With the advantages of a small footprint, wide under-bridge view, and beautiful appearance, single-column pier bridges are widely used in urban bridge networks. However, single-column pier bridges are prone to damage during earthquakes or heavy vehicle use, which can seriously affect normal operations and post-disaster recoveries. Therefore, there is an urgent need to carry out the seismic resilience assessment of single-column pier bridges and formulate disaster prevention and mitigation measures from the aspects of design, maintenance, and post-earthquake recovery. This paper first establishes a resilience assessment framework for the single-column pier bridge and optimizes a functionality recovery model after an earthquake. Then, a numerical model of a sample bridge is built for resilience fragility analysis. Nonlinear dynamic time history analysis is performed to build a probabilistic seismic demand model, and moment-curvature analysis is performed to build a probabilistic seismic capacity model. Finally, a seismic resilience assessment of the single-column pier bridge is obtained based on the seismic fragility, and a sensitivity analysis is carried out for the pier height, pier section dimension, span and vehicle load level to improve the resilience of the single-column pier bridge.
{"title":"Fragility Analysis and Resilience Assessment of the Single-Column Pier Steel-Concrete Composite Bridge Subjected to Seismic Loads","authors":"Tong Wang, Q. Gao, Yidian Dong, Hao Xu, Yang Liu","doi":"10.1115/1.4064647","DOIUrl":"https://doi.org/10.1115/1.4064647","url":null,"abstract":"\u0000 With the advantages of a small footprint, wide under-bridge view, and beautiful appearance, single-column pier bridges are widely used in urban bridge networks. However, single-column pier bridges are prone to damage during earthquakes or heavy vehicle use, which can seriously affect normal operations and post-disaster recoveries. Therefore, there is an urgent need to carry out the seismic resilience assessment of single-column pier bridges and formulate disaster prevention and mitigation measures from the aspects of design, maintenance, and post-earthquake recovery. This paper first establishes a resilience assessment framework for the single-column pier bridge and optimizes a functionality recovery model after an earthquake. Then, a numerical model of a sample bridge is built for resilience fragility analysis. Nonlinear dynamic time history analysis is performed to build a probabilistic seismic demand model, and moment-curvature analysis is performed to build a probabilistic seismic capacity model. Finally, a seismic resilience assessment of the single-column pier bridge is obtained based on the seismic fragility, and a sensitivity analysis is carried out for the pier height, pier section dimension, span and vehicle load level to improve the resilience of the single-column pier bridge.","PeriodicalId":504755,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139868364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Júlio Oliveira Schmidt, Lucas França Aires, G. R. Hubner, Humberto Pinheiro, Daniel Fernando Tello Gamarra
This work proposes a method using a long short-term memory neural network as a diagnostic tool to detect wind turbine rotor mass imbalance. The method uses the synthetic minority oversampling technique for data augmentation in an unbalanced dataset. For this purpose, a 1.5 MW three-bladed wind turbine model was simulated at Turbsim, FAST, and Matlab Simulink to generate rotor speed data for different scenarios, simulating different wind speeds and creating a mass imbalance by changing the density of the blades in the software. Features extraction and power spectral density were also used to improve the Neural Network results. The results were compared to nine different classifiers with four different combinations of datasets and demonstrated that the technique is promising for mass imbalance detection.
{"title":"LSTM Neural Networks Using the SMOTE Algorithm for Wind Turbine Fault Prediction","authors":"Júlio Oliveira Schmidt, Lucas França Aires, G. R. Hubner, Humberto Pinheiro, Daniel Fernando Tello Gamarra","doi":"10.1115/1.4064375","DOIUrl":"https://doi.org/10.1115/1.4064375","url":null,"abstract":"\u0000 This work proposes a method using a long short-term memory neural network as a diagnostic tool to detect wind turbine rotor mass imbalance. The method uses the synthetic minority oversampling technique for data augmentation in an unbalanced dataset. For this purpose, a 1.5 MW three-bladed wind turbine model was simulated at Turbsim, FAST, and Matlab Simulink to generate rotor speed data for different scenarios, simulating different wind speeds and creating a mass imbalance by changing the density of the blades in the software. Features extraction and power spectral density were also used to improve the Neural Network results. The results were compared to nine different classifiers with four different combinations of datasets and demonstrated that the technique is promising for mass imbalance detection.","PeriodicalId":504755,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139683921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Marc Gille, Pierre Beaurepaire, N. Gayton, Antoine Dumas, T. Yalamas
Metrology is extensively used in the manufacturing industry to determine whether the dimensions of parts are within their tolerance interval. However, errors cannot be avoided. Metrology experts are of course aware of it, and able to identify the different sources that contribute to making errors. In this paper, the probability density function of measurement errors is assumed to be given as an input. Very little research has been made in metrology to develop methods that take into account such data. This work deals with a batch of measures and its statistical properties. A first method is proposed to correct the effects of the measurement errors on the distribution that characterizes the entire batch. Then a second method is proposed to estimate the true value that is hidden behind each single measure, by removing the measurement error statistically. The second method is based on the output knowledge of the first, which is integrated with Bayesian statistics. The relevance of these two methods is shown through two examples applied on simulated data.
