Diseño óptimo de tableros isostáticos de vigas artesas prefabricadas pretensadas

Los tableros de vigas prefabricadas de hormigón pretensado se utilizan habitualmente en todo el mundo para resolver estructuras de viaductos y de pasos superiores sobre carreteras. Son escasos los trabajos de inestigación encaminados a la optimización económica de estas estructuras, donde la mayoría se han centrado en la reducción de las fuerzas de pretensado y han padecido de un signo considerablemente teórico, sin mejorar el aprovechamiento de los recursos que requieren, lo que ha dificultado su aplicación sobre los proyectos de ejecución. En la búsqueda bibliográfica, no se ha encontrado trabajo alguno enfocado a la optimización heurística económica de este tipo de estructuras. Ante el hueco existente en el espectro de la investigación, el enfoque de este trabajo está basado en la aplicación de distintas técnicas de optimización económica a esta tipología. Se han elegido técnicas metaheurísticas para poder plantear el problema lo más completo posible, donde se define todo el tablero y sus armados, y se realizan las comprobaciones que marca la normativa española. Se han analizado diferentes proyectos con esta tipología estructural, donde se han estudiado diseños de vigas artesa de distintos fabricantes, elegiendo una de ellas como modelo para realizar el presente estudio, y dejando la opción para adaptar la tipología a cualquiera de los otros diseños que se fabrican. A continuación se ha desarrollado un programa informatico en lenguage FORTRAN que incluye diferentes módulos: generación de la estructura, comprobación estructural y evaluación económica. El estudio se realiza sobre un tablero de 12 metros de ancho, con 11 de calzada, luz entre apoyos de 35 metros, y separación entre vigas de 6 metros. El estudio de los métodos heurísticos sobre el tablero, se realiza mediante la comparación de ocho distintos tipos de algoritmos: la estrategia de saltos múltiples aleatorios (RW), tres métodos de búsqueda local y cuatro de búsqueda poblacional.Martí Albiñana, JV. (2010). Diseño óptimo de tableros isostáticos de vigas artesas prefabricadas pretensadas [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8640Palanci

