Analysis of a bridge failure due to fire using computational fluid dynamics and finite element models

Bridge fires are a major concern because of the consequences that these kind of events have and because they are a real threat. However, bridge fire response is under researched and not covered in the codes. This paper studies the capabilities of numerical models to predict the fire response of a bridge and provides modeling guidelines useful for improving bridge design. To reach this goal, a numerical analysis of the fire of the I-65 overpass in Birmingham, Alabama, USA in 2002 is carried out. The analyses are based on computational fluid dynamics (CFD) for creating the fire model, and finite element (FE) software for obtaining the thermo-mechanical response of the bridge. The models are validated with parametric studies that consider heat release rate of the spilled fuel, discretization of the fire temperature in the transition from CFD to FE modeling, and boundary conditions. The validated model is used in a study to evaluate the influence of fire scenario (CFD versus standard fires), and live load. Results show that numerical models are able to simulate the response of the bridge and can be used as a basis for a performance-based approach for the design of bridges under fire. Additionally, it is found that applying the Eurocode standard and hydrocarbon fires along the full length of the bridge does not adequately represent a real bridge fire response for medium-long span bridges such as this case study. The study also shows that live loads essentially do not influence the response of the bridge. (C) 2014 Elsevier Ltd. All rights reserved.Funding for this research has been provided by the Spanish Ministry of Science and Innovation (research project BIA 2011-27104) and the Universitat Politecnica de Valencia (Research and Development Support Program PAID-06-11). Funding has also been provided to Dr. Maria Garlock by the National Science Foundation (NSF) under award number CMMI-1068252. The authors are grateful to R. King from the Federal Highway Administration of the USA, J. Black and T. Colquett from the Alabama Department of Transportation, J. Glassman from Princeton University, J.V. Aguado from Ecole Centrale de Nantes and to J. Hidalgo from the University of Edinburgh for all the information and support provided. All opinions expressed in this paper are the authors' and do not necessarily reflect the policies and views of the sponsors.Alós Moya, J.; Paya-Zaforteza, I.; Garlock, ME.; Loma-Ossorio, E.; Schiffner, D.; Hospitaler Pérez, A. (2014). Analysis of a bridge failure due to fire using computational fluid dynamics and finite element models. Engineering Structures. 68:96-110. https://doi.org/10.1016/j.engstruct.2014.02.022S961106

Analysis of the influence of geometric, modeling and environmental parameters on the fire response of steel bridges subjected to realistic fire scenarios

This paper studies bridge fires by using numerical models to analyze the response of a typical girder bridge to tanker truck fires. It explains the influence of fire position, bridge configuration (vertical clearance, number of spans) and wind speed on the bridge response. Results show that the most damage is caused by tanker fires close to the abutments in single span bridges with minimum vertical clearance and under windless conditions. The paper provides new insights into modeling techniques and proves that bridge response can be predicted by FE models of the most exposed girder, which saves significant modeling and analysis times. (C) 2015 Elsevier Ltd. All rights reserved.Funding for this research was provided by the Spanish Ministry of Science and Innovation (Research Project BIA 2011-27104) and the Universitat Politecnica de Valencia (Research and Development Support Program PAID-06-11). All opinions expressed in this paper are the authors' and do not necessarily reflect the policies and views of the sponsors.Peris-Sayol, G.; Paya-Zaforteza, I.; Alós Moya, J.; Hospitaler Pérez, A. (2015). Analysis of the influence of geometric, modeling and environmental parameters on the fire response of steel bridges subjected to realistic fire scenarios. Computers and Structures. 158:333-345. https://doi.org/10.1016/j.compstruc.2015.06.003S33334515

High-speed railway tunnel monitoring using point, long gauge and distributed strain and temperature fiber optic sensors

