Safety, Risk, Reliability and Quality; Building and Construction; Civil and Structural Engineering
Disciplines :
Civil engineering
Author, co-author :
BERTOLA, Numa Joy ; University of Luxembourg ; Laboratory for Maintenance and Safety of Structures, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
Schiltz, Philippe; Laboratory for Maintenance and Safety of Structures, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
Brühwiler, Eugen ; Laboratory for Maintenance and Safety of Structures, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
External co-authors :
yes
Language :
English
Title :
A global framework for data-informed bridge examination
Publication date :
08 April 2024
Journal title :
Structure and Infrastructure Engineering - Maintenance
Abdel-Jaber, H., & Glisic, B., (2019). Monitoring of prestressing forces in prestressed concrete structures—an overview. Structural Control and Health Monitoring, 26(8), e2374. doi:10.1002/stc.2374
Afshari, S. S., Enayatollahi, F., Xu, X., & Liang, X., (2022). Machine learning-based methods in structural reliability analysis: A review. Reliability Engineering & System Safety, 219, 108223. doi:10.1016/j.ress.2021.108223
Aktan, A. E., Bartoli, I., & Karaman, S. G., (2019). Technology leveraging for infrastructure asset management: Challenges and opportunities. Frontiers in Built Environment, 5, 61. doi:10.3389/fbuil.2019.00061
Alampalli, S., Frangopol, D. M., Grimson, J., Halling, M. W., Kosnik, D. E., Lantsoght, E. O. L., … Zhou, Y. E., (2021). Bridge load testing: State-of-the-practice. Journal of Bridge Engineering, 26(3), 03120002. doi:10.1061/(ASCE)BE.1943-5592.0001678
Argyris, C., Papadimitriou, C., & Panetsos, P., (2017). Bayesian optimal sensor placement for modal identification of civil infrastructures. Journal of Smart Cities, 2(2), 69–86. doi:10.18063/JSC.2016.02.001
Bah, A. S., Sanchez, T., Zhang, Y., Sasai, K., Conciatori, D., Chouinard, L., … Zufferey, N., (2022). Assessing the condition state of a concrete bridge combining visual inspection and nonlinear deterioration model. Structure and Infrastructure Engineering, 20(2), 149–164. doi:10.1080/15732479.2022.2081987
Beck, J. L., (2010). Bayesian system identification based on probability logic. Structural Control and Health Monitoring, 17(7), 825–847. doi:10.1002/stc.424
Bertola, N., & Brühwiler, E., (2021). Risk-based methodology to assess bridge condition based on visual inspection. Structure and Infrastructure Engineering, 19(4), 575–588. doi:10.1080/15732479.2021.1959621
Bertola, N., & Brühwiler, E., (2023a). Framework to evaluate the value of information for structural performance monitoring. Structure and Infrastructure Engineering, 1–20. doi:10.1080/15732479.2023.2280727
Bertola, N., & Brühwiler, E., (2023b). Predicting the usefulness of monitoring information for structural evaluations of bridges. In F., Biondini & D., Frangopol (Eds.), Life-cycle of structures and infrastructure systems: Proceedings of the Eighth International Symposium on Life-Cycle Civil Engineering (IALCCE 2023), 2–6 July, 2023.Politecnico di Milano, Milan, Italy: Taylor and Francis/CRC Press.
Bertola, N., Costa, A., & Smith, I. F. C., (2020). Strategy to validate sensor-placement methodologies in the context of sparse measurement in complex urban systems. IEEE Sensors Journal, 20(10), 5501–5509. doi:10.1109/JSEN.2020.2969470
Bertola, N., Henriques, G., & Brühwiler, E., (2023). Assessment of the information gain of several monitoring techniques for bridge structural examination. Journal of Civil Structural Health Monitoring, 13(4–5), 983–1001. doi:10.1007/s13349-023-00685-6
Bertola, N., Henriques, G., Schumacher, T., & Brühwiler, E., (2022, September 1). Engineering of existing structures: The need and place for non-destructive evaluation. NDT-CE 2022 - The International Symposium on Nondestructive Testing in Civil Engineering, Zurich, Switzerland.
