ArticleProceedings of the National Academy of Sciences of the United States of America2019
Forecasting failure locations in 2-dimensional disordered lattices.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- A review on molecular simulations for the rupture of cross-linked polymer networks.Science and technology of advanced materials · 2025Review
- Network bypasses sustain complexity.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Vein fate determined by flow-based but time-delayed integration of network architecture.eLife · 2023Article
- Forecasting the evolution of fast-changing transportation networks using machine learning.Nature communications · 2022Article
- Edge betweenness centrality as a failure predictor in network models of structurally disordered materials.Scientific reports · 2022Article
- Link-usage asymmetry and collective patterns emerging from rich-club organization of complex networks.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Network analysis predicts failure of materials and structures.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
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3 authors.
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Abstract
Forecasting fracture locations in a progressively failing disordered structure is of paramount importance when considering structural materials. We explore this issue for gradual deterioration via beam breakage of 2-dimensional (2D) disordered lattices, which we represent as networks, for various values of mean degree. We study experimental samples with geometric structures that we construct based on observed contact networks in 2D granular media. We calculate geodesic edge betweenness centrality, which helps quantify which edges are on many shortest paths in a network, to forecast the failure locations. We demonstrate for the tested samples that, for a variety of failure behaviors, failures occur predominantly at locations that have larger geodesic edge betweenness values than the mean one in the structure. Because only a small fraction of edges have values above the mean, this is a relevant diagnostic to assess failure locations. Our results demonstrate that one can consider only specific parts of a system as likely failure locations and that, with reasonable success, one can assess possible failure locations of a structure without needing to study its detailed energetic states.
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