Service-Life Evaluation of Existing Bridges Subjected to Static and Moving Trucks Using Structural Health Monitoring System: Case Study

Kaloop, Mosbeh R.; Kim, Kyoung-Ho; Elbeltagi, Emad; Jin, Xinghan; Hu, Jong Wan

doi:10.1007/s12205-020-2128-1

Cited by 11 publications

(3 citation statements)

References 36 publications

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“…By incorporating a range of damage scenarios, they assessed the bridge’s response to the loading of a standard passing truck in both the time and frequency domains. Kaloop et al [ 23 ] collected static and dynamic data from the Dorim-Goh Bridge and the Tun Bridge [ 24 ], both located in Korea, using ambient trucks and information in time and frequency domains. Another example is the work of Hedegaard et al [ 25 ], who created a linear elastic finite element model of the concrete post-tensioned box girder I-35W St. Anthony Falls Bridge and validated it using truck-load tests.…”

Section: Introductionmentioning

confidence: 99%

Case Studies about Finite Element Modeling and Wireless Sensing of Three Pennsylvania Bridges

Enshaeian,

Ghahremani,

Rizzo

2024

Sensors

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Three Pennsylvanian bridges were studied using finite element and wireless sensor technology. A detailed finite element model of each bridge was created using a commercial software in order to calculate the strains generated by a load that simulates the presence of a standard truck. Pristine and damage scenarios were simulated, and the computed strains were compared to the experimental strains measured with proprietary wireless sensors during a truck test performed by companies not involved in the study presented in this article. The comparison demonstrated the accuracy of the model and the presence of a few non-critical anomalies in terms of load redistribution. In addition, the comparison proved the reliability of the wireless sensing system installed on the bridges, although some drift was observed. The structural monitoring program for the three bridges was also evaluated by processing more than two years of data streamed to a repository.

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Section: Introductionmentioning

confidence: 99%

Case Studies about Finite Element Modeling and Wireless Sensing of Three Pennsylvania Bridges

Enshaeian,

Ghahremani,

Rizzo

2024

Sensors

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“…Specifically, a Wavelet technique (see, e.g., [17,18]) is selected, as an efficient approach for this purpose. Being already successfully applied in the application fields of damage identification (see, e.g., [19][20][21]) and signal denoising (see, e.g., [9][10][11]22]), a Wavelet analysis can be specifically considered in bridge signal processing, as e.g., proposed in [23][24][25][26][27][28][29]. It is here applied to the same two channels of each side of the central span of the bridge only, assuming that the outcomes could easily be extended to the channels of the other two spans.…”

Section: Introductionmentioning

confidence: 99%

Signal Processing Methodology of Response Data from a Historical Arch Bridge toward Reliable Modal Identification

et al. 2022

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The paper is part of a case study concerning the structural assessment of a historical infrastructure in the local territory, a road three-span reinforced concrete arch bridge over a river, built by the end of World War I (1917). The purpose of the paper is twofold: first, in-situ acquired response data are systematically analysed by specific signal processing techniques, to form a devoted methodological procedure and to extract useful information toward possible interpretation of the current structural conditions; second, the deciphered information is elaborated, in view of obtaining peculiar conceptualisations of detailed features of the structural response, as meant to achieve quantitative descriptions and modelling, for final Structural Health Monitoring (SHM) and intervention purposes. The proposed methodology, integrating self-implemented and adapted classical signal processing methods, and refined techniques, such as Wavelet analysis and ARMA models, assembles a rather general, systematic methodological approach to signal processing, highlighting the capability to extract useful and fundamental information from acquired response data, also endowed of a non-stationary character, toward final structural interpretation, identification and modelling, thus enabling for developing a reliable and effective SHM platform, on strategic ageing infrastructures. For the present case study, non-stationary characteristics of the response signals are revealed and flattened out, to identify the underlying fundamental frequencies of the infrastructure and to advance particular interpretations of its current structural behaviour, informing an enlarging structural consciousness of the bridge at hand.

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“…As one of the main methods for bridge condition assessment, reliability theory has been widely used to evaluate the safety and serviceability of bridges [25][26][27][28]. When combined with the SHM data of deflection, strain, stress, and so on, it can predict the time-dependent reliability and residual life of a bridge [29][30][31][32][33][34][35][36]. As the most widely used parameter in SHM, modal frequency is usually adopted to reflect the dynamic characteristics and to identify structural damage.…”

Section: Introductionmentioning

confidence: 99%

Reliability Assessment Method for Simply Supported Bridge Based on Structural Health Monitoring of Frequency with Temperature and Humidity Effect Eliminated

Tan

Chu

et al. 2022

Sustainability

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Structural health monitoring (SHM) has been widely used for the performance assessment of bridges, especially the methods based on dynamic characteristics. Meanwhile, bridge modal frequency is influenced significantly by environmental factors, such as temperature and humidity. Combined with SHM, a reliability assessment of bridges with the temperature and humidity effects eliminated is proposed. Firstly, the bridge deflection verification coefficient is adopted as the evaluation indicator for bridge condition, which is the ratio of deflection-measured value to deflection-calculated value. It is obtained from the relationship between verification coefficient and modal frequency through theoretical derivation. Secondly, a back propagation (BP) neural network is improved by using an artificial bee colony algorithm and employed as a surrogate model to eliminate the effect of temperature and humidity on frequency. Thirdly, a dynamic Bayesian network is applied to establish the reliability analysis model combined with the monitoring results, so that the probability distribution of bridge parameters is updated to improve the accuracy of the reliability analysis. Finally, a simply supported bridge is used as the case study, based on the proposed method in this work. The results indicate that the proposed method can eliminate the temperature and humidity effect on frequency precisely and effectively. With the effect of temperature and humidity on frequency eliminated, the bridge condition assessment can be evaluated accurately through the reliability analysis based on SHM and the dynamic Bayesian network.

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Service-Life Evaluation of Existing Bridges Subjected to Static and Moving Trucks Using Structural Health Monitoring System: Case Study

Cited by 11 publications

References 36 publications

Case Studies about Finite Element Modeling and Wireless Sensing of Three Pennsylvania Bridges

Case Studies about Finite Element Modeling and Wireless Sensing of Three Pennsylvania Bridges

Signal Processing Methodology of Response Data from a Historical Arch Bridge toward Reliable Modal Identification

Reliability Assessment Method for Simply Supported Bridge Based on Structural Health Monitoring of Frequency with Temperature and Humidity Effect Eliminated

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