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Intertwining the Tangible and Intangible Aspects of Historic Building Information Modelling (HBIM) SEBE0025

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  • Full or part time
    Dr Suha Jaradat
    Prof John Currie
  • Application Deadline
    No more applications being accepted
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

PROJECT DESCRIPTION
We currently live in data-rich societies where we constantly collect various forms of data in both systematic and unsystematic ways. Questions arise regarding how to increase the value and sustainability of all available data. Building Information Modelling (BIM) is a process that is increasingly being used to collect data about building and infrastructure projects. At the heart of BIM is a digital model with attached data that should be used across the lifecycle of the project. Historic Building Information Modelling (HBIM), is a system used to model historic buildings and structures from laser scanning and photogrammetric data using Building Information Modelling (BIM) tools. Large data sets with analytics and visualisations have been successfully applied to the field of heritage and its sustainability. Advanced digital tools enable virtual construction of heritage structures however this process has its own limitations. Heritage is a complex concept that has been loosely defined by different people in different ways. For example, heritage defined by experts may not necessarily mean the same thing to local communities or visitors. Heritage comprises the day-to-day life of people including their political and economic contexts. The way people perceive and interrelate with heritage varies however this social aspect of interacting with heritage is usually neglected. Policy makers and designers often make assumptions about how to conserve and develop historical sites without involving local people and end users. Behavioural architecture focuses on the interactions of human beings and the built environment by considering the social, cognitive and emotional aspects in studying the spatial behaviour of users. The aim of this project is to apply behavioural architecture tools to the field of HBIM to maximize the value of heritage and improve its sustainability. The focus of this project will be on a significant case study. Behavioural architecture and digital tools will be employed to collect and stitch various pieces of the story associated with that place. This narrative will contribute to sustaining the built heritage for future generations by intertwining the tangible and the intangible aspects of HBIM.
This is a full-time vacancy therefore part-time applicants will not be considered.

Academic qualifications
A first degree (at least a 2.1) ideally in architecture with a good fundamental knowledge of BIM and HBIM.

English language requirement
IELTS score must be at least 6.5 (with not less than 6.0 in each of the four components). Other, equivalent qualifications will be accepted. Full details of the University’s policy are available online.

Essential attributes:
• Experience of fundamental Building Information Modelling (BIM) and Historic Building Information Modelling (HBIM)
• Competent in using 3D modelling BIM tools
• Knowledge of architecture, heritage and behavioural architecture
• Good written and oral communication skills
• Strong motivation, with evidence of independent research skills relevant to the project
• Good time management

Desirable attributes:
Competent in using laser scanning tools

For informal enquiries about this PhD project, please contact Dr Suha Jaradat [Email Address Removed]

Funding Notes

This is an unfunded position.

References

Sacks, R, Eastman, C, Lee, G, and Teicholz, P (2018) BIM Handbook: A Guide to Building Information Modeling for Owners, Designers, Engineers, Contractors, and Facility Managers (3rd ed.). Hoboken, New Jersey: John Wiley & Sons.

Arayici, Y., & Proquest. (2017). Heritage building information modelling. Ann Arbor, Mich.]: ProQuest.

How good is research at Edinburgh Napier University in Architecture, Built Environment and Planning?

FTE Category A staff submitted: 7.90

Research output data provided by the Research Excellence Framework (REF)

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