La monitorización de estructuras es una rama de la ingeniería estructural que está captando mucha atención actualmente. Las deformaciones y temperaturas son, habitualmente, los parámetros monitorizados porque son los que mejor representan el comportamiento estructural. De todos los tipos de sensores existentes, los basados en fibra óptica resultan especialmente interesantes debido a sus ventajas comparativas sobre los sensores convencionales. En este artículo se presentan los trabajos de monitorización de la estructura de un túnel artificial de Alta Velocidad construido en Mogente (España) mediante tres tipos de sensores ópticos desarrollados por los autores. Los resultados de los sensores se comparan con los proporcionados por un modelo teórico de elementos finitos. Esta comparación confirma que los sensores reproducen notablemente bien la pauta general de comportamiento de la estructura, incluso con pequeños niveles de deformación (5µε). Por último, el artículo discute el comportamiento de los sensores, sus mediciones y sus campos de aplicación.Structural Health Monitoring (SHM) is presently having a great development. Strains and temperatures are usually the key parameters to be monitored due to their relevance when explaining structural behavior. Several types of sensors are used in SHM, but fiber optic sensors are especially interesting due to their advantages with respect to conventional sensors. In this paper, the monitoring of a high-speed train tunnel recently built in Spain using three types of fiber optic sensors developed by the authors is shown. Results given by the sensors are compared to those provided by a theoretical model built using FEM. Comparison of measurements and theoretical results confirms that the sensors reproduced remarkably well the general patterns of the tunnel structural behavior, even when strains are relatively small (around 5 µε). Finally, the paper discusses the behavior of the sensors, their measurements and their field of application which is useful for researchers and practitioners.Los autores quieren agradecer a la Universitat Politècnica de València, al Ministerio de Educación por la financiación recibida a través del proyecto BIA2011-27104 y al Ministerio de Fomento por el apoyo recibido a través del Proyecto SOPROMAC (P41/08)

Life cycle greenhouse gas emissions of blended cement concrete including carbonation and durability

