# A convolutional neural network deep learning method for model class selection

arXiv: https://arxiv.org/abs/2511.03743 · HTML: https://mlsanity.com/paper/2511.03743/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 95 references that are in the map.
- First posted on arXiv 30 Oct 2025.

## Builds on (most cited first, 5 of 5)

- [1D Convolutional Neural Networks and Applications: A Survey](https://mlsanity.com/paper/1905.03554/) (2019)
- [All You Need is a Few Shifts: Designing Efficient Convolutional Neural Networks for Image Classification](https://mlsanity.com/paper/1903.05285/) (2019)
- [A Review of Vibration-Based Damage Detection in Civil Structures: From Traditional Methods to Machine Learning and Deep Learning Applications](https://mlsanity.com/paper/2004.04373/) (2020)
- [Managing engineering systems with large state and action spaces through deep reinforcement learning](https://mlsanity.com/paper/1811.02052/) (2018)
- [Design of experiments for the calibration of history-dependent models via deep reinforcement learning and an enhanced Kalman filter](https://mlsanity.com/paper/2209.13126/) (2022)

## Abstract (arXiv)

The response-only model class selection capability of a novel deep convolutional neural network method is examined herein in a simple, yet effective, manner. Specifically, the responses from a unique degree of freedom along with their class information train and validate a one-dimensional convolutional neural network. In doing so, the network selects the model class of new and unlabeled signals without the need of the system input information, or full system identification. An optional physics-based algorithm enhancement is also examined using the Kalman filter to fuse the system response signals using the kinematics constraints of the acceleration and displacement data. Importantly, the method is shown to select the model class in slight signal variations attributed to the damping behavior or hysteresis behavior on both linear and nonlinear dynamic systems, as well as on a 3D building finite element model, providing a powerful tool for structural health monitoring applications.