5,409 research outputs found

    La biología computacional de la Proteómica post-genómica

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    La reforma fiscal ante el esfuerzo productivo y la asunción de riesgos

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    Desde que en 1977 se inicia la Reforma Tributaria, junto a una multiplicidad de otros cambios institucionales en diversas áreas, se produce la conocida Reforma Fiscal que afectará de manera directa al ahorro, de una parte, y a las decisiones de inversión de las empresas, por otra. Se analiza cómo afecta el cambio tributario a la disposición ante el esfuerzo, la correlación entre renta y esfuerzo así como su influencia sobre las decisiones de inversión empresarial por medio de nuevos incentivos a la inversión. Este cambio tributario no puede valorarse negativamente, ni ha reducido los incentivos al ahorro, ni va a afectar negativamente las decisiones de inversión empresarial.Since in 1977 starts the Tax Reform, along with a multiplicity of other institutional changes in various areas, occurs the known Fiscal Reform that will directly affect the savings, on the one hand, and the investment decisions of firms, on the other. Discusses how it affects the tax change to the layout before the effort, the correlation between income and effort as well as their influence on the decisions of business investment through new investment incentives. This tax change cannot be assessed negatively, since it has not reduced savings incentives and it has not affected the decisions of business investment

    Multi-objective design of post-tensioned concrete road bridges using artificial neural networks

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    [EN] In order to minimize the total expected cost, bridges have to be designed for safety and durability. This paper considers the cost, the safety, and the corrosion initiation time to design post-tensioned concrete box-girder road bridges. The deck is modeled by finite elements based on problem variables such as the cross-section geometry, the concrete grade, and the reinforcing and post-tensioning steel. An integrated multi-objective harmony search with artificial neural networks (ANNs) is proposed to reduce the high computing time required for the finite-element analysis and the increment in conflicting objectives. ANNs are trained through the results of previous bridge performance evaluations. Then, ANNs are used to evaluate the constraints and provide a direction towards the Pareto front. Finally, exact methods actualize and improve the Pareto set. The results show that the harmony search parameters should be progressively changed in a diversification-intensification strategy. This methodology provides trade-off solutions that are the cheapest ones for the safety and durability levels considered. Therefore, it is possible to choose an alternative that can be easily adjusted to each need.The authors acknowledge the financial support of the Spanish Ministry of Economy and Competitiveness, along with FEDER funding (BRIDLIFE Project: BIA2014-56574-R) and the Research and Development Support Program of Universitat Politecnica de Valencia (PAID-02-15).García-Segura, T.; Yepes, V.; Frangopol, D. (2017). Multi-objective design of post-tensioned concrete road bridges using artificial neural networks. Structural and Multidisciplinary Optimization. 56(1):139-150. doi:10.1007/s00158-017-1653-0S139150561Alberdi R, Khandelwal K (2015) Comparison of robustness of metaheuristic algorithms for steel frame optimization. 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Ministerio de Fomento, MadridGarcía-Segura T, Yepes V (2016) Multiobjective optimization of post-tensioned concrete box-girder road bridges considering cost, CO2 emissions, and safety. Eng Struct 125:325–336. doi: 10.1016/j.engstruct.2016.07.012García-Segura T, Yepes V, Alcalá J (2014a) Life cycle greenhouse gas emissions of blended cement concrete including carbonation and durability. Int J Life Cycle Assess 19:3–12. doi: 10.1007/s11367-013-0614-0García-Segura T, Yepes V, Martí JV, Alcalá J (2014b) Optimization of concrete I-beams using a new hybrid glowworm swarm algorithm. Lat Am J Solids Struct 11:1190–1205. doi: 10.1590/S1679-78252014000700007García-Segura T, Yepes V, Alcalá J, Pérez-López E (2015) Hybrid harmony search for sustainable design of post-tensioned concrete box-girder pedestrian bridges. Eng Struct 92:112–122. doi: 10.1016/j.engstruct.2015.03.015Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76:60–68Giannakoglou KC (2002) Design of optimal aerodynamic shapes using stochastic optimization methods and computational intelligence. Prog Aerosp Sci 38:43–76. doi: 10.1016/S0376-0421(01)00019-7Hare W, Nutini J, Tesfamariam S (2013) A survey of non-gradient optimization methods in structural engineering. Adv Eng Softw 59:19–28. doi: 10.1016/j.advengsoft.2013.03.001Martí JV, Yepes V, González-Vidosa F (2015) Memetic algorithm approach to designing precast-prestressed concrete road bridges with steel fiber reinforcement. J Struct Eng 141:4014114. doi: 10.1061/(ASCE)ST.1943-541X.0001058Martí JV, García-Segura T, Yepes V (2016) Structural design of precast-prestressed concrete U-beam road bridges based on embodied energy. J Clean Prod 120:231–240. doi: 10.1016/j.jclepro.2016.02.024Martinez-Martin FJ, Gonzalez-Vidosa F, Hospitaler A, Yepes V (2012) Multi-objective optimization design of bridge piers with hybrid heuristic algorithms. 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    Robust Design Optimization for Low-Cost Concrete Box-Girder Bridge

