This Special Issue aims to advance Digital Twin (DT) integration into building pathology, supporting a transition from reactive defect diagnostics to predictive, data-driven adaptation systems. It brings together multidisciplinary research across data science, structural engineering and facility management, with a focus on the post-construction lifecycle of existing buildings.
The issue seeks frameworks that integrate Internet of Things (IoT) sensors, artificial intelligence (AI) and scan-to-BIM-to-DT workflows to monitor building health, diagnose hidden anomalies and simulate climate adaptation strategies. Particular attention is given to live-updating virtual models for assessing structural decay and material degradation, predictive simulations of building performance under environmental stress, and decision support for retrofitting and asset management.
Contributions should help connect theoretical models with practical decisions that extend building lifespans and improve operational resilience. The issue also seeks critical evaluations of validated diagnostic and retrofitting methods, common methodological pitfalls, and unintended building pathologies arising from poorly conceived climate adaptation measures. These themes support safer and more enduring communities, with an emphasis on SDG 9 and SDG 11.
List of Topic Areas
1. Scan-to-BIM-to-Digital Twin workflows: Methodologies for automated defect identification, geometric quality assurance and multi-sensor data fusion in existing structures.
2. AI and deep learning in building diagnostics: Machine learning applications for predictive asset modelling, automated image labelling of structural decay and text classification for facility service requests.
3. Climate adaptation and retrofitting simulations: Dynamic decision-support frameworks and AI-driven generative design for evaluating structural resilience and retrofitting under extreme environmental stress.
4. Socio-technical governance and data readiness: Organizational attributes, data governance standards, such as ISO 19650, and digital leadership frameworks required to scale Digital Twin operations in facility management.
5. Circular economy and material traceability: Integrating material recyclability tracking, predictive building maintenance plans and structural health monitoring to minimize lifecycle waste.
6. Lessons learned and do’s and don’ts: Critical evaluations of failed retrofits, unintended pathologies and methodological best practices in climate risk assessment.
Submissions Information
Submissions are made using ScholarOne Manuscripts. Registration and access are available at:
https://mc.manuscriptcentral.com/ijbpa
Author guidelines must be strictly followed. Please see:
https://www.emeraldgrouppublishing.com/journal/ijbpa#jlp_author_guidelines
Authors should select “Digital Twins for Building Pathology Diagnosis and Adaptation Decisions” from the drop-down menu at the appropriate step in the submission process, in response to “Please select the issue you are submitting to”.
Submitted articles must not have been previously published, nor should they be under consideration for publication anywhere else while under review for this journal.
Journal Information: Scopus Journal Q2, H-Index 47
Key Deadlines
The dates below are proposed in the Special Issue proposal and remain subject to agreement with the Editor and Commissioning Editor.
Opening date for manuscript submissions: 1 October 2026
Closing date for manuscript submissions: 1 April 2027
Closing date for abstract submission: Not applicable. No separate call for abstracts is planned.
Email for submissions: Not applicable. All manuscripts must be submitted through ScholarOne Manuscripts.
For enquiries about the Special Issue, contact the lead Guest Editor at arahimirahman@umpsa.edu.my
Guest Editors
Rahimi A. Rahman
University Malaysia Pahang Al-Sultan Abdullah, Malaysia
arahimirahman@umpsa.edu.my
Don Amila Sajeevan Samarasinghe
Massey University, New Zealand
D.Samarasinghe@massey.ac.nz
Mustika Sari
Universitas Indonesia, Indonesia
mustika.sari01@ui.ac.id
Syafizal Shahruddin
Universiti Sains Malaysia, Malaysia
syafizal@usm.my
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