Decision-making in bridge management has changed considerably in the past two decades and owners are additionally considering what types of interventions to implement, but correct decisions still need certain input. In Estonia, like in many countries, bridge management is based on inventory records and condition information. The main emphasis of this investigation is on improving the regular condition assessment. More accurate non-destructive testing methods and optimised inspection scheduling are proposed, to reduce condition assessment uncertainties. A conversion matrix for translating additional assessment results to the rating scale of the current Estonian Transport Administration management system is introduced and uncertainties in the condition state are analysed probabilistically. In addition, stochastic degradation models based on existing information are investigated to help considering uncertainties as a part of the overall management process. What impact the adopting of quantitative assessment, rather than qualitative visual inspection, may have on the suggested interventions schedule is also analysed. The probabilistic characteristics of the condition profiles of the most common bridge elements are computed using Markov Chain Monte Carlo stochastic simulation. The optimisation of inspection scheduling is performed considering the uncertainty of the initial deterioration model. When a threshold value, defined by the owner, is reached, the model is updated with assessment data to maintain the level of uncertainty below that threshold. The results confirm that deviations in the degradation model and assessment results influence the bridge condition uncertainty. Likewise, times of both inspection and intervention are influenced, which will ultimately impact the overall management reliability and costs.
AASHTO. 1993. Guidelines for Bridge Management Systems. American Association of State Highway and Transportation Officials.
https://standards.globalspec.com/std/164939/aashto-gbms
AASHTO. 2011. The Manual for Bridge Evaluation. American Association of State Highway and Transportation Officials, Subcommittee on Bridges and Structures. file:///C:/Users/kirjastus/Downloads/previews_AASHTO_MBE-2_2011_pre.pdf
Andrade, C. and Alonso, C. 2004. Test methods for on-site corrosion rate measurement of steel reinforcement in concrete by means of the polarization resistance method. Mater. Struct., 37(9), 623–643.
https://doi.org/10.1007/BF02483292
Bich, W., Cox, M. G. and Harris, P. M. 2006. Evolution of the ʻGuide to the Expression of Uncertainty in Measurement’. Metrologia, 43(4), S161–S166.
https://doi.org/10.1088/0026-1394/43/4/S01
Corotis, R. B., Ellis, H. J. and Jiang, M. 2005. Modeling of risk-based inspection, maintenance and life-cycle cost with partially observable Markov decision processes. Struct. Infrastruct. Eng., 1(1), 75–84.
https://doi.org/10.1080/15732470412331289305
COST Action TU1402. 2015. Memorandum of Understanding Action TU1402. Quantifying the Value of Structural Health. European Cooperation in Science & Technology (COST).
https://www.cost.eu/actions/TU1402 (accessed 2021-01-21).
COST Action TU1406. 2015. Memorandum of Understanding Action TU1406. Quality Specifications for Roadway Bridges Standardization at a European Technology Level (BridgeSpec). European Cooperation in Sciecne & Technology (COST).
https://www.cost.eu/actions/TU1406/#tabs|Name:overview (accessed 2021-01-21).
Denysiuk, R., Moreira, A. V., Matos, J. C., Oliveira, R. M. and Santos, A. 2017. Two-stage multiobjective optimization of maintenance scheduling for pavements. J. Infrastruct. Syst., 23(3), 04017001.
https://doi.org/10.1061/(ASCE)IS.1943-555X.0000355
Ferreira, C., Neves, L. C., Matos, J. C. and Soares, J. M. S. 2014. A degradation and maintenance model: Application to portuguese context. Proceedings of Bridge Maintenance, Safety, Management and Life Extension, 483–489.
https://doi.org/10.1201/b17063-307
Ghodoosi, F., Abu-Samra, S., Zeynalian, M. and Zayed, T. 2018. Maintenance cost optimization for bridge structures using system reliability analysis and genetic algorithms. J. Constr. Eng. Manage., 144(2), 04017116.
https://doi.org/10.1061/(ASCE)CO.1943-7862.0001435
Hajdin, R., Kušar, M., Mašović, S., Linneberg, P., Amado, J. and Tanasić, N. 2018. WG3 Technical Report. Establishment of a Quality Control Plan.
https://www.tu1406.eu/wp-content/uploads/2018/09/tu1406_wg3_digital_vf.pdf
Hässelbarth, W., Golze, M., Noack, S. and Subaric-Leitis, A. 2006. Guide to the Evaluation of Measurement Uncertainty for Quantitative Test Results.
https://opus4.kobv.de/opus4-bam/frontdoor/index/index/docId/13871 (accessed 2021-01-22).
