This paper proposes an intelligent damage detection strategy in terras of measured acceleration responses in practical structures, which is applicable to damage location and extent assessment. Firstly, genetic algorithm (GA) and the Newmark constant average acceleration method are employed to identify the undamaged stiffness with the acceleration responses in healthy state; Secondly, acceleration responses in damage or practical states are processed by the above same methods so as to identify the current stiffness;Thirdly,after the damage threshold is determined by the hypothesis testing, damage localization and extent detection are performed by comparing the stiffness difference between the undamaged and current stiffness. Localization results through the validation of a numerical simulation and a laboratory experiment agree well with their practical cases. This damage detection strategy not only reduces the complexity of the detection progress,but also avoids the occurrence of the time-frequency domain transform error owing to the acceleration responses in the time domain being constructed the fitness function directly. Additionally, the maximum relative error is 3.18% for damage extent prediction. This implies that the intelligent method is effective and applicable todamage detection;furthermore,it has good noise tolerance and robustness.%目的 为实现结构的损伤定位及损伤程度评估,研究一种适用于实际结构的智能损伤检测策略.方法 利用结构振动时程响应进行结构的损伤检测,提出针对结构损伤前的加速度响应,运用遗传算法和Newmark常平均加速度法识别出结构损伤前的层间刚度值;运用同样的方法对损伤后的加速度响应处理识别出层间刚度值;结合假设检验,在验证损伤阈值有效的基础上,比较结构损伤前后的层间刚度值,完成损伤定位与损伤程度检测.结果 数值模拟与实验室试验测试发现损伤定位位置与实际情况一致,该损伤检测策略直接运用时域内的加速度响应来构造适应度函数,不仅简化了损伤检测的过程,同时也避免了时频域转换时的误差,损 伤程度预测相对误差最大为3.18％.结论 笔者提出的损伤检测方法是可行、有效的,有较好的抗噪性、鲁棒性.
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