Efficient design of post-tensioned concrete box-girder road bridges based on sustainable multi-objective criteria

[EN] Bridges, as an important component of infrastructure, are expected to meet all the requirements for a modern society. Traditionally, the primary aim in bridge design has been to achieve the lowest cost while guaranteeing the structural efficiency. However, concerns regarding building a more sustainable future have change the priorities of society. Ecological and durable structures are increasingly demanded. Under these premises, heuristic optimization methods provide an effective alternative to structural designs based on experience. The emergence of new materials, structural designs and sustainable criteria motivate the need to create a methodology for the automatic and accurate design of a real post-tensioned concrete bridge that considers all these aspects. For the first time, this thesis studies the efficient design of post-tensioned concrete box-girder road bridges from a sustainable point of view. This research integrates environmental, safety and durability criteria into the optimum design of the bridge. The methodology proposed provides multiple trade-off solutions that hardly increase the cost and achieve improved safety and durability. Likewise, this approach quantifies the sustainable criteria in economic terms, and evaluates the effect of these criteria on the best values of the variables. In this context, a multi-objective optimization is formulated to provide multiple trade-off and high-performing solutions that balance economic, ecologic and societal goals. An optimization design program selects the best geometry, concrete type, reinforcement and post-tensioning steel that meet the objectives selected. A three-span continuous box-girder road bridge located in a coastal region is selected for a case study. This approach provides vital knowledge about this type of bridge in the sustainable context. The life-cycle perspective has been included through a lifetime performance evaluation that models the bridge deterioration process due to chloride-induced corrosion. The economic, environmental and societal impacts of maintenance actions required to extend the service life are examined. Therefore, the proposed goals for an efficient design have been switch from initial stage to life-cycle consideration. Faced with the large computational time of multi-objective optimization and finite-element analysis, artificial neural networks (ANNs) are integrated in the proposed methodology. ANNs are trained to predict the structural response based on the design variables, without the need to analyze the bridge response. The multi-objective optimization problem results in a set of trade-off solutions characterized by the presence of conflicting objectives. The final selection of preferred solutions is simplified by a decision-making technique. A rational technique converts a verbal pairwise comparison between criteria with a degree of uncertainty into numerical values that guarantee the consistency of judgments. This thesis gives a guide for the sustainable design of concrete structures. The use of the proposed approach leads to designs with lower life-cycle cost and emissions compared to general design approaches. Both bridge safety and durability can be improved with a little cost increment by choosing the correct design variables. In addition, this methodology is applicable to any type of structure and material.[ES] Los puentes, como parte importante de una infraestructura, se espera que reúnan todos los requisitos de una sociedad moderna. Tradicionalmente, el objetivo principal en el diseño de puentes ha sido lograr el menor coste mientras se garantiza la eficiencia estructural. Sin embargo, la preocupación por construir un futuro más sostenible ha provocado un cambio en las prioridades de la sociedad. Estructuras más ecológicas y duraderas son cada vez más demandadas. Bajo estas premisas, los métodos de optimización heurística proporcionan una alternativa eficaz a los diseños estructurales basados en la experiencia. La aparición de nuevos materiales, diseños estructurales y criterios sostenibles motivan la necesidad de crear una metodología para el diseño automático y preciso de un puente real de hormigón postesado que considere todos estos aspectos. Por primera vez, esta tesis estudia el diseño eficiente de puentes de hormigón postesado con sección en cajón desde un punto de vista sostenible. Esta investigación integra criterios ambientales, de seguridad estructural y durabilidad en el diseño óptimo del puente. La metodología propuesta proporciona múltiples soluciones que apenas encarecen el coste y mejoran la seguridad y durabilidad. Al mismo tiempo, se cuantifica el enfoque sostenible en términos económicos, y se evalúa el efecto que tienen dichos criterios en el valor óptimo de las variables. En este contexto, se formula una optimización multiobjetivo que proporciona soluciones eficientes y de compromiso entre los criterios económicos, ecológicos y sociales. Un programa de optimización del diseño selecciona la mejor combinación de geometría, tipo de hormigón, armadura y postesado que cumpla con los objetivos seleccionados. Se ha escogido como caso de estudio un puente continuo en cajón de tres vanos situado en la costa. Este método proporciona un mayor conocimiento sobre esta tipología de puentes desde un punto de vista sostenible. Se ha estudiado el ciclo de vida a través de la evaluación del deterioro estructural del puente debido al ataque por cloruros. Se examina el impacto económico, ambiental y social que produce el mantenimiento necesario para extender la vida útil del puente. Por lo tanto, los objetivos propuestos para un diseño eficiente han sido trasladados desde la etapa inicial hasta la consideración del ciclo de vida. Para solucionar el problema del elevado tiempo de cálculo debido a la optimización multiobjetivo y el análisis por elementos finitos, se han integrado redes neuronales en la metodología propuesta. Las redes neuronales son entrenadas para predecir la respuesta estructural a partir de las variables de diseño, sin la necesidad de analizar el puente. El problema de optimización multiobjetivo se traduce en un conjunto de soluciones de compromiso que representan objetivos contrapuestos. La selección final de las soluciones preferidas se simplifica mediante una técnica de toma de decisiones. Una técnica estructurada convierte los juicios basados en comparaciones por pares de elementos con un grado de incertidumbre en valores numéricos que garantizan la consistencia de dichos juicios. Esta tesis proporciona una guía que extiende y mejora las recomendaciones sobre el diseño de estructuras de hormigón dentro del contexto de desarrollo sostenible. El uso de la metodología propuesta lleva a diseños con menor coste y emisiones del ciclo de vida, comparado con diseños que siguen metodologías generales. Los resultados demuestran que mediante una correcta elección del valor de las variables se puede mejorar la seguridad y durabilidad del puente con un pequeño incremento del coste. Además, esta metodología es aplicable a cualquier tipo de estructura y material.[CA] Els ponts, com a part important d'una infraestructura, s'espera que reunisquen tots els requisits d'una societat moderna. Tradicionalment, l'objectiu principal en el disseny de ponts ha sigut aconseguir el menor cost mentres es garantix l'eficiència estructural. No obstant això, la preocupació per construir un futur més sostenible ha provocat un canvi en les prioritats de la societat. Estructures més ecològiques i durables són cada vegada més demandades. Davall estes premisses, els mètodes d'optimització heurística proporcionen una alternativa eficaç als dissenys estructurals basats en l'experiència. L'aparició de nous materials, dissenys estructurals i criteris sostenibles motiven la necessitat de crear una metodologia per al disseny automàtic i precís d'un pont real de formigó posttesat que considere tots estos aspectos. Per primera vegada, esta tesi estudia el disseny eficient de ponts de formigó posttesat amb secció en calaix des d'un punt de vista sostenible. Esta investigació integra criteris ambientals, de seguretat estructural i durabilitat en el disseny òptim del pont. La metodologia proposada proporciona múltiples solucions que a penes encarixen el cost i milloren la seguretat i durabilitat. Al mateix temps, es quantifica l'enfocament sostenible en termes econòmics, i s'avalua l'efecte que tenen els dits criteris en el valor òptim de les variables. En este context, es formula una optimització multiobjetivo que proporciona solucions eficients i de compromís entre els criteris econòmics, ecològics i socials. Un programa d'optimització del disseny selecciona la millor geometria, tipus de formigó, armadura i posttesat que complisquen amb els objectius seleccionats. S'ha triat com a cas d'estudi un pont continu en calaix de tres vans situat en la costa. Este mètode proporciona un major coneixement sobre esta tipologia de ponts des d'un punt de vista sostenible. S'ha estudiat el cicle de vida a través de l'avaluació del deteriorament estructural del pont a causa de l'atac per clorurs. S'examina l'impacte econòmic, ambiental i social que produïx el manteniment necessari per a estendre la vida útil del pont. Per tant, els objectius proposats per a un disseny eficient han sigut traslladats des de l'etapa inicial fins a la consideració del cicle de vida. Per a solucionar el problema de l'elevat temps de càlcul degut a l'optimització multiobjetivo i l'anàlisi per elements finits, s'han integrat xarxes neuronals en la metodologia proposada. Les xarxes neuronals són entrenades per a predir la resposta estructural a partir de les variables de disseny, sense la necessitat d'analitzar el pont. El problema d'optimització multiobjetivo es traduïx en un conjunt de solucions de compromís que representen objectius contraposats. La selecció final de les solucions preferides se simplifica per mitjà d'una tècnica de presa de decisions. Una tècnica estructurada convertix els juís basats en comparacions per parells d'elements amb un grau d'incertesa en valors numèrics que garantixen la consistència dels dits juís. Esta tesi proporciona una guia que estén i millora les recomanacions sobre el disseny d'estructures de formigó dins del context de desenrotllament sostenible. L'ús de la metodologia proposada porta a dissenys amb menor cost i emissions del cicle de vida, comparat amb dissenys que seguixen metodologies generals. Els resultats demostren que per mitjà d'una correcta elecció del valor de les variables es pot millorar la seguretat i durabilitat del pont amb un xicotet increment del cost. A més, esta metodologia és aplicable a qualsevol tipus d'estructura i material.García Segura, T. (2016). Efficient design of post-tensioned concrete box-girder road bridges based on sustainable multi-objective criteria [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/73147TESI

