An Iterative Approach for the Optimization of Pavement Maintenance Management at the Network Level

Pavement maintenance is one of the major issues of public agencies. Insufficient investment or inefficient maintenance strategies lead to high economic expenses in the long term. Under budgetary restrictions, the optimal allocation of resources becomes a crucial aspect. Two traditional approaches (sequential and holistic) and four classes of optimization methods (selection based on ranking, mathematical optimization, near optimization, and other methods) have been applied to solve this problem. They vary in the number of alternatives considered and how the selection process is performed.Therefore, a previous understanding of the problem is mandatory to identify the most suitable approach and method for a particular network. This study aims to assist highway agencies, researchers, and practitioners onwhen and howto apply availablemethods based on a comparative analysis of the current state of the practice. Holistic approach tackles the problem considering the overall network condition, while the sequential approach is easier to implement and understand, but may lead to solutions far from optimal. Scenarios defining the suitability of these approaches are defined. Finally, an iterative approach gathering the advantages of traditional approaches is proposed and applied in a case study. The proposed approach considers the overall network condition in a simpler and more intuitive manner than the holistic approach.The authors gratefully acknowledge members of the research group at the Pontificia Universidad Catolica de Chile for their contributions and resources during the study. The research team acknowledges Conicyt-Fondef/Decimoseptimo Concurso de Proyectos de Investigacion y Desarrollo del Fondo de Fomento al Desarrollo Cientifico y Tecnologico, Fondef/Conicyt 2009 (D09I1018) for funding this project. Support of the associated institutions is also appreciated: Ministry of Housing and Urban Development (Ministerio de Vivienda y Urbanismo), Regional Government for Metropolitan Region (Gobierno Regional de la Region Metropolitana), Municipality of Santiago (Municipalidad de Santiago), and Municipality of Macul (Municipalidad de Macul). Funding over Santander Universidades (Becas Iberoamerica Jovenes Profesionales e Investigadores, 2013) to support this work is sincerely appreciated.Torres Machí, C.; Chamorro, A.; Videla, C.; Pellicer Armiñana, E.; Yepes, V. (2014). An Iterative Approach for the Optimization of Pavement Maintenance Management at the Network Level. Scientific World Journal. 2014(4329):1-11. https://doi.org/10.1155/2014/524329S11120144329Qin, J., Ni, L., & Shi, F. (2013). Mixed Transportation Network Design under a Sustainable Development Perspective. The Scientific World Journal, 2013, 1-8. doi:10.1155/2013/549735Chamorro, A., & Tighe, S. L. (2009). Development of a Management Framework for Rural Roads in Developing Countries. Transportation Research Record: Journal of the Transportation Research Board, 2093(1), 99-107. doi:10.3141/2093-12Golroo, A., & L. Tighe, S. (2012). Optimum Genetic Algorithm Structure Selection in Pavement Management. Asian Journal of Applied Sciences, 5(6), 327-341. doi:10.3923/ajaps.2012.327.341Hsueh, S.-L., & Yan, M.