González-Pérez, Carlos Alberto

Main Affiliation
Preferred name
González-Pérez, Carlos Alberto
Official Name
González Pérez, Carlos Alberto
ORCID
0000-0003-3975-7628
Researcher ID
D-4256-2016
Scopus Author ID
55787411800

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    Effect of Signal Filtering on Metaheuristic-Based Structural Parameter Identification in Shear Building Models
    (Ital Publication, 2025-06-01)
    ;
    Jaime De-la-Colina
    ;
    Jesús Valdés-González
    This study evaluates the effectiveness of three metaheuristic algorithms—Genetic Algorithm (GA), Differential Evolution (DE), and Particle Swarm Optimization (PSO)—for identifying lateral interstory stiffness and the modal damping ratio in two-dimensional shear building models. The main objective is to estimate these parameters using time-domain displacement, velocity, and acceleration data, assuming known floor masses and unknown input excitation that primarily excites translational vibration modes. Three structural configurations with 2, 3, and 5 stories are analyzed to assess the scalability and robustness of each algorithm. To assess the effect of signal filtering on the performance of the algorithms, white noise is added to the synthetic response data at six levels ranging from 0% to 5% of the root mean square (RMS) amplitude. A sixth-order Butterworth filter is applied to evaluate the effect of signal preprocessing, and results obtained with and without filtering are compared. The results show that all three algorithms achieve acceptable levels of accuracy, even under noisy conditions. Filtering consistently improves identification accuracy, especially in high-noise conditions. In the most challenging case (5% noise, 5-story model), the average identification errors were 5.042% for GA, 5.106% for DE, and 5.035% for PSO. The findings underscore the practical value of integrating signal filtering with metaheuristic optimization for robust structural system identification in noise-contaminated environments. To account for the random nature of the algorithms, all results reported correspond to the average of 10 independent runs per identification scenario to ensure reliable performance evaluation.
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    Structural damage detection of Gerber saddles in RC bridges by monitoring the variation of their dynamic properties
    (Wiley, 2025)
    Jesús Valdés‐González
    ;
    Jaime De‐la‐Colina
    ;
    ;
    Fernando Sánchez‐Cortes
    This paper numerically studies the behavior of a Gerber saddle model in a reinforced concrete (RC) bridge. The feasibility of identifying its structural damage by monitoring the variation of its dynamic properties is investigated. The main objective of this research is to establish an index based on the changes in the model's dynamic properties able to identify both its different behavior stages and its structural damage. The model was analyzed with the ANSYS program using the extended finite element method, which allows one to explicitly model concrete cracking. The analytical model was calibrated with laboratory tests. By simulating ambient vibration tests, it was feasible to estimate the first modal vibration frequency of the model and its damping percentage. The frequency response functions (FRF) for each damage stage were also obtained from the simulated tests. The proposed index to evaluate the structural damage was the correlation coefficient r, which was computed between pairs of FRFs corresponding to two different states of damage, without and with damage. It was observed that the change in the dynamic properties (natural frequencies and critical damping percentages) can be considered an indicator of the presence of structural damage in the case study. The correlation coefficient r proved to be a useful parameter to identify the presence of damage. It was also possible to correlate the value of r with the level of damage.
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      10
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    Determination of Mass Properties in Floor Slabs from the Dynamic Response Using Artificial Neural Networks
    (2022)
    ;
    Jaime De-la-Colina
    <jats:p>Most of the research on accidental eccentricity is directed at both the evaluation of accidental eccentricity design code recommendations and the study of building torsional response. In contrast, this paper addresses how the mass properties of each of the levels of a building could be determined from the dynamic response of a building. Using the dynamic response of buildings, this paper presents the application of multilayer feed forward artificial neural networks (ANNs) to determine the magnitude, the radial distance, and the polar moment of inertia of the mass for each level of reinforced concrete (RC) buildings. Analytical models were developed for three regular buildings. Live-load magnitude and mass position are considered as random variables. Seven load cases were generated for the 1, 2 and 4-story models using two excitations. As for the input parameters of the ANNs, three different choices of input data to the network were used. The developed ANN models are able to predict with adequate accuracy the radial position, magnitude, and polar moment of inertia of masses of each level. The implementation of this method based on ANNs would allow the monitoring, either permanently or temporarily, of changes in mass properties at each building floor slab. Doi: 10.28991/CEJ-2022-08-08-01 Full Text: PDF</jats:p>
    Scopus© Citations 3  7  2
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    Proposal to compute hydrodynamic pressures and sloshing heights in ground-supported rectangular tanks subjected to earthquakes
    (2022)
    J. De-la-Colina
    ;
    <jats:p>A proposal to estimate both the hydrodynamic pressures and the maximum wave height of liquids contained in rectangular tanks subjected to earthquake ground motions is presented. The computation procedure, based on the Rayleigh-Ritz method, assumes the fluid as a continuum and it does not use concentrated masses or springs. The solution is achieved by equating the seismic input energy of the system with the total kinetic energy of the fluid assuming liquid velocity fields. The resulting design formulas are simple and they are intended to simplify the seismic design of tanks. Numerical results lead to both the liquid maximum sloshing height and hydrodynamic pressure distributions that are similar to those obtained with other simplified methods and those estimated with the finite element method. Preliminary estimations of shear forces and bending moments for a numerical example resulted 13% and 6% larger (respectively) than the corresponding values obtained with the finite element method.</jats:p>
      42  2
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    Scopus© Citations 2  29