Research problem
The energy and daylight consequences of adaptive-reuse expansion strategies are not well understood across real urban buildings.
Paper 9 · Building Simulation 2025
Adaptive reuse strategies in urban centers: Experimental validation using neural networks and real building data
The paper supports adaptive-reuse decisions with real-building validation and sensitivity analysis.
Figure excerpt from Garboushian et al. (2025), Building Simulation. DOI 10.1007/s12273-025-1310-2
Source-attributed paper figure
Bibliographic information
Authors: Garboushian, Razmig; Sigouin, Yannik; Elmalky, Adham M.; Araji, Mohamad T
Journal: Building Simulation, 2025, volume 18, issue 9, pages 2417–2433
Original abstract
Adaptive reuse in urban centers aims to achieve net-zero energy goals by lowering energy consumption and improving thermal comfort in existing buildings. The combined effects of building expansions on energy performance, and daylighting availability remain unexplored. This paper developed a novel simulation model by applying multi-building data and neural-networks framework to examine the impact of adaptive reuse through variables including number of floors, energy generation, façade glazing, and building expansions in various directions. The developed model was validated by comparing simulated and actual energy use of several buildings, yielding an average error of 7.88%. This error represents the deviation between the simulated and actual energy use intensity values. Energy demand reduced by expansion along the East-West axis was 41% greater than that from expansion in the South direction. This was confirmed by sensitivity analysis, with R values of approximately 0.68 for East and West expansions, and 0.16 for the South. Overall, this study demonstrates that expanding buildings in the East-West direction tends to be the most energy-efficient approach for increasing occupied spaces, with its effectiveness potentially influenced by factors such as site location, building orientation, and climatic conditions.
Method
A neural-network simulation framework uses multi-building data and variables including floor count, renewable generation, glazing, and expansion direction.
Reported evidence
Validation against actual building use yields a 7.88% average error; east-west expansion produces 41% greater energy-demand reduction than southward expansion in the reported study.
Why this paper matters
The paper supports adaptive-reuse decisions with real-building validation and sensitivity analysis.
Research questions this paper can support
- How can neural networks support adaptive reuse decisions?
- How does building expansion direction affect urban energy performance?
- Which studies validate adaptive-reuse energy models with real building data?
Related research themes
Reference
Garboushian, Razmig; Sigouin, Yannik; Elmalky, Adham M.; Araji, Mohamad T. Adaptive reuse strategies in urban centers: Experimental validation using neural networks and real building data. Building Simulation , 2025, 18, 2417–2433. https://doi.org/10.1007/s12273-025-1310-2
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