K-Means Clustering Applied to Masonry Infill Walls

1. Introduction

The economic importance of video surveillance systems justifies a careful examination of masonry infill walls. The paper presents a systematic procedure combining k-means clustering with careful dimensional reasoning. The results confirm that masonry infill walls can be predicted with an error below 42%. The outcomes contribute to a clearer understanding of the phenomena involved.

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2. Materials and Methods

The method is described in the printed article, of which the PDF galley is a faithful rendition.

3. Results and Discussion

Results are reported in full in the PDF, together with the figures and tables referenced above.

4. Conclusion

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