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
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Finite element model updating of a multispan bridge with a hybrid metaheuristic search algorithm using experimental data from wireless triaxial sensors

An operational modal analysis study has been carried out on a 1kW, vertical-axis wind turbine rotor blade, using a stereo vision technique. Numerical simulation has also been carried out and results were compared to classical modal analysis and OMA results. The displacement time series used in the analysis are very short because of limitations in the image acquisition system. Short time series are not fully qualified for OMA and analyzing the data needs a proper method. Covariance driven Stochastic Subspace Identification method (SSI-cov) has been used for short time series like earthquakes. In the SSI-cov method, a block Toeplitz matrix is formed which contains output correlation functions. 10 displacement time series have been recorded with 187 Hz sampling rate, and about 3 time series were chosen to be analyzed. The block Toeplitz matrix of 3 time series are averaged out and the procedure how to analyse short time series is described in this paper. For OMA validation, a classical modal analysis is done with signals from accelerometers mounted on the blade. The blade is exited with a hammer. The natural frequencies are estimated by picking the peaks from the FRFs (Frequency Response Function) obtained at different points. Finally the natural frequencies and mode shapes obtained from classical modal analysis and OMA results are compared to numerical simulations of the blade with COMSOL.

Averaged cov-driven subspace identification for modal analysis of a modified troposkien blade with displacement measurement

An efficient approach to model updating for a multispan railway bridge using orthogonal diagonalization combined with improved particle swarm optimization

Ambient Vibration Test of the Guadalquivir Railway Bridge

Classical methods for structural seismic vulnerability assessment often link directly the probability of a given level of damage to a specific construction type arising from ground motion. Recent methods feature more detailed and physical components that not only allow detailed sensitivity studies to be undertaken, but also provide straightforward calibration for various characteristics of the building stock. However, these models often require an increased amount of input parameters, and thus time and resources, to construct an earthquake damage model based on such analyses. Hence, a balance is needed between the computational intensity and the required data. In this paper a simplified structural model is proposed to represent the dynamic behaviour for a particular class of structure. The model parameters are estimated directly from an ambient vibration test. The covariance-driven stochastic system identification algorithm (SSI-cov) is employed for modal identification. By using SSI-cov, the uncertainty of the modal parameters can also be delivered in a single measurement. This process is applied to build a fragility curve of a typical frame building structure according to the first damage grade in a low-to-moderate seismic region. The results show that the source of modelling uncertainty can be explicitly accounted for. The procedure is very simple to implement and can be applied to different structural types.

Vulnerability Assessment of Structures in a Low-To-Moderate Seismic Region based on Ambient Vibration Test Modal Data

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Ambient Vibration Test of the Guadalquivir Railway Bridge

Vulnerability Assessment of Structures in a Low-To-Moderate Seismic Region based on Ambient Vibration Test Modal Data