• Smart Structures and Systems
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• v.26 no.5
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• pp.591-603
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• 2020

Dynamic deflection monitoring is an essential and critical part of structural health monitoring for high-speed railway bridges. Two critical problems need to be addressed when using inclinometer sensors for such applications. These include constructing a general representation model of inclination-deflection and addressing the ill-posed inverse problem to obtain the accurate dynamic deflection. This paper provides a dynamic deflection monitoring method with the placement of optimal inclinometer sensors for high-speed railway bridges. The deflection shapes are reconstructed using the inclination-deflection transformation model based on the differential relationship between the inclination and displacement mode shape matrix. The proposed optimal sensor configuration can be used to select inclination-deflection transformation models that meet the required accuracy and stability from all possible sensor locations. In this study, the condition number and information entropy are employed to measure the ill-condition of the selected mode shape matrix and evaluate the prediction performance of different sensor configurations. The particle swarm optimization algorithm, genetic algorithm, and artificial fish swarm algorithm are used to optimize the sensor position placement. Numerical simulation and experimental validation results of a 5-span high-speed railway bridge show that the reconstructed deflection shapes agree well with those of the real bridge.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.40 no.12
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• pp.10-16
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• 2003

To analyze the effects of the air-bridge parasitic capacitances on the performance of coplanar waveguide pseudomorphic high electron mobility transistors (CPW PHEMTs), the gate-to-air-bridge ( $C_{ag}$ ) and the drain-to air-bridge ( $C_{ad}$ ) capacitances were taken into account plus the conventional pinched-off cold. FET circuit model. To examine the effects of the parasitic capacitances due to the air-bridges, a variety routing schemes for the air-bridge interconnection were adopted for fabricating the 0.1-${\mu}{\textrm}{m}$ $\Gamma$-gate length CPW HEMT's. According to air-bridge schemes, the $S_{21}$ gain is affected considerably. From the results of the fabricated CPW PHEMT, the $C_{ag}$ and $C_{ad}$ is one of the important factor of decreasing the gain of HEMTs.

• 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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.11
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• pp.128-134
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• 2016

Estimating the repair and reinforcement (R&R) costs for each bridge member is essential for managing the life cycle of a bridge using a bridge management system (BMS). Representative members of a bridge were defined in this study, and detailed and representative R&R methods for each one were drawn in order to develop a systematic maintenance cost model that is applicable to the BMS. The unit cost for each detailed R&R method was established using the standard of estimate and historical cost data, and a systematic procedure is presented using an integration program to enable easy renewal of the R&R unit cost. Also, the average unit cost of the representative R&R methods was calculated in the form of a weighted average by considering the unit cost and application frequency of each detained R&R method. The appropriateness of the drawn average unit cost was reviewed by comparing and verifying it with the previous historical unit cost. The suggested average R&R unit cost can be used to review the validity of the required budget or the appropriateness of the R&R performance cost in the stage to establish the bridge maintenance plan. The results of this study are expected to improve the reliability of maintenance cost information and the rationality of decision making.

• Structural Engineering and Mechanics
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• v.63 no.6
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• pp.809-824
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• 2017

The accurate evaluation of wind characteristics and wind-induced structural responses during a typhoon is of significant importance for bridge design and safety assessment. This paper presents an expectation maximization (EM) algorithm-based angular-linear approach for probabilistic modeling of field-measured wind characteristics. The proposed method has been applied to model the wind speed and direction data during typhoons recorded by the structural health monitoring (SHM) system instrumented on the arch Jiubao Bridge located in Hangzhou, China. In the summer of 2015, three typhoons, i.e., Typhoon Chan-hom, Typhoon Soudelor and Typhoon Goni, made landfall in the east of China and then struck the Jiubao Bridge. By analyzing the wind monitoring data such as the wind speed and direction measured by three anemometers during typhoons, the wind characteristics during typhoons are derived, including the average wind speed and direction, turbulence intensity, gust factor, turbulence integral scale, and power spectral density (PSD). An EM algorithm-based angular-linear modeling approach is proposed for modeling the joint distribution of the wind speed and direction. For the marginal distribution of the wind speed, the finite mixture of two-parameter Weibull distribution is employed, and the finite mixture of von Mises distribution is used to represent the wind direction. The parameters of each distribution model are estimated by use of the EM algorithm, and the optimal model is determined by the values of $R^2$ statistic and the Akaike's information criterion (AIC). The results indicate that the stochastic properties of the wind field around the bridge site during typhoons are effectively characterized by the proposed EM algorithm-based angular-linear modeling approach. The formulated joint distribution of the wind speed and direction can serve as a solid foundation for the purpose of accurately evaluating the typhoon-induced fatigue damage of long-span bridges.

