Data Envelopment Analysis for Efficiency Measurement of Bridge Resilience
DOI:
https://doi.org/10.7250/bjrbe.2022-17.585Keywords:
bridge, data envelopment analysis, efficiency, prioritise, resilience, sensitivity analysisAbstract
The resilience of a bridge is computed using different quantitative and qualitative assessment methodologies. However, the resilience score obtained by these assessment approaches is insufficient for the decision-makers for setting a priority level for bridges in need of resilience improvement. To address this issue, the present study develops a methodology using the data envelopment analysis (DEA) approach. A total of 12 bridges are selected as the decision-making units in the DEA model. This study considers the variables such as age, area, design high flood level, and finish road level of the bridge as inputs, and bridge resilience index as the output variable. Based on these variables, three frameworks are developed to compute the efficiency of bridge resilience. A variable return to scale with the output-oriented formulation of DEA is selected to compute the efficiency of bridge resilience in all three frameworks. Thus, the proposed methodology enables bridge owners to set a priority level for bridges in need of resilience improvement based on the scores of the assessment methodology.
References
Andrić, J. M., & Lu, D. G. (2017). Fuzzy methods for prediction of seismic resilience of bridges. International Journal of Disaster Risk Reduction, 22, 458–468. https://doi.org/10.1016/j.ijdrr.2017.01.001
Banerjee, S., Vishwanath, B. S., & Devendiran, D. K. (2019). Multihazard resilience of highway bridges and bridge networks: a review. Structure and Infrastructure Engineering, 15(12), 1694–1714. https://doi.org/10.1080/15732479.2019.1648526
Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078–1092. https://doi.org/10.1287/mnsc.30.9.1078
Bhowmik, B., Tripura, T., Hazra, B., & Pakrashi, V. (2019). First-order eigen-perturbation techniques for real-time damage detection of vibrating systems: Theory and applications. Applied Mechanics Reviews, 71(6), Article 060801. https://doi.org/10.1115/1.4044287
Bhowmik, B., Tripura, T., Hazra, B., & Pakrashi, V. (2020). Real time structural modal identification using recursive canonical correlation analysis and application towards online structural damage detection. Journal of Sound and Vibration, 468, Article 115101. https://doi.org/10.1016/j.jsv.2019.115101
Biondini, F., Camnasio, E., & Titi, A. (2015). Seismic resilience of concrete structures under corrosion. Earthquake Engineering & Structural Dynamics, 44(14), 2445–2466. https://doi.org/10.1002/eqe.2591
Bocchini, P., & Frangopol, D. M. (2012a). Restoration of bridge networks after an earthquake: Multicriteria intervention optimisation. Earthquake Spectra, 28(2), 427–455. https://doi.org/10.1193%2F1.4000019
Bocchini, P., & Frangopol, D. M. (2012b). Optimal resilience-and cost-based postdisaster intervention prioritisation for bridges along a highway segment. Journal of Bridge Engineering, 17(1), 117–129. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000201
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444. https://doi.org/10.1016/0377-2217(78)90138-8
Coelli, T. (1996). A guide to DEAP version 2.1: a data envelopment analysis (computer) program. CEPA Working paper 96/08, University of New England. http://www.owlnet.rice.edu/~econ380/DEAP.PDF
Decò, A., Bocchini, P., & Frangopol, D. M. (2013). A probabilistic approach for the prediction of seismic resilience of bridges. Earthquake Engineering & Structural Dynamics, 42(10), 1469–1487. https://doi.org/10.1002/eqe.2282
Domaneschi, M., & Martinelli, L. (2016). Earthquake-resilience-based control solutions for the extended benchmark cable-stayed bridge. Journal of Structural Engineering, 142(8), Article C4015009. https://doi.org/10.1061/(ASCE)ST.1943-541X.0001392
