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Determining uncertainties in AI applications in AEC sector and their corresponding mitigation strategies

An, Yi, Li, Haijiang, Su, Tengxiang and Wang, Yitong 2021. Determining uncertainties in AI applications in AEC sector and their corresponding mitigation strategies. Automation in Construction 131 , 103883. 10.1016/j.autcon.2021.103883

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Abstract

The Artificial Intelligence (AI) methodologies and techniques have been used to solve a wide spectrum of engineering problems in Architectural, Engineering and Construction (AEC) industry with the aim of improving overall productivity and optimized decision throughout full project life cycle (planning, design, construction and maintenance). However, many AI applications are facing different limitations and constrains due to the lack of comprehensive understanding about the inherent uncertainty fundamentally and mathematically, hence the use of AI has not achieved a satisfactory level. It requires different actions to tackle different types of uncertainties which varies according to different types of applications. This paper therefore reviews 5 type of popular AI algorithms, including Primary Component Analysis, Multilayer Perceptron, Fuzzy Logic, Support Vector Machine and Genetic Algorithm; then examines how these artificial intelligence techniques can assist the decision-making process by mitigating uncertainty meanwhile achieving the expected high efficiency. The paper reviews each germane technique, mathematical explanation, analysis of reasons causing uncertainty, and concludes a set of guidelines and an application framework for optimizing their informed uncertainty for AEC applications. This work will pave the way for the fundamental understanding and in turn to provide a valuable reference for applying AI techniques in AEC sector properly to achieve better overall performance.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Additional Information: This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
Publisher: Elsevier
ISSN: 0926-5805
Date of First Compliant Deposit: 9 September 2021
Date of Acceptance: 8 August 2021
Last Modified: 31 Jan 2022 07:44
URI: https://orca.cardiff.ac.uk/id/eprint/143487

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