TRUSS-ITN Research Collection

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TRUSS (Training in Reducing Uncertainty in Structural Safety) is a Marie Skłodowska-Curie Innovative Training Network (ITN) funded by the European Union under the Horizon 2020 Programme (call H2020-MSCA-ITN-2014).

TRUSS ITN gathers this understanding by bringing together an intersectoral and multidisciplinary collaboration between 4 Universities, 11 Industry participants and 1 research institute from 5 European countries. The consortium combines and shares expertise to offer training at an advanced level as new concepts for monitoring, modelling and reliability analysis of structures are emerging all the time.

This project has received funding from the Eurpean Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 642453.

For more information, please visit the official website.

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Recent Submissions

Now showing 1 - 5 of 69
  • Publication
    A holistic approach to risk-based decision on inspection and design of fatigue-sensitive structures
    Design and operation of large welded structural systems (e.g. ship and offshore structures) are challenging due to numerous fatigue-sensitive details, limited available budgets, uncertainties in fatigue damages, inspection & maintenance activities, etc. Traditionally, fatigue design and maintenance planning have been almost disconnected, which restricts coherent decision-making and optimum safety management. Structural design optimization, without quantitatively incorporating the effects of operational maintenance, can hardly result in a structural plan that is optimum in terms of life cycle costs. Also, if the design of a structure is not optimum, maintenance optimization alone cannot really yield a optimum maintenance plan. As operational inspections and maintenance are essential, there are merits to utilize their effects on structural design and meanwhile optimize them at the initial design stage when impacts of decisions are greater. This paper proposes a risk-based approach to holistic decision-making enveloping decisions and uncertainties affecting design, inspection and maintenance of fatigue-sensitive components. Decisions variables in structural scantling and operational maintenance are obtained holistically at the structural design stage by risk-based optimization, based on quantitative assessment of the effectiveness of both structural scantling and maintenance interventions. Optimum fatigue reliability level is also obtained, informed by the effects of uncertainties and failure consequences. The method captures combined benefits of structural scantling and operational maintenance to fatigue reliability and risk mitigation and achieves optimum resource utilization and life cycle cost reduction. Advantages of the proposed method have been demonstrated via a numerical example, in comparison to alternative methods.
      151Scopus© Citations 2
  • Publication
    Bayesian maintenance decision optimisation based on computing the information value from condition inspections
    A challenge in marine and offshore engineering is structural integrity management (SIM) of assets such as ships, offshore structures, mooring systems, etc. Due to harsh marine environments, fatigue cracking and corrosion present persistent threats to structural integrity. SIM for such assets is complicated because of a very large number of rewelded plates and joints, for which condition inspections and maintenance are difficult and expensive tasks. Marine SIM needs to take into account uncertainty in material properties, loading characteristics, fatigue models, detection capacities of inspection methods, etc. Optimising inspection and maintenance strategies under uncertainty is therefore vital for effective SIM and cost reductions. This paper proposes a value of information (VoI) computation and Bayesian decision optimisation (BDO) approach to optimal maintenance planning of typical fatigue-prone structural systems under uncertainty. It is shown that the approach can yield optimal maintenance strategies reliably in various maintenance decision making problems or contexts, which are characterized by different cost ratios. It is also shown that there are decision making contexts where inspection information doesn’t add value, and condition based maintenance (CBM) is not cost-effective. The CBM strategy is optimal only in the decision making contexts where VoI > 0. The proposed approach overcomes the limitation of CBM strategy and highlights the importance of VoI computation (to confirm VoI > 0) before adopting inspections and CBM.
      160Scopus© Citations 2
  • Publication
    Fatigue inspection and maintenance optimization: A comparison of information value, life cycle cost and reliability based approaches
    Fatigue cracks increase structural failure risk and timely maintenance is very important. Maintenance planning is often formulated as a probabilistic optimization problem, considering uncertainties in structural and load modelling, material properties, damage measurements, etc. A decision rule or strategy, e.g. condition based maintenance (CBM), needs to be set up, and then an optimal maintenance criterion or threshold is derived via solving the optimization problem. This paper develops a probabilistic maintenance optimization approach exploiting value of information (VoI) computation and Bayesian decision optimization. The VoI based approach explicitly quantifies added values from future inspections, and gives an optimal decision (or strategy) by direct modelling decision alternatives and evaluating their expected outcomes, rather than a pre-defined strategy. A comparative study on VoI, life cycle cost (LCC) and reliability based optimization approaches is conducted. It is shown that the VoI based approach takes all available maintenance strategies into account (both with and without involving inspections), and can reliably yield optimal maintenance strategies, whether the VoI is larger than or equal to zero. When the VoI is equal to zero, LCC and reliability based CBM optimization can lead to suboptimal maintenance strategies. The differences in the approaches are illustrated on fatigue-sensitive components in a marine structure.
      135Scopus© Citations 9
  • Publication
    A simplified method for holistic value of information computation for informed structural integrity management under uncertainty
    Collecting structural information by inspection or monitoring is important means to reduce uncertainty and improve the qualities of maintenance decisions in structural integrity management. However, information collecting inevitably involves some costs. When information collecting brings added value and to what extent uncertainty reduction suffices are questions that are often not fully accounted for before information collecting activities are carried out. Value of information (VoI) computation helps justifying investments and informing efficient strategies for information collecting. This paper develops a holistic approach to quantify the VoI from multiple inspections in the lifetime of an engineering structure, taking into account combined effects of lifetime maintenance interventions. The approach can be used for holistic planning and optimization of lifetime inspections at an early stage. Also, a simplified VoI computation approach is developed for some maintenance decision cases based on an alignment decision strategy (ADS). The approaches are exemplified on a typical marine structure, and sensitivities of VoI to the number of planned interventions, cost ratio, inspection times and methods are studied. It is shown that the ADS and the simplified method are well applicable when the number of planned interventions is large. The optimal maintenance decisions and inspection times obtained by VoI-based and cost-based optimization methods are compared.
      138
  • Publication
    An integrated probabilistic approach for optimum maintenance of fatigue-critical structural components
    Inspection and maintenance are important means to validate or recover reliability of metallic structural systems, which usually degrade over time due to fatigue, corrosion and other mechanisms. These inspection and maintenance actions generally account for a large part of lifetime costs, which necessitate an efficient maintenance strategy to satisfy the requirements on reliability and costs. Most often, an optimum maintenance/repair crack size criterion is derived by probabilistic cost-benefit analysis, e.g. by minimization of expected lifetime costs, which are assessed based on cost models. This paper proposes an integrated approach to derive an optimum range for repair (crack size) criterion using both reliability-based and cost-based optimization. It is found that an optimum repair criterion exists which leads to the maximum lifetime fatigue reliability. A smaller repair criterion than the reliability-optimum do not lead to a higher lifetime fatigue reliability but leads to higher lifetime costs. Hence, a limit for repair criterion is defined by the reliability-optimum criterion, which can be obtained without cost models. The reliability-optimum criterion is found to be smaller than the cost-optimum criterion and thus an optimum range between the reliability-optimum and cost-optimum criterion is established.
      173Scopus© Citations 5