Resilience-based multi-objective optimization for determining the optimal sequence of bridge retrofit projects

• Autorzy: Nezhad Mehdi Dezfuli , Raoufi Reza , Dalvand Ahmad

Identyfikatory

DOI

10.7409/rabdim.022.017

Warianty tytułu

PL

Zastosowanie optymalizacji wielokryterialnej do wyznaczenia optymalnej kolejności modernizacji obiektów mostowych z uwzględnieniem odporności na zakłócenia sieci transportowej

• Języki publikacji: EN PL

Abstrakty

EN

This study intends to provide a methodology for determination of the optimal sequence of bridge retrofit projects in the pre-disaster phase. A two-stage optimization model is proposed. In the first stage, single-objective optimization is used, and the weighted average number of reliable independent pathways (WIPW) is adopted as the measure of network resilience (MOR) to be maximized. In the second stage, multi-objective optimization is used, and two objective functions are introduced to be maximized: the measure of strategy implementation sequence (MOS) and the measure of strategy implementation time (MOT). The proposed methodology is illustrated using a hypothetical community road system. The results show that there is an inverse relationship between MOS and MOT. By considering these two new objectives in the process of pre-disaster risk mitigation planning, network owners can determine the trade-off between MOS and MOT and select a proper sequence of bridge retrofit projects based on predictability of the examined disruptive events.

PL

Celem pracy jest przedstawienie metodyki określania optymalnej kolejności planowanych modernizacji obiektów mostowych w fazie poprzedzającej wystąpienie katastrofy budowlanej. Zaproponowano dwustopniowy model optymalizacji. W pierwszym etapie wykorzystuje się optymalizację jednokryterialną, a jako miarę zapewnienia maksymalnej odporności na zakłócenia sieci transportowej (MOR) przyjmuje się średnią ważoną z liczby niezawodnych, niezależnych ścieżek (WIPW) między jej węzłami. W drugim etapie stosowana jest optymalizacja wielokryterialna, przy czym dla osiągnięcia maksymalnej odporności na zakłócenia sieci wprowadza się dwie funkcje celu: miarę kolejności wdrażania strategii (MOS) oraz miarę czasu realizacji strategii (MOT). Proponowaną metodykę zilustrowano na przykładzie hipotetycznej sieci dróg lokalnych. Wyniki przeprowadzonej analizy wykazały, że między parametrami MOS i MOT występuje korelacja ujemna. Uwzględniając te dwie nowe funkcje celu w procesie planowania ograniczenia ryzyka przed katastrofą, zarządcy dróg mogą określić kompromis w relacji pomiędzy wartościami MOS oraz MOT i w ten sposób w oparciu o analizę przewidywalności wystąpienia zdarzeń zaburzających funkcjonowanie sieci transportowej dokonać wyboru optymalnej kolejności modernizacji obiektów mostowych.

Słowa kluczowe

EN

bridge retrofitting; multi-objective optimization; optimal sequence; resilience transportation networks; risk mitigation

PL

modernizacja mostów; odporność sieci transportowych; optymalizacja wielokryterialna; optymalna sekwencja; zarządzanie ryzykiem

• Wydawca: Instytut Badawczy Dróg i Mostów
• Czasopismo: Roads and Bridges - Drogi i Mosty
• Rocznik: 2022
• Tom: Vol. 21, no. 4
• Strony: 293--308
• Opis fizyczny: Bibliogr. 35 poz., rys., tab.

Twórcy

autor

Nezhad Mehdi Dezfuli

• Islamic Azad University of Ahvaz, Department of Civil Engineering, Farhangshahr, Golestan Blvd., 6134937333 Ahvaz, Iran

• https://orcid.org/0000-0003-1386-2155

autor

Raoufi Reza

• Islamic Azad University of Ahvaz, Department of Civil Engineering, Farhangshahr, Golestan Blvd., 6134937333 Ahvaz, Iran

• https://orcid.org/0000-0002-0328-1886

autor

Dalvand Ahmad

• Islamic Azad University of Ahvaz, Department of Civil Engineering, Farhangshahr, Golestan Blvd., 6134937333 Ahvaz, Iran