A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem

[EN] The counterfort retaining wall is one of the most frequent structures used in civil engineering. In this structure, optimization of cost and CO2 emissions are important. The first is relevant in the competitiveness and efficiency of the company, the second in environmental impact. From the point of view of computational complexity, the problem is challenging due to the large number of possible combinations in the solution space. In this article, a k-means cuckoo search hybrid algorithm is proposed where the cuckoo search metaheuristic is used as an optimization mechanism in continuous spaces and the unsupervised k-means learning technique to discretize the solutions. A random operator is designed to determine the contribution of the k-means operator in the optimization process. The best values, the averages, and the interquartile ranges of the obtained distributions are compared. The hybrid algorithm was later compared to a version of harmony search that also solved the problem. The results show that the k-mean operator contributes significantly to the quality of the solutions and that our algorithm is highly competitive, surpassing the results obtained by harmony search.The first author was supported by the Grant CONICYT/FONDECYT/INICIACION/11180056, the other two authors were supported by the Spanish Ministry of Economy and Competitiveness, along with FEDER funding (Project: BIA2017-85098-R).García, J.; Yepes, V.; Martí Albiñana, JV. (2020). A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem. Mathematics. 8(4):1-22. https://doi.org/10.3390/math8040555S12284García, J., Altimiras, F., Peña, A., Astorga, G., & Peredo, O. (2018). A Binary Cuckoo Search Big Data Algorithm Applied to Large-Scale Crew Scheduling Problems. Complexity, 2018, 1-15. doi:10.1155/2018/8395193García, J., Moraga, P., Valenzuela, M., Crawford, B., Soto, R., Pinto, H., … Astorga, G. (2019). A Db-Scan Binarization Algorithm Applied to Matrix Covering Problems. Computational Intelligence and Neuroscience, 2019, 1-16. doi:10.1155/2019/3238574Al-Madi, N., Faris, H., & Mirjalili, S. (2019). Binary multi-verse optimization algorithm for global optimization and discrete problems. International Journal of Machine Learning and Cybernetics, 10(12), 3445-3465. doi:10.1007/s13042-019-00931-8Kim, M., & Chae, J. (2019). Monarch Butterfly Optimization for Facility Layout Design Based on a Single Loop Material Handling Path. Mathematics, 7(2), 154. doi:10.3390/math7020154García, J., Crawford, B., Soto, R., & Astorga, G. (2019). A clustering algorithm applied to the binarization of Swarm intelligence continuous metaheuristics. Swarm and Evolutionary Computation, 44, 646-664. doi:10.1016/j.swevo.2018.08.006García, J., Lalla-Ruiz, E., Voß, S., & Droguett, E. L. (2020). Enhancing a machine learning binarization framework by perturbation operators: analysis on the multidimensional knapsack problem. International Journal of Machine Learning and Cybernetics, 11(9), 1951-1970. doi:10.1007/s13042-020-01085-8García, J., Moraga, P., Valenzuela, M., & Pinto, H. (2020). A db-Scan Hybrid Algorithm: An Application to the Multidimensional Knapsack Problem. Mathematics, 8(4), 507. doi:10.3390/math8040507Saeheaw, T., & Charoenchai, N. (2018). A comparative study among different parallel hybrid artificial intelligent approaches to solve the capacitated vehicle routing problem. International Journal of Bio-Inspired Computation, 11(3), 171. doi:10.1504/ijbic.2018.091704Valdez, F., Castillo, O., Jain, A., & Jana, D. K. (2019). Nature-Inspired Optimization Algorithms for Neuro-Fuzzy Models in Real-World Control and Robotics Applications. Computational Intelligence and Neuroscience, 2019, 1-2. doi:10.1155/2019/9128451Penadés-Plà, V., García-Segura, T., & Yepes, V. (2020). Robust Design Optimization for Low-Cost Concrete Box-Girder Bridge. Mathematics, 8(3), 398. doi:10.3390/math8030398García-Segura, T., Yepes, V., Frangopol, D. M., & Yang, D. Y. (2017). Lifetime reliability-based optimization of post-tensioned box-girder bridges. Engineering Structures, 145, 381-391. doi:10.1016/j.engstruct.2017.05.013Yepes, V., Martí, J. V., & García, J. (2020). Black Hole Algorithm for Sustainable Design of Counterfort Retaining Walls. Sustainability, 12(7), 2767. doi:10.3390/su12072767Marti-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.2013.12.2.187Fu, W., Tan, J., Zhang, X., Chen, T., & Wang, K. (2019). Blind Parameter Identification of MAR Model and Mutation Hybrid GWO-SCA Optimized SVM for Fault Diagnosis of Rotating Machinery. Complexity, 2019, 1-17. doi:10.1155/2019/3264969Sierra, 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.2017.12.140Crawford, B., Soto, R., Astorga, G., García, J., Castro, C., & Paredes, F. (2017). Putting Continuous Metaheuristics to Work in Binary Search Spaces. Complexity, 2017, 1-19. doi:10.1155/2017/8404231Hatamlou, A. (2013). Black hole: A new heuristic optimization approach for data clustering. Information Sciences, 222, 175-184. doi:10.1016/j.ins.2012.08.023Pan, W.