• 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.

• Journal of Korean Society of Disaster and Security
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• v.16 no.4
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• pp.123-128
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• 2023

This paper presents the results of an analysis of the discrimination accuracy of a bridge load carrying capacity estimation model based on data from inspection reports. The load carrying rate estimation model was derived using statistical methods through the collection of 2,161 inspection reports. By entering the bridge specifications and maintenance information, you can check the estimated load carrying capacity of the bridge. In order to verify the discrimination accuracy of the estimated load carrying rate model, the estimated load carrying rate was compared with the load carrying rate in the inspection and diagnosis report for 164 public bridges for which data was available. Although there are differences depending on the bridge type, the results were obtained with an accuracy of over 80% in determining the estimated load carrying capacity.

• Smart Structures and Systems
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• v.17 no.3
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• pp.445-470
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• 2016

In this study, the Bayesian probabilistic framework is investigated for modal identification and modal identifiability based on the field measurements provided in the structural health monitoring benchmark problem of an instrumented cable-stayed bridge named Ting Kau Bridge (TKB). The comprehensive structural health monitoring system on the cable-stayed TKB has been operated for more than ten years and it is recognized as one of the best test-beds with readily available field measurements. The benchmark problem of the cable-stayed bridge is established to stimulate investigations on modal identifiability and the present paper addresses this benchmark problem from the Bayesian prospective. In contrast to deterministic approaches, an appealing feature of the Bayesian approach is that not only the optimal values of the modal parameters can be obtained but also the associated estimation uncertainty can be quantified in the form of probability distribution. The uncertainty quantification provides necessary information to evaluate the reliability of parametric identification results as well as modal identifiability. Herein, the Bayesian spectral density approach is conducted for output-only modal identification and the Bayesian model class selection approach is used to evaluate the significance of different modes in modal identification. Detailed analysis on the modal identification and modal identifiability based on the measurements of the bridge will be presented. Moreover, the advantages and potentials of Bayesian probabilistic framework on structural health monitoring will be discussed.

• Interaction and multiscale mechanics
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• v.3 no.2
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• pp.173-191
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• 2010

Damage detection plays a very important role to the maintenance of bridge structures. Traditional damage detection methods are usually based on structural dynamic properties, which are acquired from pre-installed sensors on the bridge. This is not only time-consuming and costly, but also suffers from poor sensitivity to damage if only natural frequencies and mode shapes are concerned in a noisy environment. Recently, the idea of using the dynamic responses of a passing vehicle shows a convenient and economical way for damage detection of bridge structures. Inspired by this new idea and the well-established tap test in the field of non-destructive testing, this paper proposes a new method for obtaining the damage information through the acceleration of a passing vehicle enhanced by a tapping device. Since no finger-print is required of the intact structure, this method can be easily implemented in practice. The logistics of this method is illustrated by a vehicle-bridge interaction model, along with the sensitivity analysis presented in detail. The validity of the method is proved by some numerical examples, and remarks are given concerning the potential implementation of the method as well as the directions for future research.

• Journal of KIBIM
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• v.3 no.3
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• pp.1-8
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• 2013

In general, a two-dimensional platform were used to manage the structural inspection information. But we performed a study on utilizing 3D model to input and display the information of structure inspection. Coarse and Fine model of structure were used to input the information. 3D model combined with database built from record plan and field inspections data and rating will provide more intuitive and effective environment for inspectors in bridge maintenance.