Technical Papers
Apr 18, 2020

Structural Health-Monitoring and Assessment in Tunnels: Hybrid Simulation Approach

Publication: Journal of Performance of Constructed Facilities
Volume 34, Issue 4

Abstract

During the operation of tunnels, structural performance could inevitably degrade due to stochastic and adverse factors. To reduce the randomness and uncertainty of the tunnel operation, this paper provides a novel global sensitivity analysis (GSA) approach for monitoring parameters. The Wuhan Yangtze River Tunnel was utilized as a case study to verify the applicability of the proposed approach. The particle swarm optimization–least-squares support vector machine (PSO-LSSVM) was used to establish the model relationship between the input and output parameters, and the variance-based extended Fourier amplitude sensitivity test (EFAST) algorithm was employed to investigate the parameters sensitivities. Results of this GSA quantified the parameter sensitivities, and the sensitive and insensitive parameters were distinguished. The sensitive parameters can be identified as major factors for structural health monitoring and proactive maintenance in tunnels. This study evaluated the variations of sensitivity index with various target functions, parameter ranges, and distributions. The variations of parameter sensitivities were observed under various conditions, which indicated that sensitive and insensitive parameters may be different under different conditions. The GSA in this research can aid in optimizing tunnel design, construction, and operating period safety management.

Get full access to this article

View all available purchase options and get full access to this article.

Data Availability Statement

Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

This research was supported by the project funded by the National Natural Science Foundation of China (Grant Nos. 71732001 and 71801101) and the China Postdoctoral Science Foundation (Grant No. 2018M632880). The authors gratefully acknowledge the support.

