Selecting right positions for composite-integrated sensors for monitoring cure during manufacturing and loads during product use presents challenges for engineering design. Since an optimal sensor placement (OSP) methodology for both phases is not emphasised enough in literature, a new methodology is proposed. This methodology is based on a Genetic Algorithm and strain gauges, temperature sensors and interdigitated electrode sensors for cure monitoring and physics-informed neural network-based load detection. Additionally, it includes sensor node positions optimization in a sensor network.

Proper monitor planning is a vital component of structural health monitoring (SHM) project. An extremely important part of the monitor planning is the placement of sensors, usually in the form of acceleration sensors. For the placement of three-dimensional acceleration sensors, the state of practice is to select the sensor configuration by previous experiences. However, this results in a waste of many sensors. A novel method called siege ant colony algorithm (SAC) is proposed in this paper. This method is built on the previous ant colony optimization (ACO) in the direction of improving efficiency and accuracy when applied to optimal sensor placement (OSP) problems in large-scale structure monitoring. This method is applied and compared with standard approaches using the Hanjiang transmission tower.