The final publication is available at Springer via http://dx.doi.org/10.1007/s11367-013-0614-0Purpose Blended cements use waste products to replace Portland cement, the main contributor to CO2 emissions in concrete manufacture. Using blended cements reduces the embodied greenhouse gas emissions; however, little attention has been paid to the reduction in CO2 capture (carbonation) and durability. The aim of this study is to determine if the reduction in production emissions of blended cements compensates for the reduced durability and CO2 capture. Methods This study evaluates CO2 emissions and CO2 capture for a reinforced concrete column during its service life and after demolition and reuse as gravel filling material. Concrete depletion, due to carbonation and the unavoidable steel embedded corrosion, is studied, as this process consequently ends the concrete service life. Carbonation deepens progressively during service life and captures CO2 even after demolition due to the greater exposed surface area. In this study, results are presented as a function of cement replaced by fly ash (FA) and blast furnace slag (BFS). Results and discussion Concrete made with Portland cement, FA (35%FA), and BFS blended cements (80%BFS) captures 47, 41, and 20 % of CO2 emissions, respectively. The service life of blended cements with high amounts of cement replacement, like CEM III/A (50 % BFS), CEM III/B (80 % BFS), and CEMII/B-V (35%FA), was about 10%shorter, given the higher carbonation rate coefficient. Compared to Portland cement and despite the reduced CO2 capture and service life, CEM III/B emitted 20 % less CO2 per year. Conclusions To obtain reliable results in a life cycle assessment, it is crucial to consider carbonation during use and after demolition. Replacing Portland cement with FA, instead of BFS, leads to a lower material emission factor, since FA needs less processing after being collected, and transport distances are usually shorter. However, greater reductions were achieved using BFS, since a larger amount of cement can be replaced. Blended cements emit less CO2 per year during the life cycle of a structure, although a high cement replacement reduces the service life notably. If the demolished concrete is crushed and recycled as gravel filling material, carbonation can cut CO2 emissions by half. A case study is presented in this paper demonstrating how the results may be utilized.This research was financially supported by the Spanish Ministry of Science and Innovation (research project BIA2011-23602). The authors thank the anonymous reviewers for their constructive comments and useful suggestions. The authors are also grateful for the thorough revision of the manuscript by Dr. Debra Westall.García Segura, T.; Yepes Piqueras, V.; Alcalá González, J. (2014). Life cycle greenhouse gas emissions of blended cement concrete including carbonation and durability. International Journal of Life Cycle Assessment. 19(1):3-12. https://doi.org/10.1007/s11367-013-0614-0S312191Aïtcin PC (2000) Cements of yesterday and today: concrete of tomorrow. Cem Concr Res 30(9):1349–1359Angst U, Elsener B, Larsen C (2009) Critical chloride content in reinforced concrete—a review. Cement Concr Res 39(12):1122–1138Berge B (2000) The ecology of building materials. Architectural Press, OxfordBertolini L, Elsener B, Pedeferri P, Polder R (2004) Corrosion of Steel in Concrete—Prevention Diagnosis. Repair, Wiley-VCH, WeinheimBörjesson P, Gustavsson L (2000) Greenhouse gas balances in building construction: wood versus concrete from life cycle and forest land-use perspectives. Energy Policy 28(9):575–588Camp CV, Huq F (2013) CO2 and cost optimization of reinforced concrete frames using a big bang-crunch algorithm. Eng Struct 48:363–372CEN (2011) EN 197–1: Cement. Part 1: Composition, specifications and conformity criteria for common cements. European Committee for Standardization, BrusselsCIWMB (2000) Designing with vision: a technical manual for materials choices in sustainable construction. California Integrated Waste Management Board, SacramentoCollins F (2010) Inclusion of carbonation during the life cycle of built and recycled concrete: influence on their carbon footprint. Int J Life Cycle Assess 15(6):549–556Database BEDEC (2012) Institute of Construction Technology of Catalonia. Barcelona, SpainDodoo A, Gustavsson L, Sathre R (2009) Carbon implications of end-of-life management of building materials. Resour Conserv Recy 53(5):276–286ECO-SERVE Network Cluster 3 (2004) Baseline Report for the Aggregate and Concrete Industries in Europe. European Commission, Hellerup: http://www.eco-serve.net/uploads/479998_baseline_report_final.pdf , accessed 10 September 2012European Federation of Concrete Admixtures Associations (2006) Environmental Product Declaration (EPD) for Normal Plasticizing admixtures. Environmental Consultant, Sittard: http://www.efca.info/downloads/324%20ETG%20Plasticiser%20EPD.pdf , accessed 13 October 2012Galán I (2011) Carbonatación del hormigón: combinación de CO2. Dissertation, Universidad Complutense de Madrid, SpainGalán I, Andrade C, Mora P, Sanjuan MA (2010) Sequestration of CO2 by concrete carbonation. Environ Sci Technol 44(8):3181–3186Flower DJM, Sanjayan JG (2007) Greenhouse gas emissions due to concrete manufacture. Int J Life Cycle Assess 12(5):282–288Guzmán S, Gálvez JC, Sancho JM (2011) Cover cracking of reinforced concrete due to rebar corrosion induced by chloride penetration. Cement Concr Res 41(8):893–902Houst YF, Wittmann FH (2002) Depth profiles of carbonates formed during natural carbonation. C Cement Concr Res 32(12):1923–1930Institute for Diversification and Energy Saving (2010) Conversion factors of primary energy and CO2 emissions of 2010. M. Industria, Energía y Turismo, Madrid, Spain: http://www.idae.es/index.php/mod.documentos/mem.descarga?file=/documentos_Factores_Conversion_Energia_y_CO2_2010_0a9cb734.pdf , accessed 10 September 2012ISO (2005) ISO/TC 71—Business plan. Concrete, reinforced concrete and prestressed concrete. International Organization for Standardization (ISO), Geneva, SwitzerlandISO (2006) ISO 14040: Environmental management—life-cycle assessment—principles and framework. International Organization for Standardization, Geneva, SwitzerlandJiang L, Lin B, Cai Y (2000) A model for predicting carbonation of high-volume fly ash concrete. Cement Concr Res 30(5):699–702Jönsson A, Björklund T, Tillman AM (1988) LCA of concrete and steel building frames. Int J Life Cycle Assess 3(4):216–224Knoeri C, Sanyé-Mengual E, Althaus HJ (2013) Comparative LCA of recycled and conventional concrete for structural applications. Int J Life Cycle Assess 18(5):909–918Lagerblad B (2005) Carbon dioxide uptake during concrete life-cycle: State of the art. Swedish Cement and Concrete Research Institute, StockholmLeber I, Blakey FA (1956) Some effects of carbon dioxide on mortars and concrete. J Am Concr Inst 53:295–308Fomento M (2008) EHE-08; Code of Structural Concrete. M. Fomento, Madrid, SpainMarinkovic S, Radonjanin V, Malešev M, Ignjatovic I (2010) Comparative environmental assessment of natural and recycled aggregate concrete. Waste Manag 30(11):2255–2264Martinez-Martin FJ, Gonzalez-Vidosa F, Hospitaler A, Yepes V (2012) Multi-objective optimization design of bridge piers with hybrid heuristic algorithms. J Zhejiang Univ-SCI A 13(6):420–432O’Brien KR, Ménaché J, O’Moore LM (2009) Impact of fly ash content and fly ash transportation distance on embodied greenhouse gas emissions and water consumption in concrete. Int J Life-cycle Assess 14(7):621–629Pade C, Guimaraes M (2007) The CO2 uptake of concrete in a 100-year perspective. Cem Concr Res 37(9):1384–1356Papadakis VG, Vayenas CG, Fardis MN (1991) Fundamental modeling and experimental investigation of concrete carbonation. ACI Mater J 88(4):363–373Payá I, Yepes V, González-Vidosa F, Hospitaler A (2008) Multiobjective optimization of reinforced concrete building by simulated annealing. Comput-Aided Civ Inf 23(8):596–610Payá-Zaforteza I, Yepes V, Hospitaler A, González-Vidosa F (2009) CO2-efficient design of reinforced concrete building frames. Eng Struct 31(7):1501–1508Saassouh B, Lounis Z (2012) Probabilistic modeling of chloride-induced corrosion in concrete structures using first- and second-order reliability methods. Cement Concrete Comp 34(9):1082–1093The Concrete Centre (2009) The Concrete Industry Sustainability Performance Report. The Concrete Center, Camberley: http://www.admixtures.org.uk/downloads/Concrete%20Industry%20Sustainable%20Performance%20Report%202009.pdf , accessed 9 September 2012Tuutti K (1982) Corrosion of steel in Concrete. CBI Forskning Research Report, Swedish Cem Concr Res Inst. Stockholm, SwedenWeil M, Jeske U, Schebek L (2006) Closed-loop recycling of construction and demolition waste in Germany in view of stricter environmental threshold values. Waste Manage Res 24(3):197–206World Steel Association (2010) Fact sheet: the three Rs of sustainable Steel. World Steel Association, Brussels: http://www.steel.org/Sustainability/~/media/Files/SMDI/Sustainability/3rs.ashx , accessed 15 September 2012Worrell E, Price L, Martin N, Hendriks C, Meida LO (2001) Carbon dioxide emissions from the global cement industry. Annu Rev Energy Environ 26:303–329Yepes V, González-Vidosa F, Alcalá J, Villalba P (2012) CO2-optimization design of reinforced concrete retaining walls based on a VNS-threshold acceptance strategy. J Comput Civ Eng 26(3):378–386Yiwei T, Qun Z, Jian G (2011) Study on the Life-cycle Carbon Emission and Energy-efficiency Management of the Large-scale Public Buildings in Hangzho. China. International Conference on Computer and Management, Wuhan, pp 546–552Zornoza E, Payá J, Monzó J, Borrachero MV, Garcés P (2009) The carbonation of OPC mortars partially substituted with spent fluid catalytic catalyst (FC3R) and its influence on their mechanical properties. Const Build Mater 23(3):1323–132