• https://orcid.org/0000-0003-3042-451X

Bibliografia

• 1. Twumasi-Boakye R., Sobanjo J.O.: Resilience of regional transportation networks subjected to hazard-induced bridge damages. Journal of Transportation Engineering, Part A: Systems, 144, 10, 2018, 04018062, DOI: 10.1061/JTEPBS.0000186
• 2. Li Z., Jin C., Hu P., Wang C.: Resilience-based transportation network recovery strategy during emergency recovery phase under uncertainty. Reliability Engineering & System Safety, 188, 2019, 503-514, DOI: 10.1016/j.ress.2019.03.052
• 3. Twumasi-Boakye R., Sobanjo J.O.: Evaluating transportation user costs based on simulated regional network models. Transportation Research Record, 2612, 1, 2017, 121-131, DOI: 10.3141/2612-14
• 4. Vugrin E.D., Turnquist M.A., Brown N.J.: Optimal recovery sequencing for enhanced resilience and service restoration in transportation networks. International Journal of Critical Infrastructures, 10, 3-4, 2014, 218-246, DOI: 10.1504/IJCIS.2014.066356
• 5. Hosseini S., Barker K., Ramirez-Marquez J.E.: A review of definitions and measures of system resilience. Reliability Engineering & System Safety, 145, 2016, 47-61, DOI: 10.1016/j.ress.2015.08.006
• 6. National Infrastructure Advisory Council: Critical infrastructure resilience: Final report and recommendations. Washington, D.C., 2009
• 7. Kameshwar S., Cox D.T., Barbosa A.R., Farokhnia K., Park H., Alam M.S., van de Lindt J.W.: Probabilistic decision-support framework for community resilience: Incorporating multi-hazards, infrastructure interdependencies, and resilience goals in a Bayesian network. Reliability Engineering & System Safety, 191, 2019, ID article 106568, DOI: 10.1016/j.ress.2019.106568
• 8. Bruneau M., Chang S.E., Eguchi R.T., Lee G.C., O’Rourke T.D., Reinhorn A.M., Shinozuka M., Tierney K., Wallace W.A., von Winterfeldt D.: A framework to quantitatively assess and enhance the seismic resilience of communities. Earthquake spectra, 19, 4, 2003, 733-752, DOI: 10.1193/1.1623497
• 9. Frangopol D.M., Nakib R.: Redundancy in highway bridges. Engineering Journal, 28, 1, 1991, 45-50
• 10. Ghosn M., Moses F., Frangopol D.M.: Redundancy and robustness of highway bridge superstructures and substructures. Structure and Infrastructure Engineering, 6, 1-2, 2010, 257-278, DOI: 10.1080/15732470802664498
• 11. Zhang W., Wang N.: Resilience-based risk mitigation for road networks. Structural Safety, 62, 2016, 57-65, DOI: 10.1016/j.strusafe.2016.06.003
• 12. Faturechi R., Miller-Hooks E.: Measuring the performance of transportation infrastructure systems in disasters: A comprehensive review. Journal of Infrastructure Systems, 21, 1, 2015, ID article 04014025, DOI: 10.1061/(ASCE)IS.1943-555X.0000212
• 13. Wan C., Yang Z., Zhang D., Yan X., Fan S.: Resilience in transportation systems: a systematic review and future directions. Transport Reviews, 38, 4, 2018, 479-498, DOI: 10.1080/01441647.2017.1383532
• 14. Zhou Y., Wang J., Yang H.: Resilience of transportation systems: concepts and comprehensive review. IEEE Transactions on Intelligent Transportation Systems, 20, 12, 2019, 4262-4276, DOI: 10.1109/TITS.2018.2883766
• 15. Liu C., Fan Y., Ordóńez F.: A two-stage stochastic programming model for transportation network protection. Computers & Operations Research, 36, 5, 2009, 1582-1590, DOI: 10.1016/j.cor.2008.03.001
• 16. Chang L., Peng F., Ouyang Y., Elnashai A.S., Spencer B.F. Jr.: Bridge seismic retrofit program planning to maximize postearthquake transportation network capacity. Journal of Infrastructure Systems, 18, 2, 2012, 75-88, DOI: 10.1061/(ASCE)IS.1943-555X.0000082
• 17. Dong Y., Frangopol D.M., Saydam D.: Pre-earthquake multi-objective probabilistic retrofit optimization of bridge networks based on sustainability. Journal of Bridge Engineering, 19, 6, 2014, ID article 04014018, DOI: 10.1061/(ASCE)BE.1943-5592.0000586