-T. (2012). A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example. Knowledge-Based Systems, 26, 69-74. doi:10.1016/j.knosys.2011.07.001Rashedi, E., Nezamabadi-pour, H., & Saryazdi, S. (2009). GSA: A Gravitational Search Algorithm. Information Sciences, 179(13), 2232-2248. doi:10.1016/j.ins.2009.03.004Calvet, L., Armas, J. de, Masip, D., & Juan, A. A. (2017). Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs. Open Mathematics, 15(1), 261-280. doi:10.1515/math-2017-0029Talbi, E.-G. (2015). Combining metaheuristics with mathematical programming, constraint programming and machine learning. Annals of Operations Research, 240(1), 171-215. doi:10.1007/s10479-015-2034-yJuan, A. A., Faulin, J., Grasman, S. E., Rabe, M., & Figueira, G. (2015). A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems. Operations Research Perspectives, 2, 62-72. doi:10.1016/j.orp.2015.03.001Chou, J.-S., & Nguyen, T.-K. (2018). Forward Forecast of Stock Price Using Sliding-Window Metaheuristic-Optimized Machine-Learning Regression. IEEE Transactions on Industrial Informatics, 14(7), 3132-3142. doi:10.1109/tii.2018.2794389Sayed, G. I., Tharwat, A., & Hassanien, A. E. (2018). Chaotic dragonfly algorithm: an improved metaheuristic algorithm for feature selection. Applied Intelligence, 49(1), 188-205. doi:10.1007/s10489-018-1261-8De León, A. D., Lalla-Ruiz, E., Melián-Batista, B., & Marcos Moreno-Vega, J. (2017). A Machine Learning-based system for berth scheduling at bulk terminals. Expert Systems with Applications, 87, 170-182. doi:10.1016/j.eswa.2017.06.010García, J., Crawford, B., Soto, R., Castro, C., & Paredes, F. (2017). A k-means binarization framework applied to multidimensional knapsack problem. Applied Intelligence, 48(2), 357-380. doi:10.1007/s10489-017-0972-6Molina-Moreno, F., Martí, J. V., & Yepes, V. (2017). Carbon embodied optimization for buttressed earth-retaining walls: Implications for low-carbon conceptual designs. Journal of Cleaner Production, 164, 872-884. doi:10.1016/j.jclepro.2017.06.246Asta, S., Özcan, E., & Curtois, T. (2016). A tensor based hyper-heuristic for nurse rostering. Knowledge-Based Systems, 98, 185-199. doi:10.1016/j.knosys.2016.01.031Martin, S., Ouelhadj, D., Beullens, P., Ozcan, E., Juan, A. A., & Burke, E. K. (2016). A multi-agent based cooperative approach to scheduling and routing. European Journal of Operational Research, 254(1), 169-178. doi:10.1016/j.ejor.2016.02.045Ghazali, R., Deris, M. M., Nawi, N. M., & Abawajy, J. H. (Eds.). (2018). Recent Advances on Soft Computing and Data Mining. Advances in Intelligent Systems and Computing. doi:10.1007/978-3-319-72550-5Veček, N., Mernik, M., Filipič, B., & Črepinšek, M. (2016). Parameter tuning with Chess Rating System (CRS-Tuning) for meta-heuristic algorithms. Information Sciences, 372, 446-469. doi:10.1016/j.ins.2016.08.066Ries, J., & Beullens, P. (2015). A semi-automated design of instance-based fuzzy parameter tuning for metaheuristics based on decision tree induction. Journal of the Operational Research Society, 66(5), 782-793. doi:10.1057/jors.2014.46Yalcinoz, T., & Altun, H. (2001). Power economic dispatch using a hybrid genetic algorithm. IEEE Power Engineering Review, 21(3), 59-60. doi:10.1109/39.911360Kaur, H., Virmani, J., Kriti, & Thakur, S. (2019). A genetic algorithm-based metaheuristic approach to customize a computer-aided classification system for enhanced screen film mammograms. U-Healthcare Monitoring Systems, 217-259. doi:10.1016/b978-0-12-815370-3.00010-4Faris, H., Hassonah, M. A., Al-Zoubi, A. M., Mirjalili, S., & Aljarah, I. (2017). A multi-verse optimizer approach for feature selection and optimizing SVM parameters based on a robust system architecture. Neural Computing and Applications, 30(8), 2355-2369. doi:10.1007/s00521-016-2818-2Faris, H., Aljarah, I., & Mirjalili, S. (2017). Improved monarch butterfly optimization for unconstrained global search and neural network training. Applied Intelligence, 48(2), 445-464. doi:10.1007/s10489-017-0967-3Chou, J.