Large-scale distributed fiber optic sensing network for short and long-term integrity monitoring of tunnel linings

Monsberger, Christoph; Bauer, Peter; Buchmayer, Fabian; Lienhart, Werner

doi:10.1007/s13349-022-00560-w

Cited by 19 publications

(3 citation statements)

References 9 publications

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“…Proper decision-making will ensure the reasonable structural integrity and safety of tunnels. In fact, structural health monitoring based on automation and smart techniques is becoming indispensable for the accurate prediction of tunnel safety and stability [68][69][70][71][72]. In addition, the monitoring system must be sufficient to provide all the required information regarding the health status of the tunnel.…”

Section: Discussionmentioning

confidence: 99%

Seepage Actions and Their Consequences on the Support Scheme of Deep-Buried Tunnels Constructed in Soft Rock Strata

Frenelus,

Peng,

Zhang

2024

Infrastructures

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The stability of deep soft rock tunnels under seepage conditions is of particular concern. Aiming at thoroughly discussing seepage actions and their consequences on the support schemes of such structures, the host rocks of the Weilai Tunnel situated in the Guangxi province of China are used as the research subject. Emphasis is placed on adequately examining the seepage conditions, stresses, displacements and plastic zone radii along the surrounding rocks of such tunnels, taking into consideration the Mogi–Coulomb strength criterion and the elastic-plastic theory. Explicitly, this article proposes analytical solutions for stresses, displacements and plastic radii around deep tunnels in soft rocks under seepage conditions by considering the aforesaid criterion and nonlinear elastoplastic approaches. Subsequently, based on the strain-softening model, the coupled actions of seepage and softening on the rocks surrounding the tunnel are studied. In order to investigate the effects of relevant influencing factors on tunnel stability, parametric studies are thoroughly examined. According to the results, it is revealed that the support scheme of deep soft rock tunnels must be of the highest resistance possible to better decrease the plastic zone and the tangential stress along the host rocks. Moreover, throughout the surrounding rocks, the dissemination of pore water pressure is strongly affected by the uneven permeability coefficient under anisotropic seepage states. The combined effects of softening and seepage are very dangerous for the surrounding rocks of deep-buried tunnels. It is also shown that the seepage pressure substantially affects the plastic radii and tunnel displacements. Under high seepage pressure, the surface displacements of the tunnel are excessive, easily exceeding 400 mm. To better guarantee the reasonable longevity of such tunnels, the long-term monitoring of their support structures with reliable remote sensors is strongly recommended.

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Section: Discussionmentioning

confidence: 99%

Seepage Actions and Their Consequences on the Support Scheme of Deep-Buried Tunnels Constructed in Soft Rock Strata

Frenelus,

Peng,

Zhang

2024

Infrastructures

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“…One major challenge in manufacturing fiber-optic-based sensors is their complexity and cost. The process typically involves integrating interferometers and carefully managing amplitude modulation in the output spectrum, which can be cumbersome and laborious [20].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Applications in Optical Fiber Sensing: A Research Agenda

Reyes-Vera,

Valencia-Arias,

García-Pineda

et al. 2024

Sensors

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The constant monitoring and control of various health, infrastructure, and natural factors have led to the design and development of technological devices in a wide range of fields. This has resulted in the creation of different types of sensors that can be used to monitor and control different environments, such as fire, water, temperature, and movement, among others. These sensors detect anomalies in the input data to the system, allowing alerts to be generated for early risk detection. The advancement of artificial intelligence has led to improved sensor systems and networks, resulting in devices with better performance and more precise results by incorporating various features. The aim of this work is to conduct a bibliometric analysis using the PRISMA 2020 set to identify research trends in the development of machine learning applications in fiber optic sensors. This methodology facilitates the analysis of a dataset comprised of documents obtained from Scopus and Web of Science databases. It enables the evaluation of both the quantity and quality of publications in the study area based on specific criteria, such as trends, key concepts, and advances in concepts over time. The study found that deep learning techniques and fiber Bragg gratings have been extensively researched in infrastructure, with a focus on using fiber optic sensors for structural health monitoring in future research. One of the main limitations is the lack of research on the use of novel materials, such as graphite, for designing fiber optic sensors. One of the main limitations is the lack of research on the use of novel materials, such as graphite, for designing fiber optic sensors. This presents an opportunity for future studies.

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“…DFOSs enable continuous measurements along the entire length of an optical fiber, which can be up to hundreds of kilometers. This has already made DFOSs attractive for a wide range of applications, including structural health monitoring of civil and geotechnical structures [1][2][3][4][5], pipeline and borehole monitoring for leak detection [6], seismic activity monitoring [7][8][9][10] or even the condition monitoring of high-voltage submarine cables [11] and deep earth dynamics in oceans [12]. Even though the most common measurands are temperature and strain, DFOSs can directly or indirectly measure the humidity [13][14][15], pressure [16], displacement [4,17], radiation [18][19][20], gas concentration [21,22], etc.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches in Brillouin Distributed Fiber Optic Sensors

Karapanagiotis

Krebber

2023

Sensors

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This paper presents reported machine learning approaches in the field of Brillouin distributed fiber optic sensors (DFOSs). The increasing popularity of Brillouin DFOSs stems from their capability to continuously monitor temperature and strain along kilometer-long optical fibers, rendering them attractive for industrial applications, such as the structural health monitoring of large civil infrastructures and pipelines. In recent years, machine learning has been integrated into the Brillouin DFOS signal processing, resulting in fast and enhanced temperature, strain, and humidity measurements without increasing the system’s cost. Machine learning has also contributed to enhanced spatial resolution in Brillouin optical time domain analysis (BOTDA) systems and shorter measurement times in Brillouin optical frequency domain analysis (BOFDA) systems. This paper provides an overview of the applied machine learning methodologies in Brillouin DFOSs, as well as future perspectives in this area.

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Large-scale distributed fiber optic sensing network for short and long-term integrity monitoring of tunnel linings

Cited by 19 publications

References 9 publications

Seepage Actions and Their Consequences on the Support Scheme of Deep-Buried Tunnels Constructed in Soft Rock Strata

Seepage Actions and Their Consequences on the Support Scheme of Deep-Buried Tunnels Constructed in Soft Rock Strata

Machine Learning Applications in Optical Fiber Sensing: A Research Agenda

Machine Learning Approaches in Brillouin Distributed Fiber Optic Sensors

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