Structural Health Monitoring (SHM) systems have a potential to reduce lifecycle costs of structures. As a result, there is a lot of active research in the area for SHM of civil and mechanical structures. Guided waves (GW) based SHM techniques allow monitoring of large plate-like structures with a few sensors and have been identified as the most promising of techniques for SHM. Fibre Bragg grating (FBG) sensors due to their low weight, and ability to be multiplexed have been long thought to be ideal sensors for SHM. The recent development of the edge filtering approach has increased their sensitivity to GW sensing and made them ideal sensors. Unfortunately the FBG sensors are passive sensors and show directional sensitivity. These operational constraints make extension of the earlier developed GW based SHM techniques for FBG sensors difficult. Recently the authors developed a technique for damage detection specifically designed for a network with FBG sensors. This paper builds on the past work by the authors and develops a methodology for a design of an actuator–sensor (AS) network for improving the damage assessment capability of the previously proposed method. The paper develops a multi objective optimization technique for the joint optimization of actuator and sensor placement for a network with FBG sensors. The joint optimization of the actuators and sensors is necessary due to the passive nature of the FBG sensors and also incorporates the directional nature of the FBG sensors. The paper develops an integer encoded NSGA-II for the optimization of the AS network. The objectives for the optimization are derived from the specific damage detection technique tailored for the use of FBG sensors. The objective are: coverage with at least 2 AS pairs, coverage with at least 1 edge reflected path and the cost of the deployed network. The results indicate that the encoding of the objectives of the optimization is valid and indeed the damage detection capabilities of the AS network are as predicted analytically. The paper for the first time develops a joint optimization of network for FBG sensors. It is also the first attempt at a truly multi-objective optimization of the AS network and promises to have applications on real structures.

结构健康监测 (SHM) 系统具有降低结构生命周期成本的潜力。因此，在土木和机械结构的 SHM 领域有很多活跃的研究。基于导波 (GW) 的 SHM 技术允许使用几个传感器监测大型板状结构，并且已被确定为最有前途的 SHM 技术。长期以来，光纤布拉格光栅 (FBG) 传感器因其重量轻和多路复用能力而被认为是 SHM 的理想传感器。边缘滤波方法的最新发展提高了它们对 GW 传感的敏感性，并使它们成为理想的传感器。不幸的是，FBG 传感器是无源传感器并显示方向灵敏度。这些操作限制使得早期开发的基于 GW 的 SHM 技术难以扩展到 FBG 传感器。最近，作者开发了一种专门为具有 FBG 传感器的网络设计的损伤检测技术。本文以作者过去的工作为基础，开发了一种设计执行器-传感器 (AS) 网络的方法，以提高先前提出的方法的损坏评估能力。本文开发了一种多目标优化技术，用于联合优化具有 FBG 传感器的网络的执行器和传感器放置。由于 FBG 传感器的被动特性，并且还结合了 FBG 传感器的方向特性，因此有必要对执行器和传感器进行联合优化。该论文开发了一个整数编码的 NSGA-II，用于优化 AS 网络。优化目标源自为使用 FBG 传感器量身定制的特定损坏检测技术。目标是：覆盖至少 2 个 AS 对，覆盖至少 1 个边缘反射路径以及部署网络的成本。结果表明优化目标的编码是有效的，并且实际上 AS 网络的损坏检测能力与分析预测的一样。该论文首次为FBG传感器开发了网络联合优化。这也是对 AS 网络进行真正多目标优化的第一次尝试，并有望在真实结构上得到应用。结果表明优化目标的编码是有效的，并且实际上 AS 网络的损坏检测能力与分析预测的一样。该论文首次为FBG传感器开发了网络联合优化。这也是对 AS 网络进行真正多目标优化的第一次尝试，并有望在真实结构上得到应用。结果表明优化目标的编码是有效的，并且实际上 AS 网络的损坏检测能力与分析预测的一样。该论文首次为FBG传感器开发了网络联合优化。这也是对 AS 网络进行真正多目标优化的第一次尝试，并有望在真实结构上得到应用。