Sesión temática en CMN2021: Optimization, metaheuristics and evolutionary algorithms in civil engineering

En el marco del próximo congreso CMN2021 (Congress on Numerical Methods in Engineering) que se celebrará en Las Palmas de Gran Canaria del 28 al 30 de junio de 2021, hemos organizado una sesión temática coordinada por David Greiner, Diogo Ribeiro y Víctor Yepes que versa sobre optimización, metaheurísticas y algoritmos evolutivos en ingeniería civil. Os dejo a continuación una breve descripción del congreso y un resumen de la sesión temática propuesta.

El objetivo del Congreso de Métodos Numéricos en Ingeniería (CMN) es actuar como un foro en que se recopilen los trabajos científicos y técnicos más relevantes en el área de los métodos numéricos y la mecánica computacional, así como sus aplicaciones prácticas. CMN 2021, organizado conjuntamente por las sociedades de métodos numéricos española (SEMNI), portuguesa (APMTAC) y por el Instituto Universitario de Sistemas Inteligentes y Aplicaciones Numéricas en Ingeniería (SIANI) de la Universidad de Las Palmas de Gran Canaria (ULPGC). Los anteriores congresos conjuntos de ambas sociedades fueron celebrados en Madrid (2002), en Lisboa (2004), en Granada (2005), Porto (2007), Barcelona (2009), Coimbra (2011), Bilbao (2013), Lisboa (2015), Valencia (2017) y Minho (2019). Habiendo sido Las Palmas de Gran Canaria la sede del Primer Congreso CMN organizado por SEMNI en 1990, (General Chairs: Gabriel Winter y Miguel Galante), retorna 31 años después a su primera sede. El programa científico del CMN 2021 estará estructurado en sesiones temáticas según las distintas especialidades de los métodos numéricos. Las comunicaciones presentadas en el congreso constituirán una referencia de los avances recientes y de las líneas de trabajo futuras. Asimismo, investigadores internacionales de reconocido prestigio impartirán una serie de conferencias plenarias. El enlace a la web del congreso es la siguiente: https://congress.cimne.com/cmn2021

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Special Issue “Deep Learning and Hybrid-Metaheuristics: Novel Engineering Applications”

 

 

 

 

 

Mathematics (ISSN 2227-7390) is a peer-reviewed open access journal which provides an advanced forum for studies related to mathematics, and is published monthly online by MDPI. The European Society for Fuzzy Logic and Technology (EUSFLAT) and International Society for the Study of Information (IS4SI) are affiliated with Mathematics and their members receive a discount on article processing charges.

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Special Issue “Deep Learning and Hybrid-Metaheuristics: Novel Engineering Applications”

Deadline for manuscript submissions: 30 April 2021.

Special Issue Editors

Prof. Dr. Víctor Yepes Website SciProfiles
Guest Editor
ICITECH, Universitat Politècnica de València, Valencia, Spain
Interests: multiobjective optimization; structure optimization; lifecycle assessment; social sustainability of infrastructures; reliability-based maintenance optimization; optimization and decision-making under uncertainty
Special Issues and Collections in MDPI journals
Dr. José Antonio García Website
Guest Editor
Pontificia Universidad Católica de Valparaíso, Chile
Interests: optimization; deep learning; operations research; artificial intelligence applications to industrial problems

Special Issue Information

Dear Colleagues,

Hybrid metaheuristic methods have shown very good performances in different combinatorial problems. Additionally, the rise of machine learning techniques has created a space to develop metaheuristic algorithms that use these techniques in order to tackle NP-hard problems and improve the convergence of algorithms. In this Special Issue, we invite researchers to submit papers in this optimization line, applying hybrid algorithms to industrial problems, including but not limited to industrial applications, and challenging problems arising in the fields of big data, construction, sustainability, transportation, and logistics, among others.

Deep learning techniques have also been important tools in extracting features, classifying situations, predicting events, and assisting in decision making. Some of these tools have been applied, for example, to Industry 4.0. Among the main techniques used are feedforward networks (FNN), convolutional networks (CNN), long-term short memory (LSTM), autoencoders (AE), enerative adversarial networks, and deep Q-networks (DQNs). Contributions on practical deep learning applications and cases are invited to this Special Issue, including but not limited to applications to the industry of computational vision, natural language processing, supervised learning applied to industry, unsupervised learning applied to industry, and reinforcement learning, among others.

