Search

Your search keyword '"Chang, Minwoo"' showing total 30 results
Start Over Author "Chang, Minwoo"
30 results on '"Chang, Minwoo"'

4. Smart bridge bearing monitoring: Predicting seismic responses with a multi‐head attention‐based CNN‐LSTM network.

Author

Yazdanpanah, Omid, Chang, Minwoo, Park, Minseok, and Mangalathu, Sujith

Subjects
SHORT-term memory, LONG short-term memory, AXIAL loads, RUBBER bearings, LATERAL loads, SEISMIC response
Abstract

This paper introduces a novel method to spontaneously predict displacement time histories and hysteresis curves of bridge lead rubber bearings under seismic loads and axial forces. The method leverages a stacked convolutional‐bidirectional Cuda Long Short Term Memory network, enhanced with multi‐head attention, skip connections, exponential learning rate scheduler, and a hybrid activation function to improve performance. The framework utilizes the functional application programming interface provided by the Python Keras library to build a model that takes input features such as horizontal and vertical ground accelerations, actuator loads in both lateral and vertical directions, and the superstructure mass. The effectiveness of the deep learning model is evaluated using a considerable experimental dataset of 53 real‐time hybrid simulations, spanning various earthquake intensities and superstructure masses (Chi‐Chi: 15 scenarios, El Centro: 15 scenarios, Kobe: 13 scenarios, and Northridge: 10 scenarios). Initially, Northridge earthquake data serves as unseen data, while the rest is used for training and validation. In a subsequent trial, the unseen data is centered on Kobe earthquake scenarios. By employing a hybrid loss function merging mean square and mean absolute errors, the model exhibits a substantial correlation of over 83% between predicted displacement time series and empirical measurements for the unseen data. In summary, the proposed model offers miscellaneous benefits, including time and cost savings in experimental efforts by decreasing the need for additional tests. It further delivers a swift and precise insight into the bridge bearing performance and its energy dissipation, facilitating timely and accurate bridge design in different scenarios for engineers. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

13. Modal Identification Using SMIT

Author

Chang, Minwoo, Pakzad, Shamim N., Leonard, Rebecca, Caicedo, J.M., editor, Catbas, F.N., editor, Cunha, A., editor, Racic, V., editor, Reynolds, P., editor, and Salyards, K., editor

Published
2012
Full Text
View/download PDF

22. Investigating and Improving Bridge Management System Methodologies Under Uncertainty

Author

Chang, Minwoo

Subjects
deterministic deterioration model, Civil and Environmental Engineering, Engineering, explanatory variables, structural health monitoring system, stochastic deterioration model
Abstract

This dissertation presents a novel procedure to select explanatory variables, without the influence of human bias, for deterioration model development using National Bridge Inventory (NBI) data. Using NBI information, including geometric data and climate information, candidate explanatory variables can be converted into normalized numeric values and analyzed prior to the development of deterministic or stochastic deterioration models. The prevailing approach for explanatory variable selection is to use expert opinion solicited from experienced engineers. This may introduce human influenced biases into the deterioration modeling process. A framework using Least Absolute Shrinkage and Selection Operator (LASSO) penalized regression and covariance analysis are combined to compensate for this potential bias. Additionally, the cross validation analysis and solution path is used as a standard for the selection of minimum number of explanatory variables. The proposed method is demonstrated through the creation of deterministic deterioration models for deck, superstructure, and substructure for Wyoming bridges and compared to explanatory variables using the expert selection method. The comparison shows a significant decrease in error using the presented framework based on the L2 relative error norm. The final chapter presents a new method to develop stochastic deterioration models using logistic regression. The relative importance amongst explanatory variables is used to develop a classification tree for Wyoming bridges. The bridges in a subset are commonly associated with several explanatory variables, so that the deterioration models can be more representative and accurate than using a single explanatory variable. The logistic regression is used to introduce the stochastic contribution into the deterioration models. In order to avoid missing data problems, the binary categories condition rating, either remaining the same or decreased, are considered for logistic regression. The probability of changes in bridges��� condition rating is obtained and the averages for same condition ratings are used to create transition probability matrix for each age group. The deterioration model based on Markov chain are developed for Wyoming bridges and compared with the previous model based on percentage prediction and optimization approach. The prediction error is analyzed, which demonstrates the considerable performance of the proposed method and is suitable for relatively small data samples.

Published
2016

30. Developing deterioration models for Wyoming bridges.

Author

Wyoming. Department of Transportation, United States. Federal Highway Administration, Chang, Minwoo, Maguire, Marc, Utah State University. Department of Civil and Environmental Engineering, Wyoming. Department of Transportation, United States. Federal Highway Administration, Chang, Minwoo, Maguire, Marc, and Utah State University. Department of Civil and Environmental Engineering

Abstract

RS04214, Deterioration models for the Wyoming Bridge Inventory were developed using both stochastic and deterministic models., The selection of explanatory variables is investigated and a new method using LASSO regression to eliminate human bias, in explanatory variable selection. The cross validation technique is used to determine the minimum number of explanatory, variables. The relative significance of candidate variables is used to rank the explanatory variables in hierarchical order., The deterministic deterioration models are developed by using curve-fitting methods for the mean of bridge ages for each, condition rating. In order to improve the accuracy in the model, bridges are split into the multiple subsets using first two, explanatory variables for deck, superstructure, and substructure. Although the deterministic deterioration model is, insufficient to predict condition ratings for a specific bridge, it is worthy to observe a general feature of how the, functionality of bridges becomes worse over time., The stochastic models are developed to capture the uncertainty in the deterioration process using the Markov chain. The, transition probability matrix is estimated using percentage prediction method, which counts the numbers corresponding to, the element of transition probability matrix. The same subsets used in the deterministic deterioration models are considered., For each subset, zoning technique is used such that the bridge data is grouped for every 30 years to estimate transition, probability matrix separately., The source codes are provided for the future update of bridge inventory and stochastic deterioration models. A computer, program is used develop and plot deterioration models. A simple guideline is also included so that the user can access the, source codes conveniently., Author ORCID information, Minwoo Chang; ORCID number 0000-0002-5749-078X, Marc Maguire; ORCID number 0000-0002-7897-0344

Catalog

Books, media, physical & digital resources
See catalog results