- -

Optimal design of steel¿concrete composite bridge based on a transfer function discrete swarm intelligence algorithm

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Optimal design of steel¿concrete composite bridge based on a transfer function discrete swarm intelligence algorithm

Mostrar el registro sencillo del ítem

Ficheros en el ítem

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
dc.description.references Afzal M, Liu Y, Cheng JC, Gan VJ (2020) Reinforced concrete structural design optimization: a critical review. J Clean Prod 260:120623 es_ES
dc.description.references Aslan M, Gunduz M, Kiran MS (2019) JayaX: Jaya algorithm with XOR operator for binary optimization. Appl Soft Comput 82:105576 es_ES
dc.description.references BEDEC (N.D.) BEDEC ITEC materials database. Catalonia Institute of Construction Technology. https://metabase.itec.cat/vide/es/bedec. Accessed Jan 2021 es_ES
dc.description.references Briseghella B, Fenu L, Lan C, Mazzarolo E, Zordan T (2013) Application of topological optimization to bridge design. J Bridge Eng 18:790–800 es_ES
dc.description.references Camacho VT, Horta N, Lopes M, Oliveira CS (2020) Optimizing earthquake design of reinforced concrete bridge infrastructures based on evolutionary computation techniques. Struct Multidisc Optim 61(3):1087–1105 es_ES
dc.description.references CEN (2013a) Eurocode 2: design of concrete structures. European Committee for Standardization, Brussels es_ES
dc.description.references CEN (2013b) Eurocode 3: design of steel structures. European Committee for Standardization, Brussels es_ES
dc.description.references CEN (2013c) Eurocode 4: design of composite steel and concrete structures. European Committee for Standardization, Brussels es_ES
dc.description.references CEN (2017) EN 10365:2017: hot rolled steel channels. I and H sections, dimensions and masses. European Committee for Standardization, Brussels es_ES
dc.description.references CEN (2019) Eurocode 1: actions on structures. European Committee for Standardization, Brussels es_ES
dc.description.references García J, Crawford B, Soto R, Castro C, Paredes F (2018) A k-means binarization framework applied to multidimensional knapsack problem. Appl Intell 48(2):357–380 es_ES
dc.description.references García-Segura T, Yepes V, Frangopol DM (2017) Multi-objective design of post-tensioned concrete road bridges using artificial neural networks. Struct Multidisc Optim 56:139–150 es_ES
dc.description.references Ghosh KK, Guha R, Bera SK, Kumar N, Sarkar R (2021) S-shaped versus V-shaped transfer functions for binary manta ray foraging optimization in feature selection problem. Neural Comput Appl 33:11027–11041 es_ES
dc.description.references Hare W, Nutini J, Tesfamariam S (2013) A survey of non-gradient optimization methods in structural engineering. Adv Eng Softw 59:19–28 es_ES
dc.description.references Hays WL, Winkler RL (1970) Statistics: probability, inference, and decision. Technical report es_ES
dc.description.references Hussien AG, Hassanien AE, Houssein EH, Amin M, Azar AT (2020) New binary whale optimization algorithm for discrete optimization problems. Eng Optim 52(6):945–959 es_ES
dc.description.references Jaouadi Z, Abbas T, Morgenthal G, Lahmer T (2020) Single and multi-objective shape optimization of streamlined bridge decks. Struct Multidisc Optim 61(4):1495–1514 es_ES
dc.description.references Kaveh A, Zarandi MMM (2019) Optimal design of steel–concrete composite I-girder bridges using three meta-heuristic algorithms. Period Polytech Civ Eng 63(2):317–337 es_ES
dc.description.references Kaveh A, Bakhshpoori T, Barkhori M (2014) Optimum design of multi-span composite box girder bridges using cuckoo search algorithm. Steel Compos Struct 17(5):703–717 es_ES
dc.description.references Kirkpatrick S, Gelatt CDJ, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680 es_ES
dc.description.references Lanza-Gutierrez JM, Crawford B, Soto R, Berrios N, Gomez-Pulido JA, Paredes F (2017) Analyzing the effects of binarization techniques when solving the set covering problem through swarm optimization. Expert Syst Appl 70:67–82 es_ES
dc.description.references Liu J, Liu P, Feng L, Wu W, Li D, Chen YF (2020) Automated clash resolution for reinforcement steel design in concrete frames via Q-learning and building information modeling. Autom Constr 112:103062 es_ES