-R. (2013). A Multimethodology Contractor Assessment Model for Facilitating Green Innovation: The View of Energy and Environmental Protection. The Scientific World Journal, 2013, 1-14. doi:10.1155/2013/624340Jiao, Y., Liu, H., Zhang, P., Wang, X., & Wei, H. (2013). Unsupervised Performance Evaluation Strategy for Bridge Superstructure Based on Fuzzy Clustering and Field Data. The Scientific World Journal, 2013, 1-6. doi:10.1155/2013/427072Shah, Y. U., Jain, S. S., & Parida, M. (2012). Evaluation of prioritization methods for effective pavement maintenance of urban roads. International Journal of Pavement Engineering, 15(3), 238-250. doi:10.1080/10298436.2012.657798De la Garza, J. M., Akyildiz, S., Bish, D. R., & Krueger, D. A. (2011). Network-level optimization of pavement maintenance renewal strategies. Advanced Engineering Informatics, 25(4), 699-712. doi:10.1016/j.aei.2011.08.002Gao, L., Xie, C., Zhang, Z., & Waller, S. T. (2011). Network-Level Road Pavement Maintenance and Rehabilitation Scheduling for Optimal Performance Improvement and Budget Utilization. Computer-Aided Civil and Infrastructure Engineering, 27(4), 278-287. doi:10.1111/j.1467-8667.2011.00733.xAmador-Jiménez, L. E., & Mrawira, D. (2009). Roads Performance Modeling and Management System from Two Condition Data Points: Case Study of Costa Rica. Journal of Transportation Engineering, 135(12), 999-1007. doi:10.1061/(asce)te.1943-5436.0000074Gao, L., & Zhang, Z. (2008). Robust Optimization for Managing Pavement Maintenance and Rehabilitation. Transportation Research Record: Journal of the Transportation Research Board, 2084(1), 55-61. doi:10.3141/2084-07Ng, M., Zhang, Z., & Travis Waller, S. (2011). The price of uncertainty in pavement infrastructure management planning: An integer programming approach. Transportation Research Part C: Emerging Technologies, 19(6), 1326-1338. doi:10.1016/j.trc.2011.03.003Ferreira, A., Antunes, A., & Picado-Santos, L. (2002). Probabilistic Segment-linked Pavement Management Optimization Model. Journal of Transportation Engineering, 128(6), 568-577. doi:10.1061/(asce)0733-947x(2002)128:6(568)Yoo, J., & Garcia-Diaz, A. (2008). Cost-effective selection and multi-period scheduling of pavement maintenance and rehabilitation strategies. Engineering Optimization, 40(3), 205-222. doi:10.1080/03052150701686937Farhan, J., & Fwa, T. F. (2012). Incorporating Priority Preferences into Pavement Maintenance Programming. Journal of Transportation Engineering, 138(6), 714-722. doi:10.1061/(asce)te.1943-5436.0000372Fwa, T. F., & Farhan, J. (2012). Optimal Multiasset Maintenance Budget Allocation in Highway Asset Management. Journal of Transportation Engineering, 138(10), 1179-1187. doi:10.1061/(asce)te.1943-5436.0000414Tsunokawa, K., Van Hiep, D., & Ul-Islam, R. (2006). True Optimization of Pavement Maintenance Options with What-If Models. Computer-Aided Civil and Infrastructure Engineering, 21(3), 193-204. doi:10.1111/j.1467-8667.2006.00427.xChou, J.-S., & Le, T.-S. (2011). Reliability-based performance simulation for optimized pavement maintenance. Reliability Engineering & System Safety, 96(10), 1402-1410. doi:10.1016/j.ress.2011.05.005Flintsch, G. W., & Chen, C. (2004). Soft Computing Applications in Infrastructure Management. Journal of Infrastructure Systems, 10(4), 157-166. doi:10.1061/(asce)1076-0342(2004)10:4(157)Chan, W. T., Fwa, T. F., & Tan, J. Y. (2003). Optimal Fund-Allocation Analysis for Multidistrict Highway Agencies. Journal of Infrastructure Systems, 9(4), 167-175. doi:10.1061/(asce)1076-0342(2003)9:4(167)Chootinan, P., Chen, A., Horrocks, M. R., & Bolling, D. (2006). A multi-year pavement maintenance program using a stochastic simulation-based genetic algorithm approach. Transportation Research Part A: Policy and Practice, 40(9), 725-743. doi:10.1016/j.tra.2005.12.003Meneses, S., & Ferreira, A. (2013). Pavement maintenance programming considering two objectives: maintenance costs and user costs. International Journal of Pavement Engineering, 14(2), 206-221. doi:10.1080/10298436.2012.727994Fwa, T. F., & Chan, W. T. (1993). Priority Rating of Highway Maintenance Needs by Neural Networks. Journal of Transportation Engineering, 119(3), 