• Computers and Concrete
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• v.20 no.5
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• pp.555-562
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• 2017

The accuracy and integrity of stress data acquired by bridge heath monitoring system is of significant importance for bridge safety assessment. However, the missing and abnormal data are inevitably existed in a realistic monitoring system. This paper presents a data reconstruction approach for bridge heath monitoring based on the wavelet multi-resolution analysis and support vector machine (SVM). The proposed method has been applied for data imputation based on the recorded data by the structural health monitoring (SHM) system instrumented on a prestressed concrete cable-stayed bridge. The effectiveness and accuracy of the proposed wavelet-based SVM prediction method is examined by comparing with the traditional autoregression moving average (ARMA) method and SVM prediction method without wavelet multi-resolution analysis in accordance with the prediction errors. The data reconstruction analysis based on 5-day and 1-day continuous stress history data with obvious preternatural signals is performed to examine the effect of sample size on the accuracy of data reconstruction. The results indicate that the proposed data reconstruction approach based on wavelet multi-resolution analysis and SVM is an effective tool for missing data imputation or preternatural signal replacement, which can serve as a solid foundation for the purpose of accurately evaluating the safety of bridge structures.

• Smart Structures and Systems
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• v.29 no.4
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• pp.599-616
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• 2022

Bridge displacement contains vital information for bridge condition and performance. Due to the limits of direct displacement measurement methods, the indirect displacement reconstruction methods based on the strain or acceleration data are also developed in engineering applications. There are still some deficiencies of the displacement reconstruction methods based on strain or acceleration in practice. This paper proposed a novel method based on long short-term memory (LSTM) networks to reconstruct the bridge dynamic displacements with the strain and acceleration data source. The LSTM networks with three hidden layers are utilized to map the relationships between the measured responses and the bridge displacement. To achieve the data fusion, the input strain and acceleration data need to be preprocessed by normalization and then the corresponding dynamic displacement responses can be reconstructed by the LSTM networks. In the numerical simulation, the errors of the displacement reconstruction are below 9% for different load cases, and the proposed method is robust when the input strain and acceleration data contains additive noise. The hyper-parameter effect is analyzed and the displacement reconstruction accuracies of different machine learning methods are compared. For experimental verification, the errors are below 6% for the simply supported beam and continuous beam cases. Both the numerical and experimental results indicate that the proposed data fusion method can accurately reconstruct the displacement.

• Journal of the Korea institute for structural maintenance and inspection
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• v.15 no.1
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• pp.148-158
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• 2011

As critical infrastructure, bridges play an indispensable role in facilitating the distribution of goods. When bridges reach their end of useful life or get damaged by natural disasters such as earthquakes or storms, they have to be removed and reconstructed. When bridges in service need to be reconstructed, user costs occur from vehicle detours and traffic congestions, and social costs occur from noise and dust during construction periods. However, these user and social costs are not considered during reconstruction and the evaluation methods of those costs are vague. Thus, there is lack of appropriate bridge types that consider these costs. Therefore, this paper identifies the social overhead costs that occur during bridge reconstruction, which is also called, users' socioeconomic values. Next, it proposes a method to evaluate user costs during bridge reconstruction, and appraises the method. User costs are evaluated based on traffic information, social and material volumes including the bridge's daily traffic volume, peak hours, detour distance and time. In addition, time delay costs due to traffic operational costs and bridge reconstruction are also taken into consideration.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.9
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• pp.533-542
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• 2016

The periodic maintenance of bridges is necessary once they have been constructed and its cost depends on various factors, such as their condition, environmental conditions and so on. To make a decision support system, it is essential to establish a basic reconstruction cost model. In this study, a regression model is suggested for calculating the reconstruction cost for typical cases and influential factors, depending on the type of bridge and its components, by analyzing the basic bridge specifications based on the data of the Bridge Management System (BMS). The details for each case were estimated in consideration of the cost calculation variables. The details for each case were estimated in consideration of the cost calculation variables. The cost model for the new construction of the superstructure, substructure and foundation and the temporary bridge construction and demolition costs were drawn from the regression analysis of the estimation results of typical cases according to the cost calculation variables. The reconstruction costs for different types of bridge were obtained using the cost model and compared with those in the literature. The cost model developed herein is expected to be utilized effectively in maintenance decision making.