Hofer, E. 2018. The Uncertainty Analysis of Model Results: A Practical Guide. Springer, Cham.
https://doi.org/10.1007/978-3-319-76297-5
Ilbeigi, M. and Pawar, B. 2020. A probabilistic model for optimal bridge inspection interval. Infrastructures, 5(6), 47.
https://doi.org/10.3390/infrastructures5060047
Jackson, C. H. 2011. Multi-state models for panel data: the msm package for R. J. Stat. Softw., 38(8), 1–28.
https://doi.org/10.18637/jss.v038.i08
JCGM 2008. JCGM 100:2008(E).
https://www.iso.org/sites/JCGM/GUM/JCGM100/C045315e-html/C045315e.html?csnumber=50461 (accessed 2021-01-13).
Kallen, M. J. and van Noortwijk, J. M. 2006. Statistical inference for Markov deterioration models of bridge conditions in the Netherlands. In Proceedings of the Third International Conference on Bridge Maintenance, Safety and Management (IABMAS), Porto, Portugal, July 16–19, 2006 (Cruz, P. J. S., Frangopol, D. M. and Neves, L. C., eds), pp. 16–19, Taylor & Francis, London.
Kang, M. and Adams, T. 2010. Sensitivity analysis of bridge health nidex by various element failure costs and element conditions. In TRB 89th Annual Meeting Compendium of Papers DVD, Washington, D.C., USA, January 10–14, 2010, 10-2544.
Kušar, M. 2014. Development of bridge management system for roads and highways. Doctoral dissertation. University of Ljubljana, 2014 (in Slovenian).
Kušar, M. and Šelih, J. 2014. Analysis of bridge condition on state network in Slovenia. Građevinar, 66(9), 811–822.
https://doi.org/10.14256/JCE.1047.2014
Kušar, M., Galvão, N. and Sein, S. 2019. Regular bridge inspection data improvement using non-destructive testing. In Proceedings of Life Cycle Analysis and Assessment in Civil Engineering: Towards an Integrated Vision, Ghent, Belgium, 2018 (Caspule, Taerwe and Frangopol, eds), pp.1793–1797, Taylor & Francis, London.
Minister of Economic Affairs and Infrastructure. 2018. Requirements for Road Conditions. State Gazette (in Estonian).
https://www.riigiteataja.ee/akt/102112018003?leiaKehtiv (accessed 2021-01-27).
Mirzaei, Z., Adey, B. T., Klatter, L. and Kong, J. S. 2014. The IABMAS Bridge Management Committee Overview of Existing Bridge Management Systems. International Association for Bridge Maintenance and Safety (IABMAS).
Neves, L. C. and Frangopol, D. M. 2005. Condition, safety and cost profiles for deteriorating structures with emphasis on bridges. Reliab. Eng. Syst. Saf., 89(2), 185–198.
https://doi.org/10.1016/j.ress.2004.08.018
Neves, L. C. and Frangopol, D. M. 2008. Life-cycle performance of structures: combining expert judgment and results of inspection. In Life-Cycle Civil Engineering (Neves, L. C. and Frangopol. D. M., eds), pp. 429–434, CRC Press, London.
https://doi.org/10.1201/9780203885307-66
Neves, L. A., Frangopol, D. M. and Cruz, P. J. 2006. Probabilistic lifetime-oriented multiobjective optimization of bridge maintenance: Single maintenance type. J. Struct. Eng., 132(6), 991–1005.
https://doi.org/10.1061/(ASCE)0733-9445(2006)132:6(991)
Neves, L. A., Frangopol, D. M. and Petcherdchoo, A. 2006. Probabilistic lifetime-oriented multiobjective optimization of bridge maintenance: Combination of maintenance types. J. Struct. Eng., 132(11), 1821–1834.
https://doi.org/10.1061/(ASCE)0733-9445(2006)132:11(1821)
Phares, B. M., Washer, G. A., Rolander, D. D., Graybeal, B. A. and Moore, M. 2004. Routine highway bridge inspection condition documentation accuracy and reliability. J. Bridge Eng., 9(4), 403–413.
https://doi.org/ 10.1061/(ASCE)1084-0702(2004)9:4(403)
Regan, H. M., Colyvan, M. and Burgman, M. A. 2002. A taxonomy and treatment of uncertainty for ecology and conservation biology. Ecol. Appl., 12(2), 618–628.
https://doi.org/10.1890/1051-0761(2002)012[0618:ATATOU]2.0.CO;2
Roberts, J. E. and Shepard, R. 2000. Bridge management for the 21st century. Transp. Res. Rec., 1696(1), 197–203.
https://doi.org/10.3141/1696-63
Rouhan, A. and Schoefs, F. 2003. Probabilistic modeling of inspection results for offshore structures. Struct. Saf., 25(4), 379–399.
https://doi.org/10.1016/S0167-4730(03)00016-X
Rücker, W., Hille, F. and Rohrmann, R. 2006. Guideline for the Assessment of Existing Structures. SAMCO Final Report.
