Dataset of the paper "Simulation-driven machine learning for real-time damage prognosis in masonry structures"

Abstract

This dataset, developed as part of the Horizon 2020 HOLAHERIS project, contains data, models and results related to a machine learning predictor for damage prognosis in cracked masonry walls based on mechanically consistent crack patterns induced by external actions (earthquake-like loads and differential settlements). The stress increase indicator machine learning predictor is trained through more than 100 crack patterns generated by an accurate block-based numerical model, and the related stress increase indicator. Good predictions on masonry piers with features different from those used in the training data support the generalization potential of the proposed method. Accordingly, the training data set could be straightforwardly enlarged also by using numerical models for masonry (e.g., utilized in other research groups). The machine learning predictor is implemented within a Python code which is released in this dataset, together with input data which are collected within the same Python code