基于云模型和离散卡尔曼滤波的临时看台结构动荷载识别

Dynamic load identification of temporary stands based on the cloud model and discrete Kalman filter

人群荷载的作用,包括人群的拥挤和跳跃、摇摆等产生的动荷载常常是看台等临时设施结构安全的重要影响因素,而国内外学者对看台等临时结构荷载识别工作研究较少,因此有必要对此类结构进行人群等荷载的快速识别研究。提出基于云模型和离散卡尔曼滤波的临时看台结构动荷载识别方法,首先介绍了云模型和离散卡尔曼滤波的基本原理,然后基于离散卡尔曼滤波器推导出荷载识别算法并利用云模型技术进行优化,以有效解决了测量误差、环境噪声等带来的干扰问题,最后应用临时看台结构进行了数值仿真,并与未优化卡尔曼滤波算法的荷载识别方法进行对比。数值仿真结果表明该方法对临时看台结构具有良好的动荷载识别性能。

The effect of crowd loads,including crowding load and dynamic loads generated by crowd jumping and swaying,is often an important factor affecting the structural safety of temporary structures such as stands.However,scholars at home and abroad have little research on the load identification of temporary stands structures,so it is necessary to create a rapid identification method crowd loads on such structures.In the paper a dynamic load identification method for temporary stand structures was proposed based on the cloud model and discrete Kalman filter.First,the basic principles of the cloud model and discrete Kalman filter were introduced.Then,the load identification algorithm was derived based on discrete Kalman filter,and the cloud model technology was used to optimize the algorithm to effectively solve the interference problems caused by measurement errors and environmental noise.Finally,a certain temporary stand structure was taken as a numerical simulation,example,and the results were compared with those by the load identification method of the unoptimized Kalman filter algorithm.The numerical simulation results show that the proposed method has good dynamic load identification performance for temporary stand structures.