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

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

Hall-effect and resistivity measurements in CdTe and ZnTe at high pressure: Electronic structure of impurities in the zincblende phase and the semi-metallic or metallic character of the high-pressure phases

We carried out high-pressure resistivity and Hall-effect measurements in single crystals of CdTe and ZnTe up to 12 GPa. Slight changes of transport parameters in the zincblende phase of CdTe are consitent with the shallow character of donor impurities. Drastic changes in all the transport parameters of CdTe were found around 4 GPa, i.e. close to the onset of the cinnabar to rock-salt transition. In particular, the carrier concentration increases by more than five orders of magnitude. Additionally, an abrupt decrease of the resistivity was detected around 10 GPa. These results are discussed in comparison with optical, thermoelectric, and x-ray diffraction experiments. The metallic character of the Cmcm phase of CdTe is confirmed and a semi-metallic character is determined for the rock-salt phase. In zincblende ZnTe, the increase of the hole concentration by more than two orders of magnitude is proposed to be due to a deep-to-shallow transformation of the acceptor levels. Between 9 and 11 GPa, transport parameters are consistent with the semiconducting character of cinnabar ZnTe. A two orders of magnitude decrease of the resistivity and a carrier-type inversion occurs at 11 GPa, in agreement with the onset of the transition to the Cmcm phase of ZnTe. A metallic character for this phase is deduced.Comment: 20 pages, 4 figure

Environmental and social impact assessment of optimized post-tensioned concrete road bridges

[EN] Most of the definitions of sustainability include three basic pillars: economic, environmental, and social. The economic pillar has always been evaluated but not necessarily in the sense of economic sustainability. On the other hand, the environmental pillar is increasingly being considered, while the social pillar is weakly developed. Focusing on the environmental and social pillars, the use of methodologies to allow a wide assessment of these pillars and the integration of the assessment in a few understandable indicators is crucial. This article is structured into two parts. In the first part, a review of life cycle impact assessment methods, which allow a comprehensive assessment of the environmental and social pillars, is carried out. In the second part, a complete environmental and social sustainability assessment is made using the ecoinvent database and ReCiPe method, for the environmental pillar, and SOCA database and simple Social Impact Weighting method, for the social pillar. This methodology was used to compare three optimized bridges: two box-section post-tensioned concrete road bridges with a variety of initial and maintenance characteristics, and a pre-stressed concrete precast bridge. The results show that there is a high interrelation between the environmental and social impact for each life cycle stage.This research was funded by the Ministerio de Economia, Ciencia y Competitividad and FEDER funding grant number [BIA2017-85098-R]Penades-Pla, V.; Martínez-Muñoz, D.; García-Segura, T.; Navarro, IJ.; Yepes, V. (2020). Environmental and social impact assessment of optimized post-tensioned concrete road bridges. Sustainability. 12(10):4265-01-4265-18. https://doi.org/10.3390/su12104265S4265-014265-18121

Life cycle assessment of cost-optimized buttress earth-retaining walls: A parametric study

In this paper life cycle assessments are carried out on 30 optimized earth-retaining walls of various heights (4e13 m) and involving different permissible soil stresses (0.2, 0.3 and 0.4 MPa) in Spain. Firstly, the environmental impacts considered in the assessment method developed by the Leiden University (CML 2001) are analyzed for each case, demonstrating the influence of the wall height and permissible soil stress. Secondly, this paper evaluates the contribution range of each element to each impact. The elements considered are: concrete, landfill, machinery, formwork, steel, and transport. Moreover, the influence of the wall height on the contribution of each element over the total impact is studied. This paper then provides the impact factors per unit of concrete, steel, and formwork. These values enable designers to quickly evaluate impacts from available measurements. Finally, the influence of steel recycling on the environmental impacts is highlighted. Findings indicate that concrete is the biggest contributor to all impact categories, especially the global warming potential. However, the steel doubles its contribution when the wall heights increase from 4 m to 13 m. Results show that recycling rates affect impacts differently.The authors acknowledge the financial support of the Spanish Ministry of Economy and Competitiveness, along with the FEDER funding (BRIDLIFE Project: BIA2014-56574-R) and from the European Institute of Innovation and Technology under grant agreement no 20140262 Low Carbon Strategy in the Construction Industry (PGA_APED0094_2014-2.1-278_P066-10). Additionally, authors acknowledge the contributions of Nadia Ata and Vicent Penades in the overall review process.Zastrow, P.; Molina Moreno, F.; García-Segura, T.; Martí Albiñana, JV.; Yepes, V. (2017). Life cycle assessment of cost-optimized buttress earth-retaining walls: A parametric study. Journal of Cleaner Production. 140(3):1037-1048. https://doi.org/10.1016/j.jclepro.2016.10.085S10371048140

Microwave-driven synthesis of bisphosphonate nanoparticles allows in vivo visualisation of atherosclerotic plaque

A fast and reproducible microwave-driven process has allowed us to synthesise neridronate-functionalised nanoparticles. Contrary to tradition, the phosphate groups decorate the outside layer of the particles providing Ca2+ binding properties in vitro and selective accumulation in vivo in the atheroma plaque. In vivo and ex vivo detection by T2-weighted MRI is demonstrated and validated by histology. The accumulation in the plaque takes place in less than one hour following the intravenous injection, which is particularly suitable for clinical applications