Bertola, N., Pai, S. G. S., & Smith, I. F. C., (2021). A methodology to design measurement systems when multiple model classes are plausible. Journal of Civil Structural Health Monitoring, 11(2), 315–336. doi:10.1007/s13349-020-00454-9
Bertola, N., Papadopoulou, M., Vernay, D., & Smith, I. F. C., (2017). Optimal multi-type sensor placement for structural identification by static-load testing. Sensors, 17(12), 2904. doi:10.3390/s17122904
Bertola, N., Proverbio, M., & Smith, I. F. C., (2020). Framework to approximate the value of information of bridge load testing for reserve capacity assessment. Frontiers in Built Environment, 6, 65. doi:10.3389/fbuil.2020.00065
Bertola, N., Reuland, Y., & Brühwiler, E., (2023). Sensing the structural behavior: A perspective on the usefulness of monitoring information for bridge examination. Frontiers in Built Environment, 8, 1045134. doi:10.3389/fbuil.2022.1045134
Beven, K., (2006). A manifesto for the equifinality thesis. Journal of Hydrology, 320(1–2), 18–36. doi:10.1016/j.jhydrol.2005.07.007
Biondini, F., & Frangopol, D. M., (2016). Life-cycle performance of deteriorating structural systems under uncertainty: Review. Journal of Structural Engineering, 142(9), F4016001. doi:10.1061/(ASCE)ST.1943-541X.0001544
Bocchini, P., Frangopol, D., Ummenhofer, T., & Zinke, T., (2014). Resilience and sustainability of civil infrastructure: Toward a unified approach. Journal of Infrastructure Systems, 20(2), 04014004. doi:10.1061/(ASCE)IS.1943-555X.0000177
Brownjohn, J. M., (2007). Structural health monitoring of civil infrastructure. Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences, 365(1851), 589–622. doi:10.1098/rsta.2006.1925
Brownjohn, J. M. W., De Stefano, A., Xu, Y.-L., Wenzel, H., & Aktan, A. E., (2011). Vibration-based monitoring of civil infrastructure: Challenges and successes. Journal of Civil Structural Health Monitoring, 1(3-4), 79–95. doi:10.1007/s13349-011-0009-5
Cao, W.-J., Koh, C. G., & Smith, I. F. C., (2019). Enhancing static-load-test identification of bridges using dynamic data. Engineering Structures, 186, 410–420. doi:10.1016/j.engstruct.2019.02.041
Capellari, G., Chatzi, E., Mariani, S., & Azam, S. E., (2017). Optimal design of sensor networks for damage detection. X International Conference on Structural Dynamics, EURODYN 2017, Rome, Italy (vol. 199, pp. 1864–1869).
Catbas, F., Kijewski-Correa, T., Lynn, T., & Aktan, A., (2013). Structural identification of constructed systems. Reston, VI: American Society of Civil Engineers.
Catbas, F. N., Susoy, M., & Frangopol, D. M., (2008). Structural health monitoring and reliability estimation: Long span truss bridge application with environmental monitoring data. Engineering Structures, 30(9), 2347–2359. doi:10.1016/j.engstruct.2008.01.013
Cheng, H., Chen, H., Jiang, G., & Yoshihira, K., (2007). Nonlinear feature selection by relevance feature vector machine. In P. Perner (Ed.), Machine Learning and Data Mining in Pattern Recognition. MLDM 2007. Lecture Notes in Computer Science (Vol. 4571, pp. 144–159). Berlin, Heidelberg: Springer.
Cosenza, E., & Losanno, D., (2021). Assessment of existing reinforced-concrete bridges under road-traffic loads according to the new Italian guidelines. Structural Concrete, 22(5), 2868–2881. doi:10.1002/suco.202100147
COST Action TU1402. (2014). Home—COST action TU1402—quantifying the value of structural health monitoring—COST action TU 1402. Retrieved from https://www.Cost-Tu1402.Eu. https://www.cost-tu1402.eu/.
Ercan, T., & Papadimitriou, C., (2021). Optimal sensor placement for reliable virtual sensing using modal expansion and information theory. Sensors, 21(10), 3400. doi:10.3390/s21103400
Farrar, C. R., & Worden, K., (2010). An introduction to structural health monitoring. In New trends in vibration based structural health monitoring (pp. 1–17). New York City: Springer.
