Influence of Maintenance Funds on Improve Road Steadiness with the Curva Expert Program

Ary Setyawan, Wahyuningsih Tri Hermani, Budi Yulianto, Evi Gravitiani


Sustainable road construction is instrumental in improving connectivity among regions and economies while also offering road users a diverse range of options within the traffic network. To ensure optimal road performance for users, it becomes essential to allocate adequate maintenance funds that correlate with the planned service life. This necessity originates from a profound understanding of the significant influence maintenance funds have on road steadiness. Therefore, this study aims to establish a comprehensive road steadiness model, investigating the influence of toll roads as new routes and the impact on maintenance funds. The analysis included national roads across 15 cities in Central Java Province, Indonesia, covering a distance of 759.75 km from 2018–2023. Using a quantitative approach, the study adopted the Curva Expert program to evaluate the values of road steadiness and maintenance funds. The results showed a 5.78% enhancement in road steadiness over the period from 2018 to 2023, underscoring the positive impact of sustainable road construction practices and the allocation of adequate maintenance funds. The establishment of relationship between road steadiness and maintenance funds was established through a regression value of R2=0.94. This statistical correlation is represented by the equation y= 90.521 + 0.022x, providing a quantitative understanding of how maintenance funds influence road steadiness. The insights obtained from the outcomes of road steadiness modeling reiterate the significance of investing in additional routes and ensuring sufficient maintenance funds to improve performance.


Doi: 10.28991/CEJ-2024-010-02-014

Full Text: PDF


IRI; Road Steadiness; Maintenance Funds; Curva Expert.


Sharma, A., & Aggarwal, P. (2023). IRI Prediction using Machine Learning Models. WSEAS Transactions on Computer Research, 11, 111–116. doi:10.37394/232018.2023.11.10.

Noma-Osaghae, E., Okokpujie, K., Daniel, F., & John, S. N. (2022). The Validity of a Decentralised Simulation-Based System for the Resolution of Road Traffic Congestion. Journal of Applied Engineering Science, 20(3), 821–830. doi:10.5937/jaes0-28642.

Suryani, F., Mutiawati, C., & Faisal, R. (2023). The influence of service performance and passenger satisfaction on public transport loyalty in a small city in a developing country. Journal of Applied Engineering Science, 21(2), 644–655. doi:10.5937/jaes0-41716.

Benedetto, A., Benedetto, F., & Tosti, F. (2012). GPR applications for geotechnical stability of transportation infrastructures. Nondestructive Testing and Evaluation, 27(3), 253–262. doi:10.1080/10589759.2012.694884.

Singh, A. P., Sharma, A., Mishra, R., Wagle, M., & Sarkar, A. K. (2018). Pavement condition assessment using soft computing techniques. International Journal of Pavement Research and Technology, 11(6), 564–581. doi:10.1016/j.ijprt.2017.12.006.

Hermani, W. T., Setyawan, A., Syafi’i, & Gravitiani, E. (2023). Decreased Performance at Unsignaled Intersections Affects the Construction of the Solo-Yogya Road With the Least Square Method. Journal of Applied Engineering Science, 21(3), 963–971. doi:10.5937/jaes0-45137.

Simini, F., Barlacchi, G., Luca, M., & Pappalardo, L. (2021). A Deep Gravity model for mobility flows generation. Nature Communications, 12(1), 6576. doi:10.1038/s41467-021-26752-4.

Tosti, F., Bianchini Ciampoli, L., D’Amico, F., Alani, A. M., & Benedetto, A. (2018). An experimental-based model for the assessment of the mechanical properties of road pavements using ground-penetrating radar. Construction and Building Materials, 165, 966–974. doi:10.1016/j.conbuildmat.2018.01.179.

Karballaeezadeh, N., Mohammadzadeh S, D., Shamshirband, S., Hajikhodaverdikhan, P., Mosavi, A., & Chau, K. wing. (2019). Prediction of remaining service life of pavement using an optimized support vector machine (case study of Semnan–Firuzkuh road). Engineering Applications of Computational Fluid Mechanics, 13(1), 188–198. doi:10.1080/19942060.2018.1563829.

Choi, S., & Do, M. (2020). Development of the road pavement deterioration model based on the deep learning method. Electronics (Switzerland), 9(1), 3. doi:10.3390/electronics9010003.

Pérez-Acebo, H., Linares-Unamunzaga, A., Rojí, E., & Gonzalo-Orden, H. (2020). IRI performance models for flexible pavements in two-lane roads until first maintenance and/or rehabilitation work. Coatings, 10(2), 97. doi:10.3390/coatings10020097.