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

[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. Eng Struct 102:40–60. doi: 10.1016/j.engstruct.2015.08.012Beume N, Naujoks B, Emmerich M (2007) SMS-EMOA: Multiobjective selection based on dominated hypervolume. Eur J Oper Res 181:1653–1669. doi: 10.1016/j.ejor.2006.08.008Cai H, Aref AJ (2015) A genetic algorithm-based multi-objective optimization for hybrid fiber reinforced polymeric deck and cable system of cable-stayed bridges. Struct Multidiscip Optim 52:583–594. doi: 10.1007/s00158-015-1266-4Cao MS, Pan LX, Gao YF, Novák D, Ding ZC, Lehký D, Li XL (2015) Neural network ensemble-based parameter sensitivity analysis in civil engineering systems. Neural Comput Appl 1–8. doi: 10.1007/s00521-015-2132-4Chatterjee S, Sarkar S, Hore S, Dey N, Ashour AS, Balas VE (2016) Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings. Neural Comput Appl. 1–12. doi: 10.1007/s00521-016-2190-2Coello CAC (2002) Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Comput Methods Appl Mech Eng 191:1245–1287. doi: 10.1016/S0045-7825(01)00323-1Coello CAC, Lamont GB, Veldhuizen DA Van (2006) Evolutionary algorithms for solving multi-objective problems. Springer-Verlag New York, IncComputers and Structures Inc. (2015) Introduction to CSiBridge. Integrated 3D bridge analysis, design and rating. Berkeley, California, USADeb K (2011) Multi-objective optimisation using evolutionary algorithms: an introduction. In: Wang L, Ng AHC, Deb K (eds) Multi-objective evolutionary optimisation for product design and manufacturing. Springer, London, pp 3–34Deb K, Nain PKS (2007) An evolutionary multi-objective adaptive meta-modeling procedure using artificial neural networks. In: Yang S, Ong Y-S, Jin Y (eds) Evolutionary computation in dynamic and uncertain environments. Springer, Berlin, pp 297–322Dong Y, Frangopol DM, Saydam D (2013) Time-variant sustainability assessment of seismically vulnerable bridges subjected to multiple hazards. Earthq Eng Struct Dyn 42:1451–1467. doi: 10.1002/eqe.2281Emmerich M, Naujoks B (2004) Metamodel assisted multiobjective optimisation strategies and their application in airfoil design. In: Parmee IC (ed) Adaptive computing in design and manufacture VI. Springer, London, pp 249–260European Committee for Standardisation (2003) EN 1991–2:2003. Eurocode 1: Actions on structures-Part 2: Traffic loads bridgesEuropean Committee for Standardisation (2005) EN1992-2:2005. Eurocode 2: Design of concrete structures- Part 2: Concrete Bridge-Design and detailing rules. BrusselsFomento M (2008) EHE-08: code on structural concrete. Ministerio de Fomento, MadridFomento M (2011) IAP-11: code on the actions for the design of road bridges. 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. J Zhejiang Univ Sci A 13:420–432. doi: 10.1631/jzus.A1100304Martini K (2011) Harmony search method for multimodal size, shape, and topology optimization of structural frameworks. J Struct Eng 137:1332–1339. doi: 10.1061/(ASCE)ST.1943-541X.0000378Marti-Vargas JR, Ferri FJ, Yepes V (2013) Prediction of the transfer length of prestressing strands with neural networks. Comput Concr 12:187–209. doi: 10.12989/cac.2013.12.2.187McGee R (1999) Modeling of durability performance of Tasmanian bridges. In: Melchers R, M.G S (eds) Applications of statistics and probability: civil engineering, reliability and risk analysis. A.A. Balkema, Rotterdam, pp 297–306Papadakis VG, Roumeliotis AP, Fardis MN, Vagenas CG (1996) Mathematical modelling of chloride effect on concrete du-rability and protection measures. In: Dhir RK, Jones MR (eds) Concrete repair, rehabilitation and protection. E&FN Spon, London, pp 165–174Paya I, Yepes V, González-Vidosa F, Hospitaler A (2008) Multiobjective optimization of reinforced concrete building frames by simulated annealing. Comput Civ Infrastruct Eng 23:596–610. doi: 10.1111/j.1467-8667.2008.00561.xProtopapadakis E, Schauer M, Pierri E et al (2016) A genetically optimized neural classifier applied to numerical pile integrity tests considering concrete piles. Comput Struct 162:68–79. doi: 10.1016/j.compstruc.2015.08.005Quaglia CP, Yu N, Thrall AP, Paolucci S (2014) Balancing energy efficiency and structural performance through multi-objective shape optimization: Case study of a rapidly deployable origami-inspired shelter. Energ Build 82:733–745. doi: 10.1016/j.enbuild.2014.07.063Ricart J, Hüttemann G, Lima J, Barán B (2011) Multiobjective harmony search algorithm proposals. Electron Notes Theor Comput Sci 281:51–67. doi: 10.1016/j.entcs.2011.11.025Sanad A, Saka MP (2001) Prediction of ultimate shear strength of reinforced-concrete deep beams using neural networks. J Struct Eng 127:818–828. doi: 10.1061/(ASCE)0733-9445(2001)127:7(818)Sarma KC, Adeli H (1998) Cost optimization of concrete structures. J Struct Eng 124:570–578. doi: 10.1061/(ASCE)0733-9445(1998)124:5(570)Shi X (2016) Experimental and modeling studies on installation of arc sprayed Zn anodes for protection of reinforced concrete structures. Front Struct Civ Eng 10:1–11. doi: 10.1007/s11709-016-0312-7Sreehari VM, Maiti DK (2016) Buckling load enhancement of damaged composite plates under hygrothermal environment using unified particle swarm optimization. Struct Multidiscip Optim 1–11. doi: 10.1007/s00158-016-1498-yTorres-Machi C, Chamorro A, Pellicer E et al (2015) Sustainable pavement management: Integrating economic, technical, and environmental aspects in decision making. Transp Res Rec J Transp Res Board 2523:56–63. doi: 10.3141/2523-07Vu KAT, Stewart MG (2000) Structural reliability of concrete bridges including improved chloride-induced corrosion models. Struct Saf 22:313–333. doi: 10.1016/S0167-4730(00)00018-7Xu H, Gao XZ, Wang T, Xue K (2010) Harmony search optimization algorithm: application to a reconfigurable mobile robot prototype. In: Geem ZW (ed) Recent advances in harmony search algorithm. Springer, Berlin, pp 11–22Yepes V, García-Segura T, Moreno-Jiménez JM (2015a) A cognitive approach for the multi-objective optimization of RC structural problems. Arch Civ Mech Eng 15:1024–1036. doi: 10.1016/j.acme.2015.05.001Yepes V, Martí JV, García-Segura T (2015b) Cost and CO2 emission optimization of precast–prestressed concrete U-beam road bridges by a hybrid glowworm swarm algorithm. Autom Constr 49:123–134. doi: 10.1016/j.autcon.2014.10.013Zavala GR, Nebro AJ, Luna F, Coello Coello CA (2013) A survey of multi-objective metaheuristics applied to structural optimization. Struct Multidiscip Optim 49:537–558. doi: 10.1007/s00158-013-0996-4Zavrtanik N, Prosen J, Tušar M, Turk G (2016) The use of artificial neural networks for modeling air void content in aggregate mixture. Autom Constr 63:155–161. doi: 10.1016/j.autcon.2015.12.009Zitzler E, Thiele L (1998) Multiobjective optimization using evolutionary algorithms - a comparative case study. In: Eiben AE, Bäck T, Schoenauer M, Schwefel H-P (eds) Conference on parallel problem solving from nature- PPSN V. Springer Berlin Heidelberg, Amsterdam, The Netherlands, pp 292–30

Robust Design Optimization for Low-Cost Concrete Box-Girder Bridge

[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. Journal of Mechanical Science and Technology, 20(8), 1169-1182. doi:10.1007/bf02916016Carbonell, A., González-Vidosa, F., & Yepes, V. (2011). Design of reinforced concrete road vaults by heuristic optimization. Advances in Engineering Software, 42(4), 151-159. doi:10.1016/j.advengsoft.2011.01.002Ahsan, R., Rana, S., & Ghani, S. N. (2012). Cost Optimum Design of Posttensioned I-Girder Bridge Using Global Optimization Algorithm. Journal of Structural Engineering, 138(2), 273-284. doi:10.1061/(asce)st.1943-541x.0000458García-Segura, T., Yepes, V., Martí, J. V., & Alcalá, 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/s1679-78252014000700007Pnevmatikos, N. G., & Thomos, G. C. (2013). Stochastic structural control under earthquake excitations. Structural Control and Health Monitoring, 21(4), 620-633. doi:10.1002/stc.1589García-Segura, T., & Yepes, V. (2016). 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). Emerging artificial intelligence methods in structural engineering. Engineering Structures, 171, 170-189. doi:10.1016/j.engstruct.2018.05.084Jin, R., Du, X., & Chen, W. (2003). The use of metamodeling techniques for optimization under uncertainty. Structural and Multidisciplinary Optimization, 25(2), 99-116. doi:10.1007/s00158-002-0277-0Penadés-Plà, V., García-Segura, T., & Yepes, V. (2019). Accelerated optimization method for low-embodied energy concrete box-girder bridge design. Engineering Structures, 179, 556-565. doi:10.1016/j.engstruct.2018.11.015Chuang, C. H., Yang, R. J., Li, G., Mallela, K., & Pothuraju, P. (2007). Multidisciplinary design optimization on vehicle tailor rolled blank design. Structural and Multidisciplinary Optimization, 35(6), 551-560. doi:10.1007/s00158-007-0152-0Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246-1266. doi:10.2113/gsecongeo.58.8.1246Simpson, T. W., Mauery, T. M., Korte, J. J., & Mistree, F. (2001). 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). 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

