README file
Data Set Title: Dataset of the paper “Simulation-driven machine learning for real-time damage prognosis in masonry structures”
Data Set Author/s: 
D'Altri, Antonio Maria	University of Bologna	0000-0002-4932-4554
Pereira, Mauricio		Princeton University
de Miranda, Stefano		University of Bologna
Glisic, Branko			Princeton University

Data Set Contact Person/s: 
D'Altri, Antonio Maria	University of Bologna	am.daltri@unibo.it

Data Set License: this data set is distributed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, https://creativecommons.org/licenses/by/4.0/

Publication Year: 2025
Project Info: HOLAHERIS (A holistic structural analysis method for cultural heritage structures conservation), funded by European Union, Horizon 2020 Programme. Grant Agreement no. 101029792; https://doi.org/10.3030/101029792; https://site.unibo.it/holaheris/en.
Data set Contents
The data set consists of:
•	a README file in .rtf format 
HOLAHERIS_StressMachineLearning _README.rtf
•	StressMachineLearning_Data_Code.py contains the Python code which implements the stress increase indicator prediction method proposed in the reference paper (indicated in the section “Notes” of this README file). A simple run generates plots similar to Fig. 9 of the reference paper. The .py file also contains all the input data in the array “X”, in terms of crack width exceedance curves (first five columns), axial load ratio (6th column), type of load (7th column), boundary condition (8th column), and wall size (9th column). Tested with Python 3.+. All packages should be readily available using Conda packages.

Data set Documentation
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.
Notes 
The data, models and results are presented in the following publication:
D’Altri, A. M., Pereira, M., de Miranda, S., & Glisic, B. (2025). Simulation-driven machine learning for real-time damage prognosis in masonry structures. International Journal of Mechanical Sciences, 289, 110055. https://doi.org/10.1016/j.ijmecsci.2025.110055