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Lookup NU author(s): Dr Manuel HerreraORCiD
This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, 2025.
For re-use rights please refer to the publisher's terms and conditions.
Risk assessment is essential for asset management, particularly in accounting for interdependencies between assets. This paper introduces a framework that analyses risk at both asset and system levels. A Multinomial Regression (MR) approach is employed to predict the probability of performance for individual assets, while Bayesian Neural Network (BNN) are used to model interdependencies between assets. The BNN method is well-suited to handling Boolean, categorical, and numerical inputs, and it effectively captures uncertainty in performance predictions. The framework is validated using data from the tracks and drainage systems of four railway routes in the UK. The results demonstrate that this approach is a reliable tool for asset performance evaluation and uncertainty quantification, offering valuable insights for improving asset management practices.
Author(s): Pan N, Sasidharan M, Okazaki S, Herrera M, Cho SI, Kookalani S, Parlikad AK
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: 1st International Workshop on Bayesian Approach in Civil Engineering (IWOBA 2025)
Year of Conference: 2025
Online publication date: 12/01/2025
Acceptance date: 02/04/2018
Date deposited: 03/03/2025
Publisher: Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University
URL: https://www.polyu.edu.hk/cee/news-and-events/events/2025/01/20250110-12_event/