{"title":"Statistical Approaches for the Reduction of Measurement Errors in Metrology","authors":"Marc Gille, Pierre Beaurepaire, N. Gayton, Antoine Dumas, T. Yalamas","doi":"10.1115/1.4064284","DOIUrl":"https://doi.org/10.1115/1.4064284","url":null,"abstract":"Metrology is extensively used in the manufacturing industry to determine whether the dimensions of parts are within their tolerance interval. However, errors cannot be avoided. Metrology experts are of course aware of it, and able to identify the different sources that contribute to making errors. In this paper, the probability density function of measurement errors is assumed to be given as an input. Very little research has been made in metrology to develop methods that take into account such data. This work deals with a batch of measures and its statistical properties. A first method is proposed to correct the effects of the measurement errors on the distribution that characterizes the entire batch. Then a second method is proposed to estimate the true value that is hidden behind each single measure, by removing the measurement error statistically. The second method is based on the output knowledge of the first, which is integrated with Bayesian statistics. The relevance of these two methods is shown through two examples applied on simulated data.","PeriodicalId":504755,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139178052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Sreekumar, I. Kougioumtzoglou, S. Triantafyllou
An approximate efficient stochastic dynamics technique is developed for determining response statistics of linear systems with frequency-dependent parameters, which are used for modeling wave propagation through rigid porous media subject to stochastic excitation. This is done in conjunction with a filter approximation of the system frequency response function. The technique exhibits the following advantages compared to alternative solution treatments in the literature. First, relying on an input-output relationship in the frequency domain, the response power spectrum matrix is integrated analytically for determining the stationary response covariance matrix, at zero computational cost. Second, the proposed filter approximation facilitates a state-variable formulation of the governing stochastic differential equations in the time domain. This yields a coupled system of deterministic differential equations to be solved numerically for the response covariance matrix. Thus, the non-stationary (transient) response covariance can be computed in the time domain at a relatively low computational cost. Various numerical examples are considered for demonstrating the accuracy and computational efficiency of the herein developed technique. Comparisons with pertinent Monte Carlo simulation data are included as well.
{"title":"Filter Approximations for Random Vibroacoustics of Rigid Porous Media","authors":"A. Sreekumar, I. Kougioumtzoglou, S. Triantafyllou","doi":"10.1115/1.4064286","DOIUrl":"https://doi.org/10.1115/1.4064286","url":null,"abstract":"An approximate efficient stochastic dynamics technique is developed for determining response statistics of linear systems with frequency-dependent parameters, which are used for modeling wave propagation through rigid porous media subject to stochastic excitation. This is done in conjunction with a filter approximation of the system frequency response function. The technique exhibits the following advantages compared to alternative solution treatments in the literature. First, relying on an input-output relationship in the frequency domain, the response power spectrum matrix is integrated analytically for determining the stationary response covariance matrix, at zero computational cost. Second, the proposed filter approximation facilitates a state-variable formulation of the governing stochastic differential equations in the time domain. This yields a coupled system of deterministic differential equations to be solved numerically for the response covariance matrix. Thus, the non-stationary (transient) response covariance can be computed in the time domain at a relatively low computational cost. Various numerical examples are considered for demonstrating the accuracy and computational efficiency of the herein developed technique. Comparisons with pertinent Monte Carlo simulation data are included as well.","PeriodicalId":504755,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139177892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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