Work

An expert-driven digital platform for decision support in sustainable building retrofitting

Year: 2025

Type: article

Abstract: • Developed an expert-driven digital platform for sustainable building retrofit decision-making. • Supports both global and property-specific configurations with adaptable decision criteria. • Integrates a structured three-step decision-making workflow. • Combines subjective and objective weighting with hybrid analysis for transparent rankings. • Validated through a mid-terrace case study using expert input and location-specific environmental data. This study introduces an expert-guided decision-support platform developed to improve the selection of building retrofit measures for energy efficiency. The platform addresses a key gap in existing tools by combining expert input with systematic decision logic, offering a more transparent and adaptable approach to retrofit planning. Unlike simulation-based or policy-orientated systems, this platform focuses on supporting real-world decisions at the property level. It allows for both general (global) and building-specific configurations, giving users the flexibility to define and adjust decision criteria based on retrofit needs. A three-phase analytical workflow supports users via assigning expert importance, classifying assessment criteria, and deciding on a ranking method. The strategy combines data-driven and expert-based weighing methodologies, resulting in balanced and context-aware outputs. The system includes an explainable AI module that generates editable reasons for final recommendations, allowing stakeholders to better understand and discuss decisions. The platform’s efficacy was demonstrated through a case study of a mid-terrace house, showing strong potential for supporting consistent, stakeholder-informed, and auditable retrofit decisions. This work contributes a flexible and scalable solution of practical value to planners, housing authorities, and retrofit consultants.

Source: Energy and Buildings

DOI: 10.1016/j.enbuild.2025.116770

Authors: Hafiz Muhammad Athar Farid, Shamaila Iram, Hafiz Muhammad Shakeel, Richard Hill, Vladimir Simic

Institutions: University of Huddersfield, Leeds Beckett University, Doğuş University, Széchenyi István University, Yuan Ze University

Language: English

Topic: Building Energy and Comfort Optimization

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Climate action, Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $4,260