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dc.contributor.author | Martínez-Muñoz, D. | es_ES |
dc.contributor.author | García, Jose | es_ES |
dc.contributor.author | Martí Albiñana, José Vicente | es_ES |
dc.contributor.author | Yepes, V. | es_ES |
dc.date.accessioned | 2022-11-24T19:03:17Z | |
dc.date.available | 2022-11-24T19:03:17Z | |
dc.date.issued | 2022-11 | es_ES |
dc.identifier.issn | 1615-147X | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/190164 | |
dc.description.abstract | [EN] Bridge optimization can be complex because of the large number of variables involved in the problem. In this paper, two box-girder steel¿concrete composite bridge single objective optimizations have been carried out considering cost and CO¿ emissions as objective functions. Taking CO¿ emissions as an objective function allows adding sustainable criteria to compare the results with cost. SAMO2, SCA, and Jaya metaheuristics have been applied to reach this goal. Transfer functions have been implemented to fit SCA and Jaya to the discontinuous nature of the bridge optimization problem. Furthermore, a Design of Experiments has been conducted to tune the algorithm and set its parameters. Consequently, it has been observed that SCA shows similar values for objective cost function as SAMO2 but improves computational time by 18% while also getting lower values for the objective function result deviation. From a cost and CO¿ optimization analysis, it has been observed that a reduction of 2.51 kg CO¿ is obtained by each euro reduced using metaheuristic techniques. Moreover, for both optimization objectives, it is observed that adding cells to bridge cross-sections improves not only the section behavior but also the optimization results. Finally, it is observed that the proposed design of double composite action in the supports allows this study to remove continuous longitudinal stiffeners in the bottom flange. | es_ES |
dc.description.sponsorship | Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This research has been made possible thanks to funding received from the following research projects: Grant PID2020-117056RB-I00 funded by MCIN/AEI/10.13039/501100011033 and by "ERDF A way of making Europe", Grant FPU-18/01592 funded by MCIN/AEI/10.13039/501100011033 and by "ESF invests in your future" and Grant CONICYT/FONDECYT/INICIACION/11180056. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Springer-Verlag | es_ES |
dc.relation.ispartof | Structural and Multidisciplinary Optimization | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | Swarm intelligence | es_ES |
dc.subject | Steel-concrete composite structures | es_ES |
dc.subject | Bridges | es_ES |
dc.subject | Optimization | es_ES |
dc.subject | Metaheuristics | es_ES |
dc.subject | Sustainability | es_ES |
dc.subject.classification | INGENIERIA DE LA CONSTRUCCION | es_ES |
dc.title | Optimal design of steel¿concrete composite bridge based on a transfer function discrete swarm intelligence algorithm | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1007/s00158-022-03393-9 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-117056RB-I00/ES/OPTIMIZACION HIBRIDA DEL CICLO DE VIDA DE PUENTES Y ESTRUCTURAS MIXTAS Y MODULARES DE ALTA EFICIENCIA SOCIAL Y MEDIOAMBIENTAL BAJO PRESUPUESTOS RESTRICTIVOS/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/ //FPU18%2F01592//AYUDA PREDOCTORAL FPU-MARTINEZ MUÑOZ/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/FONDECYT//11180056//Concurso Iniciación en Investigación/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros de Caminos, Canales y Puertos - Escola Tècnica Superior d'Enginyers de Camins, Canals i Ports | es_ES |
dc.description.bibliographicCitation | Martínez-Muñoz, D.; García, J.; Martí Albiñana, JV.; Yepes, V. (2022). Optimal design of steel¿concrete composite bridge based on a transfer function discrete swarm intelligence algorithm. Structural and Multidisciplinary Optimization. 65(11):1-25. https://doi.org/10.1007/s00158-022-03393-9 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1007/s00158-022-03393-9 | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 25 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 65 | es_ES |
dc.description.issue | 11 | es_ES |
dc.relation.pasarela | S\474736 | es_ES |
dc.contributor.funder | AGENCIA ESTATAL DE INVESTIGACION | es_ES |
dc.contributor.funder | MINISTERIO DE CIENCIA E INNOVACION | es_ES |
dc.contributor.funder | Fondo Nacional de Desarrollo Científico y Tecnológico, Chile | es_ES |
dc.contributor.funder | Universitat Politècnica de València | es_ES |
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dc.subject.ods | 09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación | es_ES |