Schembri, J;
Gentile, R;
Galasso, C;
(2022)
Enhancing Natural-Hazard Exposure Modeling Using Natural Language Processing: a Case-Study for Maltese Planning Applications.
In:
Procedia Structural Integrity.
(pp. pp. 1720-1727).
Elsevier
Preview |
PDF
1-s2.0-S2452321623002287-main.pdf - Published Version Download (1MB) | Preview |
Abstract
The algorithmic processing of written language for tools such as predictive text, sentiment analysis, and translation services has become commonplace. The segment of computer science concerned with the interpretation of human language, NLP (Natural Language Processing), is a versatile and fast-developing field. In this paper, NLP is deployed unconventionally to gather insights into a building's multi-hazard exposure characteristics consistent with the GED4ALL attributes. NLP is used in this study to "read" the contents of digitally-submitted planning applications made on the Maltese archipelago. Maltese architects/engineers submit a concise but detailed description of the proposed works on any given site as part of a planning process. It is suggested that valuable insights exist within this description that can assist in classifying buildings within the bounds of the GED4ALL taxonomy. NLP can be used to layer additional, building-by-building information onto existing exposure models based on more conventional data. Although the results of this study are preliminary, NLP may prove a valuable tool for enhancing exposure modeling for multi-hazard risk quantification and management.
| Type: | Proceedings paper |
|---|---|
| Title: | Enhancing Natural-Hazard Exposure Modeling Using Natural Language Processing: a Case-Study for Maltese Planning Applications |
| Event: | XIX ANIDIS Conference, Seismic Engineering in Italy |
| ISBN-13: | 9781713870418 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.prostr.2023.01.220 |
| Publisher version: | https://doi.org/10.1016/j.prostr.2023.01.220 |
| Language: | English |
| Additional information: | © 2023 The Author(s). Published by Elsevier B.V. under a Creative Commons license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
| Keywords: | Natural-hazard modeling, natural language processing, text mining, exposure modeling |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Inst for Risk and Disaster Reduction |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10171402 |
Archive Staff Only
 |
View Item |
