Simeon, GillSteve, HayesKristina, BrubacherAlhassawi, Ruqey Ali2026-06-092026https://hdl.handle.net/20.500.14154/79164Significant challenges persist in realising the full potential of technology related to accurate and inclusive body dimension variation and garment sizing and fit. Traditional methods often fail to capture the complexity of human body morphology, highlighting the value of more detailed approaches to analysing body dimension variation. This doctoral research aims to support the visual analysis of anthropometric population data through the integration of artificial intelligence (AI) and machine learning (ML) techniques, addressing limitations in traditional anthropometric methods used for apparel sizing and body–to–pattern mapping. A mixed–methods approach was employed across five interconnected phases, leveraging 3D body scanning (3DBS) technology to analyse and compare real–world body dimensions, classical garment sizing classifications and garment patterns. The research involved: (1) a comprehensive analysis of 3DBS data to establish body dimension diversity, (2) a critical reassessment of the traditional 8–head figure ratio, (3) clustering algorithms (Hierarchical, self–organizing map (SOM), k–means) to classify body types, (4) application of support vector machine (SVM) and principal component analysis–SVM (PCA–SVM) models for accurate size prediction, and (5) enhanced regression analysis to develop a data–driven approach for garment pattern adjustment. A dataset of 677 female participants from a range of ethnic backgrounds was utilised. Significant dimensional variations within conventional size groups were identified, revealing limitations in traditional measurement-based sizing systems within the study sample. Key findings demonstrate frequent deviations from the classical 8–head figure proportion model, emphasising the need for a more comprehensive approach. Clustering algorithms successfully delineated distinct morphological categories, while SVM modelling exposed trade–offs between predictive accuracy and computational complexity. Regression analysis established quantitative relationships between body measurements and pattern block parameters, offering a means of examining how body dimension variation relates to patternmaking practice. This research makes several theoretical, methodological and practical contributions. Theoretically, it provides data-based evidence of body proportion variability within standard size categories, challenges the classical 8-head figure proportion model using measured data, and identifies distinct body shape clusters within the study sample. Methodologically, it applies an integrated analytical framework – combining 3D body scan data, statistical analysis, ML clustering and classification, and regression analysis – to examine body dimension variation and body-to-pattern relationships. Practically, it provides how data-driven analysis of anthropometric variation may inform patternmaking considerations, subject to further applied investigation. This research examines the integration of 3D body scanning and computational techniques within anthropometric analysis. The use of data-based derived visual tools provides a means of representing and exploring body variation within the study sample. The findings highlight the potential relevance of data-driven approaches to sizing and may inform further investigation into how body diversity is represented within garment sizing systems.435enArtificial intelligenceMachine Learning3D body scanningThesis