A numerical investigation on the fire response of a steel girder bridge

The response of bridges subject to fire is an under researched topic despite the number of bridge failures caused by fire. Since available data shows that steel girder bridges are especially vulnerable to fire, this paper delves into their fire response by analyzing with a 3D numerical model the response of a typical bridge of 12.20 m span length. A parametric study is performed considering: (1) two possibilities for the axial restraint of the bridge deck, (2) four types of structural steel for the girders (carbon steel and stainless steel grades 1.4301, 1.4401, and 1.4462), (3) three different constitutive models for carbon steel, (4) four live loads, and (5) two alternative fire loads (the hydrocarbon fire defined by Eurocode 1 and a fire corresponding to a real fire event). Results show that restraint to deck expansion coming from an adjacent span or abutment should be considered in the numerical model. In addition, times to collapse are very small when the bridge girders are built with carbon steel (between 8.5 and 18 min) but they can almost double if stainless steel is used for the girders. Therefore, stainless steel is a material to consider for steel girder bridges in a high fire risk situation, especially if the bridge is located in a corrosive environment and its aesthetics deserves special attention. The methodology developed in this paper and the results obtained are useful for researchers and practitioners interested in developing and applying a performance-based approach for the design of bridges against fire. © 2012 Elsevier Ltd. All rights reserved.Funding for this research has been provided to Dr. Paya-Zaforteza by the Spanish Ministry of Education (contract number EX-2008-0669 of the Program for Postdoctoral Stays), the Spanish Ministry of Economy and Competitiveness (research project BIA 2011-27104) and the Universitat Politecnica de Valencia (Research and Development Support Program PAID-06-11). Funding has also been provided to Dr. Maria Garlock by the National Science Foundation (NSF) under award number CMMI-1068252. All opinions expressed in this paper are the authors' and do not necessarily reflect the policies and views of the sponsors.Paya-Zaforteza, I.; Garlock, ME. (2012). A numerical investigation on the fire response of a steel girder bridge. Journal of Constructional Steel Research. 75:93-103. https://doi.org/10.1016/j.jcsr.2012.03.012S931037

