• Smart Structures and Systems
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• v.32 no.3
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• pp.153-166
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• 2023

Health monitoring of civil infrastructures, in particular, old bridges that are still in service, has become more than necessary, given the risk that a possible degradation or failure of these infrastructures can induce on the safety of users in addition to the resulting commercial and economic impact. Bridge integrity assessment has attracted significant research efforts over the past forty years with the aim of developing new damage identification methods applicable to real structures. The bridge of Ouled Mimoun (Tlemcen, Algeria) is one of the oldest railway structure in the country. It was built in 1889. This bridge, which is too low with respect to the level of the road, has suffered multiple shocks from various machines that caused considerable damage to its central part. The present work aims to analyze the stability of this bridge by identifying damages and evaluating the damage rate in different parts of the structure on the basis of a finite element model. The applied method is based on an inverse analysis of the normal stress responses that were calculated from the corresponding recorded strains, during the passage of a real train, by means of a set of strain gauges placed on certain elements of the bridge. The results obtained from the inverse analysis made it possible to successfully locate areas that were really damaged and to estimate the damage rate. These results were also used to detect an excessive rigidity in certain elements due to the presence of plates, which were neglected in the numerical reference model. In the case of the continuous bridge monitoring, this developed method will be a very powerful tool as a smart health monitoring system, allowing engineers to take in time decisions in the event of bridge damage.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.2
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• pp.165-173
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• 2023

The data management and the evaluation of defects in the bridge are generally conducted based on inspection and diagnosis data, including the exterior damage map and defect quantity table prepared by periodic inspection. Since most of these data are written in 2D-based documents and are difficult to digitize in a standardized manner, it is challenging to utilize them beyond the defined functionality. This study proposed methods to efficiently build a BIM (Building Information Modeling)-based bridge damage model from raw data of inspection report and to manage and utilize the damage information linking to bridge model through the spread sheet data generated by COBie (Construction Operations Building Information Exchange). In addition, a method to conduct the condition assessment of defects in bridge was proposed based on an automatic evaluation process using digitized bridge member and damage information. The proposed methods were tested using superstructure of PSC-I girder concrete bridge, and the efficiency and effectiveness of the methods were verified.

• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.6
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• pp.206-216
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• 2020

As the number of aging railway bridges in Korea increases, maintenance costs due to aging are increasing and continuous management is becoming more important. However, while the number of old facilities to be managed increases, there is a shortage of professional personnel capable of inspecting and diagnosing these old facilities. To solve these problems, this study presents an improved model that can detect Local damage to structures using machine learning techniques of AI technology. To construct a damage detection machine learning model, an analysis model of the bridge was set by referring to the design drawing of a non-ballasted plate-girder railroad bridge. Static strain data according to the damage scenario was extracted with the analysis model, and the Local damage index based on the reliability of the bridge was presented using statistical techniques. Damage was performed in a three-step process of identifying the damage existence, the damage location, and the damage severity. In the estimation of the damage severity, a linear regression model was additionally considered to detect random damage. Finally, the random damage location was estimated and verified using a machine learning-based damage detection classification learning model and a regression model.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.04a
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• pp.208-209
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• 2022

This study aims to analyze the risk factors caused by object damage and third-party damage loss in actual bridge construction based on past insurance premium payment data from major domestic insurers for bridge construction projects, and develop a quantitative loss prediction model. For the development of quantitative bridge construction loss model, the dependent variable was selected as the loss ratio, and the independent variable adopted 1) Technical factors: superstructure type, foundation type, construction method, and bridge length 2) Natural hazards: flood anf Typhoon, 3) Project information: total construction duration, total cost and ranking. Among the selected independent variables, superstructure type, construction method, and project period were shown to affect the ratio of bridge construction losses, while superstructure, foundation, flood and ranking were shown to affect the ratio of the third-party losses.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.4
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• pp.621-625
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• 2018

This paper proposes a method for bridge damage factor recognition from inspection reports using deep learning. Bridge inspection reports contains inspection results including identified damages and causal analysis results. However, collecting such information from inspection reports manually is limited due to their considerable amount. Therefore, this paper proposes a model for recognizing bridge damage factor from inspection reports applying Named Entity Recognition (NER) using deep learning. Named Entity Recognition, Word Embedding, Recurrent Neural Network, one of deep learning methods, were applied to construct the proposed model. Experimental results showed that the proposed model has abilities to 1) recognize damage and damage factor included in a training data, 2) distinguish a specific word as a damage or a damage factor, depending on its context, and 3) recognize new damage words not included in a training data.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2001.04a
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• pp.454-461
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• 2001