419-432. doi:10.1061/(asce)0733-947x(1993)119:3(419)Moazami, D., Behbahani, H., & Muniandy, R. (2011). Pavement rehabilitation and maintenance prioritization of urban roads using fuzzy logic. Expert Systems with Applications, 38(10), 12869-12879. doi:10.1016/j.eswa.2011.04.079Khurshid, M. B., Irfan, M., Ahmed, A., & Labi, S. (2013). Multidimensional benefit–cost evaluation of asphaltic concrete overlays of rigid pavements. Structure and Infrastructure Engineering, 10(6), 792-810. doi:10.1080/15732479.2013.767842Khurshid, M. B., Irfan, M., & Labi, S. (2009). Comparison of Methods for Evaluating Pavement Interventions. Transportation Research Record: Journal of the Transportation Research Board, 2108(1), 25-36. doi:10.3141/2108-03Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671-680. doi:10.1126/science.220.4598.671Martí, J. V., Gonzalez-Vidosa, F., Yepes, V., & Alcalá, J. (2013). Design of prestressed concrete precast road bridges with hybrid simulated annealing. Engineering Structures, 48, 342-352. doi:10.1016/j.engstruct.2012.09.01

Creative innovation in Spanish construction firms

"This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil Engineers"Small and medium-sized contractors are characterized by organizational structures that are highly focused on control. As a result, employees concentrate on day-to-day activities with little time or motivation to generate creative ideas. Generally, the technological improvements of these companies arise as a result of problem-solving at the construction site. Nevertheless, the actual status quo is changing. In fact, some Spanish public agencies are already considering innovation as an added value in public procurement; thus, large contractors are starting to systemize their innovative efforts. This means that small and medium-sized enterprises must modify their attitudes towards innovation in order to sustain their competitiveness. The implementation of a system that enhances innovation and acquisition of knowledge may be the solution to overcome this disadvantage. The authors analyzed the implementation of an innovation management system in a Spanish construction firm of medium size for nine years. The system builds on a set of processes aimed to generate innovation projects that allow the contractor to document the innovation, not only for internal purposes related to knowledge management, but also for external ones associated with obtaining better results in public tenders. These processes are: (a) technology watch; (b) creativity; (c) planning and executing innovation projects; (d) technology transfer; and (e) protection of results. The last step is the feedback of the entire process through the assessment of the final outcomes. The implementation of the innovation system is ensured within the organization, through training of personnel, participation of stakeholders and encouragement of the innovation culture.The research reported in this paper was partially funded by the Universidad Catolica del Maule (UCM) [Project Mejoramiento de la Calidad y Equidad de la Educacion Superior (MECESUP)-UCM0205], the Spanish Ministry of Infrastructure (Project 2004-36), and the Universitat Politecnica de Valencia (UPV) (Contract UPV-2008-0629). Francisco Vea, Ricardo Lacort, and Manuel Civera are thanked for their help and support throughout the implementation of the system. Dr. Debra Westall is thanked for revising the text.Yepes, V.; Pellicer Armiñana, E.; Fernando Alarcón, L.; Correa Becerra, CL. (2015). Creative innovation in Spanish construction firms. Journal of Professional Issues in Engineering Education and Practice. 141:04015006-1-04015006-10. https://doi.org/10.1061/(ASCE)EI.1943-5541.0000251S04015006-104015006-1014

Optimization of concrete I-beams using a new hybrid glowworm swarm algorithm

