This study investigates the use of Ultra-High Molecular Weight Polyethylene (UHMWPE) fibers and fabric to enhance the flexural performance of Ultra-High-Performance Concrete (UHPC). A total of 45 specimens were tested to examine the effects of fiber type, fabric material, adhesive, and various combined strengthening techniques. The main findings are that incorporating UHMWPE fiber into the ultra-high-strength mortar (HSM) matrix provides superior performance compared to steel fiber, particularly in enhancing crack resistance and energy absorption. UHMWPE fiber-reinforced UHPC achieved a flexural toughness of 307 KJ/m3, over three times higher than that of steel fiber-reinforced UHPC (98 KJ/m3). The use of UHMWPE fabrics was more effective in improving the ductility and toughness of the composites than the use of glass fabrics. The bonding effect of using epoxy resin with UHMWPE fabric is better than using magnesium phosphate cement (MPC). Increasing the number of fabric layers improved the flexural properties of externally bonded fabric but had no impact on internal reinforcement techniques. The best strengthening method in this study was a combination of incorporating UHMWPE fiber internally and externally bonded fabric on a concrete surface, yielding the highest toughness of 580 KJ/m3.

Engineering Asset Management (EAM) is a strategic approach focused on the optimal management of physical assets throughout their lifecycle. By integrating engineering principles with financial and operational strategies, EAM aims to enhance asset performance, reliability, and longevity while minimizing risks and costs. This holistic methodology ensures that machinery, equipment, and infrastructure operate efficiently, thereby reducing failures and maximizing productivity. A critical component of EAM is understanding the criticality of each asset within a system. Criticality analysis evaluates the potential impact of different failure modes, considering factors such as failure likelihood, consequences, system interdependencies, cost implications, and associated risks. This analysis is essential for prioritizing maintenance efforts and allocating resources effectively. Risk assessment plays a pivotal role in this context, involving the systematic identification, analysis, evaluation, and management of potential risks associated with asset failures. However, traditional risk assessment methods often face challenges due to subjectivity and variability in evaluations, which can lead to inconsistencies in maintenance decision-making. To address these challenges, this paper proposes a novel multi-stage quantitative Failure Modes, Effects, and Criticality Analysis (FMECA) framework. This approach systematically analyses failure rates, downtime, and cost implications, providing a comprehensive understanding of each failure mode's impact. By integrating these quantitative parameters, the framework enhances objectivity in risk assessment and supports more informed decision-making. It enables organisations to systematically prioritize maintenance activities and optimize resource allocation. This approach not only mitigates operational risks but also aligns asset management practices with overarching business objectives, leading to improved efficiency and reduced costs. The proposed methodology is particularly beneficial in industries such as mining, manufacturing, and aerospace, where unplanned downtime and maintenance costs can have significant operational and financial repercussions. By adopting this multi-dimensional approach, organizations can improve asset performance, enhance safety, and achieve more sustainable operations.

Maintenance optimisation remains a persistent challenge in asset-intensive industries due to the combined uncertainties of failure behaviour and the economic consequences of strategy selection. Traditional decision frameworks typically rely on static cost comparisons or rule-based policies, which do not adequately reflect how hazard rates evolve across different lifecycle stages. This limitation creates a critical gap in linking risk-based reliability analysis with cost-driven evaluation, leaving decision makers without a robust basis for aligning maintenance strategies to both asset condition and economic impact. The objective of this study is to develop and test a framework that integrates hazard rate modelling with cost-based analysis to guide optimised maintenance decision-making. The approach applies simulation-based methods using Weibull distributions to characterise hazard rates across infant, random, and wear-out phases. Strategy performance is then evaluated through a process of normalisation and binning, which enables comparability across heterogeneous datasets and accounts for differences in data richness. Opportunity cost is introduced as the central decision metric, capturing the economic penalty incurred when a suboptimal strategy is implemented under specific hazard conditions. Findings reveal that no maintenance strategy is universally superior across all conditions. Corrective maintenance is viable only in low-hazard situations, but quickly becomes economically untenable as hazard rates increase. Time-based maintenance consistently demonstrates effectiveness in predictable environments and dominates in wear-out stages where escalating risk demands proactive intervention. Condition-based maintenance proves particularly valuable in situations where monitoring can offset uncertainty, offering a cost-effective balance between reactivity and prevention. Importantly, the results show that the framework is flexible: it remains meaningful in data-scarce contexts, where transparent assumptions allow useful insights, while in data-rich scenarios it produces stable parameter estimates and clearer economic benchmarks. This study contributes to the academic literature by bridging the gap between reliability modelling and economic evaluation. By integrating hazard functions with opportunity cost benchmarking, it advances beyond static cost comparisons to a dynamic, lifecycle-aware assessment of strategy performance. Practically, the framework provides maintenance managers with a transparent and adaptable decision-support tool that clarifies both when a strategy is most effective and what is lost when the wrong choice is made. In doing so, it enhances the ability of organisations to make defensible, data-informed decisions that minimise risk and maximise economic efficiency in asset management.

Effective evaluation of equipment criticality is a key concern in Engineering Asset Management, particularly in operationally intensive industries such as mining. While the concept of criticality is often subjective, it can be assessed more objectively using quantifiable indicators such as cost, downtime, and failure rate. This paper presents a data-driven approach to assess equipment-level criticality by analysing the impact of individual equipment downtimes on overall system performance. Focusing on a case study from a gold mining operation in Australia, the study demonstrates how equipment-level performance can be used to prioritise maintenance efforts and support more informed decision-making. One of the key contributions of this work lies in its integration of statistical modelling and probabilistic analysis to identify critical equipment within a system. Unlike conventional methods that often overlook uncertainty or assume uniform equipment influence, this approach quantifies the impact of individual equipment failures on system-level outcomes. The analysis treats subsystems independently, acknowledging the absence of interdependency data while still capturing meaningful insights about their relative importance. By leveraging a combination of platforms – Excel for data preprocessing, R for simulation, and Netica for network-based evaluation – the study offers a replicable and scalable methodology for criticality assessment. Sensitivity analysis within the Bayesian Network model further enhances the framework by highlighting components with the highest influence on system reliability. The outcome is a transparent, objective, and practically applicable tool for maintenance prioritisation, offering significant value in data-intensive and reliability-critical environments like mining. This paper contributes to the growing body of research focused on integrating operational data with advanced modelling techniques to improve asset performance management.