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ZENODO
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Blind Prediction Competition: Multi-Leaf Rubble Stone Masonry Walls

Authors: Shah, Mati Ullah; Saloustros, Savvas; Beyer, Katrin;

Blind Prediction Competition: Multi-Leaf Rubble Stone Masonry Walls

Abstract

This repository provides access to the dataset of geometrical digital twins of multi-leaf rubble stone masonry walls for the blind prediction competition on the monotonic in-plane shear–compression response of multi-leaf masonry walls. It also includes the competition guidelines, instructions, submission templates, and preliminary mechanical properties of the wall constituents. The competition focuses on predicting the structural response of walls with diverse microstructures before experimental testing, which will take place in May 2026 at the Structural Engineering Platform (GIS), EPFL, Lausanne, Switzerland. A README file details the dataset structure and folder contents. For more information, visit the competition webpage: https://mati-shah.github.io/Blind-prediction-competition-/.

Keywords

Rubble stone, Geometrical digital twins, Blind prediction, Stone masonry, Microstructure

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average