
and tunnels. The magnitude of the fire problem is illustrated, and the recent increase in fire …

Despite ongoing research efforts, we continue to fall short of arriving at a consistent
representation of fire-induced spalling of concrete. This is often attributed to the complexity …

Cold-formed steel channels are made with staggered courses of slots for reduced thermal
conductivity and improved energy efficiency of cold-formed steel buildings. The reduced …

Explosive spalling is an unfavorable phenomenon observed in concrete when exposed to
heating load. It is a great potential threat to safety of concrete structures subjected to …

This paper presents a framework for integrating Explainable and Anomalous Machine
Learning (EAML) into a digital twin to enable finetuning of mixtures as a mean to realize next …