Investigation of the Effects of the Classification of Building Stock Geometries Determined Using Clustering Techniques on the Vulnerability of Galvanized Iron Roof Covers Against Severe Wind Loading

• Published in: IOP conference series. Materials Science and Engineering 2021-05, Vol.1150 (1), p.12024
• Main Authors: Tan, Liezl Raissa E., Acosta, Timothy John S., Gumaro, Joshua Joseph C., Agar, Joshua C., Tingatinga, Eric Augustus J., Plamenco, Dean Ashton D., Ereno, Mary Nathalie C., Musico, John Kenneth B., Pacer, Jihan S., Baniqued, Julius Rey D., Hernandez, Jaime Y., Villalba, Imee Bren O.
• Format: Article
• Language: English
• Subjects: Algorithms; Aspect ratio; Buildings; Cluster analysis; Clustering; Computing time; Developing countries; Galvanized iron; Galvanizing; LDCs; Parameters; Pressure distribution; Risk assessment; Roofing; Roofs; Stress concentration; Vector quantization; Wind loads
• ISSN: 1757-8981; 1757-899X
• DOI: 10.1088/1757-899X/1150/1/012024

In the risk assessment of buildings against severe wind loading, the vulnerability component of risk is highly affected by the geometry of the structure. Parameters such as the height, width, length, aspect ratio, eaves length, and roof slope, affect the pressure distribution around the structure, which in turn affects the response of galvanized iron (GI) roof covers to wind loadings. In developing countries, there is a large variation in the building geometric parameters which poses a challenge in determining the archetypes that would best represent the building population for risk assessment. This paper aims to develop and propose a method in determining the building archetypes based on its geometry. The hierarchy for grouping of geometries started with the roof type. These were gable type roofs, mono-slope type roofs and hip type roofs. The building datasets per roofing type were then clustered using a two-stage approach involving Hierarchical and K-means clustering which were based on the aforementioned geometric parameters. These algorithms will aggregate buildings having similar sets of geometric parameters but the number of clusters must be specified. In order to determine the optimal number of clusters, this study employs various validation tools or measures namely – dendrograms, variation of the variance ratio criterion (VRC) across number of clusters, validity indices such as, Davies Bouldin, Silhouette and Calinski Harabasz, and the elbow method. Although guided by these validation methods, the final selection of the number of clusters were determined considering computational time and resources. To define an archetype, the mean values of each parameter per cluster were selected. Resulting to 5, 3, and 3, archetypes for gable, hip, and mono-slope roof buildings, respectively. The selection of the archetype was further evaluated by investigating its effects on the vulnerability of GI roof covers in order to see how distinct each archetype would behave. A Kruskal Wallis test on the vulnerability curves of the different building archetypes showed that there is a significant difference between vulnerability curves under a roof shape category, which reinforces the distinction between the selected building archetypes.