dc.description.references Lv N, Fan L (2014) Optimization of quickly assembled steel–concrete composite bridge used in temporary. Mod Appl Sci 8(4):134–143 es_ES
dc.description.references Martínez-Muñoz D, Martí JV, Yepes V (2020) Steel–concrete composite bridges: design, life cycle assessment, maintenance, and decision-making. Adv Civ Eng 2020:8823370 es_ES
dc.description.references Martins AM, Simões LM, Negrão JH (2020) Optimization of cable-stayed bridges: a literature survey. Adv Eng Softw 149:102829 es_ES
dc.description.references Mathern A, Penadés-Plà V, Armesto Barros J, Yepes V (2022) Practical metamodel-assisted multi-objective design optimization for improved sustainability and buildability of wind turbine foundations. Struct Multidisc Optim 65(2):46 es_ES
dc.description.references Medina JR (2001) Estimation of incident and reflected waves using simulated annealing. J Waterw Port Coast Ocean Eng 127(4):213–221 es_ES
dc.description.references MFOM (2011) IAP-11: code on the actions for the design of road bridges. Ministerio de Fomento, Madrid es_ES
dc.description.references Minitab (2019) Minitab 19 statistical software. Minitab, State College es_ES
dc.description.references Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl Based Syst 96:120–133 es_ES
dc.description.references Monleón S (2017) Diseño estructural de puentes. Universitat Politècnica de València, València (in Spanish) es_ES
dc.description.references Montgomery DC (2013) Design and analysis of experiments. Wiley, Hoboken es_ES
dc.description.references Mundry R, Fischer J (1998) Use of statistical programs for nonparametric tests of small samples often leads to incorrect p values: examples from animal behaviour. Anim Behav 56(1):256–259 es_ES
dc.description.references Musa YI, Diaz MA (2007) Design optimization of composite steel box girder in flexure. Pract Period Struct Des Constr 12(3):146–152 es_ES
dc.description.references Otsuki Y, Li D, Dey SS, Kurata M, Wang Y (2021) Finite element model updating of an 18-story structure using branch-and-bound algorithm with epsilon-constraint. J Civ Struct Health Monit 11(3):575–592 es_ES
dc.description.references Payá-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 es_ES
dc.description.references Pedro RL, Demarche J, Miguel LFF, Lopez RH (2017) An efficient approach for the optimization of simply supported steel-concrete composite I-girder bridges. Adv Eng Softw 112:31–45 es_ES
dc.description.references Penadés-Plà V, García-Segura T, Yepes V (2019) Accelerated optimization method for low-embodied energy concrete box-girder bridge design. Eng Struct 179:556–565 es_ES
dc.description.references Rao R (2016) Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput 7(1):19–34 es_ES
dc.description.references Rempling R, Mathern A, Tarazona Ramos D, Luis Fernández S (2019) Automatic structural design by a set-based parametric design method. Autom Constr 108:102936 es_ES
dc.description.references Richardson A (2010). In: Corder GW, Foreman DI (eds) Nonparametric statistics for non-statisticians: a step-by-step approach. Wiley, Hoboken es_ES
dc.description.references Sarma KC, Adeli H (1998) Cost optimization of concrete structures. J Struct Eng 124(5):570–578 es_ES
dc.description.references Van Rossum G, Drake FL (2009) Python 3 Reference Manual. CreateSpace, Scotts Valley es_ES
dc.description.references Vayas I, Iliopoulos A (2017) Design of steel–concrete composite bridges to Eurocodes. CRC Press, Boca Raton es_ES
dc.description.references Venkata Rao R (2016) Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput 7:19–34 es_ES
dc.description.references Yepes V, Alcala J, Perea C, González-Vidosa F (2008) A parametric study of optimum earth-retaining walls by simulated annealing. Eng Struct 30(3):821–830 es_ES
dc.description.references Yepes V, Gonzalez-Vidosa F, Alcala J, Villalba P (2012) CO$$_{2}$$-optimization design of reinforced concrete retaining walls based on a VNS-threshold acceptance strategy. J Comput Civ Eng 26(3):378–386 es_ES
dc.description.references Yepes V, Martí JV, García-Segura T (2015) Cost and CO$$^{2}$$ emission optimization of precast-prestressed concrete U-beam road bridges by a hybrid glowworm swarm algorithm. Autom Constr 49:123–134 es_ES
dc.subject.ods 09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem