Data-Driven Bridge Maintenance Cost Estimation Framework for Annual Expenditure Planning
Publication: Journal of Management in Engineering
Volume 40, Issue 2
Abstract
Bridge maintenance costs have increased due to the growth in the number of facilities and their extended periods of use. The distribution of the limited maintenance budget by considering the conditions and properties of the bridge is highly required. While much research exists to provide rough estimates of maintenance costs based on indicators, there has been a lack of a framework that incorporates historical management data at the bridge element and repair method levels. This study proposed a framework for estimating bridge maintenance costs using historical records collected in the Korean bridge management system (BMS), involving several phases of data integration. First, unit cost estimation models by repair methods were generated using the extreme gradient boosting algorithm, utilizing repair project records. A total of 31 models were developed with the average weighted f1-score of 0.82. Subsequently, these models were employed to update expected repair costs for each bridge defect recorded in the inspection reports. As a result, the updated individual costs were aggregated to estimate the required expenditure of the bridge maintenance based on its condition grade, which can be used to estimate the annual maintenance expenditure for the following years. The proposed framework underwent validation focusing on bridge elements such as deck, pier/abutment, and pavement. The error rates differed with 25.37% for deck, 8.27% for pier/abutment, and 27.18% for pavement, influenced by data availability and variation element characteristics. This framework contributes to the body of knowledge regarding bridge maintenance by incorporating historical data with data-driven approaches. As a result, it assists decision-makers in gaining a better understanding of the required cost information, ultimately enhancing the decision-making process.
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Data Availability Statement
Some data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This research was respectfully supported by the BK21 PLUS research program of the National Research Foundation of Korea and supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (Nos. RS-2022-00197868, RS-2023-00241758). The authors sincerely acknowledge the bridge management data support from the Korea Institute of Civil Engineering and Building Technology (KICT).
References
Adams, T. M. 2011. Estimating cost per lane mile for routine highway operations and maintenance. Madison, WI: Midwest Regional Univ. Transportation Center.
Ali, M. 2020. “PyCaret: An open source, low-code machine learning library in Python.” Accessed September 2, 2023. https://www.pycaret.org.
Al-Mansour, A. I., and K. C. Sinha. 1994. “Economic analysis of effectiveness of pavement preventive maintenance.” Transp. Res. Rec. 1442 (1): 31–37.
ARTBA (American Road & Transportation Builders). 2023. “ARTBA bridge report.” Accessed September 2, 2023. https://artbabridgereport.org/.
Barakchi, M., O. Torp, and A. M. Belay. 2017. “Cost estimation methods for transport infrastructure: A systematic literature review.” Procedia Eng. 196 (June): 270–277. https://doi.org/10.1016/j.proeng.2017.07.199.
Barco, A. L. 1994. “Budgeting for facility repair and maintenance.” J. Manage. Eng. 10 (4): 28–34. https://doi.org/10.1061/(ASCE)9742-597X(1994)10:4(28).
Chen, T., and C. Guestrin. 2016. “XGBoost: A scalable tree boosting system.” In Proc., 22nd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD '16), 785–794. New York: Association for Computing Machinery. https://doi.org/10.1145/2939672.2939785.
Elmousalami, H. H. 2020. “Artificial intelligence and parametric construction cost estimate modeling: State-of-the-art review.” J. Constr. Eng. Manage. 146 (1): 03119008. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001678.
France-Mensah, J., and W. J. O’Brien. 2018. “Budget allocation models for pavement maintenance and rehabilitation: Comparative case study.” J. Manage. Eng. 34 (2): 1–13. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000599.
Ghahari, S. A., M. Volovski, S. Alqadhi, and M. Alinizzi. 2019. “Estimation of annual repair expenditure for interstate highway bridges.” Infrastruct. Asset Manage. 6 (1): 40–47. https://doi.org/10.1680/jinam.17.00021.
Grandini, M., E. Bagli, and G. Visani. 2020. “Metrics for multi-class classification: An overview.” Preprint, submitted August 13, 2020. https://doi.org/10.48550/arXiv.2008.05756.
Guevara, J., M. J. Garvin, and N. Ghaffarzadegan. 2017. “Capability trap of the U.S. highway system: Policy and management implications.” J. Manage. Eng. 33 (4): 1–14. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000512.
Gunning, D., M. Stefik, J. Choi, T. Miller, S. Stumpf, and G. Z. Yang. 2019. “XAI-Explainable artificial intelligence.” Sci. Rob. 4 (37): 7120. https://doi.org/10.1126/scirobotics.aay7120.
Jeong, Y., W. S. Kim, I. Lee, and J. Lee. 2018. “Bridge inspection practices and bridge management programs in China, Japan, Korea, and US.” J. Struct. Integrity Maint. 3 (2): 126–135. https://doi.org/10.1080/24705314.2018.1461548.
Ke, G., Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, and T. Y. Liu. 2017. “LightGBM: A highly efficient gradient boosting decision tree.” In Proc., 31st Int. Conf. on Neural Information Processing Systems, 3149–3157. Red Hook, NY: Curran Associates.
KICT. 2023. “Construction cost management center.” Accessed March 1, 2023. https://cost.kict.re.kr/#/.
Kotsiantis, S., and D. Kanellopoulos. 2006. “Discretization techniques: A recent survey.” GESTS Int. Trans. Comput. Sci. Eng. 32 (1): 47–58.