Dong, Y., & Frangopol, D. M. (2015). Risk and resilience assessment of bridges under main shock and aftershocks incorporating uncertainties. Engineering Structures, 83, 198–208. https://doi.org/10.1016/j.engstruct.2014.10.050
Dong, Y., & Frangopol, D. M. (2016). Probabilistic time-dependent multihazard life-cycle assessment and resilience of bridges considering climate change. Journal of Performance of Constructed Facilities, 30(5), Article 04016034. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000883
Emrouznejad, A., & Yang, G. L. (2018). A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016. Socio-Economic Planning Sciences, 61, 4–8. https://doi.org/10.1016/j.seps.2017.01.008
Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society: Series A (General), 120(3), 253–281. https://doi.org/10.2307/2343100
Freckleton, D., Heaslip, K., Louisell, W., & Collura, J. (2012). Evaluation of transportation network resiliency with consideration for disaster magnitude. Transportation Research Record, 2284(1), 109–116. https://doi.org/10.3141%2F2284-13
Ghasemi, S. H., & Yun Lee, J. (2021). Measuring instantaneous resilience of a highway bridge subjected to earthquake events. Transportation Research Record, 2675(9), 1681–1692. https://doi.org/10.1177%2F03611981211009546
Giunta, M. (2017). Sustainability and resilience in the rehabilitation of road infrastructures after an extreme event: An integrated approach. The Baltic Journal of Road and Bridge Engineering, 12(3), 154–160. https://doi.org/10.3846/bjrbe.2017.18
Ikpong, A., & Bagchi, A. (2015). New method for climate change resilience rating of highway bridges. Journal of Cold Regions Engineering, 29(3), Article 04014013. https://doi.org/10.1061/(ASCE)CR.1943-5495.0000079
Karamlou, A., & Bocchini, P. (2014). Optimal bridge restoration sequence for resilient transportation networks. In Structures Congress 2014, (pp. 1437–1447). Boston, Massachusetts, United States. https://doi.org/10.1061/9780784413357.127
Karamlou, A., & Bocchini, P. (2015). Computation of bridge seismic fragility by large‐scale simulation for probabilistic resilience analysis. Earthquake Engineering & Structural Dynamics, 44(12), 1959–1978. https://doi.org/10.1002/eqe.2567
Karamlou, A., & Bocchini, P. (2016). Sequencing algorithm with multiple-input genetic operators: Application to disaster resilience. Engineering Structures, 117, 591–602. https://doi.org/10.1016/j.engstruct.2016.03.038
Lin, L. C., & Hong, C. H. (2006). Operational performance evaluation of international major airports: An application of data envelopment analysis. Journal of Air Transport Management, 12(6), 342–351. https://doi.org/10.1016/j.jairtraman.2006.08.002
Lucko, G., & Rojas, E. M. (2010). Research validation: Challenges and opportunities in the construction domain. Journal of Construction Engineering and Management, 136(1), 127–135. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000025
McGuire, B., Atadero, R., Clevenger, C., & Ozbek, M. (2016). Bridge information modeling for inspection and evaluation. Journal of Bridge Engineering, 21(4), Article 04015076. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000850
McLeod, S. (2019). Z-score: definition, calculation and interpretation. Simply Psychology. https://www.simplypsychology.org/z-score.html
Minaie, E., & Moon, F. (2017). Practical and simplified approach for quantifying bridge resilience. Journal of Infrastructure Systems, 23(4), Article 04017016. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000374
Morita, H., Hirokawa, K., & Zhu, J. (2005). A slack-based measure of efficiency in context-dependent data envelopment analysis. Omega, 33(4), 357–362. https://doi.org/10.1016/j.omega.2004.06.001
Nassif, H., Ozbay, K., Deka, D., Lou, P., Zhu, Y., Na, C., Mudigonda, S., Zhu, Y., Morgul, E. F., Bartin, B., & El-Awar, A. (2017). Performance measures to assess resiliency and efficiency of transit systems. Mineta National Transit Research Consortium (Project No. CA-MNTRC-16-1242). https://scholarworks.sjsu.edu/mti_publications/226/
