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 …

In recent years, fiber-reinforced polymers (FRP) in reinforced concrete (RC) members have
gained significant attention due to their exceptional properties, including lightweight …

This paper presents a data-driven approach to determine the load and flexural capacities of
reinforced concrete (RC) beams strengthened with fabric reinforced cementitious matrix …

Recent years have witnessed a surge in the application of machine learning techniques for
solving hard to solve structural engineering problems. The application of machine learning …

Fiber-reinforced polymer (FRP) composites bonded externally to reinforced concrete beams
have shown promise for increasing shear load-carrying capacity. However, accurately …

Accurately predicting the interfacial bond strength (IBS) between concrete and fiber
reinforced polymers (FRPs) has been a challenging problem in the evaluation and …

With the booming prosperity of artificial intelligence (AI) technology, it triggers a paradigm
shift in engineering fields including material science. The integration of AI and machine …

In this paper, we propose an efficient Artificial Neural Network (ANN) based on the global
search capacity of evolutionary algorithms (EAs) to identify damages in laminated composite …

Concrete-filled steel tubes (CFSTs) are popularly used in structural applications. The
accurate prediction of their ultimate strength is a key for the safety of the structure. Extensive …