README file
Data Set Title: Dataset of the paper "Crack pattern–based machine learning prediction of residual drift capacity in damaged masonry walls "
Data Set Author/s: 
D'Altri, Antonio Maria	University of Bologna	0000-0002-4932-4554

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: 2024
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://site.unibo.it/holaheris/en.  
Data set Contents
The data set consists of:
•	a README file in .rtf format
HOLAHERIS_ CrackMachineLearning _README.rtf
•	a compressed folder, named HOLAHERIS_ CrackMachineLearning-data.zip, containing:
o	15 .inp files (2x3_MON_CANT_15, 2x3_MON_FIX_15, 2x3_CYC_CANT_15, 2x3_CYC_FIX_15, 2x3_SETTL_15, 3x3_MON_CANT_15, 3x3_MON_FIX_15, 3x3_CYC_CANT_15, 3x3_CYC_FIX_15, 3x3_SETTL_15, 5x3_MON_CANT_15, 5x3_MON_FIX_15, 5x3_CYC_CANT_15, 5x3_CYC_FIX_15, 5x3_SETTL_15) which are Abaqus input files containing the models and the loading conditions for different walls sizes and boundary conditions, loaded with 15% axial load ratio. Note that the size 5x3 refers to actual size 4.2x3 m. “MON” means monotonic loading, “CYC” means cyclic loading, “SETTL” means settlement, “CANT” means cantilever conditions, “FIX” means fixed-guided conditions. These files can be opened anyway as standard text file. Accordingly, the block assembly, the nodes coordinates matrix and the element connectivity matrix can be extracted from each .inp file. To change the axial load ratio, it is possible to simply change the value of the pressure load in the step “Grav_step”.
o	3x3_10_CANT_Pushovers.xlsx containing data about pushover analyses, in terms of force – displacement curves, for the wall 3x3, with 10% axial load ratio, in cantilever condition. Forces are expressed in N and displacements are expressed in m.
o	3x3_10_CRACK_cumulative.xlsx containing the crack width cumulative distributions and the crack width exceedance curves for the wall 3x3, with 10% axial load ratio. Crack width is expressed in m.
o	DATA_ML_RESULTS.py contains the Python code which implements the drift loss prediction method proposed in the reference paper. A simple run generates plots similar to Fig. 13 of the reference paper. The file contains all the input data, in terms of crack width exceedance curves, wall size, axial load ratio, boundary condition, type of load, and drift loss. 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 residual drift capacity in damaged masonry walls based on mechanically consistent crack patterns induced by external actions (earthquake-like loads and differential settlements). The machine learning predictor is trained through 100 crack patterns generated by an accurate block-based numerical model, and the related residual displacement capacity estimated by means of the numerical model within a pushover analysis framework. 12 additional cases, with different geometries, textures, axial load ratios and sizes, are also used to validate the approach a posteriori. 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 the numerical models developed and examples of extractions of pushover curves and crack width cumulative distributions. All input and output data are collected within the same Python code.
Notes 
The data, models and results are presented in the following publication:
Pereira, M., D’Altri, A. M., de Miranda, S., & Glisic, B. (2024). Crack pattern–based machine learning prediction of residual drift capacity in damaged masonry walls. Computer-Aided Civil and Infrastructure Engineering, 1–15. https://doi.org/10.1111/mice.13212