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Cost and CO2 emission optimization of precast prestressed concrete U-beam road bridges by a hybrid glowworm swarm algorithm

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Cost and CO2 emission optimization of precast prestressed concrete U-beam road bridges by a hybrid glowworm swarm algorithm

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dc.contributor.author Yepes Piqueras, Víctor es_ES
dc.contributor.author Martí Albiñana, José Vicente es_ES
dc.date.accessioned 2015-04-17T08:41:30Z
dc.date.available 2015-04-17T08:41:30Z
dc.date.issued 2015-01
dc.identifier.issn 0926-5805
dc.identifier.uri http://hdl.handle.net/10251/48918
dc.description.abstract This paper describes a methodology to optimize cost and CO2 emissions when designing precast-prestressed concrete road bridges with a double U-shape cross-section. To this end, a hybrid glowworm swarm optimization algorithm (SAGSO) is used to combine the synergy effect of the local search with simulated annealing (SA) and the global search with glowworm swarm optimization (GSO). The solution is defined by 40 variables, including the geometry, materials and reinforcement of the beam and the slab. Regarding the material, high strength concrete is used as well as self-compacting concrete in beams. Results provide engineers with useful guidelines to design PC precast bridges. The analysis also revealed that reducing costs by 1 Euro can save up to 1.75 kg in CO2 emissions. Finally, the parametric study indicates that optimal solutions in terms of monetary costs have quite a satisfactory environmental outcome and differ only slightly from the best possible environmental solution obtained. (C) 2014 Elsevier B.V. All rights reserved. es_ES
dc.description.sponsorship This study was funded by the Spanish Ministry of Science and Innovation (Research Project BIA2011-23602) and by the Universitat Politecnica de Valencia (Research Project SP20120341). The authors are grateful to the anonymous reviewers for their constructive comments and useful suggestions. The authors are also grateful to Dr. Debra Westall for her thorough revision of the manuscript en_EN
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Automation in Construction es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Optimization es_ES
dc.subject Glowworm swarm algorithm es_ES
dc.subject Computer aided design es_ES
dc.subject Structural design es_ES
dc.subject Sustainable design es_ES
dc.subject Precast concrete es_ES
dc.subject Bridges es_ES
dc.subject.classification INGENIERIA DE LA CONSTRUCCION es_ES
dc.title Cost and CO2 emission optimization of precast prestressed concrete U-beam road bridges by a hybrid glowworm swarm algorithm es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.autcon.2014.10.013
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//BIA2011-23602/ES/DISEÑO EFICIENTE DE ESTRUCTURAS CON HORMIGONES NO CONVENCIONALES BASADOS EN CRITERIOS SOSTENIBLES MULTIOBJETIVO MEDIANTE EL EMPLEO DE TECNICAS DE MINERIA DE DATOS/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/UPV//SP20120341/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería de la Construcción y de Proyectos de Ingeniería Civil - Departament d'Enginyeria de la Construcció i de Projectes d'Enginyeria Civil es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto de Ciencia y Tecnología del Hormigón - Institut de Ciència i Tecnologia del Formigó es_ES
dc.description.bibliographicCitation Yepes Piqueras, V.; Martí Albiñana, JV. (2015). Cost and CO2 emission optimization of precast prestressed concrete U-beam road bridges by a hybrid glowworm swarm algorithm. Automation in Construction. 49:123-134. https://doi.org/10.1016/j.autcon.2014.10.013 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1016/j.autcon.2014.10.013 es_ES
dc.description.upvformatpinicio 123 es_ES
dc.description.upvformatpfin 134 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 49 es_ES
dc.relation.senia 280045
dc.contributor.funder Ministerio de Ciencia e Innovación es_ES
dc.contributor.funder Universitat Politècnica de València es_ES


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