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 …

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 …

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 …

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 …

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

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 …

In reinforced concrete structures, the utilization of composite rebar has been increased by
considering their high corrosion resistance, anti-magnetic properties, and significant tensile …

Abstract Machine learning (ML) continues to rise as an effective and affordable method of
tackling engineering problems. Unlike other disciplines, the integration of ML into structural …

Abstract Machine learning algorithms have emerged as a powerful technique to predict the
engineering properties of composite materials and structures where traditional statistical …