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Study on Building Simulation
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Study on Building Simulation

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Construction Management, and Computers & Digitization".

Deadline for manuscript submissions: closed (20 June 2024) | Viewed by 6423

Special Issue Editors

Dr. Valentina Villa

E-Mail Website
Guest Editor
Department of Structural, Geotechnical and Building Engineering, Politecnico di Torino, 10129 Turin, Italy
Interests: BIM (building information model/modeling); building simulation; construction management; construction safety; construction sites; facility management; project management; energy efficiency in building
Dr. Marco Domaneschi

E-Mail Website
Guest Editor
Department of Structural, Geotechnical and Building Engineering, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129 Turin, TO, Italy
Interests: disaster resilience; earthquake engineering; numerical simulations; special structures; structural control; structural monitoring; structural analysis, control and monitoring; structural and community resilience; structural degradation and damage detection; seismic risk; emergency and evacuation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will contribute to the field of building simulation, including consolidated and emerging digital methodologies and technologies for management and decision-making. This includes Digital Twins and Building Information Modeling, Agent-Based Modeling for emergency evacuation, Finite Element Modeling for energy and structural analysis, Artificial Intelligence algorithms for data processing (e.g., unusual behavior detection),  Virtual and Mixed Reality for health and safety training or controls, Virtual Construction Design and Process, innovative sensing units and SHM algorithms, protection strategies to reduce seismic vulnerability, energy modeling, and others.

Research on current building types will be considered and new design concepts are also welcome. Building technologies have been extended to reinforced concrete buildings, masonry buildings, and prefabricated solutions (concrete, steel, and timber). This will contribute to the Next-Generation EU programs listed in the Operational Arrangements. Component 3 of Mission 2, M2C3 “Energy Efficiency and Redevelopment of Buildings”, includes improving the efficiency and safety of the public building stock, and seismic retrofitting of the private building stock and social housing.

This Special Issue will present excellent contributions and high-impact work from academics, professionals, and the industrial sector.

Dr. Valentina Villa
Dr. Marco Domaneschi
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. Buildings is an international peer-reviewed open access monthly 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.

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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

17 pages, 8393 KiB  
Article
Confidentiality Preserved Federated Learning for Indoor Localization Using Wi-Fi Fingerprinting
by Rajeev Kumar, Renu Popli, Vikas Khullar, Isha Kansal and Ashutosh Sharma
Buildings 2023, 13(8), 2048; https://doi.org/10.3390/buildings13082048 - 10 Aug 2023
Cited by 1 | Viewed by 1137
Abstract
For the establishment of future ubiquitous location-aware applications, a scalable indoor localization technique is essential technology. Numerous classification techniques for indoor localization exist, but none have proven to be as quick, secure, and dependable as what is now needed. This research proposes an [...] Read more.
For the establishment of future ubiquitous location-aware applications, a scalable indoor localization technique is essential technology. Numerous classification techniques for indoor localization exist, but none have proven to be as quick, secure, and dependable as what is now needed. This research proposes an effective and privacy-protective federated architecture-based framework for location classification via Wi-Fi fingerprinting. The federated indoor localization classification (f-ILC) system that was suggested had distributed client–server architecture with data privacy for any and all related edge devices or clients. To try and evaluate the proposed f-ILC framework, different data from different sources on the Internet were collected and given in a format that had already been processed. Experiments were conducted with standard learning, federated learning with a single client, and federated learning with several clients to make sure that federated deep learning models worked correctly. The success of the f-ILC framework was computed using a number of factors, such as validation of accuracy and loss. The results showed that the suggested f-ILC framework performed better than traditional distributed deep learning-based classifiers in terms of accuracy and loss while keeping data secure. Due to its innovative design and superior performance over existing classifier tools, edge devices’ data privacy makes this proposed architecture the ideal solution. Full article
(This article belongs to the Special Issue Study on Building Simulation)
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24 pages, 9044 KiB  
Article
A General Contractor’s Perspective on Construction Digital Twin: Implementation, Impacts and Challenges
by Iraj Esmaeili and Davide Simeone
Buildings 2023, 13(4), 978; https://doi.org/10.3390/buildings13040978 - 7 Apr 2023
Cited by 4 | Viewed by 2214
Abstract
The Digital Twin (DT), as a real-time and data-connected virtual replica of a physical asset, introduces a new paradigm in the construction industry. To date, the use of DT in the construction phase has not been addressed sufficiently. Hence, this research studies the [...] Read more.
The Digital Twin (DT), as a real-time and data-connected virtual replica of a physical asset, introduces a new paradigm in the construction industry. To date, the use of DT in the construction phase has not been addressed sufficiently. Hence, this research studies the implementation of DT during the construction phase of projects to support general contractors’ decisions and operations. Starting from existing literature, a Construction Digital Twin (CDT) framework has been developed from a general contractor’s perspective and a case study was implemented as an application of the proposed CDT to validate the framework and demonstrate its benefits. In the selected project, the simulation of the construction operations in evaluating various “what-if” scenarios for optimum resource allocation and operation management proved the benefits of using a CDT in the construction phase of projects for general contractors. By implementing the proposed CDT framework, several impacts such as reduced costs, improved collaboration and information exchange and data-driven construction management can be anticipated. Full article
(This article belongs to the Special Issue Study on Building Simulation)
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17 pages, 8159 KiB  
Article
Algorithm-Aided Design for Composite Bridges
by Valentina Boretti, Laura Sardone, Luis Alberto Bohórquez Graterón, Davide Masera, Giuseppe Carlo Marano and Marco Domaneschi
Buildings 2023, 13(4), 865; https://doi.org/10.3390/buildings13040865 - 26 Mar 2023
Cited by 2 | Viewed by 2173
Abstract
Bridges are geometrically complex infrastructures, and their designs usually exhibit significant geometric variations between different structural solutions. The modelling complexity implies a low degree of model reuse in comparable projects; moreover, with the development of new technologies and design ways, the AEC industry [...] Read more.
Bridges are geometrically complex infrastructures, and their designs usually exhibit significant geometric variations between different structural solutions. The modelling complexity implies a low degree of model reuse in comparable projects; moreover, with the development of new technologies and design ways, the AEC industry often requires computational cost reduction, less time for model developments and analysis, and little-to-zero material waste in the face of the environmental emergency. The present document proposes a generative approach to enhance the bridge design process, increasing efficiency by reducing computational costs and modelling efforts, tackling the aforementioned objectives. The following methodology relies on a workflow to create flexible geometric models, introducing parameters and numerical relationships between all the design variables. Therefore, from a generative development, different geometric solutions of a bridge’s family are created by modifying the parameter settings within the same model. Then, the present work aims to define a modelling and analysis strategy for a multi-girder composite bridge project based on parametric development, structural analysis, and optimization. The results integrate building information modeling (BIM) to explore and create high-potential designs with complex geometries and find cost-effective solutions. Full article
(This article belongs to the Special Issue Study on Building Simulation)
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