Hacia un mapa de conocimiento algorítmico de optimización de la industria AEC-AI (Arquitectura, Ingeniería, Construcción e Inteligencia Artificial)

Acaban de publicarnos un artículo en la revista IEEE Access, revista de alto impacto indexada en el JCR. En este caso se ha realizado un análisis conceptual macroscópico de la industria AEC-AI (Arquitectura, Ingeniería, Construcción e Inteligencia Artificial). El trabajo se enmarca dentro del proyecto de investigación HYDELIFE que dirijo como investigador principal en la Universitat Politècnica de València.

La industria de la arquitectura, la ingeniería y la construcción (AEC) constituye uno de los sectores productivos más relevantes, por lo que también produce un alto impacto en los equilibrios económicos, la estabilidad de la sociedad y los desafíos globales en el cambio climático. En cuanto a su adopción de tecnologías, aplicaciones y procesos también se reconoce por su status-quo, su lento ritmo de innovación, y los enfoques conservadores. Sin embargo, una nueva era tecnológica -la Industria 4.0 alimentada por la IA- está impulsando los sectores productivos en un panorama sociopolítico y de competencia tecnológica global altamente presionado. En este trabajo, desarrollamos un enfoque adaptativo para la minería de contenido textual en el corpus de investigación de la literatura relacionada con las industrias de la AEC y la IA (AEC-AI), en particular en su relación con los procesos y aplicaciones tecnológicas. Presentamos un enfoque de primera etapa para una evaluación adaptativa de los algoritmos de IA, para formar una plataforma integradora de IA en la industria AEC, la industria AEC-AI 4.0. En esta etapa, se despliega un método adaptativo macroscópico para caracterizar la “Optimización”, un término clave en la industria AEC-AI, utilizando una metodología mixta que incorpora el aprendizaje automático y el proceso de evaluación clásico. Nuestros resultados muestran que el uso eficaz de los metadatos, las consultas de búsqueda restringidas y el conocimiento del dominio permiten obtener una evaluación macroscópica del concepto objetivo. Esto permite la extracción de un mapeo de alto nivel y la caracterización de la estructura conceptual del corpus bibliográfico. Los resultados son comparables, a este nivel, a las metodologías clásicas de revisión de la literatura. Además, nuestro método está diseñado para una evaluación adaptativa que permita incorporar otras etapas.

Abstract:

The Architecture, Engineering, and Construction (AEC) Industry is one of the most important productive sectors, hence also produce a high impact on the economic balances, societal stability, and global challenges in climate change. Regarding its adoption of technologies, applications and processes is also recognized by its status-quo, its slow innovation pace, and the conservative approaches. However, a new technological era – Industry 4.0 fueled by AI- is driving productive sectors in a highly pressurized global technological competition and sociopolitical landscape. In this paper, we develop an adaptive approach to mining text content in the literature research corpus related to the AEC and AI (AEC-AI) industries, in particular on its relation to technological processes and applications. We present a first stage approach to an adaptive assessment of AI algorithms, to form an integrative AI platform in the AEC industry, the AEC-AI industry 4.0. At this stage, a macroscopic adaptive method is deployed to characterize “Optimization,” a key term in AEC-AI industry, using a mixed methodology incorporating machine learning and classical evaluation process. Our results show that effective use of metadata, constrained search queries, and domain knowledge allows getting a macroscopic assessment of the target concept. This allows the extraction of a high-level mapping and conceptual structure characterization of the literature corpus. The results are comparable, at this level, to classical methodologies for the literature review. In addition, our method is designed for an adaptive assessment to incorporate further stages.

Keywords:

Architecture, engineering and construction, AEC, artificial intelligence, literature corpus, machine learning, optimization algorithms, knowledge mapping and structure

Reference:

MAUREIRA, C.; PINTO, H.; YEPES, V.; GARCÍA, J. (2021). Towards an AEC-AI industry optimization algorithmic knowledge mapping. IEEE Access, 9:110842-110879. DOI:10.1109/ACCESS.2021.3102215

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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.

  • Open Access—free for readers, with article processing charges (APC) paid by authors or their institutions.
  • High Visibility: Indexed in the Science Citation Indexed Expanded – SCIE (Web of Science) from Vol. 4 (2016) and Scopus.
  • Rapid Publication: manuscripts are peer-reviewed and a first decision provided to authors approximately 16.4 days after submission; acceptance to publication is undertaken in 4.6 days (median values for papers published in this journal in the first half of 2020).
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Impact Factor: 1.747 (2019) (First decile JCR)

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

Impacto de la crisis económica en la construcción: lo que opinan los estudiantes

ABSTRACT:

The current economic crisis has specially affected the Spanish construction industry, causing the loss of 1.2 million jobs in the last four years. The increase in the unemployment rate is particularly worrisome for recent graduates in the construction industry. This fact leads to changes in the university degrees related to construction: undergraduate students should be prepared for a new professional environment and recent graduate find it hard to enter the labor market. Low employment opportunities entail a lack of motivation that can cause a significant decrease in the achievement of learning outcomes. This paper seeks to analyze the impact of the crisis in the construction industry from the point of view of the students of a M.Sc. in Construction Management, analyzing the evolution of student’s perception on unemployment and their motivations to enroll in the master degree. For this purpose, a questionnaire was handed out to students of three consecutive classes of the M.Sc. in Construction Management at the Universitat Politècnica de València (Spain) from 2010 to 2012. A statistical analysis of the survey was developed. This way, some interesting points can be highlighted on the impact of crisis on young construction professionals.