Wang, C., Xu, S., & Yang, J. (2021). Adaboost algorithm in artificial intelligence for optimizing the IRI prediction accuracy of asphalt concrete pavement. Sensors, 21(17), 5682. doi:10.3390/s21175682.

Paplauskas, P., Vaitkus, A., & Simanavičienė, R. (2023). Road Pavement Condition Index Deterioration Model for Network-Level Analysis of National Road Network Based on Pavement Condition Scanning Data. Baltic Journal of Road and Bridge Engineering, 18(3), 70–101. doi:10.7250/bjrbe.2023-18.609.

Guan, X., Zhang, H., Du, X., Zhang, X., Sun, M., & Bi, Y. (2023). Optimization for Asphalt Pavement Maintenance Plans at Network Level: Integrating Maintenance Funds, Pavement Performance, Road Users, and Environment. Applied Sciences (Switzerland), 13(15), 8842. doi:10.3390/app13158842.

Jurkevičius, M., Puodžiukas, V., & Laurinavičius, A. (2020). Implementation of road performance calculation models used in strategic planning systems for Lithuania conditions. Baltic Journal of Road and Bridge Engineering, 15(3), 146–156. doi:10.7250/bjrbe.2020-15.489.

van Rensburg, J. A., & Krygsman, S. C. (2019). Funding for roads: Understanding the South African road funding framework. Journal of Transport and Supply Chain Management, 13. doi:10.4102/jtscm.v13i0.453.

Song, Y., Thatcher, D., Li, Q., McHugh, T., & Wu, P. (2021). Developing sustainable road infrastructure performance indicators using a model-driven fuzzy spatial multi-criteria decision-making method. Renewable and Sustainable Energy Reviews, 138, 110538. doi:10.1016/j.rser.2020.110538.

Hermani, W., Setyawan, A., & Syafi, S. (2023). The effect of toll road operation on national road performance in Central Java province. Journal of Applied Engineering Science, 21(2), 741–748. doi:10.5937/jaes0-43041.

Forkenbrock, D. J., & Weisbrod, G. (2001). Guidebook for Assessing the Social and Economic Effects of Transportation Projects, NCHRP Report 456, American Association of State Highway and Transportation Officials, Washington, United States.

Zhustareva, E. V., & Bochkarev, V. I. (2020). The complex method of estimation of highway maintenance quality taking into account the International Roughness Index. IOP Conference Series: Materials Science and Engineering, 832(1), 012035. doi:10.1088/1757-899X/832/1/012035.

Gao, Q., Fan, L., Wei, S., Li, Y., Du, Y., & Liu, C. (2023). Differences Evaluation of Pavement Roughness Distribution Based on Light Detection and Ranging Data. Applied Sciences (Switzerland), 13(14), 8080. doi:10.3390/app13148080.

Naseri, H., Shokoohi, M., Jahanbakhsh, H., Karimi, M. M., & Waygood, E. O. D. (2023). Novel Soft-Computing Approach to Better Predict Flexible Pavement Roughness. Transportation Research Record, 2677(10), 246–259. doi:10.1177/03611981231161051.

Isradi, M., Prasetijo, J., Prasetyo, Y. D., Hartatik, N., & Rifai, A. I. (2023). Prediction of Service Life Base on Relationship Between Psi and Iri for Flexible Pavement. Proceedings on Engineering Sciences, 5(2), 267–274. doi:10.24874/PES05.02.009.

Shivananda, P., & Khatua, S. K. (2022). Study on International Road Roughness index (IRI) using Smart phone application from REVA University to Kodigehalli gate, Bangalore, India. IOP Conference Series: Materials Science and Engineering, 1255(1), 012020. doi:10.1088/1757-899x/1255/1/012020.

Abdulrazak Hasach Albasri, N., Al-Jawari, S. M., & Al-Mosherefawi, O. J. (2022). Prediction of Urban Spatial Changes Pattern Using Markov Chain. Civil Engineering Journal (Iran), 8(4), 710–722. doi:10.28991/CEJ-2022-08-04-07.

Frangopol, D. M., & Liu, M. (2019). Maintenance and management of civil infrastructure based on condition, safety, optimization, and life-cycle cost. Structures and Infrastructure Systems, 96-108. doi:10.1201/9781351182805-6.

Full Text: PDF

DOI: 10.28991/CEJ-2024-010-02-014


  • There are currently no refbacks.

Copyright (c) 2024 Ary Setyawan, Wahyuningsih Tri Hermani, Budi Yulianto, Evi Gravitiani

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