A Case-Based Reasoning and Random Forest Framework for Selecting Preventive Maintenance of Flexible Pavement Sections

Saleh Abu Dabous, Khaled Hamad, Rami Al-Ruzouq, Waleed Zeiada, Maher Omar, Lubna Obaid

Abstract


Pavement maintenance decision-making is receiving significant attention in recent research, since pavement infrastructure is aging and deteriorating. The decision-making process is mainly related to selecting the most appropriate maintenance intervention for pavement sections to ensure performance and enhance safety. Several preventive maintenance methods have been proposed in the previous studies, yet the potential of implementing Case-Based Reasoning (CBR) in pavement maintenance decision-making has been investigated rarely. The CBR is an artificial intelligence technique, it is knowledge-based on several known cases, which are used to adapt a solution for a new case through retrieving similar cases. This research introduces the CBR to the area of pavement management to select the most appropriate preventive maintenance strategy for flexible pavement sections. The needed database was extracted from maintenance cases at Long-Term Pavement Performance Program. The criteria used to characterize condition of each section were identified based on the common practices in pavement maintenance published in the literature and implemented in the field. To assign weights to the selected criteria, different machine learning techniques were tested, and subsequently, Random Forest (RF) algorithm was selected to be integrated with the proposed CBR method producing the CBR-RF framework. A case study was analyzed to validate the proposed framework and a sensitivity analysis was conducted to assess the influence of each criterion on case retrieval accuracy and overall framework performance. Results indicated that the CBR-RF approach could assist effectively in the preventive maintenance decision-making with regard to new cases by learning from the previous similar cases. Accordingly, several agencies can depend on the proposed framework, while facing similar decision-making problems. Future research can compare the CBR-RF framework with other machine learning algorithms using the same dataset included in this research.


Keywords:

case-based reasoning; decision-making; flexible pavement; preventive maintenance; random forest

Full Text:

PDF

References


AASHTO. (2016). The Asphalt Pavement Rehabilitation Series. https://tc3. transportation.org/

Abaza, K. A., & Ashur, S. A. (1999). Optimum decision policy for management of pavement maintenance and rehabilitation. Transportation Research Record, 1655(1), 8–15. https://doi.org/10.3141/1655-02

Abo-Hashema, M. A., & Sharaf, E. A. (2009). Development of maintenance decision model for flexible pavements. International Journal of Pavement Engineering, 10(3), 173–187. https://doi.org/10.1080/10298430802169457

Abu-Samra, S., Zayed, T., & Tabra, W. (2017). Pavement Condition Rating Using Multiattribute Utility Theory. Journal of Transportation Engineering, Part B: Pavements, 143(3), Article 04017011. https://doi.org/10.1061/JPEODX.0000011

Abu Dabous, S., & Al-Khayyat, G. (2018). A flexible bridge rating method based on analytical evidential reasoning and Monte Carlo simulation. Advances in Civil Engineering, 2018, Article 1290632. https://doi.org/https://doi.org/10.1155/2018/1290632

Abu Dabous, S., Al-Khayyat, G., & Feroz, S. (2020). Utility-based road maintenance prioritization method using pavement overall condition rating. Baltic Journal of Road and Bridge Engineering, 15(1), 126–146. https://doi.org/10.7250/bjrbe.2020-15.464

Abu Dabous, S., Zeiada, W., Al-Ruzouq, R., Hamad, K., & Al-Khayyat, G. (2021). Distress-based evidential reasoning method for pavement infrastructure condition assessment and rating. International Journal of Pavement Engineering, 22(4), 455–466. https://doi.org/10.1080/10298436.2019.1622012

Ahmida, A. A., & Norwawi, N. M. (2008). Mobile case-based reasoning for reservoir gate operation decision recommendation. 3rd International Conference on Information and Communication Technologies: From Theory to Applications, Damascus, Syria, 1–6. https://doi.org/10.1109/ICTTA.2008.4530323

Al-Mansour, A. I., Sinha, K. C., & Kuczek, T. (1994). Effects of routine maintenance on flexible pavement condition. Journal of Transportation Engineering, 120(1), 65–73. https://doi.org/10.1061/(ASCE)0733-947X(1994)120:1(65)

Amarasiri, S., & Muhunthan, B. (2020). Evaluating the effectiveness of pavement preventive-maintenance treatments in mitigating longitudinal cracks in wet-freeze climatic zones. Journal of Transportation Engineering, Part B: Pavements, 146(2), 1–9. https://doi.org/10.1061/JPEODX.0000158

ASCE. (2017). A Comprehensive Assessment of America’s Infrastructure.

Carnahan, J. V. (1988). Analytical framework for optimizing pavement maintenance. Journal of Transportation Engineering, 114(3), 307–322. https://doi.org/10.1061/(ASCE)0733-947X(1988)114:3(307)

Chen, C., Flintsch, G. W., & Al-Qadi, I. L. (2004). Fuzzy logic-based life-cycle costs analysis model for pavement and asset management. 6th International Conference on Managing Pavements, Australia.

