【摘要】 结构的损伤检测常转化为求解约束优化问题,针对粒子群算法容易出现早熟问题,增大算法后期的粒子位置的改变量,从而增加粒子位置的差异,因而能够增强其在求解约束优化问题时抵抗局部极小的能力。两层刚架单损伤和多损伤识别的数值结果和收敛曲线表明了改进后的粒子群算法优于传统的带惯性因子的粒子群算法。三层框架结构的4种损伤工况的试验研究进一步说明了该算法应用于结构损伤检测领域的有效性。更多还原

【Abstract】 Structure damage detection is often converted into a constrained optimization problem. An improved Particle Swarm Optimization (PSO) algorithm has been proposed to avoid the premature convergence of traditional PSO algorithm in solving the constrained optimization problem by increasing the diversity of the particles in the later phase of traditional PSO algorithm. The numerical results of detecting damages at one or multiple sites for a two-storey rigid frame and the convergence curves of PSO algorithms show that the proposed improved PSO algorithm is more effective to solve the constrained optimization problem than the traditional PSO algorithm with an inertia factor. Moreover, the experimental study of damage detection in 4 cases for a three-storey building model demonstrates the feasibility of the proposed improved PSO in structural damage detection.更多还原