Organizational Factors that Drive to BIM Effectiveness: Technological Learning, Collaborative Culture, and Senior Management Support

[EN] Senior management support is a key dynamic capacity for design companies in the architecture, engineering, and construction (AEC) industry, given the fact that they must identify changes in the competitive environment, which are increasingly becoming more and more technological. In addition, senior management support is obliged to react in the most efficient and effective way. Currently, the project design teams that have adopted building information modeling (BIM) are subject to constant changes in the technological environment, of which the activity is influenced by the behavior of senior management support. This research focuses on this issue by analyzing the role played by the variables of technological learning, collaborative culture, and support provided by senior management as precedents of BIM technology effectiveness. The data set has been obtained from 92 AEC companies in Spain. Using partial least squares (PLS), this research finds evidence of the previously mentioned relationships and the existence of partial mediation effects generated by technological learning and collaborative culture within the support of senior management in BIM technology effectiveness. In addition, this model achieves an appropriate level of predictive validation to explain BIM technology effectiveness in engineering project designs. The results highlight that senior management support needs to promote a technological learning and collaborative culture to improve the technological capabilities. The contribution and original value of the paper is to provide empirical evidence that the effectiveness of BIM factors in project design teams is influenced by the behavior of top management support.Villena-Manzanares, F.; García-Segura, T.; Pellicer, E. (2021). Organizational Factors that Drive to BIM Effectiveness: Technological Learning, Collaborative Culture, and Senior Management Support. Applied Sciences. 11(1):1-16. https://doi.org/10.3390/app11010199S116111Kassem, M., Brogden, T., & Dawood, N. (2012). BIM and 4D planning: a holistic study of the barriers and drivers to widespread adoption. Journal of Construction Engineering and Project Management, 2(4), 1-10. doi:10.6106/jcepm.2012.2.4.001Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319. doi:10.2307/249008Holden, R. J., & Karsh, B.-T. (2010). The Technology Acceptance Model: Its past and its future in health care. Journal of Biomedical Informatics, 43(1), 159-172. doi:10.1016/j.jbi.2009.07.002Lee, Y., Kozar, K. A., & Larsen, K. R. T. (2003). The Technology Acceptance Model: Past, Present, and Future. Communications of the Association for Information Systems, 12. doi:10.17705/1cais.01250Lee, S., & Yu, J. (2016). Comparative Study of BIM Acceptance between Korea and the United States. Journal of Construction Engineering and Management, 142(3), 05015016. doi:10.1061/(asce)co.1943-7862.0001076Ahuja, R., Jain, M., Sawhney, A., & Arif, M. (2016). Adoption of BIM by architectural firms in India: technology–organization–environment perspective. Architectural Engineering and Design Management, 12(4), 311-330. doi:10.1080/17452007.2016.1186589Xu, H., Feng, J., & Li, S. (2014). Users-orientated evaluation of building information model in the Chinese construction industry. Automation in Construction, 39, 32-46. doi:10.1016/j.autcon.2013.12.004Ahmed, A. L., & Kassem, M. (2018). A unified BIM adoption taxonomy: Conceptual development, empirical validation and application. Automation in Construction, 96, 103-127. doi:10.1016/j.autcon.2018.08.017Ullah, K., Lill, I., & Witt, E. (2019). An Overview of BIM Adoption in the Construction Industry: Benefits and Barriers. Emerald Reach Proceedings Series, 297-303. doi:10.1108/s2516-285320190000002052Latorre Uriz, A., Sanz, C., & Sánchez, B. (2019). Aplicación de un modelo Lean-BIM para la mejora de la productividad en redacción de proyectos de edificación. Informes de la Construcción, 71(556), 313. doi:10.3989/ic.67222Lu, N., & Korman, T. (2010). Implementation of Building Information Modeling (BIM) in Modular Construction: Benefits and Challenges. Construction Research Congress 2010. doi:10.1061/41109(373)114Gu, N., & London, K. (2010). Understanding and facilitating BIM adoption in the AEC industry. Automation in Construction, 19(8), 988-999. doi:10.1016/j.autcon.2010.09.002Gurevich, U., Sacks, R., & Shrestha, P. (2017). BIM adoption by public facility agencies: impacts on occupant value. Building Research & Information, 45(6), 610-630. doi:10.1080/09613218.2017.1289029Lee, S., & Yu, J. (2016). Discriminant model of BIM acceptance readiness in a construction organization. KSCE Journal of Civil Engineering, 21(3), 555-564. doi:10.1007/s12205-016-0555-9VDC Use in 2007: Significant Value, Dramatic Growth, and Apparent Business Opportunityhttp://cife.stanford.eduYuan, H., Yang, Y., & Xue, X. (2019). Promoting Owners’ BIM Adoption Behaviors to Achieve Sustainable Project Management. Sustainability, 11(14), 3905. doi:10.3390/su11143905Dodgson, M. (1993). Organizational Learning: A Review of Some Literatures. Organization Studies, 14(3), 375-394. doi:10.1177/017084069301400303Xue, W., Wang, Y., & Man, Q. (2015). Research on information models for the construction schedule management based on the IFC standard. Journal of Industrial Engineering and Management, 8(3). doi:10.3926/jiem.1283Lee, G., & Borrmann, A. (2020). BIM policy and management. Construction Management and Economics, 38(5), 413-419. doi:10.1080/01446193.2020.1726979Mahamadu, A.-M., Mahdjoubi, L., & Booth, C. A. (2017). Critical BIM qualification criteria for construction pre-qualification and selection. Architectural Engineering and Design Management, 13(5), 326-343. doi:10.1080/17452007.2017.1296812Arayici, Y., Coates, P., Koskela, L., Kagioglou, M., Usher, C., & O’Reilly, K. (2011). BIM adoption and implementation for architectural practices. Structural Survey, 29(1), 7-25. doi:10.1108/02630801111118377Alwisy, A., Al-Hussein, M., & Al-Jibouri, S. H. (2012). BIM Approach for Automated Drafting and Design for Modular Construction Manufacturing. Computing in Civil Engineering (2012). doi:10.1061/9780784412343.0028Song, J., Migliaccio, G. C., Wang, G., & Lu, H. (2017). Exploring the Influence of System Quality, Information Quality, and External Service on BIM User Satisfaction. Journal of Management in Engineering, 33(6), 04017036. doi:10.1061/(asce)me.1943-5479.0000549Orlikowski, W. J. (2000). Using Technology and Constituting Structures: A Practice Lens for Studying Technology in Organizations. Organization Science, 11(4), 404-428. doi:10.1287/orsc.11.4.404.14600Elmualim, A., & Gilder, J. (2013). BIM: innovation in design management, influence and challenges of implementation. Architectural Engineering and Design Management, 10(3-4), 183-199. doi:10.1080/17452007.2013.821399Ismail, N. A. A., Chiozzi, M., & Drogemuller, R. (2017). An overview of BIM uptake in Asian developing countries. doi:10.1063/1.5011596Hosseini, M. R., Banihashemi, S., Chileshe, N., Namzadi, M. O., Udaeja, C., Rameezdeen, R., & McCuen, T. (2016). BIM adoption within Australian Small and Medium-sized Enterprises (SMEs): an innovation diffusion model. Construction Economics and Building, 16(3), 71-86. doi:10.5130/ajceb.v16i3.5159Harrison, C., & Thurnell, D. (2015). BIM implementation in a New Zealand consultingquantity surveying practice. International Journal of Construction Supply Chain Management, 5(1), 1-15. doi:10.14424/ijcscm501015-01-15Bryde, D., Broquetas, M., & Volm, J. M. (2013). The project benefits of Building Information Modelling (BIM). International Journal of Project Management, 31(7), 971-980. doi:10.1016/j.ijproman.2012.12.001Yilmaz, G., Akcamete, A., & Demirors, O. (2019). A reference model for BIM capability assessments. Automation in Construction, 101, 245-263. doi:10.1016/j.autcon.2018.10.022Koutamanis, A. (2020). Dimensionality in BIM: Why BIM cannot have more than four dimensions? Automation in Construction, 114, 103153. doi:10.1016/j.autcon.2020.103153Mayouf, M., Gerges, M., & Cox, S. (2019). 5D BIM: an investigation into the integration of quantity surveyors within the BIM process. Journal of Engineering, Design and Technology, 17(3), 537-553. doi:10.1108/jedt-05-2018-0080Mesároš, P., Smetanková, J., & Mandičák, T. (2019). The Fifth Dimension of BIM – Implementation Survey. IOP Conference Series: Earth and Environmental Science, 222, 012003. doi:10.1088/1755-1315/222/1/012003Ghaffarianhoseini, A., Tookey, J., Ghaffarianhoseini, A., Naismith, N., Azhar, S., Efimova, O., & Raahemifar, K. (2017). Building Information Modelling (BIM) uptake: Clear benefits, understanding its implementation, risks and challenges. Renewable and Sustainable Energy Reviews, 75, 1046-1053. doi:10.1016/j.rser.2016.11.083Ragu-Nathan, B. S., Apigian, C. H., Ragu-Nathan, T. S., & Tu, Q. (2004). A path analytic study of the effect of top management support for information systems performance. Omega, 32(6), 459-471. doi:10.1016/j.omega.2004.03.001Cao, D., Wang, G., Li, H., Skitmore, M., Huang, T., & Zhang, W. (2015). Practices and effectiveness of building information modelling in construction projects in China. Automation in Construction, 49, 113-122. doi:10.1016/j.autcon.2014.10.014Sharma, & Yetton. (2003). The Contingent Effects of Management Support and Task Interdependence on Successful Information Systems Implementation. MIS Quarterly, 27(4), 533. doi:10.2307/30036548Young, R., & Jordan, E. (2008). Top management support: Mantra or necessity? International Journal of Project Management, 26(7), 713-725. doi:10.1016/j.ijproman.2008.06.001Rosenberg, N. (1976). On Technological Expectations. The Economic Journal, 86(343), 523. doi:10.2307/2230797Succar, B. (2009). Building information modelling framework: A research and delivery foundation for industry stakeholders. Automation in Construction, 18(3), 357-375. doi:10.1016/j.autcon.2008.10.003Baiden, B. K., Price, A. D. F., & Dainty, A. R. J. (2006). The extent of team integration within construction projects. International Journal of Project Management, 24(1), 13-23. doi:10.1016/j.ijproman.2005.05.001Chan, A. P. C., Scott, D., & Chan, A. P. L. (2004). Factors Affecting the Success of a Construction Project. Journal of Construction Engineering and Management, 130(1), 153-155. doi:10.1061/(asce)0733-9364(2004)130:1(153)Cassel, C., Hackl, P., & Westlund, A. H. (1999). Robustness of partial least-squares method for estimating latent variable quality structures. Journal of Applied Statistics, 26(4), 435-446. doi:10.1080/02664769922322Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial Least Squares Structural Equation Modeling: Rigorous Applications, Better Results and Higher Acceptance. Long Range Planning, 46(1-2), 1-12. doi:10.1016/j.lrp.2013.01.001Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24. doi:10.1108/ebr-11-2018-0203Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: updated guidelines. Industrial Management & Data Systems, 116(1), 2-20. doi:10.1108/imds-09-2015-0382Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879-903. doi:10.1037/0021-9010.88.5.879Reinartz, W., Haenlein, M., & Henseler, J. (2009). An empirical comparison of the efficacy of covariance-based and variance-based SEM. International Journal of Research in Marketing, 26(4), 332-344. doi:10.1016/j.ijresmar.2009.08.001Marcoulides, & Saunders. (2006). Editor’s Comments: PLS: A Silver Bullet? MIS Quarterly, 30(2), iii. doi:10.2307/25148727SmartPLS 3. Boenningstedt: SmartPLS GmbHhttp://www.smartpls.comChin, W. W., Marcolin, B. L., & Newsted, P. R. (2003). A Partial Least Squares Latent Variable Modeling Approach for Measuring Interaction Effects: Results from a Monte Carlo Simulation Study and an Electronic-Mail Emotion/Adoption Study. Information Systems Research, 14(2), 189-217. doi:10.1287/isre.14.2.189.16018Podsakoff, N. P., Shen, W., & Podsakoff, P. M. (s. f.). The Role of Formative Measurement Models in Strategic Management Research: Review, Critique, and Implications for Future Research. Research Methodology in Strategy and Management, 197-252. doi:10.1016/s1479-8387(06)03008-6Hair, J. F., Sarstedt, M., & Ringle, C. M. (2019). Rethinking some of the rethinking of partial least squares. European Journal of Marketing, 53(4), 566-584. doi:10.1108/ejm-10-2018-0665F. Hair Jr, J., Sarstedt, M., Hopkins, L., & G. Kuppelwieser, V. (2014). Partial least squares structural equation modeling (PLS-SEM). European Business Review, 26(2), 106-121. doi:10.1108/ebr-10-2013-012