References

Anoop, M. B., B. K. Raghuprasad, and K. B. Rao. 2012. “A refined methodology for durability-based service life estimation of reinforced concrete structural elements considering fuzzy and random uncertainties.” Comput.-Aided Civ. Infrastruct. Eng. 27 (3): 170–186. https://doi.org/10.1111/j.1467-8667.2011.00730.x.
Antoneli, F., F. M. Passos, L. R. Lopes, and M. R. S. Briones. 2018. “A Kolmogorov-Smirnov test for the molecular clock based on Bayesian ensembles of phylogenies.” PLoS One 13 (2): e0193633. https://doi.org/10.1371/journal.pone.0190826.
Bursi, O. S., N. Tondini, M. Fassin, and A. Bonelli. 2016. “Structural monitoring for the cyclic behaviour of concrete tunnel lining sections using FBG sensors.” Struct. Control Health Monit. 23 (4): 749–763. https://doi.org/10.1002/stc.1807.
Cusson, D., Z. Lounis, and L. Daigle. 2011. “Durability monitoring for improved service life predictions of concrete bridge decks in corrosive environments.” Comput.-Aided Civ. Infrastruct. Eng. 26 (7): 524–541. https://doi.org/10.1111/j.1467-8667.2010.00710.x.
Frangopol, D. M., and M. Soliman. 2016. “Life-cycle of structural systems: Recent achievements and future directions.” Struct. Infrastruct. Eng. 12 (1): 1–20. https://doi.org/10.1080/15732479.2014.999794.
Gao, L., B. A. Bryan, M. Nolan, J. D. Connor, X. D. Song, and G. Zhao. 2016. “Robust global sensitivity analysis under deep uncertainty via scenario analysis.” Environ. Model. Software 76 (Feb): 154–166. https://doi.org/10.1016/j.envsoft.2015.11.001.
Gao, X. Q., W. G. Qiu, and S. F. Li. 2006. “FEM analysis the boundary value of high water pressure upon lining in deep-lying one track railway tunnel.” Tunnelling Underground Space Technol. 3 (21): 350. https://doi.org/10.1016/j.tust.2005.12.169.
Helton, J. C. 1997. “Uncertainty and sensitivity analysis in the presence of stochastic and subjective uncertainty.” J. Stat. Comput. Simul. 57 (1–4): 3–76. https://doi.org/10.1080/00949659708811803.
Huang, H. W., D. M. Zhang, and B. M. Ayyub. 2017a. “An integrated risk sensing system for geo-structural safety.” J. Rock Mech. Geotech. Eng. 9 (2): 226–238. https://doi.org/10.1016/j.jrmge.2016.09.005.
Huang, H. W., Y. J. Zhang, D. M. Zhang, and B. M. Ayyub. 2017b. “Field data-based probabilistic assessment on degradation of deformational performance for shield tunnel in soft clay.” Tunnelling Underground Space Technol. 67 (Aug): 107–119. https://doi.org/10.1016/j.tust.2017.05.005.
Huang, Y., J. L. Beck, S. Wu, and H. Li. 2014. “Robust Bayesian compressive sensing for signals in structural health monitoring.” Comput.-Aided Civ. Infrastruct. Eng. 29 (3): 160–179. https://doi.org/10.1111/mice.12051.
Kala, Z., and J. Vales. 2017. “Global sensitivity analysis of lateral-torsional buckling resistance based on finite element simulations.” Eng. Struct. 134 (Mar): 37–47. https://doi.org/10.1016/j.engstruct.2016.12.032.
Kolmogorov, A. 1992. On the empirical determination of a distribution function. New York: Springer.
Koyama, Y. 2003. “Present status and technology of shield tunneling method in Japan.” Tunnelling Underground Space Technol. 18 (2–3): 145–159. https://doi.org/10.1016/S0886-7798(03)00040-3.
Lan, C. G., Z. Zhou, and J. P. Ou. 2014. “Monitoring of structural prestress loss in RC beams by inner distributed Brillouin and fiber Bragg grating sensors on a single optical fiber.” Struct. Control Health Monit. 21 (3): 317–330. https://doi.org/10.1002/stc.1563.
Lauret, P., E. Fock, and T. A. Mara. 2006. “A node pruning algorithm fulased on a Fourier amplitude sensitivity test method.” IEEE Trans. Neural Networks 17 (2): 273–293. https://doi.org/10.1109/TNN.2006.871707.
Li, D. Z., R. Jin, J. Zhou, and J. Kang. 2015. “Analysis and reduction of the uncertainties in soil moisture estimation with the L-MEB model using EFAST and ensemble retrieval.” IEEE Geosci. Remote Sens. Lett. 12 (6): 1337–1341. https://doi.org/10.1109/LGRS.2015.2399776.
Lin, S. W., K. C. Ying, S. C. Chen, and Z. J. Lee. 2008. “Particle swarm optimization for parameter determination and feature selection of support vector machines.” Expert Syst. Appl. 35 (4): 1817–1824. https://doi.org/10.1016/j.eswa.2007.08.088.
Liu, W., L. Cai, J. Chen, Y. Wang, and H. Wu. 2020. “Reliability analysis of operational metro tunnel based on a dynamic Bayesian copula model.” J. Comput. Civ. Eng. 34 (3): 05020002. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000886.
Liu, W., and L. Ding. 2020. “Global sensitivity analysis of influential parameters for excavation stability of metro tunnel.” Autom. Constr. 113 (May): 103080. https://doi.org/10.1016/j.autcon.2020.103080.
Liu, W., X. Wu, L. Zhang, and Y. Wang. 2018a. “Probabilistic analysis of tunneling-induced building safety assessment using a hybrid FE-copula model.” Struct. Infrastruct. Eng. 14 (8): 1065–1081. https://doi.org/10.1080/15732479.2017.1386691.
Liu, W. L., X. G. Wu, L. M. Zhang, Y. Y. Wang, and J. Y. Teng. 2018b. “Sensitivity analysis of structural health risk in operational tunnels.” Autom. Constr. 94 (Oct): 135–153. https://doi.org/10.1016/j.autcon.2018.06.008.
Liu, W. L., X. G. Wu, L. M. Zhang, J. J. Zheng, and J. Y. Teng. 2017. “Global sensitivity analysis of tunnel-induced building movements by a precise metamodel.” J. Comput. Civ. Eng. 31 (5): 04017037. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000681.
Liu, X., Y. Bai, Y. Yuan, and H. A. Mang. 2016. “Experimental investigation of the ultimate bearing capacity of continuously jointed segmental tunnel linings.” Struct. Infrastruct. Eng. 12 (10): 1364–1379. https://doi.org/10.1080/15732479.2015.1117115.
Ma, C. F., X. Li, and S. G. Wang. 2015. “A global sensitivity analysis of soil parameters associated with backscattering using the advanced integral equation model.” IEEE Trans. Geosci. Remote Sens. 53 (10): 5613–5623. https://doi.org/10.1109/TGRS.2015.2426194.
Mesgouez, A., S. Buis, G. Lefeuve-Mesgouez, and G. Micolau. 2017. “Use of global sensitivity analysis to assess the soil poroelastic parameter influence.” Wave Motion 72 (Jul): 377–394. https://doi.org/10.1016/j.wavemoti.2017.04.001.
Minh, L. Q., P. L. T. Duong, and M. Lee. 2018. “Global sensitivity analysis and uncertainty quantification of crude distillation unit using surrogate model based on Gaussian process regression.” Ind. Eng. Chem. Res. 57 (14): 5035–5044. https://doi.org/10.1021/acs.iecr.7b05173.
