The Optimized Social Distance Lab: A Methodology for Automated Building Layout Redesign for Social Distancing

Des Fagan; Ruth Conroy Dalton

doi:10.1007/978-3-030-95470-3_37

The Optimized Social Distance Lab: A Methodology for Automated Building Layout Redesign for Social Distancing

Des Fagan^*, Ruth Conroy Dalton

^*Corresponding author for this work

Architecture and Built Environment Department

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

The research considers buildings as a test case for the development and implementation of multi-objective optimized social distance layout redesign. This research aims to develop and test a unique methodology using software Wallacei and the NSGA-II algorithm to automate the redesign of an interior layout to automatically provide compliant social distancing using fitness functions of social distance, net useable space and total number of users. The process is evaluated in a live lab scenario, with results demonstrating that the methodology provides an agile, accurate, efficient and visually clear outcome for automating a compliant layout for social distancing.

Original language	English
Title of host publication	Machine Learning, Optimization, and Data Science
Subtitle of host publication	7th International Conference, LOD 2021, Grasmere, UK, October 4–8, 2021, Revised Selected Papers, Part II
Editors	Giuseppe Nicosia, Varun Ojha, Emanuele La Malfa, Gabriele La Malfa, Giorgio Jansen, Panos M. Pardalos, Giovanni Giuffrida, Renato Umeton
Place of Publication	Cham, Switzerland
Publisher	Springer
Pages	496-501
Number of pages	6
Volume	13164
Edition	1st
ISBN (Electronic)	9783030954703
ISBN (Print)	9783030954697
DOIs	https://doi.org/10.1007/978-3-030-95470-3_37
Publication status	Published - 2 Feb 2022
Event	7th International Conference on Machine Learning, Optimization, and Data Science, LOD 2021 - Virtual, Online Duration: 4 Oct 2021 → 8 Oct 2021

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	13164
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	7th International Conference on Machine Learning, Optimization, and Data Science, LOD 2021
City	Virtual, Online
Period	4/10/21 → 8/10/21

Keywords

Architecture
Optimization
Signage
Social distancing
Wayfinding

Access to Document

10.1007/978-3-030-95470-3_37

Cite this

Fagan, D., & Dalton, R. C. (2022). The Optimized Social Distance Lab: A Methodology for Automated Building Layout Redesign for Social Distancing. In G. Nicosia, V. Ojha, E. La Malfa, G. La Malfa, G. Jansen, P. M. Pardalos, G. Giuffrida, & R. Umeton (Eds.), Machine Learning, Optimization, and Data Science: 7th International Conference, LOD 2021, Grasmere, UK, October 4–8, 2021, Revised Selected Papers, Part II (1st ed., Vol. 13164, pp. 496-501). (Lecture Notes in Computer Science; Vol. 13164). Springer. https://doi.org/10.1007/978-3-030-95470-3_37

Fagan, Des ; Dalton, Ruth Conroy. / The Optimized Social Distance Lab : A Methodology for Automated Building Layout Redesign for Social Distancing. Machine Learning, Optimization, and Data Science: 7th International Conference, LOD 2021, Grasmere, UK, October 4–8, 2021, Revised Selected Papers, Part II. editor / Giuseppe Nicosia ; Varun Ojha ; Emanuele La Malfa ; Gabriele La Malfa ; Giorgio Jansen ; Panos M. Pardalos ; Giovanni Giuffrida ; Renato Umeton. Vol. 13164 1st. ed. Cham, Switzerland : Springer, 2022. pp. 496-501 (Lecture Notes in Computer Science).

