Integration of Structural Health Monitoring and Intelligent Transportation Systems for Bridge Condition Assessment: Current Status and Future Direction

Khan, Sakib Mahmud; Atamturktur, Sez; Chowdhury, Mashrur; Rahman, Mizanur

doi:10.1109/tits.2016.2520499

Cited by 71 publications

(28 citation statements)

References 52 publications

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“…Sistem pengawasan lalu lintas telah dikembangkan ke arah penerapan analisis respons jembatan terhadap perilaku arus lalu lintas dengan berbagai muatan atau selama peristiwa kritis yang menyebabkan pola getaran di luar batas normal [10]. Untuk mengukur berat dan jenis kendaraan, dapat digunakan sensor Weight in Motion (WIM) [11].…”

Section: Pendahuluanunclassified

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Integrasi Sistem Pengawasan Kesehatan Jembatan dengan Sistem Pengawasan Lalu Lintas

Wibawa

Ubay

Putra

et al. 2020

JNTETI

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Sistem pengawasan kesehatan jembatan adalah sistem pengumpulan data waktu-nyata yang terdiri atas komponen, respons, perubahan, dan proses konstruksi struktur jembatan. Tujuan sistem adalah untuk membantu menilai risiko keamanan struktural yang dapat merusak struktur jembatan dengan memanfaatkan Wireless Sensor Network (WSN) dalam pengambilan data. Sistem ini berfokus pada parameter struktur jembatan, tetapi tidak memperhatikan faktor kendaraan seperti berat kendaraan, pola kendaraan, dan jenis kendaraan yang melewati jembatan, yang juga dapat menjadi faktor penilaian kesehatan jembatan. Sistem lain, yaitu sistem kontrol lalu lintas, mengukur berat kendaraan menggunakan Weight in Motion (WIM). WIM dapat digunakan untuk memperoleh faktor-faktor kendaraan, terutama berat kendaraan, yang juga membantu dalam menilai kesehatan jembatan. Makalah ini mengusulkan integrasi kedua sistem untuk menciptakan sistem yang dapat mengukur kesehatan struktur jembatan serta memantau berat kendaraan. Melalui integrasi ini, dimungkinkan untuk mengelola waktu tidur dan bangun WSN yang membantu menghemat energi. Hasil eksperimen menunjukkan bahwa sistem dapat mengukur frekuensi alami mendekati nilai Finite Element Analysis (FEA) yang menunjukkan validitas sistem yang dikembangkan.

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Section: Pendahuluanunclassified

“…Otomasi di bidang pengawasan lalu lintas sangat dibutuhkan karena jumlah kendaraan di jalan mulai meningkat. Dengan semakin meningkatnya jumlah kendaraan, muncul tantangan untuk mengatur lalu lintas [10]. TMS merupakan sebuah sistem yang memberi saran pada sistem kendali perintah dan sistem persinyalan.…”

Section: B Traffic Monitoring System (Tms)unclassified

Integrasi Sistem Pengawasan Kesehatan Jembatan dengan Sistem Pengawasan Lalu Lintas

Wibawa

Ubay

Putra

et al. 2020

JNTETI

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“…Higher vehicle speeds also cause the spectral resolution of sensors to decrease and the extracted frequencies to shift away from their actual values [17,20]. On-going traffic has been demonstrated to actually aid in the drive-by health monitoring paradigm by increasing the acceleration amplitude associated with extracted bridge frequencies [20,22], but others note that traffic can also cause these frequencies to fluctuate by several percent as a result of vehicle masses altering the effective bridge mass [23,24,25]. Temperature has a similar effect, causing frequencies to fluctuate by several percent over a 24-hour period, and by an even larger magnitude throughout the year [21,26,27].…”

Section: Introductionmentioning

confidence: 99%

Using Drive-by Health Monitoring to Detect Bridge Damage Considering Environmental and Operational Effects

Locke¹,

Sybrandt²,

Redmond³

et al. 2018

Preprint

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Drive-by Health Monitoring utilizes accelerometers mounted on vehicles to gather dynamic response data that can be used to continuously evaluate the health of bridges faster and with less equipment than traditional structural health monitoring practices. Because vehicles and bridges create a coupled system, vehicle acceleration data contains information about bridge frequencies that can be used as health indicators. However, for drive-by health monitoring to be viable, variabilities in dynamic measurements caused by environmental and operational parameters, such as temperature, vehicle speed, traffic, and surface roughness need to be considered. In this paper, a finite element model of a simply supported bridge is developed considering the aforementioned variabilities and various levels of structural damage. Vehicle acceleration data obtained from the model is analyzed in the frequency domain and processed using a neural network architecture. This method is used to determine the relationships between noise inducing variables and changes in vehicle dynamic response spectrum; these relationships are leveraged to predict the overall health of the subject bridge. The results from this study indicate that the proposed approach can serve as a viable health monitoring strategy and should be further tested on physical bridge systems.

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“…In the United States highways are being transformed by this connectivity including the emergence of connected vehicles and intelligent transportation systems (ITS). The SHM of bridges can greatly benefit from these advances including integration of SHM data with other ITS data such as camera feeds and direct quantification of vertical loading imposed by heavy trucks [3]. This study explores the integration of three sources of data relevant to bridge SHM: wireless sensor data collected from instrumented bridges, traffic cameras monitoring traffic flow, and data from weigh-in-motion (WIM) systems.…”

Section: Introductionmentioning

confidence: 99%

Camera-based Triggering of Bridge Structural Health Monitoring Systems using a Cyber-physical System Framework

Hou

Jeong

Wang

et al. 2017

Structural Health Monitoring 2017

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A cyber-physical framework is proposed in this paper to integrate the measured response of two highway bridges and a weigh-in-motion system located along the same highway corridor. Leveraging traffic images captured by existing traffic cameras, computer vision techniques such as histogram of oriented gradients are utilized to detect trucks in images and synchronize measured vehicle-induced bridge data and truck weights with images of vehicle loads. Those images then act as a cyber-based linkage connecting the bridge monitoring systems and a weigh-in-motion system. Such a data fusion strategy makes it possible to establish a clear input-output model for each bridge system and explore the correlation between the responses of different bridges to same trucks. In addition, the camera system can also be used to trigger bridge monitoring systems intelligently to measure more truck events with a limited monitoring system power supply.

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Integration of Structural Health Monitoring and Intelligent Transportation Systems for Bridge Condition Assessment: Current Status and Future Direction

Cited by 71 publications

References 52 publications

Integrasi Sistem Pengawasan Kesehatan Jembatan dengan Sistem Pengawasan Lalu Lintas

Integrasi Sistem Pengawasan Kesehatan Jembatan dengan Sistem Pengawasan Lalu Lintas

Using Drive-by Health Monitoring to Detect Bridge Damage Considering Environmental and Operational Effects

Camera-based Triggering of Bridge Structural Health Monitoring Systems using a Cyber-physical System Framework

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