Sein, S., Matos, J. C. and Idnurm, J. 2017. Statistical analysis of reinforced concrete bridges in Estonia. Baltic J. Road Bridge Eng., 12(4), 225–233.
https://doi.org/10.3846/bjrbe.2017.28
Sein, S., Idnurm, J. and Matos, J. C. 2019. Uncertainty in condition prediction of bridges based on assessment method – Case study in Estonia. International Association for Bridge and Structural Engineering (IABSE).
http://repositorium.sdum.uminho.pt/ (accessed 2021-01-13).
https://doi.org/10.2749/guimaraes.2019.1758
Sheils, E., OʼConnor, A., Schoefs, F. and Breysse, D. 2012. Investigation of the effect of the quality of inspection techniques on the optimal inspection interval for structures. Struct. Infrastruct. Eng., 8(6), 557–568.
https://doi.org/10.1080/15732479.2010.505377
Straub, D. and Faber, M. H. 2003. Modeling dependency in inspection performance. In Proceedings of the 9th International Conference on Application of Statistics and Probability in Civil Engineering, San Francisco, CA, USA, July 6–9, 2003 (Der Kiureghian, A., Madanat, S. and Pestana, J. M., eds), pp. 1123–1130, Millpress, Rotterdam.
Taffe, A. 2018. Condition assessment: From good choice of methods to reliable results that meet the customer demand. MATEC Web of Conferences, 199, 01008.
https://doi.org/10.1051/matecconf/201819901008
Taffe, A. and Gehlen, C. 2009. Methodology for the validation of NDT-CE methods using transit time measurement. In Proceedings of the 7th International Symposium on Non Destructive Testing in Civil Engineering, Nantes, France, June 30–July 3, 2009 (Derobert, X. and Abraham, O., eds), pp. 997–1002, NDTCE.
Thompson, P. D. and Shepard, R. W. 2000. AASHTO. Commonly-recognized bridge elements. In Materials for National Workshop on Commonly Recognized Measures for Maintenance, Scottsdale, AZ.
USSR Mintransstroy. 1962. Standard project. Issue 56-addition. Selection of structures of reinforced concrete precast spans without diaphragms with periodic profile frame reinforcement. Spans: 7.5 m; 10.0 m; 12.5 m; 15.0 m. Loads: N-13 and NG-60; N-18 and NK-80. Dimensions: G-6; G-7 and G-8 with pavement widths of 0.75 and 1.5 m (in Russian).
https://meganorm.ru/Index2/1/4293831/4293831715.htm (accessed 2021-01-28).
USSR Mintransstroy. 1963. Standard project. Issue 167. Reinforced concrete prefabricated spans without diaphragms with periodic profile frame reinforcement made of 35GS steel. Spans: 7.5; 10.0; 12.5 and 15.0 m. Load: N-30 and NK-80. Dimensions: G-7, G-8, G-9 and G-10,5 with pavement width of 1.0 and 1.5 m(in Russian).
https://meganorm.ru/Index2/1/4293777/4293777392.htm (accessed 2021-02-28).
Zambon, I., Vidovic, A., Strauss, A. and Matos, J. 2018. Prediction of the remaining service life of existing concrete bridges in infrastructural networks based on carbonation and chloride ingress. Smart Struct. Syst., 21(3), 305–320.
https://doi.org/10.12989/sss.2018.21.3.305
Zambon, I., Vidovic, A., Strauss, A. and Matos, J. 2019. Condition prediction of existing concrete bridges as a combination of visual inspection and analytical models of deterioration. Appl. Sci., 9(1), 148.
https://doi.org/10.3390/app9010148
Zhang, C., Zayed, T. and Hammad, A. 2008. Resource management of bridge deck rehabilitation: Jacques Cartier bridge case study. J. Constr. Eng. Manage., 134(5), 311–319.
https://doi.org/10.1061/(ASCE)0733-9364(2008)134:5(311)
