Semantic Scholar Open Access 2023 19 sitasi

Data-Driven Multi-Scale Study of Historic Urban Environments by Accessing Earth Observation and Non-Destructive Testing Information via an HBIM-Supported Platform

G. Artopoulos P. Fokaides V. Lysandrou Marissia Deligiorgi Panos Sabatakos +1 lainnya

Abstrak

ABSTRACT Digital analytical tools combined with 3D documentation are used incrementally in building rehabilitation in the conservation state analysis process. In the last decade, due to the current advancements in the Architecture Engineering Construction (AEC) industry, the application of BIM methods in heritage building conservation started becoming more attractive for specialists and practitioners. In light of the latest concepts in data management at city level, as a result of the discussion about smart city representations, the use of a shared digital environment that caters to technical studies related to conservation analysis, building provenance, structural changes, and urban context transformations can lead to reduced time, improved quality, and lowered cost of city management for all domain experts and city stakeholders. This paper explores the benefits of multi-scale and discipline digitization for the restoration of heritage buildings, highlighting the potential impact of innovative data integration, methods, and workflows on architectural renovation and energy upgrades. Specifically, it focuses on the integration of conservation information for heritage buildings and large-scale environmental analysis data for historic clusters in modern cities.

Penulis (6)

G

G. Artopoulos

P

P. Fokaides

V

V. Lysandrou

M

Marissia Deligiorgi

P

Panos Sabatakos

A

A. Agapiou

Format Sitasi

Artopoulos, G., Fokaides, P., Lysandrou, V., Deligiorgi, M., Sabatakos, P., Agapiou, A. (2023). Data-Driven Multi-Scale Study of Historic Urban Environments by Accessing Earth Observation and Non-Destructive Testing Information via an HBIM-Supported Platform. https://doi.org/10.1080/15583058.2023.2199408

Akses Cepat

Informasi Jurnal
Tahun Terbit
2023
Bahasa
en
Total Sitasi
19×
Sumber Database
Semantic Scholar
DOI
10.1080/15583058.2023.2199408
Akses
Open Access ✓