Prioritization Comparison of TOPSIS and AHP to IRC: A Case Study of Kurukshetra Roads

Document Type : Regular Paper

Authors

1 Ph.D. Candidate, Department of Civil Engineering, National Institute of Technology Kurukshetra, Haryana-136119, India

2 Professor, Department of Civil Engineering, National Institute of Technology Kurukshetra, Haryana-136119, India

3 Vice Chancellor, Kurukshetra University, Kurukshetra, Haryana-136119, India

Abstract

The ever-growing requirements for pavements need regular maintenance. Prioritization of pavement maintenance using the multi-criteria-decision-making (MCDM) method is an established method. This study prioritizes roads in Kurukshetra district, Haryana, India, using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Analytical Hierarchy Process (AHP). Indian Roads Congress (IRC) standard for prioritization of roads in India is used as a benchmark to compare the ranking provided by both methods. TOPSIS outperformed the two methods, with a Spearman correlation coefficient value of 0.78 and an Index of Agreement value of 0.88. Compared to the IRC method's fixed weight assignment for rank calculation, the TOPSIS method offers a distinct advantage.

Graphical Abstract

Prioritization Comparison of TOPSIS and AHP to IRC: A Case Study of Kurukshetra Roads

Highlights

  • Focus: Pavement maintenance prioritization in Kurukshetra district, India.
  • Methodology: Multi-Criteria Decision-Making (MCDM) approach using TOPSIS and AHP.
  • Benchmark: Indian Roads Congress (IRC) standard for road prioritization.
  • Key Finding: TOPSIS outperforms AHP and IRC in prioritizing road maintenance.
  • TOPSIS Advantage: Ranks alternatives based on ideal best and worst solutions, leading to a more robust prioritization compared to the fixed weight scheme of IRC.

