Abstract
The use of recursive subspace-based identification methods is analyzed in the estimation of the most significant vibration frequencies along an elevated railroad segment of México City Metro Line 12 using ambient vibration measurements recorded from 2012, when the line was opened, to 2018. Due to the railroad characteristics, the use of high-order models and the systematic tuning of the methods are required to achieve low uncertainty in frequency estimation. A frequency history is generated using these high-order models in order to check for variations along the seven years where important events took place: two low- and one high-intensity earthquakes, paving, and construction of sidewalks and planters around the sensor station. Results are consistent for all methods under analysis in the identified frequencies, suggesting that the system has preserved its structural health. To produce independent results, spectral analysis was performed showing that the associated frequency history is again consistent with that generated with recursive subspace-based identification methods. Overall, results indicate that these subspace methods are suitable for frequency monitoring in the studied system offering, in the case of recursive N4SID, important advantages in terms of low computational cost, real-time implementation, and smaller uncertainty.
Highlights
Parameter identification in structural models is an important topic in civil engineering applications such as structural health monitoring, damage detection, and vibration control systems
The multivariable output error state space (MOESP) method demands a high computational effort, the orthogonal projection involves a LQ decomposition whose computational processing increases exponentially as the order of the identified model increases. Studies as those of Chen and Loh [21] focus on improving the MOESP method by reducing its demand for computational resources. e use of improved subspace identification methods, most of them derived from the original ones, was left for future work because the goal in this paper is to evaluate its use as monitoring systems and later integration into an alert system for the studied system
It can be observed that the standard deviation is higher when using spectral analysis, but it is lower than 5% of the average frequency values
Summary
Parameter identification in structural models is an important topic in civil engineering applications such as structural health monitoring, damage detection, and vibration control systems. E goal of this work is to evaluate the effectiveness of two recursive subspace identification methods: numerical algorithms for subspace state space system identification (N4SID) and multivariable output error state space (MOESP) in a structural health application Both methods are used to monitor the two most significant radial frequencies in a curved segment of an elevated railway in Mexico City Metro Line 12, opened in 2012, where all the mentioned critical aspects are involved. To achieve this task, ambient vibration records from 2012 to 2018 are studied using the subspace methods previously mentioned and spectral analysis. The identification can be affected by the presence of nonstructural elements
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