Gaber, M., Diab, A., Elbeltagi, E., Wahaballa, A. (2023). Integrated Safety-Pavement Maintenance Management System (SPMS) for Local Authorities in Egypt. JES. Journal of Engineering Sciences, 51(2), 125-147. doi: 10.21608/jesaun.2023.175063.1182
Mohamed Gaber; Aboelkasim Diab; Emad E. Elbeltagi; Amr M. Wahaballa. "Integrated Safety-Pavement Maintenance Management System (SPMS) for Local Authorities in Egypt". JES. Journal of Engineering Sciences, 51, 2, 2023, 125-147. doi: 10.21608/jesaun.2023.175063.1182
Gaber, M., Diab, A., Elbeltagi, E., Wahaballa, A. (2023). 'Integrated Safety-Pavement Maintenance Management System (SPMS) for Local Authorities in Egypt', JES. Journal of Engineering Sciences, 51(2), pp. 125-147. doi: 10.21608/jesaun.2023.175063.1182
Gaber, M., Diab, A., Elbeltagi, E., Wahaballa, A. Integrated Safety-Pavement Maintenance Management System (SPMS) for Local Authorities in Egypt. JES. Journal of Engineering Sciences, 2023; 51(2): 125-147. doi: 10.21608/jesaun.2023.175063.1182
Integrated Safety-Pavement Maintenance Management System (SPMS) for Local Authorities in Egypt
1Department of Civil Engineering, Faculty of Engineering, Aswan University, Egypt
2Department of Civil Engineering, Faculty of Engineering, Mansoura University, Egypt
Abstract
Road maintenance management is a fundamental strategy for achieving the infrastructure sustainability. In Egypt, maintenance decision depends only on the pavement condition, and traffic safety is not considered. This study aims to develop an integrated safety-pavement management system that enables managing roads network according to the available funds and assures the safety concept throughout the service life of roads. It develops a probabilistic performance model and an optimization decision tool based on roads condition, safety levels, and maintenance costs to provide a proper maintenance decision. The system was validated for the road network in southern Egypt. Results show that an inadequate maintenance budget causes a decrease in safety levels and pavement conditions in some sections due to late maintenance decisions. However, the results indicate the applicability for determining the economic maintenance plan which keeps safety at the targeted level and enhances the pavement performance through the network by saving 27 million Egyptian pounds through the analysis period.
[6] Saha, P., Ksaibati, K., & Atadero, R. (2017). Developing pavement distress deterioration models for pavement management system using Markovian probabilistic process. Advances in Civil Engineering.
[7] Haas, R., Hudson, W. R. (2015). Pavement asset management. John Wiley & Sons.
[8] Durango, P. L., and Madanat, S. M. (2002). "Optimal Maintenance and Repair Policies in Infrastructure Management under Uncertain Facility Deterioration Rates: An Adaptive Control Approach." Transportation Research Part A: Policy and Practice, 36(9), 763-778.
[9] Li, Z. (1997). Development of a probabilistic Based, Integrated Pavement Management System. Waterloo, Ontario, Canada: University of Waterloo.
[10] Soncim, S. P., de Oliveira, I. C. S., Santos, F. B., & Oliveira, C. A. D. S. (2018). Development of probabilistic models for predicting roughness in asphalt pavement. Road Materials and Pavement Design, 19(6), 1448-1457.
[11] Abaza, K. A. (2016). Back-calculation of transition probabilities for Markovian-based pavement performance prediction models. International Journal of Pavement Engineering.
[12] Surendrakumar, K., Prashant, N., & Mayuresh, P. (2013, August). Application of Markovian Probablistic Process to Develop a Decision Support System for Pavement Maintenance Management. International Journal of Scientific and Technology Research, 2(8), 295 – 303
[13] Wu, Z., Flintsch, G.W., 2009. Pavement Preservation Optimization Considering Multiple Objectives and Budget Variability. J. Transp. Eng. 135, 305–315.
[14] J.M. De La Garza, S. Akyildiz, D. R. Bish, and D. A. Krueger, “Network-level optimization of pavementmaintenance renewal strategies,” Advanced Engineering Informatics, vol. 25, no. 4, pp.699–712, 2011.
[15] Gu, W., Ouyang, Y., Madanat, S., 2012. Joint Optimisation of Pavement Maintenance and Resurfacing Planning. Transp. Res. Part B Methodology. 46, 511–519.
[16] Golroo, A., Tighe, S.L., 2012. Optimum Genetic Algorithm Structure Selection in Pavement Management. Asian J. Appl. Sci. 5 5, 327–341.
[17] Sedighpour M, Yousefikhoshbakht M, Darani Nm (2011) An effective genetic algorithm for solving the multiple traveling salesman problem. J Optim Ind Eng 8:73–79
[18] Yang, C., Remenyte-Prescott, R., Andrews, J.D., 2015. Pavement Maintenance Scheduling using Genetic Algorithms. Int. J. Performability Eng. 11, 135–152.
[19] Rifai, A. I., Hadiwardoyo, S. P., Correia, A. G., & Pereira, P. A. U. L. O. (2016). Genetic Algorithm Applied for Optimization of Pavement Maintenance under Overload Traffic: Case Study Indonesia National Highway. In Applied Mechanics and Materials (Vol. 845, pp. 369-378).
