FEM_buildings

This repository contains the codes for generating finite element models of masonry buildings. The hereby methodology was presented in the paper "Automated image-based generation of finite element models for masonry buildings" by Pantoja-Rosero et., al. (2023)

How to use it? (Note: tested for ubuntu 18.04lts)

1. Clone repository

Clone repository in your local machine. All codes related with method are inside the src directory.

2. Download data and CNN weights

Example input data can be downloaded from Dataset for automated image-based generation of finite element models for masonry buildings. This datased contains 3 main folders (weights, data and results). Extract the folders data/ and weights/ and place them inside the repository folder.

2a. Repository directory

The repository directory should look as:

FEM_buildings
└───data
└───docs
└───examples
└───src
└───weights

3. Environment

Create a conda environment and install python packages. At the terminal in the repository location.

conda create -n FEM_buildings python=3.8

conda activate FEM_buildings

pip install -r requirements.txt

4. Third party software

• Meshroom and Polyfit: The method needs as input Structure from Motion information and LOD2 model (see data folder) that are computed by Meshroom and Polyfit respectively. Please refeer to the links to know how to use their methodologies.

• FreeCAD: In addition to create the final 3D DADT building models, it is necessary FreeCAD python console and its methods. You can either download the appimage and extract their content as freecad_dev or download the folder here freecad_dev. Place the folder freecad_dev in the repository location. The repository directory should look as:

FEM_buildings
└───data
└───docs
└───examples
└───freecad_dev
  └───usr
    └───bin
    └───...
└───src
└───weights
└───LICENSE
└───README.md
└───requirements.txt
└───.gitignore

• Matlab: the some of the scripts are written in matlab (it will be updated soon to python). Follow the next steps to make matlab usable from terminal:

  • Install matlab in your pc
  • Add the path of the Matlab's binary file to the environment file -> in the terminal write: sudo vim /etc/environment and add at the end :/usr/local/MATLAB/R2022b/bin" and save
  • in the terminal export PATH=$PATH:/usr/local/MATLAB/R2022b/bin

• Julia and Amaru: If in the examples the flag for modal_analysis is true, it need Amaru FEM software for the analysis with solid elements. Follow the next steps:

  • Install Julia (tested with version 1.8.0)
    • Download binaries from Julia (Generic Linux binaries for x86)
    • Extract files
    • Copy the bin's folder path (/home/pantojas/julia-1.8.0-linux-x86_64/julia-1.8.0/bin)
    • open terminal and write sudo vim /etc/environment
    • Add at the final the :bin path (:/home/pantojas/julia-1.8.0-linux-x86_64/julia-1.8.0/bin) and save
    • in the terminal export PATH=$PATH:/home/pantojas/julia-1.8.0-linux-x86_64/julia-1.8.0/bin
  • Install Amaru
    • open terminal and call julia julia
    • press the pkg key "]" and then write dev https://github.com/NumSoftware/Amaru
    • done, you are all set up.

5. Testing method with pusblished examples

Inside the folder examples/ we have provide the input scripts that our algorithm needs. Two input scripts are necessary: ..._LOD3.py and ..._FEM.py. To run for instance the example p2_00_School_FEM.py simply open the terminal inside the src folder (with the environment activated) and write the next command:

python ../examples/p2_00_School_FEM.py

The algorithm first will create the LOD3 model and then postprocess it to generate the FEM models. Run the other examples similarly to the previous inline command.

Note: After displaying the finite element models close the windows to continue. For the macroelements is necessary to write exit() and hit enter to continue.

IMPORTANT change the paths according your pc of: 1) line 3 in the file LOD3_builder.sh; 2) line 6 in the file MAIN_2_writeAnalyses.m; 3) line 3 in the file MAIN_3_processAnalyses.m

6. Creating your own digital twin as LOD3 model

Create a folder your_example inside the data\ folder. Inside your_example create extra folders with the next structure:

FEM_buildings
└───data
  └───your_example
    └───images
      └───im1
    └───polyfit
    └───sfm
...

The methodology requires as input the next:

• sfm.json: file containing the sfm information (camera poses and structure). Add to the default Meshroom pipeline a node ConverSfMFormat and connect its input to the SfMData output from the node StructureFromMotion. In the node ConverSfMFormat options modify the SfM File Format to json. After running the modified Meshroom pipeline, this file is output in the folder MeshroomCache/ConvertSfMFormat/a_folder_id/. Copy that file inside the your_example/sfm/
• A registered view image for each facade containing the openings: For each facade, place one image in which the openings are visible in the folder data/your_example/images/im1/.
• polyfit.obj: use Polyfit pipeline either with the sparse or dense point cloud produced by the Meshroom pipeline. Note that it might be necessary to pre-process the point clouds deleting noisy points before running Polyfit. Save the output file as polyfit.obj or polyfit_dense.obj and place it in the folder data/your_example/polyfit/

Check the files of the data examples provided if neccessary to create the input data.

Finally create the two input scripts (your_example_LOD3.py and your_example_FEM.py) following the contents the given examples. Open the terminal inside the src folder (with the environment activated) and write the next command:

python ../examples/your_example_FEM.py

7. Results

The results will be saved inside results folder with the following structure:

FEM_buildings
└───results
  └───your_example
    └───EFM_analysis
    └───EFM_discretization
    └───FEM_solids
    └───LOD
...

7.a Final repository directory

The repository directory after runing the medothology looks as:

FEM_buildings
└───data
└───docs
└───examples
└───freecad_dev
└───results
└───src
└───weights
└───LICENSE
└───README.md
└───requirements.txt
└───.gitignore

8. Citation

We kindly ask you to cite us if you use this project, dataset or article as reference.

Paper:

@article{Pantoja-Rosero2023c,
title = {Automated image-based generation of finite element models for masonry buildings},
journal = {Bulleting of Earthquake engineering},
year = {2023},
doi = {https://doi.org/10.1007/s10518-023-01726-7},
url = {https://link.springer.com/article/10.1007/s10518-023-01726-7},
author = {B.G. Pantoja-Rosero and R. Achanta and K. Beyer},
}

Dataset:

@dataset{Pantoja-Rosero2023c-ds,
  author       = {Pantoja-Rosero, Bryan German and
                  Achanta, Radhakrishna and
                  Beyer, Katrin},
  title        = {Dataset for automated image-based generation of finite element models for masonry buildings},
  month        = aug,
  year         = 2023,
  publisher    = {Zenodo},
  version      = {v.0.0},
  doi          = {10.5281/zenodo.8094306},
  url          = {https://doi.org/10.5281/zenodo.8094306}
}