Computational framework for risk-based planning of inspections, maintenance and condition monitoring using discrete Bayesian networks

Nielsen, Jannie Sønderkær; Sørensen, John Dalsgaard

doi:10.1080/15732479.2017.1387155

Cited by 31 publications

(21 citation statements)

References 14 publications

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“…The model has a graphical interface to visualise the link between continuous and discrete factors and has the strength to express the probability of observations of a state given the state of prior factor. BN relies on the algorithms that consider the non-linearity of the interacting variables through risk assessments and conditional probabilities of the finite set of random variables (Nielsen and Sørensen, 2018). But, the usefulness of BN does not consider the dynamic and uncertain conditions (Codetta-Raiteri et al, 2012) that characterise the construction domain.…”

Section: Dynamic Bayesian Networkmentioning

confidence: 99%

Dynamic Bayesian network modelling for predicting adaptability of time performance during time influencing factors disruptions in construction enterprise

Nwadigo

Naismith

Ghaffarianhoseini

et al. 2020

ECAM

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PurposeA construction project is complex and requires dynamic modelling of a range of factors that deters time performance because of uncertainty and varying operating conditions. In construction project systems, the system components are the interconnected stages, which are time-dependent. Within the project stages are the activities which are the subsystems of the system components, causing a challenge to the analysis of the complex system. The relationship of construction project time management (CTM) with the construction project time influencing factors (CTFs) and the adaptability of the time-varying system is a key part of project effectiveness. This study explores the relationship between CTM and CTF, including the potentials to add dynamical changes on every project stage.Design/methodology/approachThis study proposed a dynamic Bayesian network (DBN) model to examine the relationship between CTM and CTF. The model investigates the time performance of a construction project that enhances decision-making. First, the paper establishes a model of probabilistic reasoning and directed acrylic graph (DAG). Second, the study tests the dynamic impact (IM) of CTM-CTF on the project stages over a specific time, including the adaptability of time performance during disruptive CTF events. In demonstrating the effectiveness of the model, the authors selected one-organisation-single-location road-improvement project as the case study. Next, the confirmation of the model internal validity relied on conditional probabilities and the project knowledge experts' selected from the case company.FindingsThe study produced structural dependencies of CTM and CTF with probability observations at each stage. A predictive time performance analysis of the model at different scenarios evaluates the adaptability of CTM during CTF uncertain events. The case demonstration of the model application shows that CTFs have effects on CTM strategy, creating the observations to help time performance restorations after disruptions.Research limitations/implicationsAlthough the case company experts' panel confirms the internal validity of the results for managing time, the model used conditional probability table (CPT) and project state values from a project contract. A project-wide application then will require multi-case data and data-mining process for generating the CPTs.Practical implicationsThe study developed a method for evaluating both quantitative and qualitative relationships between CTM and CTF, besides the knowledge to enhance CTM practice and research. In construction, the project team can use model observations to implement time performance restorations after a predictive or reactive disruption, which enhances decision-making.Originality/valueThe model used qualitative and qualitative data of a complex system to generate results, bounded by a range of probability distributions for CTM-CTF interconnections during time performance disruptions and restorations. The research explores the approach that can complement the mental CTM-CTF modeling of the project team. The CTM-CTF relationship model developed in this research is fundamental knowledge for future research, besides the valuable insight into CTF influence on CTM.

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Section: Dynamic Bayesian Networkmentioning

confidence: 99%

Dynamic Bayesian network modelling for predicting adaptability of time performance during time influencing factors disruptions in construction enterprise

Nwadigo

Naismith

Ghaffarianhoseini

et al. 2020

ECAM

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“…The framework based on discrete Bayesian networks has been developed for cost-efficient decisions for Condition based maintenance (Nielsen & Sørensen, 2018…”

Section: Common Stochastic Deterioration Mathematical Models Includinmentioning

confidence: 99%

An optimal condition based maintenance scheduling for metal structures based on a multidisciplinary research approach