Sustainable bridge design by metamodel-assisted multi-objective optimization and decision-making under uncertainty

[EN] Today, bridge design seeks not only to minimize cost, but also to minimize adverse environmental and social impacts. This multi-criteria decision-making problem is subject to variability of the opinions of stakeholders regarding the importance of criteria for sustainability. As a result, this paper proposes a method for designing and selecting optimally sustainable bridges under the uncertainty of criteria comparison. A Pareto set of solutions is obtained using a metamodel-assisted multi-objective optimization. A new decision-making technique introduces the uncertainty of the decision-maker's preference through triangular distributions and thereby ranks the sustainable bridge designs. The method is illustrated by a case study of a three-span post-tensioned concrete box-girder bridge designed according to the embodied energy, overall safety and corrosion initiation time. In this particular case, 211 efficient solutions are reduced to two preferred solutions which have a probability of being selected of 81.6% and 18.4%. In addition, a sensitivity analysis validates the influence of the uncertainty regarding the decision making. The approach proposed allows actors involved in the bridge design and decision-making to determine the best sustainable design by finding the probability of a given design being chosen.The authors acknowledge the financial support of the Spanish Ministry of Economy and Competitiveness, along with FEDER funding (Project: BIA2017-85098-R).García-Segura, T.; Penadés-Plà, V.; Yepes, V. (2018). Sustainable bridge design by metamodel-assisted multi-objective optimization and decision-making under uncertainty. Journal of Cleaner Production. 202:904-915. doi:10.1016/j.jclepro.2018.08.177S90491520

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

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.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 manuscriptYepes 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.2014.10.013S1231344

Structural design of precast-prestressed concrete U-beam road bridges based on embodied energy

An automated procedure for optimizing the design of precast-prestressed concrete U-beam road bridges is presented. The economic cost and the embodied energy are selected as the objective functions based on production materials, transport and placement. Heuristic optimization is used to search for the best geometry, the concrete type, the prestressing steel, and the reinforcement for the slab and the beam. The results for both objectives provide improved opportunities to learn about low-energy designs. The most influential variables for the energy efficiency goal are analyzed. The relationship between the span length and the embodied energy is described by a good parabolic fit for both optimization criteria. The findings indicate that the objectives do not exhibit conflicting behavior, and also that optimum energy designs are close to the optimum cost designs. The analysis also revealed that a reduction by 1 Euro can save up to 4 kW h. It is recommended to reduce the reinforcement in the slab as well as increase the volume of concrete in both slab and beams in order to achieve higher energy efficiency. It is also worth noting that web inclination angle should be increased when the depth increases for longer span lengths to maintain the optimum slab span lengths in the transverse direction.The authors acknowledge the financial support of the Spanish Ministry of Economy and Competitiveness along with FEDER funding (BRIDLIFE Project: BIA2014-56574-R).Martí Albiñana, JV.; 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. https://doi.org/10.1016/j.jclepro.2016.02.024S23124012

Bayesian network method for decision-making about the social sustainability of infrastructure projects

[EN] Nowadays, sustainability assessment tends to focus on the biophysical and economic aspects of the built environment. The social aspects are generally overestimated during an infrastructure evaluation. This study proposes a method to optimize infrastructure projects by assessing their social contribution. This proposal takes into account the infrastructure¿s interactions with the local environment in terms of its potential contribution in the short and long term. The method is structured in three stages: (1) preparation of a decision-making model, (2) formulation of the model, and (3) implementation of the model through optimization of infrastructure projects from the social sustainability viewpoint. The theory of Bayesian reasoning and a harmony search optimization algorithm are used to carry out the research. The paper presents the application to a case study of a set of alternatives for road infrastructure projects in El Salvador. This approach creates a model of participative decision-making. The results show that the method can distinguish socially efficient alternatives from the short and long-term contributions. In addition, the results suggest that some variables are less sensitive to the short and long-term maximization, while others vary their values to improve one objective or the other. The findings are directly applied to a real case. The method can be employed in the infrastructure formulation and prioritization phases and complemented with economic and environmental sustainability assessments.This research was funded by the Government of Chile under the Doctoral Fellowship Abroad Program (grant CONICYT-2015/72160059) and the Spanish Ministry of Economy and Competitiveness along with FEDER funding (project BIA2014-56574-R). The authors are grateful to both the United Nations Development Programme and the Ministry of Public Works, Transportation, Housing and Urban Development of El Salvador for their participation in the implementation of the proposed method (Project 00074250).Sierra-Varela, LA.; 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. https://doi.org/10.1016/j.jclepro.2017.12.140S52153417

Competitive Advantages and Barriers in International Construction: An Origin-Host Market Approach