Keywords

Main Subjects


[1]     Donev V, Hoffmann M. Optimisation of pavement maintenance and rehabilitation activities, timing and work zones for short survey sections and multiple distress types. Int J Pavement Eng 2020;21:583–607. https://doi.org/10.1080/10298436.2018.1502433.
[2]     Kumar P, Parida M, Garg PK. Applicability of Multi Criteria Decision Making For Prioritization of Indian Roads. Highw Res J 2020;11.
[3]     Ayalew GG, Meharie MG, Worku B. A road maintenance management strategy evaluation and selection model by integrating Fuzzy AHP and Fuzzy TOPSIS methods: The case of Ethiopian Roads Authority. Cogent Eng 2022;9. https://doi.org/10.1080/23311916.2022.2146628.
[4]     Shelton J, Medina M. Integrated multiple-criteria decision-making method to prioritize transportation projects. Transp Res Rec 2010:51–7. https://doi.org/10.3141/2174-08.
[5]     Ouma YO, Opudo J, Nyambenya S. Comparison of Fuzzy AHP and Fuzzy TOPSIS for Road Pavement Maintenance Prioritization: Methodological Exposition and Case Study. Adv Civ Eng 2015;2015. https://doi.org/10.1155/2015/140189.
[6]     Saluja S, Gaur A, Abbas S. Assessment of Pavement Surface Quality using TOPSIS Method. IOP Conf Ser Earth Environ Sci 2021;796:6–12. https://doi.org/10.1088/1755-1315/796/1/012015.
[7]     Pathan AI, Girish Agnihotri P, Said S, Patel D. AHP and TOPSIS based flood risk assessment- a case study of the Navsari City, Gujarat, India. Environ Monit Assess 2022;194:509. https://doi.org/10.1007/s10661-022-10111-x.
[8]     Abd El-Raof HS, Abd El-Hakim RT, El-Badawy SM, Afify HA. General Procedure for Pavement Maintenance/Rehabilitation Decisions Based on Structural and Functional Indices. In: Badawy S, Chen D-H, editors., Cham: Springer International Publishing; 2020, p. 13–24. https://doi.org/10.1007/978-3-030-34196-1_2.
[9]     Naseri H, Shokoohi M, Jahanbakhsh H, Karimi MM, Waygood EOD. Novel Soft-Computing Approach to Better Predict Flexible Pavement Roughness. Transp Res Rec 2023;2677:246–59. https://doi.org/10.1177/03611981231161051.
[10]   Chow M, Frangos D, Australia S. A Holistic Investment Prioritisation Framework for Road Assets. 2020.
[11]    Sandamal RMK, Pasindu HR. Applicability of smartphone-based roughness data for rural road pavement condition evaluation. Int J Pavement Eng 2022;23:663–72. https://doi.org/10.1080/10298436.2020.1765243.
[12]   Research, Legislative P. Demand for Grants 2023-24 Analysis: Road Transport and Highways. Delhi: n.d.
[13]   AASHTO Guide for Design of Pavement Structures. vol. 1. Washington D.C.: American Association of State Highway and Transportation officials; 1993.
[14]   Martin T, Choummanivong L. The Benefits of Long-Term Pavement Performance (LTPP) Research to Funders. Transp Res Procedia 2016;14:2477–86. https://doi.org/10.1016/j.trpro.2016.05.311.
[15]   Guidebook for Data and Information Systems for Transportation Asset Management. 2021. https://doi.org/10.17226/26126.
[16]   Baladi G, Dawson T, Musunuru G, Prohaska M, Kyle T. Pavement Performance Measures and Forecasting and the Effects of Maintenance and Rehabilitation Strategy on Treatment Effectiveness (Revised). Fhwa- Hrt-17-095 2017:1–329.
[17]   Torres-Machi C, Nasir F, Achebe J, Saari R, Tighe SL. Sustainability Evaluation of Pavement Technologies through Multicriteria Decision Techniques. J Infrastruct Syst 2019;25:1–10. https://doi.org/10.1061/(asce)is.1943-555x.0000504.
[18]   Hasan AE, Jaber FK. The Applicability of Multiple MCDM Techniques for Implementation in the Priority of Road Maintenance. J Eng 2023;29:106–25. https://doi.org/10.31026/j.eng.2023.10.07.
[19]   Sayadinia S, Beheshtinia MA. Proposing a new hybrid multi-criteria decision-making approach for road maintenance prioritization. Int J Qual Reliab Manag 2020;38:1661–79. https://doi.org/10.1108/IJQRM-01-2020-0020.
[20]   Kaya İ, Çolak M, Terzi F. Use of MCDM techniques for energy policy and decision-making problems: A review. Int J Energy Res 2018;42:2344–72. https://doi.org/10.1002/er.4016.
[21]   Nautiyal A, Sharma S. Scientific approach using AHP to prioritize low volume rural roads for pavement maintenance. J Qual Maint Eng 2022;28:411–29. https://doi.org/10.1108/JQME-12-2019-0111.
[22]   Perla B, Ramesh DA. Development of Roughness Index Model For Urban Roads Using Machine Learning Techniques and Prioritizing Using MCDM Techniques. SSRN Electron J 2022:1–13. https://doi.org/10.2139/ssrn.4160025.
[23]   Duleba S, Szádoczki Z. Comparing aggregation methods in large-scale group AHP: Time for the shift to distance-based aggregation. Expert Syst Appl 2022;196. https://doi.org/10.1016/j.eswa.2022.116667.
[24]   Sirin O, Gunduz M, Shamiyeh ME. Application of analytic hierarchy process (AHP) for sustainable pavement performance management in Qatar. Eng Constr Archit Manag 2020;28:3106–22. https://doi.org/10.1108/ECAM-02-2020-0136.
[25]   Gowda S, Kavitha G, Gupta A. Economic Analysis and Prioritisation of Non-core Roads in India: A Case Study. Int J Pavement Res Technol 2022. https://doi.org/10.1007/s42947-022-00250-2.
[26]   Kumar P, Sharma M. Modified pavement condition assessment model for asphalt concrete pavements. Int J Syst Assur Eng Manag 2023. https://doi.org/10.1007/s13198-023-02102-z.