Feng, M. Q., Kim, D. K., Yi, J.-H., & Chen, Y., (2004). Baseline models for bridge performance monitoring. Journal of Engineering Mechanics,130(5), 562–569. doi:10.7916/D8WQ0DNT
Goulet, J.-A., Kripakaran, P., & Smith, I. F. C., (2010). Multimodel structural performance monitoring. Journal of Structural Engineering, 136(10), 1309–1318. doi:10.1061/(ASCE)ST.1943-541X.0000232
Goulet, J.-A., & Smith, I. F. C., (2013). Structural identification with systematic errors and unknown uncertainty dependencies. Computers & Structures, 128, 251–258. doi:10.1016/j.compstruc.2013.07.009
Groβe, C. U., Arndt, R. W., Mähner, D., Niederleithinger, E., & Taffe, A., (2019). Zerstörungsfreie Prüfung im Bauwesen: Memorandum zur Lehre an deutschsprachigen Hochschulen. Bautechnik, 96(4), 360–368. doi:10.1002/bate.201900008
Kamariotis, A., Chatzi, E., & Straub, D., (2022). Value of information from vibration-based structural health monitoring extracted via Bayesian model updating. Mechanical Systems and Signal Processing, 166, 108465. doi:10.1016/j.ymssp.2021.108465
Kot, P., Muradov, M., Gkantou, M., Kamaris, G. S., Hashim, K., & Yeboah, D., (2021). Recent advancements in non-destructive testing techniques for structural health monitoring. Applied Sciences, 11(6), 2750. doi:10.3390/app11062750
Lantsoght, E. O. L., van der Veen, C., de Boer, A., & Hordijk, D. A., (2017). State-of-the-art on load testing of concrete bridges. Engineering Structures, 150, 231–241. doi:10.1016/j.engstruct.2017.07.050
Lydon, M., Taylor, S. E., Robinson, D., Mufti, A., & Brien, E. J. O., (2016). Recent developments in bridge weigh in motion (B-WIM). Journal of Civil Structural Health Monitoring, 6(1), 69–81. doi:10.1007/s13349-015-0119-6
Mai, C. V., Spiridonakos, M. D., Chatzi, E. N., & Sudret, B., (2016). Surrogate modeling for stochastic dynamical systems by combining nonlinear autoregressive with exogenous input models and polynomial chaos expansions. International Journal for Uncertainty Quantification, 6(4), 313–339. https://www.dl.begellhouse.com/journals/52034eb04b657aea,55c0c92f02169163,396de98e329da131.html. doi:10.1615/Int.J.UncertaintyQuantification.2016016603
Mosavi, A. A., Sedarat, H., O'Connor, S. M., Emami-Naeini, A., & Lynch, J., (2014). Calibrating a high-fidelity finite element model of a highway bridge using a multi-variable sensitivity-based optimisation approach. Structure and Infrastructure Engineering, 10(5), 627–642. doi:10.1080/15732479.2012.757793
Moustapha, M., Marelli, S., & Sudret, B., (2022). Active learning for structural reliability: Survey, general framework and benchmark. Structural Safety, 96, 102174. doi:10.1016/j.strusafe.2021.102174
OBrien, E. J., Brownjohn, J. M. W., Hester, D., Huseynov, F., & Casero, M., (2021). Identifying damage on a bridge using rotation-based bridge weigh-in-motion. Journal of Civil Structural Health Monitoring, 11(1), 175–188. doi:10.1007/s13349-020-00445-w
Pai, S. G. S., Nussbaumer, A., & Smith, I. F. C., (2018). Comparing structural identification methodologies for fatigue life prediction of a highway bridge. Frontiers in Built Environment, 3, 73. doi:10.3389/fbuil.2017.00073
Pai, S. G. S., & Smith, I. F. C., (2022). Methodology maps for model-based sensor-data interpretation to support civil-infrastructure management. Frontiers in Built Environment, 8, 801583. doi:10.3389/fbuil.2022.801583
Pasquier, R., & Smith, I. F. C., (2016). Iterative structural identification framework for evaluation of existing structures. Engineering Structures, 106, 179–194. doi:10.1016/j.engstruct.2015.09.039
Popper, K., (2005). The logic of scientific discovery. London, UK: Routledge.
