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Energies
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Algorithm and Intelligence for Optimizing Urban/Building Morphology
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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 10465

Special Issue Editors

Dr. Alberto Barbaresi

E-Mail Website
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
Prof. Pascal YIM

E-Mail Website
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

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Further information on MDPI's Special Issue polices can be found here.

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://doi.org/10.3390/en15134598 - 23 Jun 2022
Cited by 9 | Viewed by 2853
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)
►▼ Show Figures

Figure 1

Figure 1
<p>The research case study: the Claudio aqueduct and its sections in Tor Fiscale Park.</p> Full article ">Figure 2
<p>The proposed digital workflow applied to the research case study.</p> Full article ">Figure 3
<p>Section G: (<b>a</b>) Opus caementicium walls that were once placed all around the Claudio piers—ashlars have been removed, some traces visible on the top; (<b>b</b>) imprints of the peperino ashlars on opus caementicium—it is possible to recognise the square stone ashlar and the special one used for the arches; (<b>c</b>) opus caementicium arch—once the stones have been removed, the opus caementicium was exposed to weathering. This is an extreme case where the thickness of the arch has become thinner; (<b>d</b>) Hadrian structures—the arch was built with brick armchair voussoir and opus caementicium; (<b>e</b>) imprints of the peperino ashlars on opus caementicium.</p> Full article ">Figure 4
<p>Section D: (<b>a</b>) North facade—supporting structures, base that re-used peperino ashlars stones, walls with bricks and stones courses (probably late antiquity, Honorius emperor; (<b>b</b>) Brick wedges to fill the mortar joints; (<b>c</b>) Limestone or marble among the peperino ashlars; (<b>d</b>) East facade—opus caementicium around the peperino piers; (<b>e</b>) West facade—peperino ashlar restored in 2008–2009.</p> Full article ">Figure 5
<p>The materialized benchmarks “Order 0” and “Order I” (<b>a</b>); the position of the benchmarks for “Order 0” (1000-2000-3000) and “Order I” benchmarks are highlighted in red and retroreflective targets (blue) (<b>b</b>); DJI Mavic Mini (<b>c</b>); 3D models from UAV photogrammetry (<b>d</b>).</p> Full article ">Figure 6
<p>Views of the overall model of the Tor Fiscale Park (mesh and textured model).</p> Full article ">Figure 7
<p>Point clouds from Laser scanning: sections d, c, a2 and a1.</p> Full article ">Figure 8
<p>The photogrammetric textured models derived from detailed survey: from a to h section and the related size (megabytes).</p> Full article ">Figure 9
<p>A mesh textured model (Section A) compared to the auto-surfacing (mesh-to-NURBS) models. Both models are based on an automatic conversion from points to surfaces (meshes and patches) that do not allow users to represent the artefacts by architectural elements and connecting information.</p> Full article ">Figure 10
<p>As-found drawings of sections G and D, obtained from digital photogrammetry and slicing techniques.</p> Full article ">Figure 11
<p>GOG 10 application to section D: (<b>a</b>) automatic verification system (AVS) and GOA achieved; (<b>b</b>) generation of SUs and sub-elements by GOG 10; (<b>c</b>) Hmplementation outputs.</p> Full article ">Figure 12
<p>Section G, Building archaeology model and textures, peperino ashlars modelling and HBIM.</p> Full article ">Figure 13
<p>3D virtual reconstruction of peperino ashlars. The plan and section of opus caementicium walls with stones’ imprints are on the left. The imprint colours depend on their width among different ashlars courses. On the right: NURBS model of opus caementicium walls and corner ashlar modelling: imprints were identified on the textured model, edges were used to define stone surfaces and stone volume was defined as closing shape.</p> Full article ">Figure 14
<p>Building Archaeology model and database.</p> Full article ">Figure 15
<p>The four-step process for XR implementation.</p> Full article ">Figure 16
<p>De Architectura. Table XXIV of Book Tenth, (<b>a</b>) the letter “L” Iron pincer represents; (<b>b</b>) the olive grove.</p> Full article ">Figure 17
<p>The XR project of the research case study: HBIM objects have been transformed in IVOs able to interact with different types of users and devices (VR headsets, tablets, mobile phones and PCs).</p> Full article ">
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://doi.org/10.3390/en15124497 - 20 Jun 2022
Cited by 23 | Viewed by 4560
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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Figure 1