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. Journal of Computing in Civil Engineering, 26(3), 378-386. doi:10.1061/(asce)cp.1943-5487.000014

Analysis of the construction process of cable-stayed bridges built on temporary supports

The temporary supports erection method is a fast and economical way of building cable-stayed bridges. In this method the bridge deck is first erected on a set of temporary and permanent supports and then, the stays are successively placed and tensioned according to a predefined tensioning sequence. A proper definition and analysis of this sequence is very complex as the structure is highly statically indeterminate, exhibits a non linear behavior and has a changing static scheme.Despite its importance, no specific research referring to the modeling of the temporary support erection method has been found as most of the modeling procedures are proposed for the alternative erection technique, the cantilever erection method. The modeling carried out by most of these methods is based on the opposite construction sequence followed on site, this is to say, the structure is disassembled from the desired final stage (. Objective Completion Stage, . OCS).A procedure, the Backward Algorithm (. BA), is formally presented in this paper for calculation of the erection of cable-stayed bridges built on temporary supports. Because of its simplicity the . BA can be reproduced by any structural code that enables the modeling of the prestresses of the stays by means of imposed strains or imposed temperature increments. Another advantage is that no separate models are needed to calculate the evolution of stresses in the strands when the strand by strand tensioning technique is used. Furthermore, the stay elongations when prestressed can be easily obtained when the stays are prestressed in a single operation or strand by strand. This information is important to control the correct and safe prestressing of the stay on site. In addition, it also help the designer to control if the anchor wedge bites the strand in the same position several times during the prestressing process. © 2012 Elsevier Ltd.Part of this work was done through a collaborative agreement between University of Castilla-La Mancha (Spain) and Tongji University (China). This included an exchange of faculty and scholars. The financial support from Kwang-Hua Foundation from College of Civil Engineering of Tongji University and from the International Relation Office of University of Castilla-La Mancha is greatly appreciated.Lozano-Galant, JA.; Paya-Zaforteza, I.; Xu, D.; Turmo Coderque, J. (2012). Analysis of the construction process of cable-stayed bridges built on temporary supports. Engineering Structures. 40:95-106. doi:10.1016/j.engstruct.2012.02.005S951064

Forward Algorithm for the construction control of cable-stayed bridges built on temporary supports

Traditionally the construction process of cable-stayed bridges is modeled according to the backward approach, as its calculation is much easier using an elastic analysis. In this approach the bridge is disassembled according to the opposite sequence of events which occur during its erection. The main trade off of the backward approach is that time-dependent phenomena, such as creep, shrinkage or cable relaxation, cannot be directly computed as the analysis is performed according to the reversed time direction.In this paper the Forward Algorithm (FA) that is based on the forward approach is formally presented. This procedure has been applied to the temporary supports erection method as no specific investigation of this erection technique has been found by the authors. The FA has the following advantages: (1) Modifications in design and/or tensioning strategy can be easily computed. (2) The effect of time-dependent phenomena can be efficiently included into the calculation of the construction process of the bridge. (3) Differences between the temperatures in the structural elements assumed when doing the structural analysis of the bridge and the real ones when built can be calculated without the need of separate models. (4) The stresses in the mono-strands when the strand by strand prestressing technique is used are obtained directly. (5) Stay and strand elongation, which are very useful to control the prestressing operations, can be also easily computed. All these features make the FA an efficient procedure to control the construction process on site, increasing safety during construction and decreasing construction time. © 2012 Elsevier Ltd.Part of this work was done through a collaborative agreement between University of Castilla-La Mancha (Spain) and Tongji University (China). This included an exchange of faculty and scholars. The finantial support from Kwang-Hua Foundation from College of Civil Engineering of Tongji University and from the International Relation Office of University of Castilla-La Mancha is greatly appreciated.Lozano-Galant, JA.; Paya-Zaforteza, I.; Xu, D.; Turmo Coderque, J. (2012). Forward Algorithm for the construction control of cable-stayed bridges built on temporary supports. Engineering Structures. 40:119-130. doi:10.1016/j.engstruct.2012.02.022S1191304