From Data to Cultural Response: A Machine Learning–Driven Digital Twin Model for Smart Heritage Precincts in Urban Context

Shiran Geng¹, Se Yan², Hing-Wah Chau¹, Elmira Jamei¹, Zora Vrcelj¹

1. Institute of Sustainable and Liveable Cities, Victoria University, Melbourne, Australia
2. The Faculty of Architecture, Building and Planning, the University of Melbourne, Melbourne, Australia

Published in the Journal of Information Technology in Construction, volume 30, pages 1314 to 1331, September 2025, in the special issue Smart and Sustainable Built Environment (SASBE 2024), guest edited by Mahesh Babu Purushothaman, Ali GhaffarianHoseini, Amirhosein GhaffarianHoseini and Farzad Rahimian. DOI 10.36680/j.itcon.2025.053.

Abstract

In the context of Smart Cities, Smart Heritage has emerged as a forward-oriented strategy aimed at enhancing the construction, management, accessibility, and sustainability of culturally significant environments. Yet, within Smart Heritage discourse, the distinction between basic digital representations and truly responsive, sensor-informed systems remains underdeveloped. This study addresses this gap by proposing a machine learning–enhanced digital twin simulation framework that enables both real-time and anticipatory heritage interventions. Using Chinatown Melbourne as an urban heritage case study, five open-access urban datasets, pedestrian counting, on-street parking, microclimate conditions, dwelling functionality, and Microlab sensor data (CO₂, sound level, and accelerometer), were evaluated, with three integrated into a pilot simulation model. A key contribution is the inclusion of a conceptual ‘Heritage Layer’ that overlays cultural significance and symbolic meaning across all stages of system logic and design response. The model also incorporates a dedicated machine learning layer, trained on full-year 2024 sensor data, to forecast environmental and behavioural triggers such as crowd build-up. This predictive capability enables the system to shift from reactive monitoring to proactive design interventions aligned with cultural rhythms. A December 2024 simulation validated the frequency and relevance of trigger-based activations. Rather than relying on platform-specific code, the framework is designed for adaptability across construction informatics environments and heritage precincts globally. Findings demonstrate how Smart Heritage systems can bridge environmental sensing, cultural identity, and post-construction evaluation, offering a scalable methodology for digitally responsive, culturally attuned urban heritage management.

Keywords

Related Conference Paper

Exploring the Use of Open Access Data in Smart Heritage – Using Chinatown Melbourne as a Case Study, SASBE 2024 proceedings, chapter 81.