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Algorithm and Intelligence for Optimizing Urban/Building Morphology

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "G: Energy and Buildings".

Deadline for manuscript submissions: closed (20 July 2022) | Viewed by 7965

Special Issue Editors


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Guest Editor
Department of Agricultural and Food Sciences, University of Bologna, Viale Fanin 48, Bologna, Italy
Interests: energy efficiency; buildings; construction; civil engineering materials; building materials; structural analysis; sustainability; architecture; modeling and simulation
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
UMR 9189-CRIStAL-Centre de Recherche en Informatique Signal et Automatique de Lille, Centrale Lille, University of Lille, CNRS, F-59000 Lille, France
Interests: artificial intelligence; deep learning; energy; buildings

Special Issue Information

Dear Colleagues,

We are inviting submissions of research articles, literature reviews, case reports, and short communications to the Energies Special Issue on “Algorithm and Intelligence for Optimizing Urban/Building Morphology”.

In the near future, city and building design will face new challenges, mainly due to progressively increasing urbanization, the need of fuel fossil energy reduction, and the negative effects related to the climate change. These challenges cause the design requirements for both new and retrofitted interventions to be increasingly restrictive.

On the other hand, the professionals and researchers involved in urban and building design can take advantages from computer learning and solving algorithms. Several studies in the scientific literature show the efficacy of the application of computer intelligence, directly integrating it to the design at both urban and building scales.

This approach can play a fundamental role in addressing the challenges related to the urban microclimate conditions, thermal comfort at the pedestrian level, heat island effect and air pollution. At the same time, it can help to rethink the organization of building space and architectural solutions, boosting the innovations for more and more sustainable buildings.

This Special Issue aims to investigate the potentialities related to the use of computer intelligence applied mainly, but not limited to, the following topics:

  • Urban/building morphology to improve building energy needs, health, and thermal comfort;
  • Effects of urban greenery (trees, rooftop gardens and greenhouse, green walls, etc.) at urban and building scales;
  • New paradigms for residential and non-residential buildings (such as rural facilities, food storage buildings, etc.);
  • Definition of new methodologies and indicators;
  • Integration of different renewable energy sources (such as photovoltaic, thermal, and geothermal);
  • Predictive models for energy need and consumption;
  • Building energy optimization;
  • New constructions and retrofitting.

Dr. Alberto Barbaresi
Prof. Pascal YIM
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • neighborhood transformation
  • building design
  • energy simulation
  • energy performance
  • agricultural facility
  • envelope performances
  • solving algorithms
  • artificial Intelligence
  • machine learning
  • energy consumption prediction
  • digital twins
  • genetic algorithms
  • urban heat island
  • urban microclimate
  • rooftop greenhouse

Published Papers (3 papers)

