# Continual-learning-based framework for structural damage recognition

arXiv: https://arxiv.org/abs/2408.15513 · HTML: https://mlsanity.com/paper/2408.15513/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 51 references that are in the map.
- First posted on arXiv 28 Aug 2024.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Progressive Neural Networks](https://mlsanity.com/paper/1606.04671/) (2016)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Continual Learning with Deep Generative Replay](https://mlsanity.com/paper/1705.08690/) (2017)
- [Tune: A Research Platform for Distributed Model Selection and Training](https://mlsanity.com/paper/1807.05118/) (2018)
- [Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding](https://mlsanity.com/paper/1904.08582/) (2019)

## Abstract (arXiv)

Multi-damage is common in reinforced concrete structures and leads to the requirement of large number of neural networks, parameters and data storage, if convolutional neural network (CNN) is used for damage recognition. In addition, conventional CNN experiences catastrophic forgetting and training inefficiency as the number of tasks increases during continual learning, leading to large accuracy decrease of previous learned tasks. To address these problems, this study proposes a continuallearning-based damage recognition model (CLDRM) which integrates the learning without forgetting continual learning method into the ResNet-34 architecture for the recognition of damages in RC structures as well as relevant structural components. Three experiments for four recognition tasks were designed to validate the feasibility and effectiveness of the CLDRM framework. In this way, it reduces both the prediction time and data storage by about 75% in four tasks of continuous learning. Three experiments for four recognition tasks were designed to validate the feasibility and effectiveness of the CLDRM framework. By gradual feature fusion, CLDRM outperformed other methods by managed to achieve high accuracy in the damage recognition and classification. As the number of recognition tasks increased, CLDRM also experienced smaller decrease of the previous learned tasks. Results indicate that the CLDRM framework successfully performs damage recognition and classification with reasonable accuracy and effectiveness.