Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Ensemble predictive structural health monitoring of space frame structures using deep learning models. |
| Συγγραφείς: |
Kashavar, Mohsen Mokhtari, Shahbazi, Yaser, Amirkhiz, Haniyeh Seyvani, Pedrammehr, Siamak |
| Πηγή: |
Discover Civil Engineering; 8/6/2026, Vol. 3 Issue 1, p1-43, 43p |
| Θεματικοί όροι: |
Structural health monitoring, Deep learning, Space frame structures, Boosting algorithms, Machine learning, Convolutional neural networks, Ensemble learning, Finite element method |
| Περίληψη: |
This study develops a simulation-based surrogate modeling framework for predicting the structural response of space frame structures under predefined geometric, loading, and damage scenarios. The contribution lies in integrating a space-frame-specific parametric modeling workflow with Grasshopper–Karamba3D finite element analysis to generate 28,297 labelled configurations and evaluate machine-learning models for multi-output response prediction. The input variables include member geometry, loading conditions, and prescribed damage-location/radius parameters, while the outputs are total structural mass and maximum displacement. XGBoost, LightGBM, CNN, LSTM, MLP + LSTM, and a fixed equal-weight CNN–LSTM ensemble were compared using the same held-out test set and consistent metrics. The CNN–LSTM ensemble achieved the strongest overall R² and RMSE performance, predicting mass with R² = 0.9985 and RMSE = 0.9278 tons, and displacement with R² = 0.7542 and RMSE = 1.9997 cm. However, the ensemble did not dominate every metric, and the results show that gradient-boosted tree models are strong baselines for structured tabular finite element data. The high mass accuracy mainly reflects the geometry- and material-governed nature of the mass target, whereas displacement prediction is the more demanding and SHM-relevant task. The framework is positioned as a computational tool for rapid simulation-based scenario assessment, not as an inverse damage-localization or field-deployment-ready SHM system. Operational monitoring and inverse damage identification from measured response data remain subjects for future work. [ABSTRACT FROM AUTHOR] |
|
Copyright of Discover Civil Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Βάση Δεδομένων: |
Complementary Index |