Evolving and Proactive Risk Modelling in Underground Working Environments
type: CreativeWork
description: Underground environments are critical to global resource extraction and urban development, yet they are inherently hazardous due to their dynamic and unpredictable nature. Rapid changes, including methane accumulation and environmental fluctuations, threaten worker safety, operational continuity, and infrastructure integrity. Traditional risk assessment methods, which rely on static models and expert judgment, struggle to capture real-time variations in gas concentrations, ventilation, and ground stability, leading to delayed hazard detection and inadequate mitigation. Fragmented sensor data integration and the opaque nature of advanced models further hinder trust and decision-making. These limitations contribute to severe consequences, such as methane explosions, ventilation failures, and operational disruptions, highlighting the urgent need for an integrated, real-time risk modelling system capable of dynamically monitoring, predicting, and mitigating hazards in underground working spaces. This thesis aims to address these shortcomings by presenting an environmental Digital Twin (DT) framework for evolving and proactive underground risk modelling that integrates real-time Internet of Things (IoT) sensor networks, advanced probabilistic modelling, and adaptive deep learning (DL) techniques to address the critical challenges of managing hazardous underground environments. By fusing heterogeneous sensor data with robust Bayesian Networks (BNs) and online learning algorithms, the research establishes a system capable of continuous, real-time monitoring and proactive risk prediction, thereby significantly enhancing operational safety and decision-making in environments prone to hazards such as methane explosions and inadequate ventilation. The first phase of this research establishes an IoT-Bayes fusion framework that integrates diverse sensor arrays deployed in complex underground settings. IoT sensors capture critical environmental parameters, such as gas concentrati…
abstract: Underground environments are critical to global resource extraction and urban development, yet they are inherently hazardous due to their dynamic and unpredictable nature. Rapid changes, including methane accumulation and environmental fluctuations, threaten worker safety, operational continuity, and infrastructure integrity. Traditional risk assessment methods, which rely on static models and expert judgment, struggle to capture real-time variations in gas concentrations, ventilation, and ground stability, leading to delayed hazard detection and inadequate mitigation. Fragmented sensor data integration and the opaque nature of advanced models further hinder trust and decision-making. These limitations contribute to severe consequences, such as methane explosions, ventilation failures, and operational disruptions, highlighting the urgent need for an integrated, real-time risk modelling system capable of dynamically monitoring, predicting, and mitigating hazards in underground working spaces. This thesis aims to address these shortcomings by presenting an environmental Digital Twin (DT) framework for evolving and proactive underground risk modelling that integrates real-time Internet of Things (IoT) sensor networks, advanced probabilistic modelling, and adaptive deep learning (DL) techniques to address the critical challenges of managing hazardous underground environments. By fusing heterogeneous sensor data with robust Bayesian Networks (BNs) and online learning algorithms, the research establishes a system capable of continuous, real-time monitoring and proactive risk prediction, thereby significantly enhancing operational safety and decision-making in environments prone to hazards such as methane explosions and inadequate ventilation. The first phase of this research establishes an IoT-Bayes fusion framework that integrates diverse sensor arrays deployed in complex underground settings. IoT sensors capture critical environmental parameters, such as gas concentrati…
author: Mousavi, Milad ; https://orcid.org/0000-0002-0767-2972
language: en
keywords: Underground Environments, Internet of Things (IoT), Bayesian Networks (BN), Long Short-Term Memory (LSTM), Real-Time Risk Modelling, Data-Driven Safety Systems, Proactive Risk Management, Methane Explosions, anzsrc-for: 460206 Knowledge representation and reasoning, anzsrc-for: 330201 Automation and technology in building and construction
license: http://purl.org/coar/access_right/c_f1cf
license: https://creativecommons.org/licenses/by/4.0/
date published: 2025
date modified: 2026-07-24T05:33:31Z
provider: University of New South Wales
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: UNSW, Sydney

PropertyValue
property ID: Handle
value: http://hdl.handle.net/1959.4/105739

PropertyValue
property ID: DOI
value: 10.26190/unsworks/31560