Chen, X., Zhu, H., Dong, Q., & Huang, B. (2017). Optimal thresholds for pavement preventive maintenance treatments using LTPP data. Journal of Transportation Engineering, Part A: Systems, 143(6), 1–9. https://doi.org/10.1061/JTEPBS.0000044

Chou, J. (2008). Applying AHP-based CBR to estimate pavement maintenance cost. Tsinghua Science and Technology, 13(S1), 114–120. https://doi.org/10.1016/S1007-0214(08)70136-6

Chou, J. (2009). Web-based CBR system applied to early cost budgeting for pavement maintenance project. Expert Systems with Applications, 36(2/2), 2947–2960. https://doi.org/10.1016/j.eswa.2008.01.025

Chun, S., & Park, Y. (2005). Dynamic adaptive ensemble case-based reasoning: application to stock market prediction. Expert Systems with Applications, 28(3), 435–443. https://doi.org/10.1016/j.eswa.2004.12.004

Costache, R., & Tien Bui, D. (2020). Identification of areas prone to flash-flood phenomena using multiple-criteria decision-making, bivariate statistics, machine learning and their ensembles. Science of the Total Environment, 712, Article 136492. https://doi.org/10.1016/j.scitotenv.2019.136492

Elkins, G. E., Schmalzer, P. N., Thompson, T., & Simpson, A. (2003). Long-term pavement performance information management system: Pavement performance database user reference guide (Report No. FHWA-RD-03-088). Turner-Fairbank Highway Research Center.

Eltahan, A. A., Daleiden, J. F., & Simpson, A. L. (1999). Effectiveness of maintenance treatments of flexible pavements. Transportation Research Record, 1680(1), 18–25. https://doi.org/10.3141/1680-03

Erickson, S. W. (2015). Street pavement maintenance: Road condition is deteriorating due to insufficient funding (Report 15-02).

Foody, G. M. (2020). Explaining the unsuitability of the kappa coefficient in the assessment and comparison of the accuracy of thematic maps obtained by image classification. Remote Sensing of Environment, 239, Article 111630. https://doi.org/10.1016/j.rse.2019.111630

Gong, H., Dong, Q., Huang, B., & Jia, X. (2016). Effectiveness analyses of flexible pavement preventive maintenance treatments with LTPP SPS-3 experiment data. Journal of Transportation Engineering, 142(2). https://doi.org/10.1061/(ASCE)TE.1943-5436.0000818

He, W., Wang, F. K., Means, T., & Xu, L. D. (2009). Insight into interface design of web-based case-based reasoning retrieval systems. Expert Systems with Applications, 36(3/2), 7280–7287. https://doi.org/10.1016/j.eswa.2008.09.043

Herabat, P., & Tangphaisankun, A. (2005). Multi-objective optimization model using constraint-based genetic algorithms for Thailand pavement management. Journal of the Eastern Asia Society for Transportation Studies, 6, 1137–1152. https://doi.org/10.11175/easts.6.1137

Hicks, R. G., Moulthrop, J. S., & Daleiden, J. (1999). Selecting a preventive maintenance treatment for flexible pavements. Transportation Research Record, 1680(1), 1–12. https://doi.org/10.3141/1680-01

Hicks, R G, Dunn, K., & Moulthrop, J. S. (1997). Framework for selecting effective preventive maintenance treatments for flexible pavements. Transportation Research Record, 1597(1), 1–10. https://doi.org/10.3141/1597-01

Huang, Z., Fan, H., & Shen, L. (2019). Case-based reasoning for selection of the best practices in low-carbon city development. Frontiers of Engineering Management, 6(3), 416–432. https://doi.org/10.1007/s42524-019-0036-1

Hyung, W., Kim, S., & Jo, J. (2020). Improved similarity measure in case-based reasoning: a case study of construction cost estimation. Construction and Architectural Management, 27(2), 561–578. https://doi.org/10.1108/ECAM-01-2019-0035

Jia, Y., Dai, X., Wang, S., Gao, Y., Wang, J., & Zhou, W. (2020). Evaluation of long-term effectiveness of preventive maintenance treatments using LTPP SPS-3 experiment data. Construction and Building Materials, 247, Article 118585. https://doi.org/10.1016/j.conbuildmat.2020.118585

Jia, Y., Wang, J., Gao, Y., Yang, M., & Zhou, W. (2020). Assessment of short-term improvement effectiveness of preventive maintenance treatments on pavement performance using LTPP data. Journal of Transportation Engineering, Part B: Pavements, 146(3), 1–10. https://doi.org/10.1061/JPEODX.0000208

Kwon, N., Song, K., Ahn, Y., Park, M., & Jang, Y. (2020). Maintenance cost prediction for aging residential buildings based on case-based reasoning and genetic algorithm. Journal of Building Engineering, 28, 101006. https://doi.org/10.1016/j.jobe.2019.101006

