[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126403-en":3,"doc-seo-126403-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126403,962085571259,"Theodora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","From Data to Cultural Response - A Machine Learning–Driven Digital Twin Model for Smart Heritage Precincts in Urban Context","Smart Cities enable autonomous technologies in the built environment, and Smart Heritage extends smart urbanism to cultural and historical layers of cities. However, Smart Heritage discourse often lacks a mature distinction between basic digital representations and truly responsive sensor-informed systems. This study proposes a machine learning–enhanced digital twin simulation framework for real-time and anticipatory heritage interventions using Chinatown Melbourne as an urban case, validating trigger-based activations and enabling proactive, heritage-aware design supported by environmental sensing and cultural meaning.","From Data to Cultural Response: A Machine Learning–Driven Digital Twin Model for Smart Heritage Precincts in Urban Context  \nThis is the Published version of the following publication  \nGeng, Shiran, Yan, Se, Chau, Hing-Wah, Jamei, Elmira and Vrcelj, Zora (2025) From Data to Cultural Response: A Machine Learning–Driven Digital Twin Model for Smart Heritage Precincts in Urban Context. Journal of Information Technology in Construction. p. 1314. ISSN 1874-4753  \nThe publisher’s official version can be found at [https://doi.org/10.36680/j.itcon.2025.053](https://doi.org/10.36680/j.itcon.2025.053)  \nNote that access to this version may require subscription.  \nDownloaded from VU Research Repository [https://vuir.vu.edu.au/49665/](https://vuir.vu.edu.au/49665/)  \nFROM DATA TO CULTURAL RESPONSE: A MACHINE LEARNING– DRIVEN DIGITAL TWIN MODEL FOR SMART HERITAGE PRECINCTS IN URBAN CONTEXT  \nSUBMITTED: April 2025  \nREVISED: July 2025  \nPUBLISHED: September 2025  \nEDITOR: Mahesh Babu Purushothaman, Ali GhaffarianHoseini, Amirhosein Ghaffarianhoseini, Farzad Rahimian  \nDOI: 10.36680/j.itcon.2025.053  \nShiran Geng, Lecturer  \nInstitute of Sustainable and Liveable Cities, Victoria University, Melbourne, Australia  \nORCID: [https://orcid.org/0000-0001-6992-1420](https://orcid.org/0000-0001-6992-1420)  \n[shiran.geng@vu.edu.au](shiran.geng@vu.edu.au)  \nSe Yan*, PhD Candidate (corresponding author)  \nThe Faculty ofArchitecture, Building and Planning, the University of Melbourne, Melbourne, Australia ORCID: [https://orcid.org/0000-0003-2226-6654](https://orcid.org/0000-0003-2226-6654)  \n[sey@student.unimelb.edu.au](sey@student.unimelb.edu.au)  \n[Hing-Wah Chau](Hing-Wah Chau), Senior Lecturer  \nInstitute of Sustainable and Liveable Cities, Victoria University, Melbourne, Australia  \nORCID: [https://orcid.org/0000-0002-3501-9882](https://orcid.org/0000-0002-3501-9882)  \n[hing-wah.chau@vu.edu.au](hing-wah.chau@vu.edu.au)  \nElmira Jamei, Associate Professor  \nInstitute of Sustainable and Liveable Cities, Victoria University, Melbourne, Australia  \nORCID: [https://orcid.org/0000-0002-7909-9212](https://orcid.org/0000-0002-7909-9212)  \n[elmira.jamei@vu.edu.au](elmira.jamei@vu.edu.au)  \nZora Vrcelj, Professor  \nInstitute of Sustainable and Liveable Cities, Victoria University, Melbourne, Australia  \nORCID: [https://orcid.org/0000-0002-1403-7416](https://orcid.org/0000-0002-1403-7416)  \n[zora.vrcelj@vu.edu.au](zora.vrcelj@vu.edu.au)  \nSUMMARY: 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-informedsystems 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 ofsystem 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 ada","cbCailLZx1vK4eFZ","https://ap.wps.com/l/cbCailLZx1vK4eFZ","pdf",814790,5,1,20,"English","en",105,"# Introduction\n## Smart Cities and Smart Heritage Context\n# Proposed Machine Learning–Enhanced Digital Twin Framework\n## Heritage Layer Concept\n## Machine Learning Forecasting Layer\n# Case Study and Data Evaluation\n## Chinatown Melbourne Datasets and Sensors\n## Integrated Pilot Simulation Model\n# Validation and Findings\n## Trigger-Based Activation Relevance\n## Adaptability Across Environments","[{\"question\":\"What problem does the digital twin framework address in Smart Heritage?\",\"answer\":\"It targets the underdevelopment of responsive, sensor-informed systems compared with basic digital representations, enabling real-time and anticipatory heritage interventions.\"},{\"question\":\"Which data sources are evaluated in the Chinatown Melbourne case study?\",\"answer\":\"The study evaluates five open-access urban datasets plus pedestrian counting, on-street parking, microclimate conditions, dwelling functionality, and Microlab sensor data including CO₂, sound level, and accelerometer.\"},{\"question\":\"How does the model move from reactive monitoring to proactive intervention?\",\"answer\":\"A dedicated machine learning layer trained on full-year 2024 sensor data forecasts environmental and behavioral triggers (e.g., crowd build-up), supporting proactive, culturally aligned design interventions.\"}]","From Data to Cultural Response - A Machine Learning–Driven Digital Twin Model for Smart Heritage Precincts in Urban Context | PDF",1785904882,50,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"from-data-to-cultural-response-a-machine-learningdriven-digital-twin-model-for-smart-heritage-precincts-in-urban-context","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/from-data-to-cultural-response-a-machine-learningdriven-digital-twin-model-for-smart-heritage-precincts-in-urban-context/126403/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What problem does the digital twin framework address in Smart Heritage?","Question",{"text":77,"@type":78},"It targets the underdevelopment of responsive, sensor-informed systems compared with basic digital representations, enabling real-time and anticipatory heritage interventions.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Which data sources are evaluated in the Chinatown Melbourne case study?",{"text":82,"@type":78},"The study evaluates five open-access urban datasets plus pedestrian counting, on-street parking, microclimate conditions, dwelling functionality, and Microlab sensor data including CO₂, sound level, and accelerometer.",{"name":84,"@type":75,"acceptedAnswer":85},"How does the model move from reactive monitoring to proactive intervention?",{"text":86,"@type":78},"A dedicated machine learning layer trained on full-year 2024 sensor data forecasts environmental and behavioral triggers (e.g., crowd build-up), supporting proactive, culturally aligned design interventions.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,111,115,120,123,127,130,134],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":30,"slug":114},6,"Technology","technology",{"id":116,"doc_module":4,"doc_module_name":47,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":22,"slug":126},9,"Religion & Spirituality","religion-spirituality",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":128,"show_sort_weight":22,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":47,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":47,"category_name":136,"show_sort_weight":20,"slug":137},19,"General","general"]