Case-based reasoning approach for decision-making in building retrofit: A review

Li, Yangluxi, Du, Hu and Basavapatna Kumaraswamy, Satish ORCID: https://orcid.org/0000-0002-1056-0242 2024. Case-based reasoning approach for decision-making in building retrofit: A review. Building and Environment 248 , 111030. 10.1016/j.buildenv.2023.111030

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Abstract

The rapid development of computer science has brought inspirations to building retrofit. Artificial intelligence (AI) provides more possibilities in decision-making for building retrofit, could be regarded as an alternative strategy compared to the abundant research time spent in the early decision-making stage of traditional retrofit approaches. This paper reviews the application of the statistic algorithm and AI approach, including CBR, in building retrofit decision-making, and the essential process of CBR, such as workflow, similarity degree calculation method, weight factors correction manner, and input or output content using building design to provide a synthetic overview of CBR utilisation in the building retrofit realm. Among those different models, Case-Based Reasoning (CBR) is valuable in providing references and avoiding possible failures, which is a promising approach for building retrofit. Yet, current research mainly focused on its utilisation to solve specific issues. There is still a lack of systematically summarised research on Case-Based Reasoning solution. Therefore, this study analyses the methods used for CBR approach in the field of building retrofit decision-making process, aiming to find the characteristics of internal commonness. It concludes that CBR has two significant impact factors: similarity attribute type and similarity calculation manner, which determines the judgement process. The results show that the CBR solution has great application potential in further building retrofit design.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Architecture
Additional Information:	License information from Publisher: LICENSE 1: Title: This article is under embargo with an end date yet to be finalised.
Publisher:	Elsevier
ISSN:	0360-1323
Date of First Compliant Deposit:	23 November 2023
Date of Acceptance:	11 November 2023
Last Modified:	04 Dec 2023 09:50
URI:	https://orca.cardiff.ac.uk/id/eprint/164193

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