Mapping Fault Tree Analysis to Artificial Neural Networks and Implementing in Google Colab

DOI

10.2991/978-94-6463-922-3_27How to use a DOI?

Keywords

Artificial Neural Network; Fault tree; Google colab

Abstract

Quantitative risk assessment is an inevitable element in process safety analysis. Nowadays, huge volumes of data related to process safety is being generated. Traditional tools like Fault tree analysis cannot handle such complex data. This leads to the requirement of advanced technologies that address this complex data. One such approach is the use of Artificial Neural Network (ANN) system. The findings from this study demonstrate that integrating ANN into FTA enhances prediction performance, automates fault pattern recognition. Using MATLAB, ANN architectures were developed and trained on structured input output failure datasets. Multiple configurations were tested by varying the number of hidden layers and neurons to check the performance of the model. The performance evaluation was based on Mean Squared Error (MSE), with results indicating that a two-hidden-layer configuration achieved superior predictive accuracy compared to single-layer models. ANN was also developed in Google Colab, a free cloud-based platform by Google. This paper showcased the utility of Colab for rapid prototyping, visualization, and deployment. This methodology paves the way for intelligent, scalable risk assessment in safety-critical systems such as industrial operations, power plants, and manufacturing environments.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - N. Divya
AU  - A. M. Manilal
PY  - 2025
DA  - 2025/12/25
TI  - Mapping Fault Tree Analysis to Artificial Neural Networks and Implementing in Google Colab
BT  - Proceedings of the International Conference Recent Advances in Materials, Processes and Technology for Sustainability (RAMPTS 2025)
PB  - Atlantis Press
SP  - 406
EP  - 433
SN  - 2590-3217
UR  - https://doi.org/10.2991/978-94-6463-922-3_27
DO  - 10.2991/978-94-6463-922-3_27
ID  - Divya2025
ER  -