[20] Morcous G., Lounis Z., Maintenance optimization of infrastructure networks using genetic algorithms, Automation in Construction 14 (2005) 129– 142
[36] Meneses, S., Ferreira, A., Collop, A. (2013), “Multi-objective decision-aid tool for pavement management”, In Proceedings of the Institution of Civil Engineers-Transport, 166(2), 79-94.
[38] World Health Organization )2018(. Global status report on road safety 2018: Summary (No. WHO/NMH/NVI/18.20). World Health Organization.
[39] Gaber, M., Wahaballa, A. M., Othman, A. M., & Diab, A. (2017). Traffic accidents prediction model using fuzzy logic: Aswan desert road case study. J. Eng. Sci. Assiut Univ, 45, 2844.
[40] Hultkrantz L, Lindberg G, Andersson C (2006). The value of improved road safety. Journal of risk and uncertainty, 32(2), 151-170.
[41] Moussa, G. S., & Owais, M. (2021). Modeling Hot-Mix asphalt dynamic modulus using deep residual neural Networks: Parametric and sensitivity analysis study. Construction and Building Materials, 294, 123589.
[42] Moussa, G. S., & Owais, M. (2020). Pre-trained deep learning for hot-mix asphalt dynamic modulus prediction with laboratory effort reduction. Construction and Building Materials, 265, 120239.
[43] Hughes B, Newstead S, Anund A, Shu C, Falkmer T (2015). A review of models relevant to road safety. Accident Analysis & Prevention, 74, 250-270.
[45] Alkheder S, Taamneh M, Taamneh S (2017). Severity prediction of traffic accident using an artificial neural network. Journal of Forecasting 36, 100-108.
[47] Yafeng, Y., Yinhai, W., Lu, J., & Wei, W. (2011). Towards Sustainable Transportation Systems. In 11th International Conference of Chinese Transportation Professionals. American Society of Civil Engineers (Vol. 2006, pp. 1925-1933).
[48] Amiri, A. M., Nadimi, N., & Yousefian, A. (2020). Comparing the efficiency of different computation intelligence techniques in predicting accident frequency. IATSS Research.
[49] Labuschagne, F., De Beer, E., Roux, D., & Venter, K. (2017). The cost of crashes in South Africa 2016. Southern African Transport Conference.
[50] Cardoso, J. P., Mota, E. L. A., Rios, P. A. A., & Ferreira, L. N. (2020). Fatores associados à perda de produtividade em pessoas envolvidas em acidentes de trânsito: um estudo prospectivo. Revista Brasileira de Epidemiologia, 23, e200015
[51] Mofadal, A. I., & Kanitpong, K. (2016). Analysis of road traffic accident costs in Sudan using the human capital method. Open journal of civil engineering, 6(2), 203-216.
[52] Wijnen, W., Weijermars, W., Schoeters, A., Van den Berghe, W., Bauer, R., Carnis, L., ... & Martensen, H. (2019). An analysis of official road crash cost estimates in European countries. Safety science, 113, 318-327.
[53] Ali, Q., Yaseen, M. R., & Khan, M. T. I. (2019). Road traffic fatalities and its determinants in high-income countries: a continent-wise comparison. Environmental Science and Pollution Research, 26(19), 19915-19929.
[54] World Health Organization. (2010). Data systems: a road safety manual for decision-makers and practitioners.
[55] Daniels, S., Martensen, H., Schoeters, A., Van den Berghe, W., Papadimitriou, E., Ziakopoulos, A., & Weijermars, W. (2019). A systematic cost-benefit analysis of 29 road safety measures. Accident Analysis & Prevention, 133, 105292.
[56] Samir, E. (2009). Prediction Pavement Performance using Markov Chain Model. Master's Thesis, Faculty of Engineering, Cairo University, Cairo, Egypt
[57] Marzouk, M., Awad, E., & El-Said, M. (2012). An integrated tool for optimizing rehabilitation programs of highways pavement. The Baltic Journal of Road and Bridge Engineering, 7(4), 297-304.
[58] Elhadidy,A.,Elbeltagi,E.,andAmmar,M.(2015)."Optimum Analysis of Pavement
Maintenance Using Multi-Objective Genetic Algorithms". Housing and Building Research Center (HBRC) Journal,ElsevierPublisher,11(1),pp.107-113.
[59] El-Tahan(2017). " Development a pavement maintenance management system frame work using Markov Chains theory for Egyptian higway networs. Doctoral thesis. Faculty of Engi eering, A exandria Uni ersity.
[60] El-Hakim, A., El-Aziz, A., Nader, E., El-Badawy, S. M., & Afify, H. A. (2017). Validation and improvement of pavement ME flexible pavement roughness prediction model using extended LTPP database (No. 17-02203).
[61] Abdelaziz, N., Abd El-Hakim, R. T., El-Badawy, S. M., & Afify, H. A. (2020). International Roughness Index prediction model for flexible pavements. International Journal of Pavement Engineering, 21(1), 88-99.
[62] Egyptian General Authority for Roads, Bridges, and Land Transport, (GARBLT, 2022).
[63] ASTM D6433-11 (2011) "Standard Practice for Roads and Parking Lots Pavement Condition Index Surveys", ASTM International, West Conshohocken, PA, 2011, www.astm.org.