Istrazivanja i projektovanja za privreduJournal of Applied Engineering Science


DOI: 10.5937/jaes0-41212 
This is an open access article distributed under the CC BY 4.0
Creative Commons License

Volume 21 article 1101 pages: 598-607

Wahyu S. Winurseto*
Department of Civil and Environmental Engineering, Faculty of Engineering, Gadjah Mada University, Yogyakarta, Indonesia

Agus Taufik Mulyono
Department of Civil and Environmental Engineering, Faculty of Engineering, Gadjah Mada University, Yogyakarta, Indonesia

Latif Budi Supamak
Department of Civil and Environmental Engineering, Faculty of Engineering, Gadjah Mada University, Yogyakarta, Indonesia

Measuring the value of road performance requires an emphasis on optimal performance demand. In Indonesia, pavement assessment is the sole basis for evaluating performance value. However, road performance is not solely determined by pavement performance, as the performance of road shoulder and drainage systems also influences it. This study aims to create a road performance evaluation model that is quantitative in nature, taking into account both pavement performance and the frequency and size of damages to road shoulders and drainage systems. To construct the model, this study employed a Structural Equation Model. According to the findings, the condition of the road shoulder and drainage systems had an impact on the road's performance, as measured by the International Roughness Index (IRI). The subsidence factor had the most significant impact on road shoulder performance (31.1%), then followed by waterlogging (29.4%), potholes (29.2%), and pavement edge height difference and road shoulder (5.3%), in addition to shoulder slope (5.0%). The road drainage performance, on the other hand, was influenced by the cross-sectional conditions of the road drainage channel (34.6%), structural drainage (31.1%), and drainage canal slope (29.2%). The study found that pavement, road shoulder, and drainage had a respective effect of 58.1%, 20.2%, and 21.7% on road performance.

View article

1.      Arianto, T., Suprapto, M. (2018). Pavement Condition Assessment Using IRI From Roadroid And Surface Distress Index Method On National Road In Sumenep Regency. In IOP Conference Series: Materials Science and Engineering (Vol. 333, No. 1, p. 012091). IOP Publishing.

2.      Asada, T., Ha, T.V., Arimura, M., Kameyama, S., (2022). A Novel Approach for Urban Road Network Maintenance Plans Using Spatial Autocorrelation Analysis and Roadside Conditions: A Case Study of Muroran City, Japan. Sustainability, 14(23), p.16189.

3.      Aleadelat,W., Ksaibati,K., Wright, C. H. G., and Saha,P., (2018).  "Evaluation of pavement roughness using an android-based smartphone," Journal of Stomatology, vol. 144, no. 3, Sep. 2018, doi: 10.1061/JPEODX.0000058.

4.      Bandalos, D. L., & Finney, S. J. (2018). Factor analysis: Exploratory and confirmatory. In the reviewer’s guide to quantitative methods in the social sciences (pp. 98-122). Routledge.

5.      Bilodeau, J. P., Gagnon, L., & Doré, G. (2017). Assessment of the relationship between the international roughness index and dynamic loading of heavy vehicles. International Journal of Pavement Engineering, 18(8), 693-701.

6.      Civelek, M.E. (2018). "Essentials of Structural Equation Modeling" (2018). Zea E-Books. 64.

7.      Dafalla, M., Shaker, A., & Al-Shamrani, M. (2022). Sustainable Road Shoulders and Pavement Protection for Expansive Soil Zones. Transportation Research Record, 2676(10), 341–350.

8.      France-Mensah, J. and O’Brien, W.J. (2019). Developing a sustainable pavement management plan: Tradeoffs in road condition, user costs, and greenhouse gas emissions. Journal of Management in Engineering, 35(3), p.04019005.

9.      Gowda N, T., Shivananda P. (2018). Pavement Shoulder Maintenance: Case Study. International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 7 (2018) pp. 166-168

10.   Ghozali, I. (2018). Structural Equation Modeling: Theory, concepts, and applications with the Lisrel 8.80 program. Semarang: Diponegoro University Publishing Agency.

11.   Halomoan, P.A., Roza, E., Sriono, Mulyono, A.T. (2018), Evaluation of the implementation of long segment preservation of roads and bridges in North Sumatra and Riau Provinces, Proceedings of the 14th Road Engineering Regional Conference. 14. 272-285

12.   Hasanuddin, Setyawan, A., and Yulianto,B., (2018). "Evaluation of Road Performance Based on International Roughness Index and Falling Weight Deflectometer," in IOP Conference Series: Materials Science and Engineering, Apr. 2018, vol. 333, no. 1, doi: 10.1088/1757- 899X/333/1/012090.

