https://github.com/ymorsi7/seismiclstm
Mitigating the high computational costs associated with applying Bayesian model updating in inverse problems / Uncertainty Quantification and Efficient Sensitivity Analysis by using Surrogate Models
https://github.com/ymorsi7/seismiclstm
bayesian ddml lstm pbsd seismic seismology structural-engineering surrogate-modelling surrogate-models
Last synced: 3 months ago
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Mitigating the high computational costs associated with applying Bayesian model updating in inverse problems / Uncertainty Quantification and Efficient Sensitivity Analysis by using Surrogate Models
- Host: GitHub
- URL: https://github.com/ymorsi7/seismiclstm
- Owner: ymorsi7
- Created: 2024-06-04T18:13:39.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2024-06-13T04:04:02.000Z (12 months ago)
- Last Synced: 2025-01-23T09:44:00.142Z (4 months ago)
- Topics: bayesian, ddml, lstm, pbsd, seismic, seismology, structural-engineering, surrogate-modelling, surrogate-models
- Language: Python
- Homepage:
- Size: 55.3 MB
- Stars: 2
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Application of Long Short-Term Memory (LSTM) Networks-Based Surrogate Modeling for Nonlinear Structural Systems
#### Abdoul Aziz Sandotin Coulibaly, Enrique Simbort Zeballos, Yusuf Morsi, Ramin Sarange
## Abstract
Performance-based seismic design (PBSD) of structural systems relies on computationally expensive high-fidelity finite element (FE) models to predict how structures will respond to seismic excitation. For risk-based assessments, FE response simulations must be run thousands of times with different realizations of the sources of uncertainty. Consequently, data-driven machine learning (DDML) surrogate models have gained prominence as fast emulators for predicting seismic structural responses in probabilistic analyses. This paper leverages deep Long Short-Term Memory (LSTM) networks, known for their powerful and flexible framework for time series prediction tasks. The advantages of using LSTM networks include their ability to model continuous-time processes, adapt to varying temporal resolutions, maintain implicit memory of past information, model complex nonlinear dynamics, perform interpolation and extrapolation, handle noisy data robustly, and scale effectively to high-dimensional datasets. The effectiveness of the proposed method is validated through three proof-of-concept studies: one involving a linear elastic 2D 8-degree-of-freedom (DoF) shear building model, a nonlinear single degree of freedom system (NL-SDoF), and a 2D nonlinear 3DoF shear building model. The findings indicate that the proposed LSTM network is a promising, dependable, and computationally efficient technique for predicting nonlinear structural responses.
## Instructions to Set Up and Run the LSTM Model
1. **Clone the Repository**
Clone this repository to your local machine using the following command:
```bash
git clone https://github.com/ymorsi7/LSTMsForNonlinearStructuralSystems.git
cd LSTMsForNonlinearStructuralSystems
```
2. **Install Dependencies**
Ensure you have Python 3.x installed on your system. Install the required dependencies using pip:
```bash
pip install numpy matplotlib tensorflow keras scikit-learn joblib
```
3. **Prepare the Data**
Place the MATLAB data file `data_2DOF_SB_BWWN.mat` in the `LSTMsForNonlinearStructuralSystems` directory. This file should be available in the repository or provided separately.
4. **Run the Python File**
Execute the provided Python script `2DOF_ShearBuild_LSTM_f.py` to train the LSTM model:
```bash
python 2DOF_ShearBuild_LSTM_f.py
```
This will:
- Load the preprocessed data from the MATLAB file.
- Normalize the data using MinMaxScaler.
- Set up and train the LSTM model.
- Evaluate the model performance.
- Save the best-performing model.
5. **A/B Testing**
The script also includes a section for A/B testing different LSTM architectures. This can be executed within the same script to compare performance metrics between two models.
## Report
To read our report, [click here](files/paper.pdf)
## Results
The results are much more interpretable with the context on the full report (linked above). However, here are some of our output images:
### Linear Elastic 8-Story Shear Building
### Nonlinear Inelastic Single Degree Of Freedom (SDOF) Structure
### Nonlinear Inelastic Multi Degree Of Freedom (MDOF) Structure
### A/B Testing
