Social group optimization (SGO) is a human-based metaheuristic optimization technique which shows accurate results for different benchmark functions but has not been studied for civil engineering structural health monitoring problems. This article deals with the use of SGO for damage analysis of different modelled civil engineering structures and a real-life American Society of Civil Engineers (ASCE) benchmark structure using a stiffness-based objective function. It is observed that SGO is not able to identify the damage in the structures owing to the algorithm becoming trapped in local optima. To improve the performance of SGO, a modified social group optimization (MSGO) is proposed, which deals with the drawbacks of SGO in dealing with the complicated objective functions of civil structures. It is observed that MSGO shows accurate damage detection capability, with errors of less than 1%, in the civil structures considered, even in the presence of noise.

社会群体优化 (SGO) 是一种基于人类的元启发式优化技术，它对不同的基准函数显示出准确的结果，但尚未针对土木工程结构健康监测问题进行研究。本文涉及使用 SGO 对不同建模的土木工程结构和现实生活中的美国土木工程师协会 (ASCE) 基准结构进行损伤分析，使用基于刚度的目标函数。据观察，由于算法陷入局部最优，SGO 无法识别结构中的损坏。为了提高SGO的性能，提出了一种改进的社会群体优化（MSGO），它解决了SGO在处理土木结构复杂目标函数方面的缺点。