KEYWORDS:

Construction; Economic Crisis; Employment; Motivation; Labor Market; M.Sc. Degree

REFERENCIA:

TORRES-MACHÍ, C.; PELLICER, E.; YEPES, V.; PICORNELL, M. (2013). Impact of the economic crisis in construction: a perspective from graduate students. Procedia – Social and Behavioral Sciences, 89:640-645.

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Appraisal of infrastructure sustainability by graduate students using an active-learning method

file.FeedFileLoaderAppraisal of infrastructure sustainability by graduate students using an active-learning method

Abstract:

Currently many university programs in the construction field do not take sustainability into account from a holistic viewpoint. This may cause a lack of sensitivity from future professionals concerning sustainability. Academics in construction must endeavor to instill a culture of sustainability in the curricula of their students. Therefore, this study proposes an active-learning method that allows graduate students in the construction field to take into consideration infrastructure sustainability from a variety of perspectives in a participatory process. The students applied an analytical hierarchical process to determine the appraisal degree of each criterion. A cluster statistical analysis was carried out, aiming to identify the profiles that influence decision-making. This method was applied to two classes of graduate students enrolled in the Master of Planning and Management in Civil Engineering at the Universitat Politècnica de València. This method identified a correlation between the profiles toward sustainability and the characteristics of the chosen infrastructure. It was also found that the method fulfills educational purposes: most of the students obtained more than 65% of the target learning outcomes. This approach promotes awareness and sensitivity to different points of view of the sustainability in a participatory context. It can be replicated in other contexts so as to obtain appraisals regarding various criteria that help enhance decision-making.

Highlights

  • Proposal of a method that allows students to consider infrastructure sustainability.
  • Participatory learning method that promotes integral sustainability.
  • Students profiles’ identification influencing decision making toward sustainability.
  • The profiles of evaluators influence the prioritization among alternatives.

Reference:

PELLICER, E.; SIERRA, L.A.; YEPES, V. (2016). Appraisal of infrastructure sustainability by graduate students using an active-learning method. Journal of Cleaner Production, 113:884-896. DOI:10.1016/j.jclepro.2015.11.010

Os dejo a continuación la versión autor del artículo:

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Developing learning manuals for European construction project managers

Maturity model in management competences
Maturity model in management competences (Milosevic et al, 2007)

There is a need for supplementary learning and training in management applied to the construction industry, as many authors, professionals and organisations have already recognised. The assessment and up-keeping of management competencies are essential for the performance of individuals and organisations that work in the construction sector. Most of the universities syllabuses focus in traditional construction courses that do not deal with the most relevant features of management for engineers and architects in the construction industry; these graduate courses mainly cover an assortment of design-oriented issues, leaving no room for managerial topics. Thus, management is a crucial issue for professionals in the construction sector; currently, an engineer or an architect must have some knowledge of every managerial issue valuable in construction. Taking the complete life cycle of the infrastructure as a reference, a holistic attitude must be pursued. Therefore, a model for management and administration in construction is proposed in this paper. This model displays two dimensions: life cycle (per phase) and organisational level. The former is linked to time through the four well-known phases of the construction process: feasibility, design, construction and operation. The latter considers four organisational levels that can be found in the construction sector: life cycle, company, project (or team) and individual. In order to test the appropriateness and usefulness of the model, two applications are implemented. The first one is the analysis of the outputs of a European project which goal was to produce seven basic books for construction managers; this project was developed by several universities and professional associations of the European Union. The second one is the design of a new syllabus in civil engineering (M.Sc. degree) with a specialisation of 30 ECTS; right now, this proposal is being discussed in the School of Civil Engineering at the Universidad Politécnica de Valencia (Spain) to get it implemented in 2010 due to the new academic scenario according to the Bologna process. The model presented in this paper offers an innovative framework for orientation to organisations, professionals and academicians in order to improve the knowledge of management and administration in the construction industry.

Reference:

PELLICER, E.; YEPES, V.; TEIXEIRA, J.C.; CATALÁ; J. (2009). Developing learning manuals for European construction project managers, in Gómez, L.; Martí, D; Candel I. (eds.): Proceedings of International Conference on Education and New Learning Technologies, EDULEARN 09, pp. 2374-2384. 6-8 July, Barcelona, Spain. ISBN: 978-84-612-9802-0. (link)

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Environmental Assessment of Concrete Structures

2014-11-12 16.38.05In recent decades, with the objective of reaching a more sustainable development, worldwide society has increased its concern about environmental protection. Nevertheless, there are still economic sectors, such as the construction industry, which produce significant environmental impacts. Life Cycle Assessment (LCA) is a tool that enables identifying environmental issues related to both finished products and services, and allows focusing efforts to resolve them. The main objective of this paper is to asses LCA applicability on concrete structures so that construction’s environmental performance can be improved. For this purpose, an attempt is made to provide a decision-making tool for construction-sector stakeholders with reliable and accurate environmental data. The research methodologies used in this paper are based on a literature review and are applied to a case study. This review was performed to collect information on LCA methodologies currently in use and their practical application. The case study subsequently described in this paper involved identification of the most sustainable type of slab for a reinforced concrete structure in a residential building, using two different databases. It was observed that, depending on the database selected and inherent assumptions, results varied. Therefore it was concluded that in order to avoid producing incorrect results when applying LCA, it is highly recommended to develop a more constrained methodology and grant access to reliable construction-sector data. (link)