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Digital Twin-Based Predictive Modeling and Simulation for Critical Infrastructure Asset Management in Saudi Arabia
Subject area: Science,Engineering and Technology · Area of research: Predictive Modeling and Simulation
Abstract
In Saudi Arabia critical infrastructure is growing and at the same time becoming more connected, equipped with a greater number of sensors, and operationally interdependent. Because of these features there arises an asset-management problem which cannot be properly solved by means of periodic inspections, isolated condition monitoring or static risk registers. This article critically looks at the way in which digital twin-based predictive modelling and simulation can assist with making decisions throughout the lifecycle of assets in the energy, water, transport, industrial and built-environment sectors. Evidence from between 2020 and 2025 is combined in a structured integrative review, with a focus on digital-twin architecture, data synchronisation, predictive maintenance, uncertainty-aware simulation, interoperability, cyber-physical trust and decision governance. The literature shows that value is not derived simply from visualisation but comes from keeping a continuously updated representation in which operational data constrain the models, the models forecast degradation, and simulations are used to test maintenance or recovery options before any actual intervention takes place. Hybrid approaches that combine physics-based models with machine learning seem especially appropriate for safety-critical assets since they are able to retain engineering meaning while making use of high-frequency data. Yet implementation is still limited by fragmented asset information, inconsistent semantics, a lack of validation across different operating conditions, exposure to cyberattacks, uncertainty regarding the transferability of models and poor integration with existing enterprise asset-management workflows. For Saudi Arabia a step-by-step approach is suggested in which data foundations, model fidelity, human supervision and interoperable governance all develop together. The review ends by stating that digital twins should be seen as decision infrastructure rather than as software replicas, and their performance should be measured in terms of the number of failures avoided, the improvement in maintenance timing, resilience, lifecycle cost and the quality of decisions that can be audited.
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How to cite this paper
@article{1723743,
author = {Hassan Ali Khan Mohammed},
title = {Digital Twin-Based Predictive Modeling and Simulation for Critical Infrastructure Asset Management in Saudi Arabia},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {4},
pages = {470-483},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1723743.pdf},
abstract = {In Saudi Arabia critical infrastructure is growing and at the same time becoming more connected, equipped with a greater number of sensors, and operationally interdependent. Because of these features there arises an asset-management problem which cannot be properly solved by means of periodic inspections, isolated condition monitoring or static risk registers. This article critically looks at the way in which digital twin-based predictive modelling and simulation can assist with making decisions throughout the lifecycle of assets in the energy, water, transport, industrial and built-environment sectors. Evidence from between 2020 and 2025 is combined in a structured integrative review, with a focus on digital-twin architecture, data synchronisation, predictive maintenance, uncertainty-aware simulation, interoperability, cyber-physical trust and decision governance. The literature shows that value is not derived simply from visualisation but comes from keeping a continuously updated representation in which operational data constrain the models, the models forecast degradation, and simulations are used to test maintenance or recovery options before any actual intervention takes place. Hybrid approaches that combine physics-based models with machine learning seem especially appropriate for safety-critical assets since they are able to retain engineering meaning while making use of high-frequency data. Yet implementation is still limited by fragmented asset information, inconsistent semantics, a lack of validation across different operating conditions, exposure to cyberattacks, uncertainty regarding the transferability of models and poor integration with existing enterprise asset-management workflows. For Saudi Arabia a step-by-step approach is suggested in which data foundations, model fidelity, human supervision and interoperable governance all develop together. The review ends by stating that digital twins should be seen as decision infrastructure rather than as software replicas, and their performance should be measured in terms of the number of failures avoided, the improvement in maintenance timing, resilience, lifecycle cost and the quality of decisions that can be audited.},
month = {October},
}