[EN] The growing importance of international construction activity forces companies to find out what competitive advantages to provide and barriers to overcome to achieve success, considering the particular characteristics of their origin and destination countries. Seventy six articles regarding success in the international construction market in the period 2008-2017 are reviewed to identify the barriers and the competitive advantages that most affect the international success of contractors. The Chi-Square (chi(2)) statistical test is used to verify the influence of the origin and destination countries on these variables. The literature identifies the provision of trained human resources as the most important competitive advantage and cultural differences and political risks as the main barriers. The Chi-Square (chi(2)) test indicates a dependency relationship between the origin country and the competitive advantage "managerial ability" to achieve international success, and between the external barriers "link between countries", "political environment" and "economic environment" with the target market. Results of this paper provide valuable information for any construction company seeking international success.Lozano-Torró, A.; García-Segura, T.; Montalbán-Domingo, L.; Pellicer, E. (2020). Competitive Advantages and Barriers in International Construction: An Origin-Host Market Approach. Journal of Civil Engineering and Management. 26(5):475-489. https://doi.org/10.3846/jcem.2020.12180475489265Abdelghany, Y., & Ezeldin, A. S. (2010). Classification of Risks for International Construction Joint Ventures (ICJV) Projects. Construction Research Congress 2010. doi:10.1061/41109(373)126Abdul-Aziz, A.-R., Nor Azmi, H.-A. C. M., Law, Y.-H., & Pengiran, D. N. (2013). Internationalization of Construction-Related Consultants: Impact of Age and Size. Journal of Professional Issues in Engineering Education and Practice, 139(2), 148-155. doi:10.1061/(asce)ei.1943-5541.0000135Ahmed, R., & Azmi bin Mohamad, N. (2016). Exploring the Relationship Between Multi-Dimensional Top Management Support and Project Success: An International Study. Engineering Management Journal, 28(1), 54-67. doi:10.1080/10429247.2015.1136525Ajmal, M. M. (2015). Cultural Effects on Trust Building in International Projects’ Stakeholders. International Journal of Information Technology Project Management, 6(3), 41-57. doi:10.4018/ijitpm.2015070103AKGUL, B. K., OZORHON, B., DIKMEN, I., & BIRGONUL, M. T. (2016). Social network analysis of construction companies operating in international markets: case of Turkish contractors. JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT, 23(3), 327-337. doi:10.3846/13923730.2015.1073617Alashwal, A. M., Fareed, N. F., & Al-obaidi Karam Mustafa. (2017). Determining Success Criteria and Success Factors for International Construction Projects for Malaysian Contractors. Construction Economics and Building, 17(2), 62-80. doi:10.5130/ajceb.v17i2.5319Awil, A. U., & Abdul-Aziz, A. R. (2005, July). The role of government in the internationalization of Malaysian housing developers. In Queensland University of Technology Research Week International Conference (pp. 4–8), Brisbane, Australia.Bannò, M., & Redondi, R. (2014). Air connectivity and foreign direct investments: economic effects of the introduction of new routes. European Transport Research Review, 6(4), 355-363. doi:10.1007/s12544-014-0136-2BARKEMA, H. G., BELL, J. H. J., & PENNINGS, J. M. (1996). FOREIGN ENTRY, CULTURAL BARRIERS, AND LEARNING. Strategic Management Journal, 17(2), 151-166. doi:10.1002/(sici)1097-0266(199602)17:23.0.co;2-zBelassi, W., & Tukel, O. I. (1996). A new framework for determining critical success/failure factors in projects. International Journal of Project Management, 14(3), 141-151. doi:10.1016/0263-7863(95)00064-xBrewer, P. (2001). International market selection: developing a model from Australian case studies. International Business Review, 10(2), 155-174. doi:10.1016/s0969-5931(00)00049-4Burnard, P. (1991). A method of analysing interview transcripts in qualitative research. Nurse Education Today, 11(6), 461-466. doi:10.1016/0260-6917(91)90009-yCarnevalli, J. A., & Miguel, P. C. (2008). Review, analysis and classification of the literature on QFD—Types of research, difficulties and benefits. International Journal of Production Economics, 114(2), 737-754. doi:10.1016/j.ijpe.2008.03.006Carpintero, S. (2011). THE COMPETITIVE ADVANTAGES OF THE SPANISH COMPANIES IN THE INTERNATIONAL TOLL ROAD INDUSTRY / ISPANIJOS ĮMONIŲ KONKURENCINIS PRANAŠUMAS TARPTAUTINIAME MOKAMŲJŲ KELIŲ SEKTORIUJE. Journal of Civil Engineering and Management, 17(4), 483-493. doi:10.3846/13923730.2011.625632Chen, C. (2008). Entry mode selection for international construction markets: the influence of host country related factors. Construction Management and Economics, 26(3), 303-314. doi:10.1080/01446190701882382Otsuki, T., Chen, M. X., & Wilson, J. S. (2006). Do Standards Matter For Export Success ? Policy Research Working Papers. doi:10.1596/1813-9450-3809Chen, C., & Orr, R. J. (2009). Chinese Contractors in Africa: Home Government Support, Coordination Mechanisms, and Market Entry Strategies. Journal of Construction Engineering and Management, 135(11), 1201-1210. doi:10.1061/(asce)co.1943-7862.0000082Chen, C., Wang, Q., Martek, I., & Li, H. (2016). International Market Selection Model for Large Chinese Contractors. Journal of Construction Engineering and Management, 142(10), 04016044. doi:10.1061/(asce)co.1943-7862.0001122Chew, D. A. S., Yan, S., & Cheah, C. Y. J. (2008). Core capability and competitive strategy for construction SMEs in China. Chinese Management Studies, 2(3), 203-214. doi:10.1108/17506140810895898Chua, D. K. H., Wang, Y., & Tan, W. T. (2003). Impacts of Obstacles in East Asian Cross-Border Construction. Journal of Construction Engineering and Management, 129(2), 131-141. doi:10.1061/(asce)0733-9364(2003)129:2(131)Tang, X., Zheng, H., Wang, Y., Chen, W., Guo, J., Zhou, Y., & Li, X. (2016). Effect of fresh aluminum hydroxide gels on algae removal from micro-polluted water by polyaluminum chloride coagulant. Journal of the Taiwan Institute of Chemical Engineers, 63, 195-201. doi:10.1016/j.jtice.2016.03.001DeSarbo, W. S., Anthony Di Benedetto, C., Michael Song, & Sinha, I. (2004). Revisiting the Miles and Snow strategic framework: uncovering interrelationships between strategic types, capabilities, environmental uncertainty, and firm performance. Strategic Management Journal, 26(1), 47-74. doi:10.1002/smj.431Dey, I. (2003). Qualitative Data Analysis. doi:10.4324/9780203412497Diaz-Sarachaga, J. M., Jato-Espino, D., Alsulami, B., & Castro-Fresno, D. (2016). Evaluation of existing sustainable infrastructure rating systems for their application in developing countries. Ecological Indicators, 71, 491-502. doi:10.1016/j.ecolind.2016.07.033Diaz-Sarachaga, J. M., Jato-Espino, D., & Castro-Fresno, D. (2017). Methodology for the development of a new Sustainable Infrastructure Rating System for Developing Countries (SIRSDEC). Environmental Science & Policy, 69, 65-72. doi:10.1016/j.envsci.2016.12.010Downe‐Wamboldt, B. (1992). Content analysis: Method, applications, and issues. Health Care for Women International, 13(3), 313-321. doi:10.1080/07399339209516006Du, L., Tang, W., Liu, C., Wang, S., Wang, T., Shen, W., … Zhou, Y. (2016). Enhancing engineer–procure–construct project performance by partnering in international markets: Perspective from Chinese construction companies. International Journal of Project Management, 34(1), 30-43. doi:10.1016/j.ijproman.2015.09.003Dunning, J. H. (2000). The eclectic paradigm as an envelope for economic and business theories of MNE activity. International Business Review, 9(2), 163-190. doi:10.1016/s0969-5931(99)00035-9Duvholt, J. E., Follinglo, K. A., Lædre, O., & Lohne, J. (2016). International Competition on the Norwegian Infrastructure Market. Energy Procedia, 96, 855-867. doi:10.1016/j.egypro.2016.09.147Engineering News Record (ENR). (2017). The top 250 international contractors.Ercan, T., & Koksal, A. (2015). Competitive Strategic Performance Benchmarking (CSPB) model for international construction companies. KSCE Journal of Civil Engineering, 20(5), 1657-1668. doi:10.1007/s12205-015-0733-1Erramilli, M. K., Agarwal, S., & Kim, S.-S. (1997). Are Firm-Specific Advantages Location-Specific Too? Journal of International Business Studies, 28(4), 735-757. doi:10.1057/palgrave.jibs.8490117Essl, I., & Mauerhofer, V. (2018). Opportunities for mutual implementation of nature conservation and climate change policies: A multilevel case study based on local stakeholder perceptions. Journal of Cleaner Production, 183, 898-907. doi:10.1016/j.jclepro.2018.01.210Fang, D., Li, M., Fong, P. S., & Shen, L. (2004). Risks in Chinese Construction Market—Contractors’ Perspective. Journal of Construction Engineering and Management, 130(6), 853-861. doi:10.1061/(asce)0733-9364(2004)130:6(853)Faridi, A. S., & El‐Sayegh, S. M. (2006). Significant factors causing delay in the UAE construction industry. Construction Management and Economics, 24(11), 1167-1176. doi:10.1080/01446190600827033Field, A. (2013). Discovering statistics using IBM SPSS statisticsGhemawat, P. (2004). Distance still matters: The hard reality of global expansion. Harvard Business Review. https://hbr.org/2001/09/distance-still-matters-the-hard-reality-of-globalexpansionGundes, S., & Aydogan, G. (2016). Bibliometric analysis of research in international construction. Canadian Journal of Civil Engineering, 43(4), 304-311. doi:10.1139/cjce-2015-0127Gunhan, S., & Arditi, D. (2005). Factors Affecting International Construction. Journal of Construction Engineering and Management, 131(3), 273-282. doi:10.1061/(asce)0733-9364(2005)131:3(273)Han, S. H., Park, S. H., Kim, D. Y., Kim, H., & Kang, Y. W. (2007). Causes of Bad Profit in Overseas Construction Projects. Journal of Construction Engineering and Management, 133(12), 932-943. doi:10.1061/(asce)0733-9364(2007)133:12(932)Hong, Y., Chan, D. W. M., Chan, A. P. C., & Yeung, J. F. Y. (2012). Critical Analysis of Partnering Research Trend in Construction Journals. Journal of Management in Engineering, 28(2), 82-95. doi:10.1061/(asce)me.1943-5479.0000084Ika, L. A., & Donnelly, J. (2017). Success conditions for international development capacity building projects. International Journal of Project Management, 35(1), 44-63. doi:10.1016/j.ijproman.2016.10.005Jang, W., Lee, J. K., Lee, J., & Han, S. H. (2015). Naïve Bayesian Classifier for Selecting Good/Bad Projects during the Early Stage of International Construction Bidding Decisions. Mathematical Problems in Engineering, 2015, 1-12. doi:10.1155/2015/830781Javernick-Will, A. (2013). Local Embeddedness and Knowledge Management Strategies for Project-Based Multi-National Firms. Engineering Management Journal, 25(3), 16-26. doi:10.1080/10429247.2013.11431979Jewell, C. A. (2010). Exporting construction professional services in a global service economy. