PSAM 16 Conference Paper Overview
Welcome to the PSAM 16 Conference paper and speaker overview page.
Lead Author: Suyun Ham
| Study of Bridge Damage Prediction Leveraging Kinematic Contact Enforcement-based Moving Load and Deep Learning Techniques |
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| This paper presents a study on the development of hybrid models featuring structural health monitoring (SHM) inspection using bridge weigh-in-motion (BWIM) physics-based models. Artificial intelligence (AI) techniques helped improve the structural damage prediction during SHM inspection of bridges. This study introduces a comprehensive assessment of 1) a unique finite element (FE) simulation approach, which leverages the kinematic contact enforcement (KCE) method, verified with the vehicle-bridge interaction (VBI) theory, and 2) machine learning (ML) techniques to identify and automatically predict structural damages from the structural response. The KCE method is a new approach to simulating vehicle motion in a BWIM model, which is used to carry out actual structural responses to motion. Thus, KCE method provides contact conditions between elements (i.e., contact type, material properties, and element moving speed), which enables the realistic vehicle motion and structural response to be simulated. The FE model is designed with four different classes of damages with three different damage locations applied under two different load conditions (i.e., static load and moving load). These responses obtained from the FE simulation are further examined by using the feature selection method, which provides the importance rank for ML models. A prediction model includes: 1) a decision tree, 2) a support vector machine (SVM), 3) backpropagation (BP), and 4) XGBoost. Among these prediction models, the deep learning XGBoost with its assembly decision tree provided the most reliable results. The results in this paper verify that structural damage prediction can be achieved by using ML as well as the BWIM structural response, which provides high accuracy in bridge damage prediction. |
Paper S.332 Preview
Author and Presentation Info
Presentation only, a full paper is not available.
Lead Author Name: Suyun Ham (s.ham@uta.edu)
Bio: Dr. Ham is currently an Associate Professor at the University of Texas at Arlington. He is a director of the Smart Infrastructure and Testing Laboratory (SITL). He finished his Ph. D. in Civil Engineering from the University of Illinois at Urbana-Champaign. Before that, he worked in industries for seven years as a researcher and a structural engineer on projects for more than 100 buildings and infrastructure designs. His research expertise and interests with the infrastructure damage assessment field and deep learning.
Country: United States of America
Company: University of Texas at Arlington
Job Title: Associate professor
Bio: Dr. Ham is currently an Associate Professor at the University of Texas at Arlington. He is a director of the Smart Infrastructure and Testing Laboratory (SITL). He finished his Ph. D. in Civil Engineering from the University of Illinois at Urbana-Champaign. Before that, he worked in industries for seven years as a researcher and a structural engineer on projects for more than 100 buildings and infrastructure designs. His research expertise and interests with the infrastructure damage assessment field and deep learning.
Country: United States of America
Company: University of Texas at Arlington
Job Title: Associate professor