Nezhad, M. D., Raoufi, R., & Dalvand, A. (2022). A network-based importance measurement index for bridge security risk assessment and prioritisation. The Baltic Journal of Road and Bridge Engineering, 17(1), 1–30. https://doi.org/10.7250/bjrbe.2022-17.549
Ozbek, M. E., de la Garza, J. M., & Triantis, K. (2009). Data envelopment analysis as a decision-making tool for transportation professionals. Journal of Transportation Engineering, 135(11), 822–831. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000069
Ozbek, M. E., de la Garza, J. M., & Triantis, K. (2010). Efficiency measurement of bridge maintenance using data envelopment analysis. Journal of Infrastructure Systems, 16(1), 31–39. https://doi.org/10.1061/(ASCE)1076-0342(2010)16:1(31)
Patel, D. A., Lad, V. H., Chauhan, K. A., & Patel, K. A. (2020). Development of bridge resilience index using multicriteria decision-making techniques. Journal of Bridge Engineering, 25(10), Article 04020090. http://doi.org/10.1061/(ASCE)BE.1943-5592.0001622
Ramanathan, R. (2003). An introduction to data envelopment analysis: a tool for performance measurement (1st ed.). Sage, Thousand Oaks.
SMC. (2022). Surat Municipal Corporation. https://www.suratmunicipal.gov.in
Stevens, M., & Tuchscherer, R. (2020). Quantifying a bridge’s structural resilience. Practice Periodical on Structural Design and Construction, 25(4), Article 05020009. https://doi.org/10.1061/(ASCE)SC.1943-5576.0000517
Thanassoulis, E. (2001). Introduction to the theory and application of data envelopment analysis. Dordrecht: Kluwer Academic Publishers. https://doi.org/10.1007/978-1-4615-1407-7
Tyagi, P., Yadav, S. P., & Singh, S. P. (2009). Relative performance of academic departments using DEA with sensitivity analysis. Evaluation and Program Planning, 32(2), 168–177. https://doi.org/10.1016/j.evalprogplan.2008.10.002
Vishwanath, B. S., & Banerjee, S. (2019). Life-cycle resilience of aging bridges under earthquakes. Journal of Bridge Engineering, 24(11), Article 04019106. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001491
Vyas, G. S., & Jha, K. N. (2017). Benchmarking green building attributes to achieve cost effectiveness using a data envelopment analysis. Sustainable Cities and Society, 28, 127–134. https://doi.org/10.1016/j.scs.2016.08.028
Wakchaure, S. S., & Jha, K. N. (2011). Prioritisation of bridges for maintenance planning using data envelopment analysis. Construction Management and Economics, 29(9), 957–968. https://doi.org/10.1080/01446193.2011.614267
Wang, Y. M., Liu, J., & Elhag, T. M. (2008). An integrated AHP–DEA methodology for bridge risk assessment. Computers &Industrial Engineering, 54(3), 513–525. https://doi.org/10.1016/j.cie.2007.09.002
Yang, J. B., Wang, H. H., Wang, W. C., & Ma, S. M. (2016). Using data envelopment analysis to support best-value contractor selection. Journal of Civil Engineering and Management, 22(2), 199–209. https://doi.org/10.3846/13923730.2014.897984
Zheng, Y., Dong, Y., & Li, Y. (2018). Resilience and life-cycle performance of smart bridges with shape memory alloy (SMA)-cable-based bearings. Construction and Building Materials, 158, 389–400. https://doi.org/10.1016/j.conbuildmat.2017.10.031
Zhu, J. (2014). Quantitative models for performance evaluation and benchmarking: Data envelopment analysis with spreadsheets. Book series: International Series in Operations Research & Management Science (ISOR, volume 213), Springer. http://doi.org/10.1007/978-3-319-06647-9
Zhu, J. (2015). Data envelopment analysis: a handbook of models and methods. Book series: International Series in Operations Research & Management Science (ISOR, volume 221), Springer. https://doi.org/10.1007/978-1-4899-7553-9
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