Branding of Government Services: Benefits and Challenges

Till recently, brand management was perceived as a function exclusive to commercial organizations. However, the experiences of several nations have shown that branding can be an effective tool for managing government services also. This paper discusses the benefits of the current practices of branding in governance. A coherent branding strategy assists the departments in the formulation and delivery of services by having clarity of purpose and consistency in communication. The citizen benefits in various ways from a streamlined execution of the strategy. The paper also identifies the challenges for brand management to grow in the governance sphere

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. 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Influência da camada do revestimento de argamassa na penetração de cloretos em estruturas de concreto

Este trabalho estudou a influência da camada de revestimento em argamassa na penetração de cloretos no concreto. Para tanto, foram moldados corpos de prova de concreto nas dimensões 8 cm x 8 cm x 8 cm e relação água/cimento de 0,55, sobre os quais foram aplicados três tipos de argamassa de revestimento, após uma fina camada de chapisco. Vencidos os períodos de cura de 28 dias para o concreto e a argamassa de revestimento, cinco das seis faces dos CPs foram isoladas com resina epóxi para simular um fluxo unidirecional. Esses CPs foram submetidos ao ensaio de imersão e secagem por 49 dias e, após isso, foram retiradas e analisadas amostras para a obtenção dos perfis de cloretos. Os resultados indicam que as argamassas de revestimento influenciam no transporte de cloretos no concreto e que essa influência é mais pronunciada para as argamassas menos porosas e mais ricas em cimento. Também se observou um acúmulo de cloretos na região próxima à interface argamassa-concreto, o qual é explicado pelas diferenças na capacidade de transporte entre a argamassa e o concreto. Apesar de as argamassas serem mais porosas que o concreto, elas podem representar uma proteção adicional em relação ao retardamento na penetração de cloretos no concreto

Comportamento de concreto armado com adição de resíduos de tijolo cerâmico moído frente à corrosão por cloretos

Com o intuito de analisar o comportamento de concretos com substituição de cimento por resíduo de tijolo cerâmico moído (RTM) frente à corrosão por cloretos, foram moldados corpos de prova (CPs) de concreto armado de 80 x 80 x 80 mm, com faixas de substituição do RTM de 0 %, 10 % e 30 % e fator água/aglomerante de 0,55. Após uma cura úmida de 7 dias e em ambiente de laboratório até 180 dias, os CPs foram submetidos a ciclos de imersão e secagem em solução de NaCl 1M. Uma vez identificada a despassivação das armaduras, perfis de cloretos livres e totais foram obtidos. Os resultados mostram que, embora haja uma pequena diminuição da resistência mecânica e do teor crítico de cloretos com a incorporação de RTM ao concreto, os concretos com RTM tendem a apresentar uma redução da sua capacidade de transporte de massa, que pode se sobrepor ao aspecto anterior e alongar o período de iniciação da corrosão