Leśniak, A., & Z ima, K. ( 2018). Cost Calculation of Construction Projects Including Sustainability Factors Using the Case Based Reasoning (CBR) Method. Sustainability, 10(5), 1608. https://doi.org/10.3390/su10051608

Li, L., & Wang, K. C. P. (2011). Strategies for flexible pavement rehabilitation based on case-based reasoning. T&DI Congress 2011: Integrated Transportation and Development for a Better Tomorrow – Proceedings of the 1st Congress of the Transportation and Development Institute of ASCE, 479, 32–39. https://doi.org/10.1061/41167(398)4

Marcelino, P., Antunes, M. de L., Fortunato, E., & Gomes, M. C. (2019). Machine learning approach for pavement performance prediction. International Journal of Pavement Engineering, 22(3), 341–354. https://doi.org/10.1080/10298436.2019.1609673

Milad, A., Basri, N. E. A., & M., H. (2017). Prototype web-based expert system for flexible pavement maintenance. Journal of Engineering Science and Technology, 12(11), 2909–2921. https://www.researchgate.net/publication/316915090_Prototype_web-based_expert_system_for_flexible_pavement_maintenance

Morcous, G., Rivard, H., & Hanna, A. M. (2002). Modeling bridge deterioration using case-based reasoning. Journal of Infrastructure Systems, 8(3), 86–95. https://doi.org/10.1061/(ASCE)1076-0342(2002)8:3(86)

Mousa, M. R., Elseifi, M. A., Zhang, Z., & Gaspard, K. (2020). Development of a decision-making tool to select optimum preventive maintenance treatments in a hot and humid climate. Transportation Research Record, 2674(1), 44–56. https://doi.org/10.1177/0361198119898397

Salem, A. M., & Voskoglou, M. G. (2013). Applications of the CBR methodology to medicine. Egyptian Computer Science Journal, 37(7), 68–78.

SHRP2. (2015). Project inception through December 2015. Annual Report.

Stéphane, N., & Hector, R. (2010). Effective retrieval and new indexing method for case based reasoning: Application in chemical process design. Engineering Applications of Artificial Intelligence, 23(6), 880–894. https://doi.org/10.1016/j.engappai.2010.03.005

Sundin, S., & Braban‐Ledoux, C. (2001). Artificial intelligence-based decision support technologies in pavement management. Computer‐Aided Civil and Infrastructure Engineering, 16(2), 143–157. https://doi.org/10.1111/0885-9507.00220

Tabatabaee, N., Ziyadi, M., & Shafahi, Y. (2012). Two-stage support vector classifier and recurrent neural network predictor for pavement performance modeling. Journal of Infrastructure Systems, 19(3), 266–274. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000132

TRIP. (2016). The Interstate Highway System turns 60.

Waheed, A., & Adeli, H. (2005). Case-based reasoning in steel bridge engineering. Knowledge-Based Systems, 18(1), 37–46. https://doi.org/10.1016/j.knosys.2004.06.001

Wang, F. K. (2006). Applying case‐based reasoning in knowledge management to support organizational performance. Performance Improvement Quarterly, 19(2), 173–188. https://doi.org/10.1111/j.1937-8327.2006.tb00371.x

Wang, F., Zhang, Z., & Machemehl, R. B. (2003). Decision-making problem for managing pavement maintenance and rehabilitation projects. Transportation Research Record, 1853(1), 21–28. https://doi.org/10.3141/1853-03

Wang, W., Wang, Y., & Gong, W. (2012). Case-based reasoning application in e-learning. 9th International Conference on Fuzzy Systems and Knowledge Discovery, Chongqing, China, 930–933. https://doi.org/10.1109/FSKD.2012.6234117

Wei, C., & Tighe, S. (2004). Development of preventive maintenance decision trees based on cost-effectiveness analysis: an Ontario case study. Transportation Research Record, 1866(1), 9–19. https://doi.org/10.3141/1866-02

Yamin, Z., Mengmeng, Z., Xiaomin, G., Zhiwei, Z., & Jianhua, Z. (2017). Research on matching method for case retrieval process in CBR based on FCM. Procedia Engineering, 174, 267–274. https://doi.org/10.1016/j.proeng.2017.01.134

Yao, L., Dong, Q., Ni, F., Jiang, J., Lu, X., & Du, Y. (2019). Effectiveness and cost-effectiveness evaluation of pavement treatments using life-cycle cost analysis. Journal of Transportation Engineering, Part B: Pavements, 145(2). https://doi.org/10.1061/JPEODX.0000106

Yau, N., & Yang, J. (1998). Case-based reasoning in construction management. Computer‐Aided Civil and Infrastructure Engineering, 13(2), 143–150. https://doi.org/10.1111/0885-9507.00094




DOI: 10.7250/bjrbe.2022-17.562

Refbacks

  • There are currently no refbacks.


Copyright (c) 2022 Saleh Abu Dabous, Khaled Hamad, Rami Al-Ruzouq, Waleed Zeiada, Maher Omar, Lubna Obaid

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.