Kriging Metamodel-Based Seismic Fragility Analysis of Single-Bent Reinforced Concrete Highway Bridges

Hoang, Phuong H.; Phan, Hoang Nam; Nguyen, Dat Thanh; Paolacci, Fabrizio

doi:10.3390/buildings11060238

Cited by 14 publications

(7 citation statements)

References 26 publications

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“…GPR analysis is a type of regression analysis that is adopted when the dependent variable follows the Gaussian process [20,25,26]. Phan et al [27] and Hoang et al [28] used the GPR technique for seismic vulnerability and seismic fragility analysis for storage tanks and reinforced concrete highway bridges. Jung et al [20] also investigated several methods in estimating the maximum displacement and found that the GPR approach showed the best performance.…”

Section: Estimation Model Based On Gaussian Process Regressionmentioning

confidence: 99%

“…The levels for IO, LS, and CP determine the acceptance range of MIDR using the maximum height. The performance levels have threshold values considering the IO (0.7%), LS (2.5%), and CP (5.0%) to provide the MIDR limits [27,28,45,46]. Generally, the structural analysis that produces the fragility curve is conducted based on the PGA of 0.0 g in the fragility function.…”

Section: Six-floor Building Analysis For Development Of the Rapid Prediction Modelmentioning

confidence: 99%

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Gaussian Process Regression-Based Structural Response Model and Its Application to Regional Damage Assessment

Park

Jung

2021

IJGI

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Seismic activities are serious disasters that induce natural hazards resulting in an incalculable amount of damage to properties and millions of deaths. Typically, seismic risk assessment can be performed by means of structural damage information computed based on the maximum displacement of the structure. In this study, machine learning models based on GPR are developed in order to estimate the maximum displacement of the structures from seismic activities and then used to construct fragility curves as an application. During construction of the models, 13 features of seismic waves are considered, and six wave features are selected to establish the seismic models with the correlation analysis normalizing the variables with the peak ground acceleration. Two models for six-floor and 13-floor buildings are developed, and a sensitivity analysis is performed to identify the relationship between prediction accuracy and sampling size. A 10-fold cross-validation method is used to evaluate the model performance, using the R-squared, root mean squared error, Nash criterion, and mean bias. Results of the six-parameter-based model apparently indicate a similar performance to that of the 13-parameter-based model for the two types of buildings. The model for the six-floor building affords a steadily enhanced performance by increasing the sampling size, while the model for the 13-floor building shows a significantly improved performance with a sampling size of over 200. The results indicate that the heighted structure requires a larger sampling size because it has more degrees of freedom that can influence the model performance. Finally, the proposed models are successfully constructed to estimate the maximum displacement, and applied to obtain fragility curves with various performance levels. Then, the regional seismic damage is assessed in Gyeonjgu city of South Korea as an application of the developed models. The damage assessment with the fragility curve provides the structural response from the seismic activities, which can assist in minimizing damage.

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Section: Estimation Model Based On Gaussian Process Regressionmentioning

confidence: 99%

Section: Six-floor Building Analysis For Development Of the Rapid Prediction Modelmentioning

confidence: 99%

Gaussian Process Regression-Based Structural Response Model and Its Application to Regional Damage Assessment

Park

Jung

2021

IJGI

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“…The ensemble of decision trees (EDT) is a regression model capable of dealing with large amounts of data [23]. The Kriging model, which is an interpolation model, passes exactly through the experimental points and can form complex models with a limited amount of data [16]. It is also possible to analyze prediction uncertainty using this model [26].…”

Section: Metamodelingmentioning

confidence: 99%

“…In the former method, the accuracy of the metamodel significantly affects the optimal design results. In most extant studies, the experiments were conducted to approximate the relationship between the input and output variables using only one metamodel technique [15,16]. However, due to the existence of a variety of metamodeling techniques, it is important to apply the most appropriate one for each design problem.…”

Section: Introductionmentioning

confidence: 99%

Optimization of a Permanent Magnet Synchronous Motor for e-Mobility Using Metamodels

Kim

You

2022

Applied Sciences

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Permanent magnet synchronous motors (PMSMs) with rectangular coils in hairpin windings exhibit improved fill factor and reduced end turn of the coils, which in turn improve the efficiency and power density of PMSMs, making them ideal for e-mobility applications. Herein, the shape of a PMSM was optimized for torque ripple reduction using metamodels to improve the noise and vibrational performance of the motor. The objective function of the optimal design aimed to minimize the torque ripple, and the average torque and efficiency were set as constraints. The notch width and depth and barrier length were selected as the design variables to satisfy the objective function and constraints. Using the optimal Latin hypercube design technique, 27 experimental points were selected, and a finite element analysis (FEA) was performed for each point. Furthermore, a function approximation was performed using six metamodels, and the best metamodel was selected using the root mean square error test. Moreover, the optimization was performed by combining the best metamodels for each variable with a sequential two-point diagonal quadratic approximation optimization algorithm. The torque ripple was improved by approximately 1.63% compared with the initial model, whereas the constraint values remained constant. Finally, an FEA was performed on the optimal point, and the FEA results matched with those of the optimal method.

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“…The results showed that the impact of the mixer is significant, and the use of a dual-mixer results in a remarkable increase in the urea conversion rate and NH 3 uniformity index by 169.5% and 136.4%, respectively. The techniques mentioned above are optimized using methods of trial-and-error and the design of experiment (DOE) [11,12]. These methods can help in easily planning experiments; however, it is difficult to represent the performance across the entire range of the design.…”

Section: Introductionmentioning

confidence: 99%

Optimization of the Uniformity Index Performance in the Selective Catalytic Reduction System Using a Metamodel

Kim,

Park,

Yoo

et al. 2023

Sustainability

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The significance of the selective catalytic reduction system in vehicles increases in line with the high standards of emission control and enhanced selective catalytic reduction efficiency. This study aims to improve the performance of the selective catalytic reduction system through an optimization method using a metamodel. The objective function is defined as the ammonia uniformity index, and the design parameters are defined in relation to the pipe length and mixer related to the chemical reaction of the urea solution. The range of design parameters has been designated by a trial-and-error method in order to maintain the overall design drawings of the selective catalytic reduction system and prevent modeling errors. Three algorithms, namely, ensemble decision tree, Kriging, and radial basis function, are employed to develop the metamodel. The accuracy of the metamodel is verified based on three indicators: the normalized root mean square error, root mean square error, and maximum absolute error. The metamodel is generated using the Kriging model, which has the highest accuracy among the algorithms, and optimization is also performed. The predicted optimization results are confirmed by computational fluid dynamics numerical analysis with a 99.83% match. The ammonia uniformity index is improved by 1.38% compared to the base model, and it can be said that the NOx purification efficiency is improved by 30.95%. Consequently, optimizing the uniformity index performance through structural optimization is of utmost importance. Furthermore, this study reveals that the design variables related to the mixer play a crucial role in the performance. Therefore, using the metamodel to optimize the selectively catalytic reduction system’s structure should be considered significant. Finally, in the future, the analysis model can be validated using test equipment based on the findings of this study.

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Kriging Metamodel-Based Seismic Fragility Analysis of Single-Bent Reinforced Concrete Highway Bridges

Cited by 14 publications

References 26 publications

Gaussian Process Regression-Based Structural Response Model and Its Application to Regional Damage Assessment

Gaussian Process Regression-Based Structural Response Model and Its Application to Regional Damage Assessment

Optimization of a Permanent Magnet Synchronous Motor for e-Mobility Using Metamodels

Optimization of the Uniformity Index Performance in the Selective Catalytic Reduction System Using a Metamodel

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