# Machine learning for structural design models of continuous beam systems via influence zones

arXiv: https://arxiv.org/abs/2403.09454 · HTML: https://mlsanity.com/paper/2403.09454/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 62 references that are in the map.
- First posted on arXiv 14 Mar 2024.

## Builds on (most cited first, 7 of 7)

- [TopologyGAN: Topology Optimization Using Generative Adversarial Networks Based on Physical Fields Over the Initial Domain](https://mlsanity.com/paper/2003.04685/) (2020)
- [Deep D-bar: Real time Electrical Impedance Tomography Imaging with Deep Neural Networks](https://mlsanity.com/paper/1711.03180/) (2017)
- [Real-Time Topology Optimization in 3D via Deep Transfer Learning](https://mlsanity.com/paper/2102.07657/) (2021)
- [Structural engineering from an inverse problems perspective](https://mlsanity.com/paper/2106.15177/) (2021)
- [Towards Reusable Surrogate Models: Graph-Based Transfer Learning on Trusses](https://mlsanity.com/paper/2109.02689/) (2021)
- [Spatial Damage Characterization in Self-Sensing Materials via Neural Network-Aided Electrical Impedance Tomography: A Computational Study](https://mlsanity.com/paper/2010.01674/) (2020)
- [Influence zones for continuous beam systems](https://mlsanity.com/paper/2305.02211/) (2023)

## Abstract (arXiv)

This work develops a machine learned structural design model for continuous beam systems from the inverse problem perspective. After demarcating between forward, optimisation and inverse machine learned operators, the investigation proposes a novel methodology based on the recently developed influence zone concept which represents a fundamental shift in approach compared to traditional structural design methods. The aim of this approach is to conceptualise a non-iterative structural design model that predicts cross-section requirements for continuous beam systems of arbitrary system size. After generating a dataset of known solutions, an appropriate neural network architecture is identified, trained, and tested against unseen data. The results show a mean absolute percentage testing error of 1.6% for cross-section property predictions, along with a good ability of the neural network to generalise well to structural systems of variable size. The CBeamXP dataset generated in this work and an associated python-based neural network training script are available at an open-source data repository to allow for the reproducibility of results and to encourage further investigations.