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abstract = "The research considers buildings as a test case for the development and implementation of multi-objective optimized social distance layout redesign. This research aims to develop and test a unique methodology using software Wallacei and the NSGA-II algorithm to automate the redesign of an interior layout to automatically provide compliant social distancing using fitness functions of social distance, net useable space and total number of users. The process is evaluated in a live lab scenario, with results demonstrating that the methodology provides an agile, accurate, efficient and visually clear outcome for automating a compliant layout for social distancing.",

keywords = "Architecture, Optimization, Signage, Social distancing, Wayfinding",

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Fagan, D & Dalton, RC 2022, The Optimized Social Distance Lab: A Methodology for Automated Building Layout Redesign for Social Distancing. in G Nicosia, V Ojha, E La Malfa, G La Malfa, G Jansen, PM Pardalos, G Giuffrida & R Umeton (eds), Machine Learning, Optimization, and Data Science: 7th International Conference, LOD 2021, Grasmere, UK, October 4–8, 2021, Revised Selected Papers, Part II. 1st edn, vol. 13164, Lecture Notes in Computer Science, vol. 13164, Springer, Cham, Switzerland, pp. 496-501, 7th International Conference on Machine Learning, Optimization, and Data Science, LOD 2021, Virtual, Online, 4/10/21. https://doi.org/10.1007/978-3-030-95470-3_37

The Optimized Social Distance Lab: A Methodology for Automated Building Layout Redesign for Social Distancing. / Fagan, Des; Dalton, Ruth Conroy.
Machine Learning, Optimization, and Data Science: 7th International Conference, LOD 2021, Grasmere, UK, October 4–8, 2021, Revised Selected Papers, Part II. ed. / Giuseppe Nicosia; Varun Ojha; Emanuele La Malfa; Gabriele La Malfa; Giorgio Jansen; Panos M. Pardalos; Giovanni Giuffrida; Renato Umeton. Vol. 13164 1st. ed. Cham, Switzerland: Springer, 2022. p. 496-501 (Lecture Notes in Computer Science; Vol. 13164).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Fagan, Des

AU - Dalton, Ruth Conroy

PY - 2022/2/2

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N2 - The research considers buildings as a test case for the development and implementation of multi-objective optimized social distance layout redesign. This research aims to develop and test a unique methodology using software Wallacei and the NSGA-II algorithm to automate the redesign of an interior layout to automatically provide compliant social distancing using fitness functions of social distance, net useable space and total number of users. The process is evaluated in a live lab scenario, with results demonstrating that the methodology provides an agile, accurate, efficient and visually clear outcome for automating a compliant layout for social distancing.

AB - The research considers buildings as a test case for the development and implementation of multi-objective optimized social distance layout redesign. This research aims to develop and test a unique methodology using software Wallacei and the NSGA-II algorithm to automate the redesign of an interior layout to automatically provide compliant social distancing using fitness functions of social distance, net useable space and total number of users. The process is evaluated in a live lab scenario, with results demonstrating that the methodology provides an agile, accurate, efficient and visually clear outcome for automating a compliant layout for social distancing.

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KW - Optimization

KW - Signage

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KW - Wayfinding

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A2 - Nicosia, Giuseppe

A2 - Ojha, Varun

A2 - La Malfa, Emanuele

A2 - La Malfa, Gabriele

A2 - Jansen, Giorgio

A2 - Pardalos, Panos M.

A2 - Giuffrida, Giovanni

A2 - Umeton, Renato

PB - Springer

CY - Cham, Switzerland

Y2 - 4 October 2021 through 8 October 2021

ER -

Fagan D, Dalton RC. The Optimized Social Distance Lab: A Methodology for Automated Building Layout Redesign for Social Distancing. In Nicosia G, Ojha V, La Malfa E, La Malfa G, Jansen G, Pardalos PM, Giuffrida G, Umeton R, editors, Machine Learning, Optimization, and Data Science: 7th International Conference, LOD 2021, Grasmere, UK, October 4–8, 2021, Revised Selected Papers, Part II. 1st ed. Vol. 13164. Cham, Switzerland: Springer. 2022. p. 496-501. (Lecture Notes in Computer Science). Epub 2022 Feb 1. doi: 10.1007/978-3-030-95470-3_37

The Optimized Social Distance Lab: A Methodology for Automated Building Layout Redesign for Social Distancing

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