基于改进粒子群算法的应变传感器优化布置

在结构健康监测和损伤识别研究中，为了应用有限的试验设备资源获取尽可能多的有效测试信息，快速有效地解决应变传感器的优化配置问题，提出了一种基于克隆选择和离散粒子群混合算法优化新型适应度函数的应变传感器优化布置方法，并将该方法应用到拉西瓦拱坝上。结果表明，基于改进克隆选择和离散粒子群混合算法具有更强的全局寻优能力，且提出的应变类适应度函数在保证应变模态正交性和模态应变能方面更有优势。该方法能很好地识别拱坝的应变振型，可在各类结构的模态测试和损伤识别研究中进行推广。

Optimal Strain Sensor Placement Based on an Improved Particle Swarm Optimization Algorithm

Optimal sensor placement has become an important topic in the field of structural health monitoring and damage identification for obtaining the most testing results possible with limited resources. To effectively place strain sensors, a new optimal strain sensor placement (OSSP) method, which uses a hybrid algorithm of an improved discrete particle swarm optimization (DPSO) algorithm and a clonal selection algorithm (CSA) to optimize the novel fitness function, is proposed and applied to a Laxiwa arch dam. The results show that the clonal selection and discrete particle swarm hybrid algorithm (CSA-DPSO) has stronge global optimization ability, and the proposed strain fitness function has advantages in capability for both strain modal assurance and modal strain energy criteria. The scheme determined by the proposed strain sensor placement method can accurately identify strain mode shapes. This method can provide some guidance for OSSP in a variety of structures in the future.