Influence of Maintenance Funds on Improve Road Steadiness with the Curva Expert Program
Vol. 10 No. 2 (2024): February
Research Articles
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Doi: 10.28991/CEJ-2024-010-02-014
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Setyawan, A., Hermani, W. T., Yulianto, B., & Gravitiani, E. (2024). Influence of Maintenance Funds on Improve Road Steadiness with the Curva Expert Program. Civil Engineering Journal, 10(2), 546–554. https://doi.org/10.28991/CEJ-2024-010-02-014
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[1] 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.
[2] 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.
[3] 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.
[4] 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.
[5] 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.
[6] 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.
[7] 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.
[8] 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.
[9] 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.
[10] 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.
[11] 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.
[12] 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.
[13] Paplauskas, P., Vaitkus, A., & SimanaviÄienÄ—, 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.
[14] 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.
[15] JurkeviÄius, M., PuodŠ¾iukas, V., & LaurinaviÄius, 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.
[16] 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.
[17] 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.
[18] 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.
[19] 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.
[20] 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.
[21] 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.
[22] 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.
[23] 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.
[24] 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.
[25] 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.
[26] 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.
[2] 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.
[3] 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.
[4] 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.
[5] 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.
[6] 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.
[7] 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.
[8] 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.
[9] 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.
[10] 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.
[11] 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.
[12] 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.
[13] Paplauskas, P., Vaitkus, A., & SimanaviÄienÄ—, 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.
[14] 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.
[15] JurkeviÄius, M., PuodŠ¾iukas, V., & LaurinaviÄius, 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.
[16] 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.
[17] 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.
[18] 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.
[19] 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.
[20] 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.
[21] 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.
[22] 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.
[23] 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.
[24] 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.
[25] 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.
[26] 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.
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