Advanced composite materials are prone to various types of damage during-manufacturing
and long-time service. Ultrasonic detection methods have been widely employed to detect …

The optimization problems that must meet more than one objective are called multi-objective
optimization problems and may present several optimal solutions. This manuscript brings …

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 …

Deep learning techniques have found applications across diverse fields, enhancing the
efficiency and effectiveness of decision-making processes. The integration of these …

With the advancement of computing and inspired by optimal phenomena found in nature,
several algorithms capable of solving complex engineering problems have been developed …

It is often computationally expensive to monitor structural health using computer models.
This time-consuming process can be relieved using surrogate models, which provide cheap …

Predictive maintenance of production equipment is a prerequisite to ensure safe and
reliable manufacturing operations. To avoid unexpected shutdown and even casualties …

This work aims to develop a Structural Health Monitoring methodology that maximizes the
acquired modal response and minimizes the number of sensors in a helicopter's main rotor …

Large-scale group decision-making (LSGDM) is one of the main open problems where a
decision is made by many different results. Moreover, there is also a problem with how to …

Laminated composite structures performance and behavior can be affected by damage that
is not always visible on the surface. The need to monitor the health of these structures has …