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.687.4922&rep=rep1&type=pdfJung, W., Han, S. H., Park, H., & Kim, D. Y. (2010). Empirical Assessment of Internationalization Strategies for Small and Medium Construction Companies. Journal of Construction Engineering and Management, 136(12), 1306-1316. doi:10.1061/(asce)co.1943-7862.0000237Jung, W., Han, S. H., Koo, B., & Jang, W. (2012). Which Strategies Are More Effective for International Contractors during Boom and Recession Periods? Journal of Management in Engineering, 28(3), 281-290. doi:10.1061/(asce)me.1943-5479.0000087Kaplan, R. S., & Norton, D. P. (2007). Using the balanced scorecard as a strategic management system. Harvard Business Review. https://hbr.org/2007/07/using-the-balanced-scorecard-as-a-strategic-management-systemKhang, D. B., & Moe, T. L. (2008). Success Criteria and Factors for International Development Projects: A Life-Cycle-Based Framework. Project Management Journal, 39(1), 72-84. doi:10.1002/pmj.20034Kim, D. Y., Ashuri, B., & Han, S. H. (2013). Financial Valuation of Investments in International Construction Markets: Real-Options Approach for Market-Entry Decisions. Journal of Management in Engineering, 29(4), 355-368. doi:10.1061/(asce)me.1943-5479.0000152Kirsch, R. J., Laird, K. R., & Evans, T. G. (2000). The Entry of International CPA Firms into Emerging Markets: Motivational Factors and Growth Strategies. The International Journal of Accounting, 35(1), 99-119. doi:10.1016/s0020-7063(99)00048-5Kivrak, S., Arslan, G., Tuncan, M., & Birgonul, M. T. (2014). Impact of national culture on knowledge sharing in international construction projects. Canadian Journal of Civil Engineering, 41(7), 642-649. doi:10.1139/cjce-2013-0408Konijn, P., & van Tulder, R. (2015). Resources-for-infrastructure (R4I) swaps. critical perspectives on international business, 11(3/4), 259-284. doi:10.1108/cpoib-02-2013-0008Korkmaz, S., & Messner, J. I. (2008). Competitive Positioning and Continuity of Construction Firms in International Markets. Journal of Management in Engineering, 24(4), 207-216. doi:10.1061/(asce)0742-597x(2008)24:4(207)Kytle, B., & Ruggie, J. G. (2005). Corporate social responsibility as risk management. A model for multinationals (Working paper No. 10). Harvard University.LAMBERT, D. R. (1980). PRICE AS A QUALITY SIGNAL: THE TIP OF THE ICEBERG. Economic Inquiry, 18(1), 144-150. doi:10.1111/j.1465-7295.1980.tb00565.xLee, S.-H., Jeon, R.-K., Kim, J.-H., & Kim, J.-J. (2011). Strategies for Developing Countries to Expand Their Shares in the Global Construction Market: Phase-Based SWOT and AAA Analyses of Korea. Journal of Construction Engineering and Management, 137(6), 460-470. doi:10.1061/(asce)co.1943-7862.0000316Lee, K.-W., Han, S. H., Park, H., & David Jeong, H. (2016). Empirical Analysis of Host-Country Effects in the International Construction Market: An Industry-Level Approach. Journal of Construction Engineering and Management, 142(3), 04015092. doi:10.1061/(asce)co.1943-7862.0001083Lei, Z., Tang, W., Duffield, C., Zhang, L., & Hui, F. K. P. (2017). The impact of technical standards on international project performance: Chinese contractors’ experience. International Journal of Project Management, 35(8), 1597-1607. doi:10.1016/j.ijproman.2017.09.002Ling, F. Y. Y., & Hoi, L. (2006). Risks faced by Singapore firms when undertaking construction projects in India. International Journal of Project Management, 24(3), 261-270. doi:10.1016/j.ijproman.2005.11.003Liu, X., Buck, T., & Shu, C. (2005). Chinese economic development, the next stage: outward FDI? International Business Review, 14(1), 97-115. doi:10.1016/j.ibusrev.2004.12.003Lu, W., Li, H., Shen, L., & Huang, T. (2009). Strengths, Weaknesses, Opportunities, and Threats Analysis of Chinese Construction Companies in the Global Market. Journal of Management in Engineering, 25(4), 166-176. doi:10.1061/(asce)0742-597x(2009)25:4(166)Lu, W., Liu, A. M. M., Rowlinson, S., & Poon, S. W. (2013). Sharpening Competitive Edge through Procurement Innovation: Perspectives from Chinese International Construction Companies. Journal of Construction Engineering and Management, 139(3), 347-351. doi:10.1061/(asce)co.1943-7862.0000614Luu, V. T., Kim, S.-Y., & Huynh, T.-A. (2008). Improving project management performance of large contractors using benchmarking approach. International Journal of Project Management, 26(7), 758-769. doi:10.1016/j.ijproman.2007.10.002Marinič, P., & Záthurecký, V. (2014). Evaluation of the construction industry’s economic situation and expected development according to managers of SMBs. Central European Journal of Management, 1(1). doi:10.5817/cejm2014-1-5Meyer, J. W., & Rowan, B. (1977). Institutionalized Organizations: Formal Structure as Myth and Ceremony. American Journal of Sociology, 83(2), 340-363. doi:10.1086/226550Mohr, A. T., & Puck, J. F. (2005). Managing Functional Diversity to Improve the Performance of International Joint Ventures. Long Range Planning, 38(2), 163-182. doi:10.1016/j.lrp.2005.02.004Antonio J. (2011). Financial risks in construction projects. AFRICAN JOURNAL OF BUSINESS MANAGEMENT, 5(31). doi:10.5897/ajbm11.1463Mosley, J. C., & Bubshait, A. A. (2016). Performance Evaluation of International Contractors in Saudi Arabia. Journal of Professional Issues in Engineering Education and Practice, 142(1), 04015007. doi:10.1061/(asce)ei.1943-5541.0000254Ngowi, A. B., Pienaar, E., Talukhaba, A., & Mbachu, J. (2005). The globalisation of the construction industry—a review. Building and Environment, 40(1), 135-141. doi:10.1016/j.buildenv.2004.05.008Öz, Ö. (2001). Sources of competitive advantage of Turkish construction companies in international markets. Construction Management and Economics, 19(2), 135-144. doi:10.1080/01446190010009988Ozorhon, B., Arditi, D., Dikmen, I., & Birgonul, M. T. (2010). Performance of International Joint Ventures in Construction. Journal of Management in Engineering, 26(4), 209-222. doi:10.1061/(asce)me.1943-5479.0000022Papadopoulos, N., & Martín Martín, O. (2011). International market selection and segmentation: perspectives and challenges. International Marketing Review, 28(2), 132-149. doi:10.1108/02651331111122632Park, H., Lee, K.-W., Jeong, H. D., & Han, S. H. (2014). Effect of Institutional Risks on the Performance of International Construction Projects. Construction Research Congress 2014. doi:10.1061/9780784413517.216Parkhe, A. (1991). Interfirm Diversity, Organizational Learning, and Longevity in Global Strategic Alliances. Journal of International Business Studies, 22(4), 579-601. doi:10.1057/palgrave.jibs.8490315Price, A. D. F., & Newson, E. (2003). Strategic Management: Consideration of Paradoxes, Processes, and Associated Concepts as Applied to Construction. Journal of Management in Engineering, 19(4), 183-192. doi:10.1061/(asce)0742-597x(2003)19:4(183)Rutihinda, C. (2011). Factors Influencing The Internationalization Of Small And Medium Size Enterprises. International Business & Economics Research Journal (IBER), 7(12). doi:10.19030/iber.v7i12.3312Sakakibara, M., & Porter, M. E. (2001). Competing at Home to Win Abroad: Evidence from Japanese Industry. Review of Economics and Statistics, 83(2), 310-322. doi:10.1162/00346530151143842Sambasivan, M., & Soon, Y. W. (2007). Causes and effects of delays in Malaysian construction industry. International Journal of Project Management, 25(5), 517-526. doi:10.1016/j.ijproman.2006.11.007Scherer, F. M., & Ross, D. (1990). Industrial market structure and economic performance. University of Illinois at UrbanaChampaign’s Academy for Entrepreneurial Leadership Historical Research Reference in Entrepreneurship. https://ssrn.com/abstract=1496716Sierra, L. A., Pellicer, E., & Yepes, V. (2017). Method for estimating the social sustainability of infrastructure projects. Environmental Impact Assessment Review, 65, 41-53. doi:10.1016/j.eiar.2017.02.004Sierra, L. A., Yepes, V., & Pellicer, E. (2018). A review of multi-criteria assessment of the social sustainability of infrastructures. Journal of Cleaner Production, 187, 496-513. doi:10.1016/j.jclepro.2018.03.022Utama, W. P., Chan, A. P. C., Zahoor, H., & Gao, R. (2016). Review of research trend in international construction projects: a bibliometric analysis. Construction Economics and Building, 16(2), 71-82. doi:10.5130/ajceb.v16i2.4866Wang, S. Q., Tiong, R. L. K., Ting, S. K., & Ashley, D. (1999). Political Risks: Analysis of Key Contract Clauses in China’s BOT Project. Journal of Construction Engineering and Management, 125(3), 190-197. doi:10.1061/(asce)0733-9364(1999)125:3(190)Watt, D. J., Kayis, B., & Willey, K. (2010). The relative importance of tender evaluation and contractor selection criteria. International Journal of Project Management, 28(1), 51-60. doi:10.1016/j.ijproman.2009.04.003Welch, D. E., Welch, L. S., & Marschan-Piekkari, R. (2001). The Persistent Impact of Language on Global Operations. Prometheus, 19(3), 193-209. doi:10.1080/08109020110072180Winch, G. M. (2008). Internationalisation strategies in business-to-business services: the case of architectural practice. The Service Industries Journal, 28(1), 1-13. doi:10.1080/02642060701725347Wu, C.-L., Fang, D.-P., Liao, P.-C., Xue, J.-W., Li, Y., & Wang, T. (2015). Perception of corporate social responsibility: the case of Chinese international contractors. Journal of Cleaner Production, 107, 185-194. doi:10.1016/j.jclepro.2015.04.143Xia, B., Chan, A., Molenaar, K., & Skitmore, M. (2012). Determining the Appropriate Proportion of Owner-Provided Design in Design-Build Contracts: Content Analysis Approach. Journal of Construction Engineering and Management, 138(9), 1017-1022. doi:10.1061/(asce)co.1943-7862.0000522Xiaopeng, D., & Pheng, L. S. (2013). Understanding the critical variables affecting the level of political risks in international construction projects. KSCE Journal of Civil Engineering, 17(5), 895-907. doi:10.1007/s12205-013-0354-5Yalegama, S., Chileshe, N., & Ma, T. (2016). Critical success factors for community-driven development projects: A Sri Lankan community perspective. International Journal of Project Management, 34(4), 643-659. doi:10.1016/j.ijproman.2016.02.006Yang, H., Chan, A. P. C., & Yeung, J. F. Y. (2015). Niche Width, Competitive Positioning, and Performance of International Construction Contractors (1992–2009). Journal of Management in Engineering, 31(3), 04014040. doi:10.1061/(asce)me.1943-5479.0000250Ye, K., Lu, W., & Jiang, W. (2009). Concentration in the international construction market. Construction Management and Economics, 27(12), 1197-1207. doi:10.1080/01446190903222429Yildiz, A. E., Dikmen, I., Birgonul, M. T., Ercoskun, K., & Alten, S. (2014). A knowledge-based risk mapping tool for cos