In this paper a new hybrid glowworm swarm algorithm (SAGSO) for solving structural optimization problems is presented. The structure proposed to be optimized here is a simply-supported concrete I-beam defined by 20 variables. Eight different concrete mixtures are studied, varying the compressive strength grade and compacting system. The solutions are evaluated following the Spanish Code for structural concrete. The algorithm is applied to two objective functions, namely the embedded CO2 emissions and the economic cost of the structure. The ability of glowworm swarm optimization (GSO) to search in the entire solution space is combined with the local search by Simulated Annealing (SA) to obtain better results than using the GSO and SA independently. Finally, the hybrid algorithm can solve structural optimization problems applied to discrete variables. The study showed that large sections with a highly exposed surface area and the use of conventional vibrated concrete (CVC) with the lower strength grade minimize the CO2 emissionsGarcía Segura, T.; Yepes Piqueras, V.; Martí Albiñana, JV.; Alcalá González, J. (2014). Optimization of concrete I-beams using a new hybrid glowworm swarm algorithm. Latin American Journal of Solids and Structures. 11(7):1190-1205. doi:10.1590/S1679-78252014000700007S11901205117Alinia Ahandani, M., Vakil Baghmisheh, M. T., Badamchi Zadeh, M. A., & Ghaemi, S. (2012). Hybrid particle swarm optimization transplanted into a hyper-heuristic structure for solving examination timetabling problem. Swarm and Evolutionary Computation, 7, 21-34. doi:10.1016/j.swevo.2012.06.004Chen, S.-M., Sarosh, A., & Dong, Y.-F. (2012). Simulated annealing based artificial bee colony algorithm for global numerical optimization. Applied Mathematics and Computation, 219(8), 3575-3589. doi:10.1016/j.amc.2012.09.052Collins, F. (2010). Inclusion of carbonation during the life cycle of built and recycled concrete: influence on their carbon footprint. The International Journal of Life Cycle Assessment, 15(6), 549-556. doi:10.1007/s11367-010-0191-4Dutta, R., Ganguli, R., & Mani, V. (2011). Swarm intelligence algorithms for integrated optimization of piezoelectric actuator and sensor placement and feedback gains. Smart Materials and Structures, 20(10), 105018. doi:10.1088/0964-1726/20/10/105018Fan, S.-K. S., & Zahara, E. (2007). A hybrid simplex search and particle swarm optimization for unconstrained optimization. European Journal of Operational Research, 181(2), 527-548. doi:10.1016/j.ejor.2006.06.034García-Segura, T., Yepes, V., & Alcalá, J. (2013). Life cycle greenhouse gas emissions of blended cement concrete including carbonation and durability. The International Journal of Life Cycle Assessment, 19(1), 3-12. doi:10.1007/s11367-013-0614-0Gong, Q. Q., Zhou, Y. Q., & Yang, Y. (2010). Artificial Glowworm Swarm Optimization Algorithm for Solving 0-1 Knapsack Problem. Advanced Materials Research, 143-144, 166-171. doi:10.4028/www.scientific.net/amr.143-144.166Hare, W., Nutini, J., & Tesfamariam, S. (2013). A survey of non-gradient optimization methods in structural engineering. Advances in Engineering Software, 59, 19-28. doi:10.1016/j.advengsoft.2013.03.001He, S., Prempain, E., & Wu, Q. H. (2004). An improved particle swarm optimizer for mechanical design optimization problems. Engineering Optimization, 36(5), 585-605. doi:10.1080/03052150410001704854Karaboga, D., & Basturk, B. (2008). On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing, 8(1), 687-697. doi:10.1016/j.asoc.2007.05.007Khan, K., & Sahai, A. (2012). A Glowworm Optimization Method for the Design of Web Services. International Journal of Intelligent Systems and Applications, 4(10), 89-102. doi:10.5815/ijisa.2012.10.10Kicinger, R., Arciszewski, T., & Jong, K. D. (2005). Evolutionary