The LAE sensing sheet contains dense array of individual sensors, but there are some non-instrumented spaces between the sensors that are not sensitive to strain anomalies. In addition, individual sensors are sensitive to strain in one specific direction, while the damage (e.g. cracking of concrete and bowing of steel), can generate strain field anomalies that do not necessarily occur in the same direction. If the sensor network is not design correctly, all these effects can potentially lead to unsuccessful damage identification.

To address the above challenges, probabilistic approaches are needed to establish a sensor network within LAE based on the probability that this particular network can detect damage of a certain size (probability of detection). Thus, the objective of this project is to create methodology for design of sensor network for LAE sensing sheets that will enable reliable identification of damage of a given size. The methodology will be based on probabilistic approach, and it will take into account the size of the LAE sensing sheet, size of the individual sensor, angular sensitivity of the sensor to damage, and orientation and size of the damage.