• 18. Lu J., Atamturktur S., Huang Y.: Bi-level resource allocation framework for retrofitting bridges in a transportation network. Transportation Research Record, 2550, 1, 2016, 31-37, DOI: 10.3141/2550-05
• 19. Liu K., Zhai C., Dong Y.: Optimal restoration schedules of transportation network considering resilience. Structure and Infrastructure Engineering, 17, 8, 2021, 1141-1154, DOI: 10.1080/15732479.2020.1801764
• 20. Bocchini P., Frangopol D.M.: Optimal resilience-and cost-based postdisaster intervention prioritization for bridges along a highway segment. Journal of Bridge Engineering, 17, 1, 2012, 117-129, DOI: 10.1061/(ASCE)BE.1943-5592.0000201
• 21. Zhang W., Wang N., Nicholson C.: Resilience-based post-disaster recovery strategies for road-bridge networks. Structure and Infrastructure Engineering, 13, 11, 2017, 1404-1413, DOI: 10.1080/15732479.2016.1271813
• 22. Decň A., Bocchini P., Frangopol D.M.: A probabilistic approach for the prediction of seismic resilience of bridges. Earthquake Engineering & Structural Dynamics, 42, 10, 2013, 1469-1487, DOI: 10.1002/eqe.2282
• 23. Bocchini P., Frangopol D.M.: Restoration of bridge networks after an earthquake: Multicriteria intervention optimization. Earthquake Spectra, 28, 2, 2012, 427-455, DOI: 10.1193/1.4000019
• 24. Frangopol D.M., Bocchini P.: Resilience as optimization criterion for the rehabilitation of bridges belonging to a transportation network subject to earthquake. Structures Congress 2011, Las Vegas, 2011, 2044-2055, DOI: 10.1061/41171(401)178
• 25. Karamlou A., Bocchini P.: Optimal bridge restoration sequence for resilient transportation networks. Structures Congress 2014, Boston, 2014, 1437-1447, DOI: 10.1061/9780784413357.127
• 26. Merschman E., Doustmohammadi M., Salman A.M., Anderson M.: Postdisaster decision framework for bridge repair prioritization to improve road network resilience. Transportation research record, 2674, 3, 2020, 81-92, DOI: 10.1177/0361198120908870
• 27. Liu Y., McNeil S., Hackl J., Adey B.T.: Prioritizing transportation network recovery using a resilience measure. Sustainable and Resilient Infrastructure, 7, 1, 2022, 70-81, DOI: 10.1080/23789689.2019.1708180
• 28. Zhang X., Miller-Hooks E., Denny K.: Assessing the role of network topology in transportation network resilience. Journal of Transport Geography, 46, 2015, 35-45, DOI: 10.1016/j.jtrangeo.2015.05.006
• 29. Faturechi R., Miller-Hooks E.: Travel time resilience of roadway networks under disaster. Transportation Research Part B: Methodological, 70, 2014, 47-64, DOI: 10.1016/j.trb.2014.08.007
• 30. Liao T.Y., Hu T.Y., Ko Y.N.: A resilience optimization model for transportation networks under disasters. Natural Hazards, 93, 1, 2018, 469-489, DOI: 10.1007/s11069-018-3310-3
• 31. Zhang W., Wang N., Nicholson C., Tehrani M.H.: A stage-wise decision framework for transportation network resilience planning. arXiv:1808.03850, 2018, DOI: 10.48550/arXiv.1808.03850
• 32. Zhang N., Alipour A.: Two-Stage Model for Optimized Mitigation and Recovery of Bridge Network with Final Goal of Resilience. Transportation Research Record, 2674, 10, 2020, 114-123, DOI: 10.1177/0361198120935450
• 33. Sun W., Bocchini P., Davison B.D.: Resilience metrics and measurement methods for transportation infrastructure: the state of the art. Sustainable and Resilient Infrastructure, 5, 3, 2020, 168-199, DOI: 10.1080/23789689.2018.1448663
• 34. Ip W.H., Wang D.: Resilience evaluation approach of transportation networks, in: 2009 International Joint Conference on Computational Sciences and Optimization, 2, 2009, 618-622, DOI: 10.1109/CSO.2009.294
• 35. Ip W.H., Wang D.: Resilience and friability of transportation networks: evaluation, analysis and optimization. IEEE Systems Journal, 5, 2, 2011, 189-198, DOI: 10.1109/JSYST.2010.2096670

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).