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Research

37 pages, 11849 KiB  
Article
Diachronic and Synchronic Analysis for Knowledge Creation: Architectural Representation Geared to XR Building Archaeology (Claudius-Anio Novus Aqueduct in Tor Fiscale, the Appia Antica Archaeological Park)
by Fabrizio Banfi, Stefano Roascio, Francesca Romana Paolillo, Mattia Previtali, Fabio Roncoroni and Chiara Stanga
Energies 2022, 15(13), 4598; https://0-doi-org.brum.beds.ac.uk/10.3390/en15134598 - 23 Jun 2022
Cited by 8 | Viewed by 2153
Abstract
This study summarises research progress to identify appropriate quality methodologies for representing, interpreting, and modelling complex contexts such as the Claudian Aqueduct in the Appian Way Archaeological Park. The goal is to intrinsically integrate (embed) geometric survey (Laser scanning and photogrammetric) with the [...] Read more.
This study summarises research progress to identify appropriate quality methodologies for representing, interpreting, and modelling complex contexts such as the Claudian Aqueduct in the Appian Way Archaeological Park. The goal is to intrinsically integrate (embed) geometric survey (Laser scanning and photogrammetric) with the materials and construction techniques (Stratigraphic Units—SU), semantic models in order to support the design with a better understanding of the artefact considered, and also to give indications that can be implemented in the future in a continuous cognitive process. Volume stratigraphic units in the form of architectural drawings, heritage building information modelling (HBIM) and extended reality (XR) environments have been oriented to comparative analyses based on the research case study’s complex morphology. Analysis of geometries’ intersection, construction techniques and materials open up new cognitive scenarios, self-feeding a progressive knowledge and making different studies correlatable, avoiding diaspora or incommunicability. Finally, an extended reality (XR) platform aims to enhance tangible and intangible values through new human-computer interaction and information sharing levels. Full article
(This article belongs to the Special Issue Algorithm and Intelligence for Optimizing Urban/Building Morphology)
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26 pages, 11208 KiB  
Article
Digital Twin and Cloud BIM-XR Platform Development: From Scan-to-BIM-to-DT Process to a 4D Multi-User Live App to Improve Building Comfort, Efficiency and Costs
by Fabrizio Banfi, Raffaella Brumana, Graziano Salvalai and Mattia Previtali
Energies 2022, 15(12), 4497; https://0-doi-org.brum.beds.ac.uk/10.3390/en15124497 - 20 Jun 2022
Cited by 13 | Viewed by 3492
Abstract
Digital twins (DTs) and building information modelling (BIM) are proving to be valuable tools for managing the entire life cycle of a building (LCB), from the early design stages to management and maintenance over time. On the other hand, BIM platforms cannot manage [...] Read more.
Digital twins (DTs) and building information modelling (BIM) are proving to be valuable tools for managing the entire life cycle of a building (LCB), from the early design stages to management and maintenance over time. On the other hand, BIM platforms cannot manage the geometric complexities of existing buildings and the large amount of information that sensors can collect. For this reason, this research proposes a scan-to-BIM process capable of managing high levels of detail (LODs) and information (LOIs) during the design, construction site management, and construction phases. Specific grades of generation (GOGs) were applied to create as-found, as-designed, and as-built models that interact with and support the rehabilitation project of a multi-level residential building. Furthermore, thanks to the sharing of specific APIs (Revit and Autodesk Forge APIs), it was possible to switch from static representations to novel levels of interoperability and interactivity for the user and more advanced forms of building management such as a DT, a BIM cloud, and an extended reality (XR) web platform. Finally, the development of a live app shows how different types of users (professionals and non-expert) can interact with the DT, in order to know the characteristics with which the environments have been designed, as well as the environmental parameters, increasing their degree of control, from the point of view of improving comfort, use, costs, behaviour, and good practices. Finally, the overall approach was verified through a real case study where the BIM-XR platform was built for energy improvements to existing buildings and façade renovations. Full article
(This article belongs to the Special Issue Algorithm and Intelligence for Optimizing Urban/Building Morphology)
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16 pages, 9124 KiB  
Article
Application of Machine Learning Models for Fast and Accurate Predictions of Building Energy Need
by Alberto Barbaresi, Mattia Ceccarelli, Giulia Menichetti, Daniele Torreggiani, Patrizia Tassinari and Marco Bovo
Energies 2022, 15(4), 1266; https://0-doi-org.brum.beds.ac.uk/10.3390/en15041266 - 09 Feb 2022
Cited by 3 | Viewed by 1559
Abstract
Accurate prediction of building energy need plays a fundamental role in building design, despite the high computational cost to search for optimal energy saving solutions. An important advancement in the reduction of computational time could come from the application of machine learning models [...] Read more.
Accurate prediction of building energy need plays a fundamental role in building design, despite the high computational cost to search for optimal energy saving solutions. An important advancement in the reduction of computational time could come from the application of machine learning models to circumvent energy simulations. With the goal of drastically limiting the number of simulations, in this paper we investigate the regression performance of different machine learning models, i.e., Support Vector Machine, Random Forest, and Extreme Gradient Boosting, trained on a small data-set of energy simulations performed on a case study building. Among the XX algorithms, the tree-based Extreme Gradient Boosting showed the best performance. Overall, we find that machine learning methods offer efficient and interpretable solutions, that could help academics and professionals in shaping better design strategies, informed by feature importance. Full article
(This article belongs to the Special Issue Algorithm and Intelligence for Optimizing Urban/Building Morphology)
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