Prof. Dr. Víctor Yepes
Dr. José Antonio García
Guest Editors

 

Manuscript Submission Information

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Keywords

  • Construction
  • Smart cities
  • Optimization
  • Deep learning

Revisión de los métodos de optimización aplicados al consumo de energía en ferrocarriles

Acaban de publicarnos un artículo en la revista Journal of Cleaner Production, revista de ELSEVIER indexada en el primer decil del JCR. Se trata de un artículo de revisión del estado del arte donde se analizan 52 artículos científicos relacionados con el consumo energético en ferrocarriles. Se analizan dos áreas principales: las técnicas de modelización utilizadas para simular el movimiento de los trenes y el consumo de energía, y los métodos de optimización utilizados para conseguir una circulación ferroviaria más eficiente. Se describen brevemente los métodos más utilizados en cada caso y se analizan las principales tendencias encontradas. Además, se ha realizado un estudio estadístico para reconocer las relaciones entre los métodos y las variables de optimización. Se encontró que los modelos determinísticos basados en la ecuación de Davis son, con diferencia (85% de los trabajos revisados), los más comunes en términos de modelización. En cuanto a la optimización, los métodos meta-heurísticos son la opción preferida (57,8%), en particular los Algoritmos Genéticos. Este artículo forma parte de nuestra línea de investigación BRIDLIFE en la que se pretenden optimizar las infraestructuras atendiendo no sólo a su coste, sino al impacto ambiental y social que generan a lo largo de su ciclo de vida.

El artículo lo podéis descargar GRATUITAMENTE hasta el 3 de mayo de 2019 en el siguiente enlace: https://authors.elsevier.com/a/1YjHX3QCo9Uqa3

Abstract:

Railways are a rather efficient transport mean, and yet there is increasing interest in reducing their energy consumption and making them more sustainable in the current context of climate change. Many studies try to model, analyse and optimise the energy consumed by railways, and there is a wide diversity of methods, techniques and approaches regarding how to formulate and solve this problem. This paper aims to provide insight into this topic by reviewing up to 52 papers related to railways energy consumption. Two main areas are analysed: modelling techniques used to simulate train(s) movement and energy consumption, and optimisation methods used to achieve more efficient train circulations in railway networks. The most used methods in each case are briefly described and the main trends found are analysed. Furthermore, a statistical study has been carried out to recognise relationships between methods and optimisation variables. It was found that deterministic models based on the Davis equation are by far (85% of the papers reviewed) the most common in terms of modelling. As for optimisation, meta-heuristic methods are the preferred choice (57.8%), particularly Genetic Algorithms.

Keywords:

Railways
Energy efficiency
Modelling
Optimisation
Meta-heuristics

Reference:

MARTÍNEZ-FERNÁNDEZ, P.; VILLALBA-SANCHÍS, I.; INSA-FRANCO, R.; YEPES, V. (2019). A review of modelling and optimisation methods applied to railways energy consumption. Journal of Cleaner Production, 222:153-162. DOI:10.1016/j.jclepro.2019.03.037

 

 

Diseño automático de puentes pretensados con algoritmos heurísticos

Acaban de publicarnos un artículo donde se utilizan cuatro algoritmos heurísticos: Descent Local Search, Threshold Accepting Algorithm with Mutation Operation, Genetic Algorithm y Memetic Algorithm para el diseño automático de puentes pretensados.

Se puede descargar gratuitamente este artículo hasta el 10 de junio de 2017 en el siguiente enlace: https://authors.elsevier.com/a/1UwC15s1QSxbmc

Referencia: 

YEPES, 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.2017.02.006

Abstract:

This paper deals with the cost optimization of road bridges consisting of concrete slabs prepared in situ and two precast-prestressed U-shaped beams of self-compacting concrete. It shows the efficiency of four heuristic algorithms applied to a problem of 59 discrete variables. The four algorithms are the Descent Local Search (DLS), a threshold accepting algorithm with mutation operation (TAMO), the Genetic Algorithm (GA), and the Memetic Algorithm (MA). The heuristic optimization algorithms are applied to a bridge with a span length of 35 m and a width of 12 m. A performance analysis is run for the different heuristics, based on a study of Pareto optimal solutions between execution time and efficiency. The best results were obtained with TAMO for a minimum cost of 104184 euros. Among the key findings of the study, the practical use of these heuristics in real cases stands out. Furthermore, the knowledge gained from the investigation of the algorithms allows a range of values for the design optimization of such structures and pre-dimensioning of the variables to be recommended.

Keywords:

Optimization; Metaheuristics; Bridges; Overpasses; Structural design