Nakamura, K., T. Matsumura, and S. Ueha. 2005. “A load cell using a fiber Bragg grating with inherent mechanical temperature compensation.” Struct. Control Health Monit. 12 (3–4): 345–355. https://doi.org/10.1002/stc.74.
Nieto, P. J. G., E. Garcia-Gonzalo, J. R. A. Fernandez, and C. D. Muniz. 2014. “Hybrid PSO-SVM-based method for long-term forecasting of turbidity in the Nalón river basin: A case study in Northern Spain.” Ecol. Eng. 73 (Dec): 192–200. https://doi.org/10.1016/j.ecoleng.2014.09.042.
Palar, P. S., L. R. Zuhal, K. Shimoyama, and T. Tsuchiya. 2018. “Global sensitivity analysis via multi-fidelity polynomial chaos expansion.” Reliab. Eng. Syst. Saf. 170 (Feb): 175–190. https://doi.org/10.1016/j.ress.2017.10.013.
Pan, Y., L. Zhang, Z. Li, and L. Ding. 2019a. “Improved fuzzy Bayesian network-based risk analysis with interval-valued fuzzy sets and DS evidence theory.” IEEE Trans. Fuzzy Syst. 1–14. https://doi.org/10.1109/TFUZZ.2019.2929024.
Pan, Y., L. Zhang, X. Wu, W. Qin, and M. J. Skibniewski. 2019b. “Modeling face reliability in tunneling: A copula approach.” Comput. Geotech. 109 (May): 272–286. https://doi.org/10.1016/j.compgeo.2019.01.027.
Pan, Y., L. Zhang, X. Wu, and M. J. Skibniewski. 2020. “Multi-classifier information fusion in risk analysis.” Inf. Fusion 60: 121–136. https://doi.org/10.1016/j.inffus.2020.02.003.
Pianosi, F., and T. Wagener. 2015. “A simple and efficient method for global sensitivity analysis based on cumulative distribution functions.” Environ. Modell. Software 67 (May): 1–11. https://doi.org/10.1016/j.envsoft.2015.01.004.
RTRI (Railway Technical Research Institute). 1997. Design standard for railway structures (shield-driventunnel). [In Japanese.] 47–61. Tokyo: Maruzen.
Safavi, H. R., and M. Esmikhani. 2013. “Conjunctive use of surface water and groundwater: Application of support vector machines (SVMs) and genetic algorithms.” Water Resour. Manage. 27 (7): 2623–2644. https://doi.org/10.1007/s11269-013-0307-2.
Saltelli, A. 2008. Global sensitivity analysis: The primer. Hoboken, NJ: Wiley.
Saltelli, A., S. Tarantola, and K. P. S. Chan. 1999. “A quantitative model-independent method for global sensitivity analysis of model output.” Technometrics 41 (1): 39–56. https://doi.org/10.1080/00401706.1999.10485594.
Sarrazin, F., F. Pianosi, and T. Wagener. 2016. “Global sensitivity analysis of environmental models: Convergence and validation.” Environ. Modell. Software 79 (May): 135–152. https://doi.org/10.1016/j.envsoft.2016.02.005.
Shu, R. Z., J. Wei, D. T. Qin, T. C. Lim, and A. Q. Zhang. 2018. “Global sensitivity analysis and dynamic optimization of multi-motor driving transmission system.” Struct. Multidiscip. Optim. 58 (2): 797–816. https://doi.org/10.1007/s00158-018-1909-3.
Sobol, I. M. 2003. “Theorems and examples on high dimensional model representation.” Reliab. Eng. Syst. Saf. 79 (2): 187–193. https://doi.org/10.1016/S0951-8320(02)00229-6.
Sobol, I. M., S. Tarantola, D. Gatelli, S. S. Kucherenko, and W. Mauntz. 2007. “Estimating the approximation error when fixing unessential factors in global sensitivity analysis.” Reliab. Eng. Syst. Saf. 92 (7): 957–960. https://doi.org/10.1016/j.ress.2006.07.001.
Spiessl, S. M., D. A. Becker, and A. Rubel. 2012. “EFAST analysis applied to a PA model for a generic HLW repository in clay.” Reliab. Eng. Syst. Saf. 107 (Nov): 190–204. https://doi.org/10.1016/j.ress.2012.04.012.
Tan, Y., and L. Zhang. 2019. “Computational methodologies for optimal sensor placement in structural health monitoring: A review.” Struct. Health Monit. 1–22. https://doi.org/10.1177/1475921719877579.
Tarantola, S., M. Nardo, M. Saisana, and D. Gatelli. 2006. “A new estimator for sensitivity analysis of model output: An application to the e-business readiness composite indicator.” Reliab. Eng. Syst. Saf. 91 (10–11): 1135–1141. https://doi.org/10.1016/j.ress.2005.11.048.
Tondini, N., O. S. Bursi, A. Bonelli, and M. Fassin. 2015. “Capabilities of a fiber Bragg grating sensor system to monitor the inelastic response of concrete sections in new tunnel linings subjected to earthquake loading.” Comput.-Aided Civ. Infrastruct. Eng. 30 (8): 636–653. https://doi.org/10.1111/mice.12106.
van Griensven, A., T. Meixner, S. Grunwald, T. Bishop, A. Diluzio, and R. Srinivasan. 2006. “A global sensitivity analysis tool for the parameters of multi-variable catchment models.” J. Hydrol. 324 (1–4): 10–23. https://doi.org/10.1016/j.jhydrol.2005.09.008.
Vanuytrecht, E., D. Raes, and P. Willems. 2014. “Global sensitivity analysis of yield output from the water productivity model.” Environ. Modell. Software 51 (Jan): 323–332. https://doi.org/10.1016/j.envsoft.2013.10.017.
Wang, J., X. Li, L. Lu, and F. Fang. 2013. “Parameter sensitivity analysis of crop growth models based on the extended Fourier amplitude sensitivity test method.” Environ. Modell. Software 48 (Oct): 171–182. https://doi.org/10.1016/j.envsoft.2013.06.007.
Wang, X., B. Shi, G. Q. Wei, S. E. Chen, H. H. Zhu, and T. Wang. 2018. “Monitoring the behavior of segment joints in a shield tunnel using distributed fiber optic sensors.” Struct. Control Health Monit. 25 (1): e2056. https://doi.org/10.1002/stc.2056.
Wang, Y., P.-C. Liao, C. Zhang, Y. Ren, X. Sun, and P. Tang. 2019. “Crowdsourced reliable labeling of safety-rule violations on images of complex construction scenes for advanced vision-based workplace safety.” Adv. Eng. Inf. 42 (Oct): 101001. https://doi.org/10.1016/j.aei.2019.101001.
Yang, F., S. R. Cao, and G. Qin. 2018. “Performance of the prestressed composite lining of a tunnel: Case study of the Yellow River Crossing Tunnel.” Int. J. Civ. Eng. 16 (2): 229–241. https://doi.org/10.1007/s40999-016-0124-0.
Zhang, L., X. Wu, H. Zhu, and S. M. AbouRizk. 2017. “Performing global uncertainty and sensitivity analysis from given data in tunnel construction.” J. Comput. Civ. Eng. 31 (6): 04017065. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000714.
Zhang, W., K. Sun, C. Z. Lei, Y. C. Zhang, H. X. Li, and B. F. Spencer. 2014. “Fuzzy analytic hierarchy process synthetic evaluation models for the health monitoring of shield tunnels.” Comput.-Aided Civ. Infrastruct. Eng. 29 (9): 676–688. https://doi.org/10.1111/mice.12091.
Zhang, Z. G., and M. S. Huang. 2014. “Geotechnical influence on existing subway tunnels induced by multiline tunneling in Shanghai soft soil.” Comput. Geotech. 56 (Mar): 121–132. https://doi.org/10.1016/j.compgeo.2013.11.008.