Study of Social and Environmental Needs for the Selection of Sustainable Criteria in the Procurement of Public Works

This research was funded by the Spanish Ministry of Economy and Competitiveness, grant number BIA2017-85098-R and the Valencian Regional Government, grant number GV/2018//085Montalbán-Domingo, L.; Aguilar-Morocho, M.; García-Segura, T.; Pellicer, E. (2020). Study of Social and Environmental Needs for the Selection of Sustainable Criteria in the Procurement of Public Works. Sustainability. 12(18):1-21. https://doi.org/10.3390/su12187756S1211218Xia, B., Chen, Q., Xu, Y., Li, M., & Jin, X. (2015). Design-Build Contractor Selection for Public Sustainable Buildings. Journal of Management in Engineering, 31(5), 04014070. doi:10.1061/(asce)me.1943-5479.0000295Ruparathna, R., & Hewage, K. (2015). Sustainable procurement in the Canadian construction industry: current practices, drivers and opportunities. Journal of Cleaner Production, 109, 305-314. doi:10.1016/j.jclepro.2015.07.007Testa, F., Grappio, P., Gusmerotti, N. M., Iraldo, F., & Frey, M. (2015). Examining green public procurement using content analysis: existing difficulties for procurers and useful recommendations. Environment, Development and Sustainability, 18(1), 197-219. doi:10.1007/s10668-015-9634-1Directive 2014/24/EU of the European Parliament and of the Council of 26 February 2014 On Public Procurement and Repealing Directive 2004/18/EChttps://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32014L0024&rid=1Montalbán-Domingo, L., García-Segura, T., Sanz, M. A., & Pellicer, E. (2018). Social sustainability criteria in public-work procurement: An international perspective. Journal of Cleaner Production, 198, 1355-1371. doi:10.1016/j.jclepro.2018.07.083Sourani, A., & Sohail, M. (2011). Barriers to addressing sustainable construction in public procurement strategies. Proceedings of the Institution of Civil Engineers - Engineering Sustainability, 164(4), 229-237. doi:10.1680/ensu.2011.164.4.229Ugwu, O. O., Kumaraswamy, M. M., Wong, A., & Ng, S. T. (2006). Sustainability appraisal in infrastructure projects (SUSAIP). Automation in Construction, 15(2), 239-251. doi:10.1016/j.autcon.2005.05.006Shi, Ge, Yuan, Wang, Kellett, Li, & Ba. (2019). An Integrated Indicator System and Evaluation Model for Regional Sustainable Development. Sustainability, 11(7), 2183. doi:10.3390/su11072183Szopik-Depczyńska, K., Cheba, K., Bąk, I., Stajniak, M., Simboli, A., & Ioppolo, G. (2018). The study of relationship in a hierarchical structure of EU sustainable development indicators. Ecological Indicators, 90, 120-131. doi:10.1016/j.ecolind.2018.03.002Yli-Viikari, A., Hietala-Koivu, R., Huusela-Veistola, E., Hyvönen, T., Perälä, P., & Turtola, E. (2007). Evaluating agri-environmental indicators (AEIs)—Use and limitations of international indicators at national level. Ecological Indicators, 7(1), 150-163. doi:10.1016/j.ecolind.2005.11.005Antanasijević, D., Pocajt, V., Ristić, M., & Perić-Grujić, A. (2017). A differential multi-criteria analysis for the assessment of sustainability performance of European countries: Beyond country ranking. Journal of Cleaner Production, 165, 213-220. doi:10.1016/j.jclepro.2017.07.131Phillis, Y. A., Kouikoglou, V. S., & Verdugo, C. (2017). Urban sustainability assessment and ranking of cities. Computers, Environment and Urban Systems, 64, 254-265. doi:10.1016/j.compenvurbsys.2017.03.002Illankoon, I. M. C. S., Tam, V. W. Y., & Le, K. N. (2017). Environmental, Economic, and Social Parameters in International Green Building Rating Tools. Journal of Professional Issues in Engineering Education and Practice, 143(2), 05016010. doi:10.1061/(asce)ei.1943-5541.0000313Cook, D., Saviolidis, N. M., Davíðsdóttir, B., Jóhannsdóttir, L., & Ólafsson, S. (2017). Measuring countries’ environmental sustainability performance—The development of a nation-specific indicator set. Ecological Indicators, 74, 463-478. doi:10.1016/j.ecolind.2016.12.009Walker, H., & Brammer, S. (2012). The relationship between sustainable procurement and e-procurement in the public sector. International Journal of Production Economics, 140(1), 256-268. doi:10.1016/j.ijpe.2012.01.008Varnäs, A., Balfors, B., & Faith-Ell, C. (2009). Environmental consideration in procurement of construction contracts: current practice, problems and opportunities in green procurement in the Swedish construction industry. Journal of Cleaner Production, 17(13), 1214-1222. doi:10.1016/j.jclepro.2009.04.001Anthonissen, J., Van Troyen, D., Braet, J., & Van den bergh, W. (2015). Using carbon dioxide emissions as a criterion to award road construction projects: a pilot case in Flanders. Journal of Cleaner Production, 102, 96-102. doi:10.1016/j.jclepro.2015.04.020Testa, F., Annunziata, E., Iraldo, F., & Frey, M. (2016). Drawbacks and opportunities of green public procurement: an effective tool for sustainable production. Journal of Cleaner Production, 112, 1893-1900. doi:10.1016/j.jclepro.2014.09.092Ekener, E., Hansson, J., Larsson, A., & Peck, P. (2018). Developing Life Cycle Sustainability Assessment methodology by applying values-based sustainability weighting - Tested on biomass based and fossil transportation fuels. Journal of Cleaner Production, 181, 337-351. doi:10.1016/j.jclepro.2018.01.211Carnevalli, J. A., & Miguel, P. C. (2008). Review, analysis and classification of the literature on QFD—Types of research, difficulties and benefits. International Journal of Production Economics, 114(2), 737-754. doi:10.1016/j.ijpe.2008.03.006Rodríguez López, F., & Fernández Sánchez, G. (2011). Challenges for Sustainability Assessment by Indicators. Leadership and Management in Engineering, 11(4), 321-325. doi:10.1061/(asce)lm.1943-5630.0000142Dasgupta, S., & Tam, E. K. . (2005). Indicators and framework for assessing sustainable infrastructure. Canadian Journal of Civil Engineering, 32(1), 30-44. doi:10.1139/l04-101Dobrovolskienė, N., & Tamošiūnienė, R. (2015). An Index to Measure Sustainability of a Business Project in the Construction Industry: Lithuanian Case. Sustainability, 8(1), 14. doi:10.3390/su8010014Shen, L., Wu, Y., & Zhang, X. (2011). Key Assessment Indicators for the Sustainability of Infrastructure Projects. Journal of Construction Engineering and Management, 137(6), 441-451. doi:10.1061/(asce)co.1943-7862.0000315Ugwu, O. O., & Haupt, T. C. (2007). Key performance indicators and assessment