Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/228768
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dc.titleADVANCED AI AND MACHINE LEARNING FOR COMPUTATIONAL BUILDING DESIGN AND OPTIMIZATION: CURRENT TRENDS AND FUTURE PROSPECTS
dc.contributor.authorCHONG YEONG JO, ELAINE
dc.date.accessioned2022-07-18T07:11:56Z
dc.date.available2022-07-18T07:11:56Z
dc.date.issued2022
dc.identifier.citationCHONG YEONG JO, ELAINE (2022). ADVANCED AI AND MACHINE LEARNING FOR COMPUTATIONAL BUILDING DESIGN AND OPTIMIZATION: CURRENT TRENDS AND FUTURE PROSPECTS. ScholarBank@NUS Repository.
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/228768
dc.description.abstractBuilt environment sector is one of the largest contributors to climate change and faces an array of challenges which includes inadequate use of technology. To combat, digitalisation has been one of the most sought out measures to reinvent the sector into a sustainable and future-proofed industry proposed by international and governmental bodies. Computational intelligence has been introduced to further advance the sector’s move in digitalisation. In recent years, major advancements in artificial intelligence and machine learning have exploded in its potential for exploration. However, much of the studies has yet to clearly define its uses and application in the field of computational building design and optimization across different fields. This work set forth to establish and identify the current trends of artificial intelligence and machine learning while also exploring possible areas of gaps in the knowledge domain.
dc.subjectArtificial Intelligence
dc.subjectMachine Learning
dc.subjectComputational Design
dc.subjectComputational Design Optimization
dc.subjectComputational Building Design
dc.typeDissertation
dc.contributor.departmentTHE BUILT ENVIRONMENT
dc.contributor.supervisorGAN JIELONG
dc.description.degreeBACHELOR'S
dc.description.degreeconferredBACHELOR OF SCIENCE (PROJECT AND FACILITIES MANAGEMENT)
Appears in Collections:Bachelor's Theses

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