Latif

Khan

Nazir

et al. 2019

Structure and Infrastructure Engineering

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Latest research findings show that the deterioration of metal coatings results due to complex combination of material and meteorological parameters. The classical maintenance scheduling do not consider complex interface of materials and meteorological parameters to determine optimal maintenance framework. The cost of recoating can be optimised through appropriate selection of coating specifications and maintenance strategy. This research provides a multidisciplinary algorithmic approach to determine cost-effective solutions for recoating. The specifications of red oxide primer coating and structural steel substrate system are considered for simulation analysis. The results show that the appropriate selection of 10% increase in coating thickness based on coatingsubstrate system specifications resulted in 20-25% reduction in annual patch failures which reduces 5-6 % cost of recoating. Furthermore, the proposed model also simulated to compare Patch recoating and Part recoating strategy and algorithm show that the Part recoating is cost-effective as compared to Patch recoating if number of annual patch failure is greater than '2' and area of the part is '2x'times larger than the area of the patch. Contrary, the Patch recoating results in low cost if the part area is '10x' times larger than patch area and number of annual patch failures are less than '7'.

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“…2,3 A shift from a simple time-based inspection and maintenance regime to predictive maintenance based on an updated belief of the component health can lower the expected maintenance costs, especially if a risk-based approach is used to balance the expected costs of failures, preventive repairs, inspections, and monitoring. 4 The theoretical background for this was established in the Bayesian pre-posterior decision theory 5 and was adapted to civil engineering by Benjamin and Cornell. 6 For risk-based inspection planning (RBI) for offshore structures, efficient generic methods have been developed.…”

Section: Introductionmentioning

confidence: 99%

“…In many studies, online monitoring observations were assumed to be independent given the damage size. 4,39–44 Pozzi and Der Kiureghian 45 considered the assessment of VoI from monitoring and propose modeling the additive error terms as jointly normally distributed random variables, possibly correlated with a given covariance matrix. However, the estimation of the covariance matrix or the implications for the results are not considered, and the example in the article does not include the correlation.…”

Section: Introductionmentioning

confidence: 99%

Value of information of structural health monitoring with temporally dependent observations

Nielsen¹

2021

Structural Health Monitoring

Self Cite

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A Bayesian approach is often applied when updating a deterioration model using observations from inspections, structural health monitoring, or condition monitoring. The observations are stochastic variables with probability distributions that depend on the damage size. Consecutive observations are usually assumed to be independent of each other, but this assumption does not always hold, especially when using online monitoring systems. Frequent updating using dependent measurements can lead to an over-optimistic assessment of the value of information when the measurements are incorrectly modeled as being independent. This article presents a Bayesian network modeling approach for the inclusion of temporal dependency between measurements through a dependency parameter and presents a generic monitoring model based on the exceedance of thresholds for a damage index. Additionally, the model is implemented in a computational framework for risk-based maintenance planning, developed for maintenance planning for wind turbines. The framework is applied for a numerical experiment, where the expected lifetime costs are found for strategies with monitoring with and without dependency between observations, and also for the case where dependency is present but is neglected when making decisions. The numerical experiment and associated parameter study show that neglecting dependency in the decision model when the observations are in fact dependent can lead to much higher costs than expected and to the selection of non-optimal strategies. Much lower costs (down to one quarter) can be obtained when the dependency is properly modeled. In the case of temporally dependent observations, an advanced decision model using a Bayesian network as a simple digital twin is needed to make monitoring feasible compared to only using inspections.

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Computational framework for risk-based planning of inspections, maintenance and condition monitoring using discrete Bayesian networks

Cited by 31 publications

References 14 publications

Dynamic Bayesian network modelling for predicting adaptability of time performance during time influencing factors disruptions in construction enterprise

Dynamic Bayesian network modelling for predicting adaptability of time performance during time influencing factors disruptions in construction enterprise

An optimal condition based maintenance scheduling for metal structures based on a multidisciplinary research approach

Value of information of structural health monitoring with temporally dependent observations

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