methods for infrastructure sustainability—a South African construction industry perspective. Building and Environment, 42(2), 665-680. doi:10.1016/j.buildenv.2005.10.018Mihyeon Jeon, C., & Amekudzi, A. (2005). Addressing Sustainability in Transportation Systems: Definitions, Indicators, and Metrics. Journal of Infrastructure Systems, 11(1), 31-50. doi:10.1061/(asce)1076-0342(2005)11:1(31)Wu, Z., Zhang, X., & Wu, M. (2016). Mitigating construction dust pollution: state of the art and the way forward. Journal of Cleaner Production, 112, 1658-1666. doi:10.1016/j.jclepro.2015.01.015Azapagic, A. (2004). Developing a framework for sustainable development indicators for the mining and minerals industry. Journal of Cleaner Production, 12(6), 639-662. doi:10.1016/s0959-6526(03)00075-1Bajjou, M. S., Chafi, A., Ennadi, A., … Hammoumi, M. E. (2017). he Practical Relationships between Lean Construction Tools and Sustainable Development: A literature review. JOURNAL OF ENGINEERING SCIENCE AND TECHNOLOGY REVIEW, 10(4), 170-177. doi:10.25103/jestr.104.20Fuentes-Bargues, J., González-Cruz, M., & González-Gaya, C. (2017). Environmental Criteria in the Spanish Public Works Procurement Process. International Journal of Environmental Research and Public Health, 14(2), 204. doi:10.3390/ijerph14020204Faith-Ell, C., Balfors, B., & Folkeson, L. (2006). The application of environmental requirements in Swedish road maintenance contracts. Journal of Cleaner Production, 14(2), 163-171. doi:10.1016/j.jclepro.2004.11.004Sarkis, J., Meade, L. M., & Presley, A. R. (2012). Incorporating sustainability into contractor evaluation and team formation in the built environment. Journal of Cleaner Production, 31, 40-53. doi:10.1016/j.jclepro.2012.02.029Valdes-Vasquez, R., & Klotz, L. E. (2013). Social Sustainability Considerations during Planning and Design: Framework of Processes for Construction Projects. Journal of Construction Engineering and Management, 139(1), 80-89. doi:10.1061/(asce)co.1943-7862.0000566Whang, S.-W., & Kim, S. (2015). Balanced sustainable implementation in the construction industry: The perspective of Korean contractors. Energy and Buildings, 96, 76-85. doi:10.1016/j.enbuild.2015.03.019McCrudden, C. (2004). Using public procurement to achieve social outcomes. Natural Resources Forum, 28(4), 257-267. doi:10.1111/j.1477-8947.2004.00099.xPellicer, E., Sanz, M. A., Esmaeili, B., & Molenaar, K. R. (2016). Exploration of Team Integration in Spanish Multifamily Residential Building Construction. Journal of Management in Engineering, 32(5), 05016012. doi:10.1061/(asce)me.1943-5479.0000438Shiau, T.-A., & Chuen-Yu, J.-K. (2016). Developing an Indicator System for Measuring the Social Sustainability of Offshore Wind Power Farms. Sustainability, 8(5), 470. doi:10.3390/su8050470Purcell, P., Bruen, M., O’Sullivan, J., Cocchiglia, L., & Kelly-Quinn, M. (2011). Water quality monitoring during the construction of the M3 motorway in Ireland. Water and Environment Journal, 26(2), 175-183. doi:10.1111/j.1747-6593.2011.00274.xXiahou, X., Tang, Y., Yuan, J., Chang, T., Liu, P., & Li, Q. (2018). Evaluating Social Performance of Construction Projects: An Empirical Study. Sustainability, 10(7), 2329. doi:10.3390/su10072329Eurostat Eurostat Databasehttps://ec.europa.eu/eurostat/data/databaseOECD Datahttps://data.oecd.org/The sustainable development goals (SDGs) indexhttp://sdgindex.org/International Labour Organization Statistics and Datablaseshttps://www.ilo.org/global/statistics-and-databases/lang--en/index.htmUnited Nations System. Chief Executives Board for Coordination. Statisticshttps://www.unsceb.org/content/statisticsPuig, M., Wooldridge, C., & Darbra, R. M. (2014). Identification and selection of Environmental Performance Indicators for sustainable port development. Marine Pollution Bulletin, 81(1), 124-130. doi:10.1016/j.marpolbul.2014.02.006World Bank World Bank Open Datahttps://data.worldbank.org/Dos Santos, S. F., & Brandi, H. S. (2015). Model framework to construct a single aggregate sustainability indicator: an application to the biodiesel supply chain. Clean Technologies and Environmental Policy, 17(7), 1963-1973. doi:10.1007/s10098-015-0919-8Młodak, A. (2013). On the construction of an aggregated measure of the development of interval data. Computational Statistics, 29(5), 895-929. doi:10.1007/s00180-013-0469-7Kazançoğlu, Y., Özbiltekin, M., & Özkan-Özen, Y. D. (2019). Sustainability benchmarking for logistics center location decision. Management of Environmental Quality: An International Journal, 31(5), 1239-1260. doi:10.1108/meq-08-2019-0177Pohekar, S. D., & Ramachandran, M. (2004). Application of multi-criteria decision making to sustainable energy planning—A review. Renewable and Sustainable Energy Reviews, 8(4), 365-381. doi:10.1016/j.rser.2003.12.007Neofytou, H., Nikas, A., & Doukas, H. (2020). Sustainable energy transition readiness: A multicriteria assessment index. Renewable and Sustainable Energy Reviews, 131, 109988. doi:10.1016/j.rser.2020.109988Behzadian, M., Kazemzadeh, R. B., Albadvi, A., & Aghdasi, M. (2010). PROMETHEE: A comprehensive literature review on methodologies and applications. European Journal of Operational Research, 200(1), 198-215. doi:10.1016/j.ejor.2009.01.021Rehman, A. U., Abidi, M. H., Umer, U., & Usmani, Y. S. (2019). Multi-Criteria Decision-Making Approach for Selecting Wind Energy Power Plant Locations. Sustainability, 11(21), 6112. doi:10.3390/su11216112Laxe, F. G., Bermúdez, F. M., Palmero, F. M., & Novo-Corti, I. (2016). Sustainability and the Spanish port system. Analysis of the relationship between economic and environmental indicators. Marine Pollution Bulletin, 113(1-2), 232-239. doi:10.1016/j.marpolbul.2016.09.022García-Segura, T., Montalbán-Domingo, L., Sanz, M. A., & Lozano-Torró, A. (2020). Sustainable Decision-Making Module: Application to Public Procurement. Journal of Civil Engineering Education, 146(3), 04020004. doi:10.1061/(asce)ei.2643-9115.0000014Phillis, A., Grigoroudis, E., & Kouikoglou, V. S. (2020). Assessing national energy sustainability using multiple criteria decision analysis. International Journal of Sustainable Development & World Ecology, 28(1), 18-35. doi:10.1080/13504509.2020.1780646Oswald, D., Sherratt, F., & Smith, S. (2018). Problems with safety observation reporting: A construction industry case study. Safety Science, 107, 35-45. doi:10.1016/j.ssci.2018.04.00