-S., & Thedja, J. P. P. (2016). Metaheuristic optimization within machine learning-based classification system for early warnings related to geotechnical problems. Automation in Construction, 68, 65-80. doi:10.1016/j.autcon.2016.03.015Pham, A.-D., Hoang, N.-D., & Nguyen, Q.-T. (2016). Predicting Compressive Strength of High-Performance Concrete Using Metaheuristic-Optimized Least Squares Support Vector Regression. Journal of Computing in Civil Engineering, 30(3), 06015002. doi:10.1061/(asce)cp.1943-5487.0000506Göçken, M., Özçalıcı, M., Boru, A., & Dosdoğru, A. T. (2016). Integrating metaheuristics and Artificial Neural Networks for improved stock price prediction. Expert Systems with Applications, 44, 320-331. doi:10.1016/j.eswa.2015.09.029Chou, J.-S., & Pham, A.-D. (2017). Nature-inspired metaheuristic optimization in least squares support vector regression for obtaining bridge scour information. Information Sciences, 399, 64-80. doi:10.1016/j.ins.2017.02.051Kuo, R. J., Lin, T. C., Zulvia, F. E., & Tsai, C. Y. (2018). A hybrid metaheuristic and kernel intuitionistic fuzzy c-means algorithm for cluster analysis. Applied Soft Computing, 67, 299-308. doi:10.1016/j.asoc.2018.02.039Singh Mann, P., & Singh, S. (2017). Energy efficient clustering protocol based on improved metaheuristic in wireless sensor networks. Journal of Network and Computer Applications, 83, 40-52. doi:10.1016/j.jnca.2017.01.031Rosa, R. de A., Machado, A. M., Ribeiro, G. M., & Mauri, G. R. (2016). A mathematical model and a Clustering Search metaheuristic for planning the helicopter transportation of employees to the production platforms of oil and gas. Computers & Industrial Engineering, 101, 303-312. doi:10.1016/j.cie.2016.09.006Faris, H., Mirjalili, S., & Aljarah, I. (2019). Automatic selection of hidden neurons and weights in neural networks using grey wolf optimizer based on a hybrid encoding scheme. International Journal of Machine Learning and Cybernetics, 10(10), 2901-2920. doi:10.1007/s13042-018-00913-2De Rosa, G. H., Papa, J. P., & Yang, X.-S. (2017). Handling dropout probability estimation in convolution neural networks using meta-heuristics. Soft Computing, 22(18), 6147-6156. doi:10.1007/s00500-017-2678-4Rere, L. M. R., Fanany, M. I., & Arymurthy, A. M. (2016). Metaheuristic Algorithms for Convolution Neural Network. Computational Intelligence and Neuroscience, 2016, 1-13. doi:10.1155/2016/1537325Jothi, R., Mohanty, S. K., & Ojha, A. (2017). DK-means: a deterministic K-means clustering algorithm for gene expression analysis. Pattern Analysis and Applications, 22(2), 649-667. doi:10.1007/s10044-017-0673-0García, J., Pope, C., & Altimiras, F. (2017). A Distributed K-Means Segmentation Algorithm Applied to Lobesia botrana Recognition. Complexity, 2017, 1-14. doi:10.1155/2017/5137317Arunkumar, N., Mohammed, M. A., Abd Ghani, M. K., Ibrahim, D. A., Abdulhay, E., Ramirez-Gonzalez, G., & de Albuquerque, V. H. C. (2018). K-Means clustering and neural network for object detecting and identifying abnormality of brain tumor. Soft Computing, 23(19), 9083-9096. doi:10.1007/s00500-018-3618-7Abdel-Basset, M., Wang, G.-G., Sangaiah, A. K., & Rushdy, E. (2017). Krill herd algorithm based on cuckoo search for solving engineering optimization problems. Multimedia Tools and Applications, 78(4), 3861-3884. doi:10.1007/s11042-017-4803-xChi, R., Su, Y., Zhang, D., Chi, X., & Zhang, H. (2017). A hybridization of cuckoo search and particle swarm optimization for solving optimization problems. Neural Computing and Applications, 31(S1), 653-670. doi:10.1007/s00521-017-3012-xLi, J., Xiao, D., Lei, H., Zhang, T., & Tian, T. (2020). Using Cuckoo Search Algorithm with Q-Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location. Mathematics, 8(2), 149. doi:10.3390/math8020149Pan, J.-S., Song, P.-C., Chu, S.-C., & Peng, Y.-J. (2020). Improved Compact Cuckoo Search Algorithm Applied to Location of Drone Logistics Hub. Mathematics, 8(3), 333. doi:10.3390/math8030333Yepes, V., Alcala, J., Perea, C., & González-Vidosa, F. (2008). A parametric study of optimum earth-retaining walls by simulated annealing. Engineering Structures, 30(3), 821-830. doi:10.1016/j.engstruct.2007.05.023Molina-Moreno, F., García-Segura, T., Martí, J. V., & Yepes, V. (2017). Optimization of buttressed earth-retaining walls using hybrid harmony search algorithms. Engineering Structures, 134, 205-216. doi:10.1016/j.engstruct.2016.12.04