[27]   Singh AP, Sharma A, Mishra R, Wagle M, Sarkar AK. Pavement condition assessment using soft computing techniques. Int J Pavement Res Technol 2018;11:564–81. https://doi.org/10.1016/j.ijprt.2017.12.006.
[28]   Malek MS, Gundaliya PJ. Negative factors in implementing public–private partnership in Indian road projects. Int J Constr Manag 2023;23:234–42. https://doi.org/10.1080/15623599.2020.1857672.
[29]   Rejani VU, Janani L, Venkateswaralu K, Sunitha V, Mathew S. Strategic Pavement Maintenance and Rehabilitation Analysis of Urban Road Network Using HDM-4. Int J Pavement Res Technol 2023;16:927–42. https://doi.org/10.1007/s42947-022-00171-0.
[30]   MORTH. Pradhan Mantri Gram Sadak Yojana. New Delhi: 2012.
[31]   Abbaszadeh H, Daneshfaraz R, Sume V, Abraham J. Experimental investigation and application of soft computing models for predicting flow energy loss in arc-shaped constrictions. AQUA — Water Infrastructure, Ecosyst Soc 2024;73:637–61. https://doi.org/10.2166/aqua.2024.010.
[32]   Miller JS, Bellinger WY. FHWA, Distress Identification manual for the Long-Term Pavement Performance Program. Report FHWA-HRT-13-092. Fed Highw Adm 2014:142.
[33]   Chang G, Gilliland A, Rada GR, Serigos PA, Simpson AL, Kouchaki S. Successful Practices for Quality Management of Pavement Surface Condition Data Collection and Analysis Phase I: Task 2-Document of Successful Practices 2020:188.
[34]   Peterson DE. Pavement management Practices Synthesis : 135. Transportation research Board, National research council; 1987.
[35]   Hashemi A, Dowlatshahi MB, Nezamabadi-pour H. MFS-MCDM: Multi-label feature selection using multi-criteria decision making. Knowledge-Based Syst 2020;206:106365. https://doi.org/10.1016/j.knosys.2020.106365.
[36]   Saaty RW. The analytic hierarchy process-what it is and how it is used. Math Model 1987;9:161–76. https://doi.org/10.1016/0270-0255(87)90473-8.
[37]   Prakasan AC, Tiwari D, Shah YU, Parida M. Pavement maintenance prioritization of urban roads using analytical hierarchy process. Int J Pavement Res Technol 2015;8:112–22. https://doi.org/10.6135/ijprt.org.tw/2015.8(2).112.
[38]   Ahmed S, Vedagiri P, Krishna Rao K V. Prioritization of pavement maintenance sections using objective based Analytic Hierarchy Process. Int J Pavement Res Technol 2017;10:158–70. https://doi.org/10.1016/j.ijprt.2017.01.001.
[39]   Farhan J, Fwa TF. Pavement Maintenance Prioritization Using Analytic Hierarchy Process. Transp Res Rec J Transp Res Board 2009;2093:12–24. https://doi.org/10.3141/2093-02.
[40]   Owen M. Process for Setting Intervention Criteria and Allocating Budgets: Process Description and Application. 2006.
[41]   Deng Y, Shi X. Development of predictive models of asphalt pavement distresses in Idaho through gene expression programming. Neural Comput Appl 2022;34:14913–27. https://doi.org/10.1007/s00521-022-07305-2.
[42]   Qiu S, Xiao DX, Huang S, Li L, Wang KCP. A Data-Driven Method for Comprehensive Pavement-Condition Ranking. J Infrastruct Syst 2016;22:1–8. https://doi.org/10.1061/(asce)is.1943-555x.0000279.
[43]   Indian Roads Congress. Code of Practice for Maintenance of Bituminous Road Surfaces Indian Roads Congress IRC:82-2015. 2015.
[44]   Liao C-N, Kao H-P. An integrated fuzzy TOPSIS and MCGP approach to supplier selection in supply chain management. Expert Syst Appl 2011;38:10803–11. https://doi.org/10.1016/j.eswa.2011.02.031.
[45]   Behzadian M, Khanmohammadi Otaghsara S, Yazdani M, Ignatius J. A state-of the-art survey of TOPSIS applications. Expert Syst Appl 2012;39:13051–69. https://doi.org/10.1016/j.eswa.2012.05.056.
[46]   Hwang C-L, Yoon K. Multiple Attribute Decision Making. vol. 186. Berlin, Heidelberg: Springer Berlin Heidelberg; 1981. https://doi.org/10.1007/978-3-642-48318-9.
[47]   Chen S-J, Hwang C-L. Fuzzy Multiple Attribute Decision Making. vol. 375. Berlin, Heidelberg: Springer Berlin Heidelberg; 1992. https://doi.org/10.1007/978-3-642-46768-4.
[48]   Yoon K. A Reconciliation Among Discrete Compromise Solutions. J Oper Res Soc 1987;38:277–86. https://doi.org/10.1057/jors.1987.44.
[49]   Greene R, Devillers R, Luther JE, Eddy BG. GIS-Based Multiple-Criteria Decision Analysis. Geogr Compass 2011;5:412–32. https://doi.org/10.1111/j.1749-8198.2011.00431.x.
[50]   Goepel KD. Comparison of Judgment Scales of the Analytical Hierarchy Process - A New Approach. Int J Inf Technol Decis Mak 2019;18:445–63. https://doi.org/10.1142/S0219622019500044.
[51]   Saaty T. The analytic hierarchy process (AHP) for decision making. Kobe, Japan, vol. 1, 1980, p. 69.
[52]   Farhan J, Fwa TF. Use of analytic hierarchy process to prioritize network-level maintenance of pavement segments with multiple distresses. Transp Res Rec 2011:11–20. https://doi.org/10.3141/2225-02.
[53]   Farhan J, Fwa TF. Incorporating Priority Preferences into Pavement Maintenance Programming. J Transp Eng 2012;138:714–22. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000372.
[54]   AgriMetSoft. Index of agreement n.d.
[55]     Karballaeezadeh N, Danial MS, Moazemi D, Band SS, Mosavi A, Reuter U. Smart structural health monitoring of flexible pavements using machine learning methods. Coatings 2020;10:1–18. https://doi.org/doi.org/10.20944/preprints202004.0029.v1.