BRAU5 - Georgios Artopoulos скачать в хорошем качестве

BRAU5 - Georgios Artopoulos

Georgios Artopoulos “Data-driven integrated practices for the study and protection of built heritage” Historic urban environments are not given static formations disconnected from the contemporary fabric of a city, but rather a set of tangible and intangible assets subjected to dynamic pressures of economic, environmental, and social activities. The sustainable development of such environments is often threatened by urbanization, financial crises, gentrification, and migration phenomena. The cross-disciplinary nature of the pressing challenges posed by these phenomena requires the development of novel data-driven tools for social resilience, inclusion and safeguarding. Responding to this need, the Virtual Environments Lab (VELab), at the Cyprus Institute, is developing and implementing data-driven practices and tools, tested and validated in H2020, ENI-CBC-MED, Cyprus Research and Innovation Foundation and DARIAH ERIC funded projects. The aim of this activity is to advance the study of built heritage, in order to safeguard its continuous use by promoting the engagement of local communities and built asset key stakeholders through the utilisation of an integrated suite of digital tools. In this paper, the Lab’s main practices implemented in heritage management are presented through a series of research activities that vary in scale; i.e. from the scale of architectural detail to the building, and its neighbourhood. Specifically, the proposed integrated workflow for heritage asset management brings together the following data analysis and management practices that rely on: a) reality capture technologies for the in-depth annotation of the architectural features of built heritage with 3D Convolutional Neural Networks (CNN); b) Building Information Modelling (BIM) tools for the documentation, mass modelling and metadata integration of historic building assets (HBIM); c) online repositories for the open access of the public and relevant stakeholders to spatial data analytics that can be used for territorial planning, energy monitoring, education purposes and “smart city” applications. In this integrated workflow, reality capture technologies contribute to the identification and classification of the stylistic influences of architectural elements, as well as the building’s overall historic layers. In addition, BIM tools used for the incorporation of information about cost, time and energy performance, may exceed typical conservation methods for the management and preservation of heritage assets, and thus contribute to the development of retrofit proposals that are compatible with the building and cost-efficient. Main thrusts of activity for the implementation of the proposed integrated management of heritage assets are demonstrated by the paper through pilot case studies that operate in different scales. Moreover, this paper discusses the challenges for scaling up state-of-the-art data-driven methods from the building to the neighbourhood scale (i.e., to historic clusters). Finally, the presented integrated workflow aims to streamline the sustainable rehabilitation process of heritage assets and provide public administrators with useful tools for the resilient management of built heritage. Keywords: Heritage Building Information Modelling (H-BIM), Convolutional Neural Networks (CNN), Heritage Management, 3D spatial data management, Heritage Buildings and Historic Clusters.