Black Hole Algorithm for Sustainable Design of Counterfort Retaining Walls

[EN] The optimization of the cost and CO 2 emissions in earth-retaining walls is of relevance, since these structures are often used in civil engineering. The optimization of costs is essential for the competitiveness of the construction company, and the optimization of emissions is relevant in the environmental impact of construction. To address the optimization, black hole metaheuristics were used, along with a discretization mechanism based on min¿max normalization. The stability of the algorithm was evaluated with respect to the solutions obtained; the steel and concrete values obtained in both optimizations were analyzed. Additionally, the geometric variables of the structure were compared. Finally, the results obtained were compared with another algorithm that solved the problem. The results show that there is a trade-off between the use of steel and concrete. The solutions that minimize CO 2 emissions prefer the use of concrete instead of those that optimize the cost. On the other hand, when comparing the geometric variables, it is seen that most remain similar in both optimizations except for the distance between buttresses. When comparing with another algorithm, the results show a good performance in optimization using the black hole algorithm.The authors acknowledge the financial support of the financial support of the Spanish Ministry of Economy and Competitiveness, along with FEDER funding (Project: BIA2017-85098-R) to the first and second authors, and the Grant CONICYT/FONDECYT/INICIACION/11180056 to the third author.Yepes, V.; Martí Albiñana, JV.; García, J. (2020). Black Hole Algorithm for Sustainable Design of Counterfort Retaining Walls. Sustainability. 12(7):1-18. https://doi.org/10.3390/su12072767S118127Frangopol, D. M. (2011). Life-cycle performance, management, and optimisation of structural systems under uncertainty: accomplishments and challenges1. Structure and Infrastructure Engineering, 7(6), 389-413. doi:10.1080/15732471003594427Ramesh, T., Prakash, R., & Shukla, K. K. (2010). Life cycle energy analysis of buildings: An overview. Energy and Buildings, 42(10), 1592-1600. doi:10.1016/j.enbuild.2010.05.007Boesch, M. E., & Hellweg, S. (2010). Identifying Improvement Potentials in Cement Production with Life Cycle Assessment. Environmental Science & Technology, 44(23), 9143-9149. doi:10.1021/es100771kSerpell, A., Kort, J., & Vera, S. (2013). AWARENESS, ACTIONS, DRIVERS AND BARRIERS OF SUSTAINABLE CONSTRUCTION IN CHILE. Technological and Economic Development of Economy, 19(2), 272-288. doi:10.3846/20294913.2013.798597Yusof, N., Zainul Abidin, N., Zailani, S. H. M., Govindan, K., & Iranmanesh, M. (2016). Linking the environmental practice of construction firms and the environmental behaviour of practitioners in construction projects. Journal of Cleaner Production, 121, 64-71. doi:10.1016/j.jclepro.2016.01.090Wang, T., Lee, I.-S., Kendall, A., Harvey, J., Lee, E.-B., & Kim, C. (2012). Life cycle energy consumption and GHG emission from pavement rehabilitation with different rolling resistance. Journal of Cleaner Production, 33, 86-96. doi:10.1016/j.jclepro.2012.05.001Wang, E., & Shen, Z. (2013). A hybrid Data Quality Indicator and statistical method for improving uncertainty analysis in LCA of complex system – application to the whole-building embodied energy analysis. Journal of Cleaner Production, 43, 166-173. doi:10.1016/j.jclepro.2012.12.010Barandica, J. M., Fernández-Sánchez, G., Berzosa, Á., Delgado, J. A., & Acosta, F. J. (2013). Applying life cycle thinking to reduce greenhouse gas emissions from road projects. Journal of Cleaner Production, 57, 79-91. doi:10.1016/j.jclepro.2013.05.036Aguado, A., Caño, A. del, de la Cruz, M. P., Gómez, D., & Josa, A. (2012). Sustainability Assessment of Concrete Structures within the Spanish Structural Concrete Code. Journal of Construction Engineering and Management, 138(2), 268-276. doi:10.1061/(asce)co.1943-7862.0000419Molina-Moreno, F., García-Segura, T., Martí, J. V., & Yepes, V. (2017). Optimization of buttressed earth-retaining walls using hybrid harmony search algorithms. Engineering Structures, 134, 205-216. doi:10.1016/j.engstruct.2016.12.042Yepes, V., Martí, J. V., & García-Segura, T. (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. doi:10.1016/j.autcon.2014.10.013Worrell, E., Price, L., Martin, N., Hendriks, C., & Meida, L. O. (2001). CARBON DIOXIDE EMISSIONS FROM THE GLOBAL CEMENT INDUSTRY. Annual Review of Energy and the Environment, 26(1), 303-329. doi:10.1146/annurev.energy.26.1.303Molina-Moreno, F., Martí, J. V., & Yepes, V. (2017). Carbon embodied optimization for buttressed earth-retaining walls: Implications for low-carbon conceptual designs. Journal of Cleaner Production, 164, 872-884. doi:10.1016/j.jclepro.2017.06.246Yepes, V., Gonzalez-Vidosa, F., Alcala, J., & Villalba, P. (2012). CO2-Optimization Design of Reinforced Concrete Retaining Walls Based on a VNS-Threshold Acceptance Strategy. Journal of Computing in Civil Engineering, 26(3), 378-386. doi:10.1061/(asce)cp.1943-5487.0000140Yoon, Y.