computation and structural design: A survey of the state-of-the-art. Computers & Structures, 83(23-24), 1943-1978. doi:10.1016/j.compstruc.2005.03.002Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671-680. doi:10.1126/science.220.4598.671Koide, R. M., França, G. von Z. de, & Luersen, M. A. (2013). An ant colony algorithm applied to lay-up optimization of laminated composite plates. Latin American Journal of Solids and Structures, 10(3), 491-504. doi:10.1590/s1679-78252013000300003Krishnanand, K. N., & Ghose, D. (2009). Glowworm swarm optimisation: a new method for optimising multi-modal functions. International Journal of Computational Intelligence Studies, 1(1), 93. doi:10.1504/ijcistudies.2009.025340Li, L. J., Huang, Z. B., & Liu, F. (2009). A heuristic particle swarm optimization method for truss structures with discrete variables. Computers & Structures, 87(7-8), 435-443. doi:10.1016/j.compstruc.2009.01.004Liao, W.-H., Kao, Y., & Li, Y.-S. (2011). A sensor deployment approach using glowworm swarm optimization algorithm in wireless sensor networks. Expert Systems with Applications, 38(10), 12180-12188. doi:10.1016/j.eswa.2011.03.053Luo, Q. F., & Zhang, J. L. (2011). Hybrid Artificial Glowworm Swarm Optimization Algorithm for Solving Constrained Engineering Problem. Advanced Materials Research, 204-210, 823-827. doi:10.4028/www.scientific.net/amr.204-210.823Martí, J. V., Gonzalez-Vidosa, F., Yepes, V., & Alcalá, J. (2013). Design of prestressed concrete precast road bridges with hybrid simulated annealing. Engineering Structures, 48, 342-352. doi:10.1016/j.engstruct.2012.09.014Martinez-Martin, F. J., Gonzalez-Vidosa, F., Hospitaler, A., & Yepes, V. (2013). A parametric study of optimum tall piers for railway bridge viaducts. Structural Engineering and Mechanics, 45(6), 723-740. doi:10.12989/sem.2013.45.6.723Medina, J. R. (2001). Estimation of Incident and Reflected Waves Using Simulated Annealing. Journal of Waterway, Port, Coastal, and Ocean Engineering, 127(4), 213-221. doi:10.1061/(asce)0733-950x(2001)127:4(213)Parsopoulos, K. E., & Vrahatis, M. N. (2002). Natural Computing, 1(2/3), 235-306. doi:10.1023/a:1016568309421Paya-Zaforteza, I., Yepes, V., González-Vidosa, F., & Hospitaler, A. (2010). On the Weibull cost estimation of building frames designed by simulated annealing. Meccanica, 45(5), 693-704. doi:10.1007/s11012-010-9285-0Sarma, K. C., & Adeli, H. (1998). Cost Optimization of Concrete Structures. Journal of Structural Engineering, 124(5), 570-578. doi:10.1061/(asce)0733-9445(1998)124:5(570)Shieh, H.-L., Kuo, C.-C., & Chiang, C.-M. (2011). Modified particle swarm optimization algorithm with simulated annealing behavior and its numerical verification. Applied Mathematics and Computation, 218(8), 4365-4383. doi:10.1016/j.amc.2011.10.012Sideris, K. K., & Anagnostopoulos, N. S. (2013). Durability of normal strength self-compacting concretes and their impact on service life of reinforced concrete structures. Construction and Building Materials, 41, 491-497. doi:10.1016/j.conbuildmat.2012.12.042Valdez, F., Melin, P., & Castillo, O. (2011). An improved evolutionary method with fuzzy logic for combining Particle Swarm Optimization and Genetic Algorithms. Applied Soft Computing, 11(2), 2625-2632. doi:10.1016/j.asoc.2010.10.010Wang, H., Sun, H., Li, C., Rahnamayan, S., & Pan, J. (2013). Diversity enhanced particle swarm optimization with neighborhood search. Information Sciences, 223, 119-135. doi:10.1016/j.ins.2012.10.012Yepes, V., Gonzalez-Vidosa, F., Alcala, J., & Villalba, P. (2012). CO2-Optimization Design of Reinforced Concrete Retaining Walls Based on a VNS-Threshold Acceptance Strategy. Journal of Computing in Civil Engineering, 26(3), 378-386. doi:10.1061/(asce)cp.1943-5487.000014