Figure 1
<p>Digital workflow proposed and applied to the research case study.</p> Full article ">Figure 2
<p>Case study location, in the northern suburban area of Cinisello Balsamo, Milano, IT. Source: Google Earth. Main façades of the Italian demo building (<b>a</b>,<b>b</b>).</p> Full article ">Figure 3
<p>Existing façades of the Italian demo building.</p> Full article ">Figure 4
<p>Technical drawings of the building façades before the retrofitting.</p> Full article ">Figure 5
<p>Three-dimensional survey tools used for the research case study: (<b>a</b>–<b>c</b>) total station; (<b>b</b>) laser scanner.</p> Full article ">Figure 6
<p>Thermal imaging survey on the north-east façade before retrofitting.</p> Full article ">Figure 7
<p>Thermal imaging survey on the south-west façade after retrofitting.</p> Full article ">Figure 8
<p>Three-dimensional modelling of the case study: (<b>a</b>) scan-to-BIM model and laser scans; (<b>b</b>) as-found model; (<b>c</b>) as-designed BIM model; (<b>d</b>) anchoring system.</p> Full article ">Figure 9
<p>Demo building complexity and irregularities.</p> Full article ">Figure 10
<p>Application of GOG 10. The GOA of each building façade is around 2.5 mm.</p> Full article ">Figure 11
<p>Top: as-designed façades. Bottom: as-found (<b>left</b>) and as-designed phases (<b>right</b>) in the same BIM project.</p> Full article ">Figure 12
<p>The creation of custom AD-BIM objects (panels, metal anchors, insulation system, etc.) allowed a metric control of the designed process and the direct export of executive as-built details.</p> Full article ">Figure 13
<p>Steps of panel installation process and technical details: (<b>a</b>) lifting by crane; (<b>b</b>) assembly in parallel to the line of the façade via a rocker; (<b>c</b>) positioning through inclusion in the boxes of the façade; (<b>d</b>) covering panels’ positioning with a crane.</p> Full article ">Figure 14
<p>From as-designed to as-built BIM.</p> Full article ">Figure 15
<p>The HOMeBIM live app process development: from real-time data to a web platform and digital twin.</p> Full article ">Figure 16
<p>Data visualisation tests: the XR web platform reworked a large number of Data Visualisations and BIM 360 APIs.</p> Full article ">Figure 17
<p>Four-dimensional multi-user Live App to improve building comfort, efficiency, and costs: from 3D survey to BIM sensor-based digital twin and cloud XR platform: (<b>a</b>) login; (<b>b</b>) upload model procedure; (<b>c</b>) project tree; (<b>d</b>) available charts in the app; (<b>e</b>) line chart of temperatures; (<b>f</b>–<b>i</b>) different comfort charts; (<b>j</b>) BIM parameters; (<b>k</b>) heatmap parameters; and (<b>l</b>) heatmap BIM visualisation.</p> Full article ">Figure 18
<p>Four-dimensional multi-user live app: mobile version (phone and tablet).</p> Full article ">Figure 19
<p>Final energy consumption in kWh/m<sup>2</sup>year of different winter seasons from 2009 to 2016. The dotted line represents the pre–post retrofitting break.</p> Full article ">Figure 20
<p>Visible and infrared picture of a portion of the north-east elevation. The red colours identify high thermal energy dispersion due to high wall resistance.</p> Full article ">
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://doi.org/10.3390/en15041266 - 9 Feb 2022
Cited by 7 | Viewed by 2134
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)
►▼ Show Figures

Figure 1

Figure 1
<p>The case study building: (<b>a</b>) the location of the case study at regional and national scale. (<b>b</b>) the layout of the building. (<b>c</b>,<b>d</b>) the South and North front of the building with the main measures.</p> Full article ">Figure 2
<p>A view of the base model created with the Sketch-Up plugin.</p> Full article ">Figure 3
<p>Main results of the sensitivity analysis. (<b>a</b>) Trends of the energy need for different values of the roof density. (<b>b</b>) Trends of the energy need for different values of the wall density.</p> Full article ">Figure 4
<p>Spearman correlation matrix. It shows how a variable is correlated with each other. Since it is symmetrical, only the lower triangle is shown. The feature dropped from the data set were: <span class="html-italic">attenuation, superficial mass, transmittance and conductivity</span> for both <span class="html-italic">roof and wall</span> elements.</p> Full article ">Figure 5
<p>(<b>a</b>) Average MAE; (<b>b</b>) average MSE; (<b>c</b>) average <math display="inline"><semantics> <msup> <mi>R</mi> <mn>2</mn> </msup> </semantics></math> and (<b>d</b>) average prediction time for the models, computed on each fold of the outer cross-validation.</p> Full article ">Figure 6
<p>Results of the XGB model in the test set. (<b>a</b>) Predicted vs. True energy values and (<b>b</b>) Residuals vs. True energy values.</p> Full article ">Figure 7
<p>SHAP values of each feature computed from all the observations in the data set. The different colours indicates the feature value, i.e., from low to high value.</p> Full article ">Figure 8
<p>Trends of <span class="html-italic">roof resistance</span> SHAP values vs. <span class="html-italic">roof resistance</span> values. The different colours represent different values of <span class="html-italic">wall resistance</span>. From left to right: (<b>a</b>) Whole range of the roof resistance values. (<b>b</b>) Detail of portion of the plot with the highest interactions between the two features.</p> Full article ">Figure 9
<p>Trend of <span class="html-italic">wall resistance</span> SHAP values vs. <span class="html-italic">wall resistance</span> values. The different colours represent different values of <span class="html-italic">roof resistance</span>. From left to right: (<b>a</b>) Whole range of roof resistance values. (<b>b</b>) Detail of portion of the plot with the highest interactions between the two features.</p> Full article ">Figure 10
<p>Trend of <span class="html-italic">air infiltration</span> SHAP values vs. <span class="html-italic">air infiltration</span> values. The different colours represent different values of <span class="html-italic">roof resistance</span>.</p> Full article ">
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