-C., Kim, K.-H., Lee, S.-H., & Yeo, D. (2018). Sustainable design for reinforced concrete columns through embodied energy and CO2 emission optimization. Energy and Buildings, 174, 44-53. doi:10.1016/j.enbuild.2018.06.013Sierra, L. A., Pellicer, E., & Yepes, V. (2016). Social Sustainability in the Lifecycle of Chilean Public Infrastructure. Journal of Construction Engineering and Management, 142(5), 05015020. doi:10.1061/(asce)co.1943-7862.0001099Sierra, 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.2017.12.140Moayyeri, 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/math7121232Pons, J. J., Penadés-Plà, V., Yepes, V., & Martí, J. V. (2018). Life cycle assessment of earth-retaining walls: An environmental comparison. Journal of Cleaner Production, 192, 411-420. doi:10.1016/j.jclepro.2018.04.268Lu, K., Jiang, X., Tam, V. W. Y., Li, M., Wang, H., Xia, B., & Chen, Q. (2019). Development of a Carbon Emissions Analysis Framework Using Building Information Modeling and Life Cycle Assessment for the Construction of Hospital Projects. Sustainability, 11(22), 6274. doi:10.3390/su11226274De Medeiros, G. F., & Kripka, M. (2014). Optimization of reinforced concrete columns according to different environmental impact assessment parameters. Engineering Structures, 59, 185-194. doi:10.1016/j.engstruct.2013.10.045Hussain, K., Mohd Salleh, M. N., Cheng, S., & Shi, Y. (2018). Metaheuristic research: a comprehensive survey. Artificial Intelligence Review, 52(4), 2191-2233. doi:10.1007/s10462-017-9605-zZong Woo Geem, Joong Hoon Kim, & Loganathan, G. V. (2001). A New Heuristic Optimization Algorithm: Harmony Search. SIMULATION, 76(2), 60-68. doi:10.1177/003754970107600201Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671-680. doi:10.1126/science.220.4598.671Černý, V. (1985). Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm. Journal of Optimization Theory and Applications, 45(1), 41-51. doi:10.1007/bf00940812Carbonell, 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.2011.01.002Zavadskas, E., Antucheviciene, J., Vilutiene, T., & Adeli, H. (2017). Sustainable Decision-Making in Civil Engineering, Construction and Building Technology. Sustainability, 10(2), 14. doi:10.3390/su10010014Shi, X., Wu, L., & Meng, X. (2017). A New Optimization Model for the Sustainable Development: Quadratic Knapsack Problem with Conflict Graphs. Sustainability, 9(2), 236. doi:10.3390/su9020236Sierra, 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.2018.03.022García, J., Moraga, P., Valenzuela, M., Crawford, B., Soto, R., Pinto, H., … Astorga, G. (2019). A Db-Scan Binarization Algorithm Applied to Matrix Covering Problems. Computational Intelligence and Neuroscience, 2019, 1-16. doi:10.1155/2019/3238574García, J., Crawford, B., Soto, R., & Astorga, G. (2019). A clustering algorithm applied to the binarization of Swarm intelligence continuous metaheuristics. Swarm and Evolutionary Computation, 44, 646-664. doi:10.1016/j.swevo.2018.08.006Yepes, V., Alcala, J., Perea, C., & González-Vidosa, F. (2008). A parametric study of optimum earth-retaining walls by simulated annealing. Engineering Structures, 30(3), 821-830. doi:10.1016/j.engstruct.2007.05.023García-Segura, T., Yepes, V., & Alcalá, J. (2013). Life cycle greenhouse gas emissions of blended cement concrete including carbonation and durability. The International Journal of Life Cycle Assessment, 19(1), 3-12. doi:10.1007/s11367-013-0614-0Hatamlou, A. (2013). Black hole: A new heuristic optimization approach for data clustering. Information Sciences, 222, 175-184. doi:10.1016/j.ins.2012.08.023García-Segura, T., Yepes, V., Alcalá, J., & Pérez-López, E. (2015). Hybrid harmony search for sustainable design of post-tensioned concrete box-girder pedestrian bridges. Engineering Structures, 92, 112-122. doi:10.1016/j.engstruct.2015.03.015García, J., Lalla-Ruiz, E., Voß, S., & Droguett, E. L. (2020). Enhancing a machine learning binarization framework by perturbation operators: analysis on the multidimensional knapsack problem. International Journal of Machine Learning and Cybernetics, 11(9), 1951-1970. doi:10.1007/s13042-020-01085-8García, J., Crawford, B., Soto, R., Castro, C., & Paredes, F. (2017). A k-means binarization framework applied to multidimensional knapsack problem. Applied Intelligence, 48(2), 357-380. doi:10.1007/s10489-017-0972-