13.   Hasibuan, R. P., & Surbakti, M. S. (2019). Study of Pavement Condition Index (PCI) relationship with International Roughness Index (IRI) on Flexible Pavement. In MATEC web of conferences (Vol. 258, p. 03019). EDP Sciences.

14.   Hayes, A. F., Montoya, A. K., & Rockwood, N. J. (2017). The analysis of mechanisms and their contingencies: Process versus structural equation modeling. Australasian Marketing Journal (AMJ), 25(1), 76-81.

15.   Imam, R., Murad, Y., Asi, I. (2021). Predicting pavement condition Index from International Roughness Index using Gene Expression Programming. Innov. Infrastruct. Solut. 6, 139.

16.   Manurung, E. H., Sawito K., Satoto A., Tuanany N.. (2022). “Analysis of the cause of road damage”, CIVILA, vol. 7, no. 1, pp. 87–96.

17.   Makransky, G., Lilleholt, L. (2018). A structural equation modeling investigation of the emotional value of immersive virtual reality in education. Education Tech Research Dev 66, 1141–1164.

18.   Novel, R., Putranto, L.S. (2020), Indikator Kinerja Jalan long segment di Banten dengan analisis Analytical Hierarchy Process, Jurnal Muara Sains Teknologi Kedokteran dan Ilmu Kesehatan, 4(1), 131-144.

19.   Patrick, G., Soliman, H. (2019). Roughness prediction models using pavement surface distresses in different Canadian climatic regions. Canadian Journal of Civil Engineering, 46(10), pp.934-940.

20.   Piryonesi, S.M., El-Diraby, T.E. (2021). Examining the relationship between two road performance indicators: Pavement condition index and international roughness index. Transportation Geotechnics, 26, p.100441.

21.   Puig-Diví A., Escalona-Marfil C., Padullés-Riu JM, Busquets A,. Padullés-Chando X. (2019). Validity and reliability of the Kinovea program in obtaining angles and distances using coordinates in 4 perspectives. PLOS ONE 14(6): e0216448.

22.   Rahman, M. M., Tabash, M. I., Salamzadeh, A., Abduli, S., Rahaman, M. S. (2022). Sampling techniques (probability) for quantitative social science researchers: a conceptual guidelines with examples. Seeu Review, 17(1), 42-51.

23.   Republik Indonesia. (2022), Undang-Undang Nomor 02 Tahun 2022 tentang Jalan, Jakarta: Indonesia

24.   Rusmanto, U., Syafi'I, and Handayani, D. (2018). "Structural and functional prediction of pavement condition (A case study on south arterial road, Yogyakarta)," in AIP Conference Proceedings, Jun. 2018, vol. 1977, doi: 10.1063/1.5042984.

25.   Shahid, M.A. (2019), Maintenance management of pavements for expressways in Malaysia. In IOP Conference Series: Materials Science and Engineering (Vol. 512, No. 1, p. 012043). IOP Publishing.

26.   Sharma, A., Sachdeva, S.N. & Aggarwal, P. (2023). Predicting IRI using Machine Learning Techniques. Int. J. Pavement Res. Technol. 16, 128–137.

27.   Shrestha, S., & Khadka, R. (2021). Assessment of relationship between Road Roughness and Pavement Surface Condition. Journal of Advanced College of Engineering and Management, 6, 177–185.

28.   Sürücü, L. and MASLAKÇI, A. (2020). Validity and reliability in quantitative research. Business & Management Studies: An International Journal, 8(3), pp.2694-2726.

29.   Sutradhar, R., dan Pal, M. (2020), Assessment of pavement shoulder condition in rural roads, International Journal on Emerging Technologies 11(1), 91-100.

30.   Swank, J. M., & Mullen, P. R. (2017). Evaluating evidence for conceptually related constructs using bivariate correlations. Measurement and Evaluation in Counseling and Development, 50(4), 270-274.

31.   Thakkar, J.J. (2020). Applications of Structural Equation Modelling with AMOS 21, IBM SPSS. In: Structural Equation Modelling. Studies in Systems, Decision and Control, vol 285. Springer, Singapore.

32.   Tarka, P., (2018). An overview of structural equation modeling: Its beginnings, historical development, usefulness and controversies in the social sciences. Quality & quantity, 52, pp.313-354.).: Focusing on exploratory factor analysis. Tutorials in quantitative methods for psychology, 9(2), 79-94.