Proverbio, M., Bertola, N., & Smith, I. F. C., (2018). Outlier-detection methodology for structural identification using sparse static measurements. Sensors18(6), 1702. doi:10.3390/s18061702
Proverbio, M., Costa, A., & Smith, I. F. C., (2018). Adaptive sampling methodology for structural identification using radial-basis functions. Journal of Computing in Civil Engineering, 32(3), 04018008. doi:10.1061/(ASCE)CP.1943-5487.0000750
Proverbio, M., Favre, F.-X., & Smith, I. F. C., (2018). Comparison of model-based identification methods for reserve-capacity assessment of existing bridges. IABSE 2018. Copenhagen, Denmark.
Proverbio, M., Vernay, D. G., & Smith, I. F. C., (2018). Population-based structural identification for reserve-capacity assessment of existing bridges. Journal of Civil Structural Health Monitoring, 8(3), 363–382. doi:10.1007/s13349-018-0283-6
Robert-Nicoud, Y., Raphael, B., & Smith, I. F. C., (2005). System identification through model composition and stochastic search. Journal of Computing in Civil Engineering, 19(3), 239–247. doi:10.1061/(ASCE)0887-3801(2005)19:3(239)
Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., … Tarantola, S., (2008). Global sensitivity analysis: The primer. Hoboken, NJ: John Wiley & Sons.
Sawicki, B., & Brühwiler, E., (2022). Quantification of influence of monitoring duration on measured traffic action effects on fatigue of RC deck slabs of road bridges. Structure and Infrastructure Engineering, 18(10–11), 1442–1456. doi:10.1080/15732479.2022.2059527
Schellenberg, K., Vogel, T., Chèvre, M., & Alvarez, M., (2013). Assessment of bridges on the Swiss national roads. Structural Engineering International, 23(4), 402–410. doi:10.2749/101686613X13627351081759
Schlune, H., Plos, M., & Gylltoft, K., (2009). Improved bridge evaluation through finite element model updating using static and dynamic measurements. Engineering Structures, 31(7), 1477–1485. doi:10.1016/j.engstruct.2009.02.011
Schlune, H., Plos, M., & Gylltoft, K., (2012). Safety formats for non-linear analysis of concrete structures. Magazine of Concrete Research, 64(7), 563–574. doi:10.1680/macr.11.00046
Schneider, J., (2006). Introduction to safety and reliability of structures (Vol. 5). Zürich, Switzerland: IABSE.
Schwab, K., & Sala-I-Martin, X., (2017). World economic forum’s global competitiveness report, 2016–2017. Available from http://www3. Weforum. Org/Docs/GCR2016-2017/05FullReport/TheGlobalCompetitivenessReport2016-2017_FINAL. Pdf.
Šidák, Z., (1967). Rectangular confidence regions for the means of multivariate normal distributions. Journal of the American Statistical Association, 62(318), 626–633. doi:10.1080/01621459.1967.10482935
Smith, I. F. C., (2016). Studies of sensor data interpretation for asset management of the built environment. Frontiers in Built Environment, 2, 2–8. doi:10.3389/fbuil.2016.00008
Swiss Society of Engineers and Architects. (2011). SIA 269—existing structures (pp. 28). Zürich, Switzerland.
Wang, X., Niederleithinger, E., & Hindersmann, I., (2022). The installation of embedded ultrasonic transducers inside a bridge to monitor temperature and load influence using coda wave interferometry technique. Structural Health Monitoring, 21(3), 913–927. doi:10.1177/14759217211014430
Worden, K., & Dulieu-Barton, J. M., (2004). An overview of intelligent fault detection in systems and structures. Structural Health Monitoring, 3(1), 85–98. doi:10.1177/1475921704041866
Yang, D. Y., & Frangopol, D. M., (2019). Societal risk assessment of transportation networks under uncertainties due to climate change and population growth. Structural Safety, 78, 33–47. doi:10.1016/j.strusafe.2018.12.005
Ye, C., Kuok, S.-C., Butler, L. J., & Middleton, C. R., (2022). Implementing bridge model updating for operation and maintenance purposes: Examination based on UK practitioners’ views. Structure and Infrastructure Engineering, 18(12), 1638–1657. doi:10.1080/15732479.2021.1914115
Zheng, R., & Ellingwood, B. R., (1998). Role of non-destructive evaluation in time-dependent reliability analysis. Structural Safety, 20(4), 325–339. doi:10.1016/S0167-4730(98)00021-6