The buttressed walls problem: An application of a hybrid clustering particle swarm optimization algorithm

[EN] The design of reinforced earth retaining walls is a combinatorial optimization problem of interest due to practical applications regarding the cost savings involved in the design and the optimization in the amount of CO2 emissions generated in its construction. On the other hand, this problem presents important challenges in computational complexity since it involves 32 design variables; therefore we have in the order of 10^20 possible combinations. In this article, we propose a hybrid algorithm in which the particle swarm optimization method is integrated that solves optimization problems in continuous spaces with the db-scan clustering technique, with the aim of addressing the combinatorial problem of the design of reinforced earth retaining walls. This algorithm optimizes two objective functions: the carbon emissions embedded and the economic cost of reinforced concrete walls. To assess the contribution of the db-scan operator in the optimization process, a random operator was designed. The best solutions, the averages, and the interquartile ranges of the obtained distributions are compared. The db-scan algorithm was then compared with a hybrid version that uses k-means as the discretization method and with a discrete implementation of the harmony search algorithm. The results indicate that the db-scan operator significantly improves the quality of the solutions and that the proposed metaheuristic shows competitive results with respect to the harmony search algorithm.The first author was supported by the Grant CONICYT/FONDECYT/INICIACION/11180056, the other two authors were supported by the Spanish Ministry of Economy and Competitiveness, along with FEDER funding (Project: BIA2017-85098-R).Garcia, J.; Martí Albiñana, JV.; Yepes, V. (2020). The buttressed walls problem: An application of a hybrid clustering particle swarm optimization algorithm. Mathematics. 8(6):862-01-862-22. https://doi.org/10.3390/math8060862S862-01862-228

Reliability-based maintenance optimization of corrosion preventive designs under a life cycle perspective

[EN] Sustainability is of paramount importance when facing the design of long lasting, maintenance demanding structures. In particular, a sustainable life cycle design for concrete structure exposed to aggressive environments may lead to significant economic savings, and to reduced environmental consequences. The present study evaluates 18 different design alternatives for an existing concrete bridge deck exposed to chlorides, analyzing the economic and environmental impacts associated with each design as a function of the maintenance interval chosen. Results are illustrated in the context of a reliability-based maintenance optimization on both life cycle costs and life cycle environmental impacts. Maintenance optimization results in significant reductions of life cycle impacts if compared to the damage resulting from performing the maintenance actions when the end of the service life of the structure is reached. The use of concrete with 10% silica fume has been shown to be the most effective prevention strategy against corrosion of reinforcement steel in economic terms, reducing the life cycle costs of the original deck design by 76%. From an environmental perspective, maintenance based on the hydrophobic treatment of the concrete deck surface results in the best performance, allowing for a reduction of the impacts associated with the original design by 82.8%.The authors acknowledge the financial support of the Spanish Ministry of Economy and Competitiveness, along with FEDER funding (Project: BIA2017-85098-R).Navarro, I.; Martí Albiñana, JV.; Yepes, V. (2019). Reliability-based maintenance optimization of corrosion preventive designs under a life cycle perspective. Environmental Impact Assessment Review. 74:23-34. https://doi.org/10.1016/j.eiar.2018.10.001S23347

Steel-Concrete Composite Bridges: Design, Life Cycle Assessment, Maintenance, and Decision-Making

[EN] Steel-concrete composite bridges are used as an alternative to concrete bridges because of their ability to adapt their geometry to design constraints and the possibility of reusing some of the materials in the structure. In this review, we report the research carried out on the design, behavior, optimization, construction processes, maintenance, impact assessment, and decision-making techniques of composite bridges in order to arrive at a complete design approach. In addition to a qualitative analysis, a multivariate analysis is used to identify knowledge gaps related to bridge design and to detect trends in research. An additional objective is to make visible the gaps in the sustainable design of composite steel-concrete bridges, which allows us to focus on future research studies. *eresults of this work show how researchers have concentrated their studies on the preliminary design of bridges with a mainly economic approach, while at a global level, concern is directed towards the search for sustainable solutions. It is found that life cycle impact assessment and decision-making strategies allow bridge managers to improve decision-making, particularly at the end of the life cycle of composite bridges.This study was funded by the Spanish Ministry of Economy and Competitiveness, along with FEDER funding (DIMALIFE Project BIA2017-85098-R).Martínez-Muñoz, D.; Martí Albiñana, JV.; Yepes, V. (2020). Steel-Concrete Composite Bridges: Design, Life Cycle Assessment, Maintenance, and Decision-Making. Advances in Civil Engineering. 2020:1-13. https://doi.org/10.1155/2020/8823370S113202