Dynamic responses of a multi-span simply supported bridge are examined under seismic excitations considering damage of bearings. An idealized mechanical model which can consider components such as pounding, friction at the supports, abutment-soil interaction, rotational and translational motions of foundations, and the nonlinear pier motions, is developed to analyze the effects due to damage of bearings. It is assumed that the bearing's response after failure can be expressed with a sliding model with a friction coefficient between the superstructure and the pier top. It is found that the global seismic behaviors are significantly influenced by the damage of bearings and the damage of bearings may lead to unseating failure at unpredicted supports. Therefore, It can be concluded that detailed seismic response analyses of bridge systems considering damage of bearings is required for the purpose of the seismic safety evaluation.

• International Journal of Concrete Structures and Materials
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• v.9 no.1
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• pp.45-54
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• 2015

This paper describes a three-dimensional approach to modeling the nonlinear behavior of partial-depth precast prestressed concrete bridge decks under increasing static loading. Six full-size panels were analyzed with this approach where the damage plasticity constitutive model was used to model concrete. Numerical results were compared and validated with the experimental data and showed reasonable agreement. The discrepancy between numerical and experimental values of load capacities was within six while the discrepancy of mid-span displacement was within 10 %. Parametric study was also conducted to show that higher accuracy could be achieved with lower values of the viscosity parameter but with an increase in the calculation effort.

• Structural Engineering and Mechanics
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• v.12 no.3
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• pp.341-346
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• 2001

Near source earthquakes can be characterized not only by strong horizontal but also by strong vertical ground motions with broad range of dominant frequencies. The inelastic horizontal response of thin-walled L-shaped steel bridge piers, which are popularly used as highway bridge supports, subjected to simultaneous horizontal and vertical ground excitations of near source earthquakes is investigated. A comprehensive damage index and an evolutionary-degrading hysteretic model are applied. Numerical analysis reveals that the strong vertical excitation of a near source earthquake exerts considerable influences on the damage development and horizontal response of thin-walled L-shaped steel bridge piers.

• Smart Structures and Systems
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• v.23 no.5
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• pp.507-520
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• 2019

In the structural health monitoring field, damage detection has been commonly carried out based on the structural model and the engineering features related to the model. However, the extracted features are often subjected to various errors, which makes the pattern recognition for damage detection still challenging. In this study, an automated damage identification method is presented for hanger cables in a tied-arch bridge using a convolutional neural network (CNN). Raw measurement data for Fourier amplitude spectra (FAS) of acceleration responses are used without a complex data pre-processing for modal identification. A CNN is a kind of deep neural network that typically consists of convolution, pooling, and fully-connected layers. A numerical simulation study was performed for multiple damage detection in the hangers using ambient wind vibration data on the bridge deck. The results show that the current CNN using FAS data performs better under various damage states than the CNN using time-history data and the traditional neural network using FAS. Robustness of the present CNN has been proven under various observational noise levels and wind speeds.

• Smart Structures and Systems
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• v.14 no.5
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• pp.917-942
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• 2014

Multivariate statistics based damage detection algorithms employed in conjunction with novel sensing technologies are attracting more attention for long term Structural Health Monitoring of civil infrastructure. In this study, two practical data driven methods are investigated utilizing strain data captured from a 4-span bridge model by Fiber Bragg Grating (FBG) sensors as part of a bridge health monitoring study. The most common and critical bridge damage scenarios were simulated on the representative bridge model equipped with FBG sensors. A high speed FBG interrogator system is developed by the authors to collect the strain responses under moving vehicle loads using FBG sensors. Two data driven methods, Moving Principal Component Analysis (MPCA) and Moving Cross Correlation Analysis (MCCA), are coded and implemented to handle and process the large amount of data. The efficiency of the SHM system with FBG sensors, MPCA and MCCA methods for detecting and localizing damage is explored with several experiments. Based on the findings presented in this paper, the MPCA and MCCA coupled with FBG sensors can be deemed to deliver promising results to detect both local and global damage implemented on the bridge structure.