The Guadalquivir bridge is a large-scale twin steel truss bridge located in Spain that opened to traffic in 1929. Since the bridge has come into operation for a long time, structural health monitoring (SHM) is strictly necessary to guarantee safety and avoid serious incidents. This paper proposes a novel approach to model updating for the Guadalquivir bridge based on the vibration measurements combined with a hybrid metaheuristic search algorithm. Cuckoo Search (CS) is an evolutionary algorithm derived from global search techniques to look for the best solution. Nevertheless, CS contains some fundamental defects that may reduce its effectiveness in dealing with optimization issues. A main drawback of CS arises in the low convergence level because CS applies fixed values for parameters when looking for the optimal solution. In addition, CS relies a lot on the quality of original populations and does not have the capability to enhance the quality of the next generations. If the position of the original particles is far from the optimal places, it may be challenging to look for the best solution. To remedy the shortcomings of CS, we propose a hybrid metaheuristic algorithm (HGAICS) employing the advantages of both Genetic Algorithm (GA) and Improved Cuckoo Search (ICS) to solve optimization problems. HGAICS contains two outstanding characteristics as follows: (1) GA is employed to create original particles with the best quality based on the capacity of crossover and mutation operators and (2) those particles are then applied to look for the global best derived from the flexible and global search ability of ICS. This paper also presents the application of wireless triaxial sensors (WTSs) taking the place of classical wired systems (CWSs) to the measurements. The use of WTSs increases dramatically the freedom in setting up experimental measurements. The results show that the performance of the proposed hybrid algorithm not only determines uncertain parameters of the Guadalquivir bridge properly, but also is more accurate than GA, CS, and improved CS (ICS). A MATLAB package of the proposed method (HGAICS) is available via GitHub: https://github.com/HoatranCH/HGAICS.

瓜达尔基维尔桥是西班牙的大型双钢桁架桥，于1929年投入运营。由于该桥已投入使用很长时间，因此必须严格进行结构健康监测（SHM），以确保安全并避免发生严重事故。本文提出了一种基于振动测量和混合元启发式搜索算法的瓜达尔基维尔桥模型更新的新方法。布谷鸟搜索（CS）是从全局搜索技术中寻找最佳解决方案的一种进化算法。但是，CS包含一些基本缺陷，可能会降低其在处理优化问题方面的有效性。CS的主要缺点出现在低收敛水平上，因为CS在寻找最佳解决方案时会为参数应用固定值。此外，CS很大程度上取决于原始人口的素质，而没有能力提高下一代的素质。如果原始粒子的位置远离最佳位置，那么寻找最佳解决方案可能会很困难。为了弥补CS的缺点，我们提出了一种混合元启发式算法（HGAICS），该算法利用遗传算法（GA）和改进的杜鹃搜索（ICS）的优点来解决优化问题。HGAICS包含以下两个突出的特征：（1）GA被用于基于交叉和变异算子的能力来创建具有最佳质量的原始粒子，（2）然后将这些粒子用于寻找从可伸缩性中获得的全局最佳值。和ICS的全局搜索功能。本文还介绍了无线三轴传感器（WTS）的应用，它代替了传统的有线系统（CWS）进行测量。WTS的使用极大地增加了建立实验测量的自由度。结果表明，所提混合算法的性能不仅可以适当地确定瓜达尔基维尔桥的不确定参数，而且比GA，CS和改进的CS（ICS）更为准确。可通过GitHub获得所建议方法（HGAICS）的MATLAB软件包：https：//github.com/HoatranCH/HGAICS。结果表明，所提混合算法的性能不仅可以适当地确定瓜达尔基维尔桥的不确定参数，而且比GA，CS和改进的CS（ICS）更为准确。可通过GitHub获得所建议方法（HGAICS）的MATLAB软件包：https：//github.com/HoatranCH/HGAICS。结果表明，所提混合算法的性能不仅可以适当地确定瓜达尔基维尔桥的不确定参数，而且比GA，CS和改进的CS（ICS）更为准确。可通过GitHub获得所建议方法（HGAICS）的MATLAB软件包：https：//github.com/HoatranCH/HGAICS。