Information & Authors

Information

Published In

Go to Journal of Performance of Constructed Facilities
Journal of Performance of Constructed Facilities
Volume 34Issue 4August 2020

History

Received: Aug 9, 2018
Accepted: Dec 4, 2019
Published online: Apr 18, 2020
Published in print: Aug 1, 2020
Discussion open until: Sep 18, 2020

Permissions

Request permissions for this article.

Authors

Affiliations

Postdoctoral Research Fellow, School of Civil Engineering and Mechanics, Huazhong Univ. of Science and Technology, Wuhan, Hubei 430074, China. ORCID: https://orcid.org/0000-0003-3981-3693. Email: [email protected]
Professor, School of Civil Engineering and Mechanics, Huazhong Univ. of Science and Technology, Wuhan, Hubei 430074, China. Email: [email protected]
Limao Zhang, M.ASCE [email protected]
Assistant Professor, School of Civil and Environmental Engineering, Nanyang Technological Univ., 50 Nanyang Ave., Singapore 639798 (corresponding author). Email: [email protected]
Ph.D. Candidate, Dept. of Civil and Environmental Engineering, Carnegie Mellon Univ., Pittsburgh, PA 15213. Email: [email protected]
Jiaying Teng [email protected]
Lecturer, School of Economics and Management, Jilin Jianzhu Univ., Changchun 130118, China. Email: [email protected]

Metrics & Citations

Metrics

Citations

Download citation

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

Cited by

View Options

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share with email

Email a colleague

Share