Lim, S., and S. Chi. 2019. “XgBoost application on bridge management systems for proactive damage estimation.” In Advanced engineering informatics, 100922. Amsterdam, Netherlands: Elsevier.
Lundberg, S. M., and S.-I. Lee. 2017. “A unified approach to interpreting model predictions.” In Proc., 31st Int. Conf. on Neural Information Processing Systems, 30, 4768–4777. Red Hook, NY: Curran Associates.
Menendez, J., S. Siabil, P. Narciso, and N. Gharaibeh. 2013. “Prioritizing infrastructure maintenance and rehabilitation activities under various budgetary scenarios.” Transp. Res. Rec. 2361 (1): 56–62. https://doi.org/10.3141/2361-07.
Miner, N., and A. Alipour. 2022. “Bridge damage, repair costs, and fragilities for inland flood events.” J. Bridge Eng. 27 (8): 04022057. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001865.
National Academies of Sciences, Engineering, and Medicine. 2020. Proposed AASHTO guides for bridge preservation actions. Washington, DC: The National Academies Press. https://doi.org/10.17226/26013.
Ottoman, G. R., W. B. Nixon, and S. T. Lofgren. 1999. “Budgeting for facility maintenance and repair I: Methods and models.” J. Manage. Eng. 15 (4): 71–83. https://doi.org/10.1061/(ASCE)0742-597X(1999)15:4(71).
Park, J.-Y., and K. Lee. 2021. “A study on life cycle cost according to bridge condition.” J. Korea Acad.–Ind. Cooperation Soc. 22 (2): 802–809. https://doi.org/10.5762/KAIS.2021.22.2.802.
Ribeiro, M. T., S. Singh, and C. Guestrin. 2016. “‘Why should I trust you?’ Explaining the predictions of any classifier.” In Proc., 22nd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 1135–1144. https://doi.org/10.1145/2939672.2939778.
Shi, X., B. Zhao, Y. Yao, and F. Wang. 2019. “Prediction methods for routine maintenance costs of a reinforced concrete beam bridge based on panel data.” Adv. Civ. Eng. 2019 (Jun): 14–24. https://doi.org/10.1155/2019/5409802.
Sobanjo, J. O., P. D. Thompson, M. Lewis, and R. Kerr. 2002. “Estimating agency cost of maintenance, repair, and rehabilitation for Florida bridges.” Transp. Res. Rec. 1795 (1): 66–73. https://doi.org/10.3141/1795-09.
Srivastava, A. K., G. Kumar, and P. Gupta. 2020. “Estimating maintenance budget using Monte Carlo simulation.” Life Cycle Reliab. Saf. Eng. 9 (1): 77–89. https://doi.org/10.1007/s41872-020-00110-7.
Thai, H. T. 2022. “Machine learning for structural engineering: A state-of-the-art review.” In Structures, 448–491. Amsterdam, Netherlands: Elsevier.
Thoft-Christensen, P. 2009. “Life-cycle cost-benefit (LCCB) analysis of bridges from a user and social point of view.” Struct. Infrastruct. Eng. 5 (1): 49–57. https://doi.org/10.1080/15732470701322818.
Thompson, P. D., and M. J. Markow. 1996. Collecting and managing cost data for bridge management systems. Washington, DC: Transportation Research Board.
Tong, B., J. Guo, and S. Fang. 2021. “Predicting budgetary estimate of highway construction projects in China Based on GRA-LASSO.” J. Manage. Eng. 37 (3): 1–10. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000890.
Volovski, M., J. Murillo-Hoyos, T. U. Saeed, and S. Labi. 2017. “Estimation of routine maintenance expenditures for highway pavement segments: Accounting for heterogeneity using random-effects models.” J. Transp. Eng. Part A. Syst. 143 (50): 04017006.
Wang, F., C.-C. B. Lee, and N. G. Gharaibeh. 2022. “Network-level bridge deterioration prediction models that consider the effect of maintenance and rehabilitation.” J. Infrastruct. Syst. 28 (1): 1–11. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000662.
Woldemariam, W. 2022. “A framework for transportation infrastructure cost prediction: A support vector regression approach.” Transp. Lett. 14 (9): 997–1003. https://doi.org/10.1080/19427867.2021.1985895.
Woldemariam, W., J. Murillo-Hoyos, and S. Labi. 2016. “Estimating annual maintenance expenditures for infrastructure: Artificial neural network approach.” J. Infrastruct. Syst. 22 (2): 1–9. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000280.
Yang, Y. N., M. M. Kumaraswamy, H. J. Pam, and G. Mahesh. 2011. “Integrated qualitative and quantitative methodology to assess validity and credibility of models for bridge maintenance management system development.” J. Manage. Eng. 27 (3): 149–158. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000051.
Information & Authors
Information
Published In
Journal of Management in Engineering
Volume 40 • Issue 2 • March 2024
Copyright
© 2023 American Society of Civil Engineers.
History
Received: May 22, 2023
Accepted: Oct 16, 2023
Published online: Dec 21, 2023
Published in print: Mar 1, 2024
Discussion open until: May 21, 2024
ASCE Technical Topics:
- Architectural engineering
- Benefit cost ratios
- Bridge engineering
- Bridge management
- Building management
- Business management
- Construction engineering
- Construction methods
- Engineering fundamentals
- Financial management
- Historic buildings
- History and Heritage
- Information management
- Maintenance and operation
- Management methods
- Practice and Profession
- Rehabilitation
- Structural engineering
- Systems engineering
- Systems management
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