• Earthquakes and Structures
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• v.10 no.3
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• pp.645-667
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• 2016

The 750 m long "De Bosset" bridge in the Cephalonia Island of Western Greece, being the area with the highest seismicity in Europe, was constructed in 1830 by successive stone arches and stiff block-type piers. The bridge suffered extensive damages during past earthquakes, such as the strong M7.2 earthquake of 1953, followed by poorly-designed reconstruction schemes with reinforced concrete. In 2005, a multidisciplinary project for the seismic assessment and restoration of the "De Bosset" bridge was undertaken under the auspices of the Greek Ministry of Culture. The proposed retrofitting scheme combining soil improvement, structural strengthening and reconstruction of the deteriorated masonry sections was recently applied on site. Design of the rehabilitation measures and assessment of the pre- and post-interventions seismic response of the bridge were based on detailed in-situ and laboratory tests, providing foundation soil and structural material properties. In-situ inspection of the rehabilitated bridge following the strong M6.1 and M6.0 Cephalonia earthquakes of January 26th and February 3rd 2014, respectively, revealed no damages or visible defects. The efficiency of the bridge retrofitting is also proved by a preliminary performance analysis of the bridge under the recorded ground motion induced by the above earthquakes.

• Journal of Chest Surgery
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• v.54 no.6
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• pp.539-542
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• 2021

Resection and reconstruction of the chest wall for the radical surgical treatment of malignant soft tissue tumors are currently considered a substantial challenge for thoracic surgeons. We present an unusual case of infantile fibrosarcoma with tropomyosin 3-neurotrophic receptor tyrosine kinase 1 fusion in a 13-year-old patient. The surgical treatment consisted of radical resection of the right posterior chest wall and reconstruction with the use of the STRATOS (Strasbourg Thoracic Osteosynthesis System) titanium rib bridge system. The patient had a favorable postoperative course and received respiratory-ventilatory rehabilitation, adjuvant therapy with chemotherapeutic agents, immunotherapy, and radiotherapy.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1990.10a
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• pp.105-110
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• 1990

Effective maintenance system for national highway bridges has been requested due to increasing numbers of bridges. In this study, bridge data base is established in accordance with rating criteria for the structural conditions and functions of bridges. Maintenance system using PC-80386 hardware and ORACLE software is developed to rank the priority of either reconstruction or repairing of bridges and to provide with those information to bridge maintenance staff in order to perform bridge maintenance effectively. With this pre-establised data base and computerized maintenance system, data and information for planning of bridge construction could be obtained in times.

• Wind and Structures
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• v.38 no.1
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• pp.75-91
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• 2024

Reliable wind signal reconstruction can be beneficial to the operational safety of long-span bridges. Non-Gaussian characteristics of wind signals make the reconstruction process challenging. In this paper, non-Gaussian wind signals are converted into a combined prediction of two kinds of features, actual wind speeds and wind angles of attack. First, two decomposition techniques, empirical mode decomposition (EMD) and variational mode decomposition (VMD), are introduced to decompose wind signals into intrinsic mode functions (IMFs) to reduce the randomness of wind signals. Their principles and applicability are also discussed. Then, four artificial intelligence (AI) algorithms are utilized for wind signal reconstruction by combining the particle swarm optimization (PSO) algorithm with back propagation neural network (BPNN), support vector regression (SVR), long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM), respectively. Measured wind signals from a bridge site in a deep-cutting gorge are taken as experimental subjects. The results showed that the reconstruction error of high-frequency components of EMD is too large. On the contrary, VMD fully extracts the multiscale rules of the signal, reduces the component complexity. The combination of VMD-PSO-Bi-LSTM is demonstrated to be the most effective among all hybrid models.

• Magazine of the Korea Concrete Institute
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• v.8 no.3
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• pp.62-69
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• 1996

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2009.04a
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• pp.318-319
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• 2009