Structural damage identification has received considerable attention during the past
decades. Although several reviews have been presented, some new developments have …

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

To date, data mining (DM) techniques, ie artificial intelligence, machine learning, and
statistical methods have been utilized in a remarkable number of structural health monitoring …

Artificial neural networks are computational approaches based on machine learning to learn
and make predictions based on data, and have been applied successfully in diverse …

Structural damages occur in modern structures during operations due to environmental and
human factors. The damages accumulating with time may lead to a significant decrease in …

Although civil engineering problems are often characterized by significant levels of
complexity, they are generally approached and solved by combining several practitioners' …

Environmental variability is still a major challenge in structural health monitoring. Due to the
similarity of changes caused by environmental variations to damage, false positive and false …

Structural health monitoring (SHM) is a practical tool for assessing the safety and system
performance of existing structures. And structural damage identification has become the …

Artificial intelligence is a branch of computer science, involved in the research, design, and
application of intelligent computer. Traditional methods for modeling and optimizing complex …

This review identifies current machine-learning algorithms implemented in building
structural health monitoring systems and their success in determining the level of damage in …