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    [EN] The design of a structure is generally carried out according to a deterministic approach. However, all structural problems have associated initial uncertain parameters that can differ from the design value. This becomes important when the goal is to reach optimized structures, as a small variation of these initial uncertain parameters can have a big influence on the structural behavior. The objective of robust design optimization is to obtain an optimum design with the lowest possible variation of the objective functions. For this purpose, a probabilistic optimization is necessary to obtain the statistical parameters that represent the mean value and variation of the objective function considered. However, one of the disadvantages of the optimal robust design is its high computational cost. In this paper, robust design optimization is applied to design a continuous prestressed concrete box-girder pedestrian bridge that is optimum in terms of its cost and robust in terms of structural stability. Furthermore, Latin hypercube sampling and the kriging metamodel are used to deal with the high computational cost. Results show that the main variables that control the structural behavior are the depth of the cross-section and compressive strength of the concrete and that a compromise solution between the optimal cost and the robustness of the design can be reached.This research was funded by the Ministerio de Economia, Ciencia y Competitividad and FEDER funding grant number [BIA2017-85098-R].Penadés-Plà, V.; García-Segura, T.; Yepes, V. (2020). Robust Design Optimization for Low-Cost Concrete Box-Girder Bridge. Mathematics. 8(3):1-14. https://doi.org/10.3390/math8030398S11483Lee, K.-H., & Kang, D.-H. (2006). A robust optimization using the statistics based on kriging metamodel. 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Multiobjective optimization of post-tensioned concrete box-girder road bridges considering cost, CO2 emissions, and safety. Engineering Structures, 125, 325-336. doi:10.1016/j.engstruct.2016.07.012Martí, J. V., García-Segura, T., & Yepes, V. (2016). Structural design of precast-prestressed concrete U-beam road bridges based on embodied energy. Journal of Cleaner Production, 120, 231-240. doi:10.1016/j.jclepro.2016.02.024Yepes, V., Martí, J. V., García-Segura, T., & González-Vidosa, F. (2017). Heuristics in optimal detailed design of precast road bridges. Archives of Civil and Mechanical Engineering, 17(4), 738-749. doi:10.1016/j.acme.2017.02.006Sun, X., Fu, H., & Zeng, J. (2018). Robust Approximate Optimality Conditions for Uncertain Nonsmooth Optimization with Infinite Number of Constraints. Mathematics, 7(1), 12. doi:10.3390/math7010012Rodriguez-Gonzalez, P. T., Rico-Ramirez, V., Rico-Martinez, R., & Diwekar, U. M. (2019). A New Approach to Solving Stochastic Optimal Control Problems. Mathematics, 7(12), 1207. doi:10.3390/math7121207Moayyeri, N., Gharehbaghi, S., & Plevris, V. (2019). Cost-Based Optimum Design of Reinforced Concrete Retaining Walls Considering Different Methods of Bearing Capacity Computation. Mathematics, 7(12), 1232. doi:10.3390/math7121232Sierra, L. A., Yepes, V., García-Segura, T., & Pellicer, E. (2018). Bayesian network method for decision-making about the social sustainability of infrastructure projects. Journal of Cleaner Production, 176, 521-534. doi:10.1016/j.jclepro.2017.12.140Valdebenito, M. A., & Schuëller, G. I. (2010). A survey on approaches for reliability-based optimization. Structural and Multidisciplinary Optimization, 42(5), 645-663. doi:10.1007/s00158-010-0518-6Doltsinis, I., & Kang, Z. (2004). Robust design of structures using optimization methods. Computer Methods in Applied Mechanics and Engineering, 193(23-26), 2221-2237. doi:10.1016/j.cma.2003.12.055Simpson, T. W., Booker, A. J., Ghosh, D., Giunta, A. A., Koch, P. N., & Yang, R.-J. (2004). Approximation methods in multidisciplinary analysis and optimization: a panel discussion. Structural and Multidisciplinary Optimization, 27(5). doi:10.1007/s00158-004-0389-9Martínez-Frutos, J., & Martí, P. (2014). Diseño óptimo robusto utilizando modelos Kriging: aplicación al diseño óptimo robusto de estructuras articuladas. Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería, 30(2), 97-105. doi:10.1016/j.rimni.2013.01.003Jin, R., Chen, W., & Simpson, T. W. (2001). Comparative studies of metamodelling techniques under multiple modelling criteria. Structural and Multidisciplinary Optimization, 23(1), 1-13. doi:10.1007/s00158-001-0160-4Marti-Vargas, J. R., Ferri, F. J., & Yepes, V. (2013). Prediction of the transfer length of prestressing strands with neural networks. Computers and Concrete, 12(2), 187-209. doi:10.12989/cac.2013.12.2.187Salehi, H., & Burgueño, R. (2018). 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Kriging Models for Global Approximation in Simulation-Based Multidisciplinary Design Optimization. AIAA Journal, 39(12), 2233-2241. doi:10.2514/2.1234Forrester, A. I. J., & Keane, A. J. (2009). Recent advances in surrogate-based optimization. Progress in Aerospace Sciences, 45(1-3), 50-79. doi:10.1016/j.paerosci.2008.11.001Simpson, T. W., Poplinski, J. D., Koch, P. N., & Allen, J. K. (2001). Metamodels for Computer-based Engineering Design: Survey and recommendations. Engineering with Computers, 17(2), 129-150. doi:10.1007/pl00007198Camp, C. V., & Huq, F. (2013). CO2 and cost optimization of reinforced concrete frames using a big bang-big crunch algorithm. Engineering Structures, 48, 363-372. doi:10.1016/j.engstruct.2012.09.004Martí, 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.2012.09.014Medina, J. R. (2001). 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