Remote teaching in construction engineering management during COVID-19

[EN] This paper describes the impact of the change from face-to-face classes to non-face-to-face classes with students of a postgraduate course at the Universitat Politècnica de València. This study is carried out in the subjects of installation, organization and quality assurance in construction and construction procedures of both degree in public works engineering and civil engineering. This course develops the student's skills to integrate into the studies department of a construction company, as Site Manager or Production Director, from a journey through the different phases of the project-construction process. As part of this topic, the methods of scheduling activities on site are discussed. In the traditional face to-face method, several problems are solved, requiring that students have previously learned programming techniques: arrow networks, precedence networks, and how to apply the PERT method to statistically obtain the probability of completion of a building or the completion of activities related. Due to the current situation of the pandemic caused by COVID-19, face-to-face teaching has changed virtual classes in a very short time. This has required a radical shift towards distance education. This paper explains how this change has been made, what new methods have been used to teach the contents corresponding to the scheduling of assignments, and what the students' perception has been. The quality of the education received and the difficulties encountered in obtaining the knowledge and skills attributed to this subject are analyzed.The authors acknowledge the support for the Ministry of Economy and Company and FEDER funding (Project BIA2017-85098-R).Martínez-Muñoz, D.; Martí Albiñana, JV.; Yepes, V. (2021). Remote teaching in construction engineering management during COVID-19. IATED Academy. 879-887. https://doi.org/10.21125/inted.2021.0205S87988

Group analytic network process for the sustainability assessment of bridges near shore

[EN] Since the Paris Agreement was established, great interest has arisen in evaluating the sustainability performance of our structures along with their life cycles. The remarkable economic expenses, the important environmental impacts associated with the construction sector, and the great social benefits that might be derived from a well-designed infrastructure system have put the design of essential infrastructures in the spotlight of many researchers. One of today¿s main challenges is the derivation of adequate sustainability indicators that aid designers when deciding on the most sustainable design alternative. The sustainability performance of infrastructures is based on various indicators that are often conflicting given their different nature. Consequently, the obtention of such indicators usually needs to be addressed using multi-criteria decision-making methods. The present communication shows the analytic hierarchy process (ANP) for the sustainability assessment of a concrete bridge exposed to a coastal environment, involving several decision-makers. A set of nine quantitative criteria, covering the economic, environmental, and social dimensions of sustainability, has been considered here.Grant PID2020-117056RB-I00 funded by MCIN/AEI/ 10.13039/501100011033 and by ERDF A way of making Europe.Navarro, IJ.; Martí Albiñana, JV.; Yepes, V. (2022). Group analytic network process for the sustainability assessment of bridges near shore. WIT Transactions on the Built Environment (Online). 209:143-154. https://doi.org/10.2495/HPSU22013114315420

Application of the response surface methodology in a postgraduate optimization course

[EN] This paper describes the introduction of response surface methodology in a postgraduate course. This case study is carried out in the subject of the predictive models of optimization of concrete structures subject. This subject is inside the curricula of concrete engineering master. In this course students learn concepts such as structures¿ optimization using algorithms, multi-criteria decision making, techniques do design of experiments, and metamodels such as the response surface in order to obtain optimum results. In this case study, the objective is to obtain a design solution of a reinforced concrete wall, using the CO2 emissions as an objective function to reduce its impact. In order to apply this methodology, the students need to use specific software. On the one hand, to carry out the statistical analysis that allow obtaining the response surface Minitab software has been used by students. On the other hand, students need to check the strength of the structure using Cype structural calculation software. As a result of applying this methodology to obtain an optimum reinforced concrete wall allow students to reach a better level in transversal competencies, such as design and project, critical thinking, analysis and problem solving or the use of specific software. This paper will introduce future research studies related to the use of structures optimization techniques by students applying other different optimization techniques.The authors acknowledge the support for the Ministry of Economy and Company and FEDER funding (Project BIA2017-85098-R).Yepes, V.; Martínez-Muñoz, D.; Martí Albiñana, JV. (2021). Application of the response surface methodology in a postgraduate optimization course. IATED Academy. 869-878. https://doi.org/10.21125/inted.2021.0204S86987

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