Optimal Heuristics for Reliability-Based Inspection and Maintenance Planning

Author(s):  
Daniel Straub ◽  
Elizabeth Bismut
Author(s):  
Jose´ G. Rangel-Rami´rez ◽  
John D. So̸rensen

Deterioration processes such as fatigue and corrosion are typically affecting offshore structures. To “control” this deterioration, inspection and maintenance activities are developed. Probabilistic methodologies represent an important tool to identify the suitable strategy to inspect and control the deterioration in structures such as offshore wind turbines (OWT). Besides these methods, the integration of condition monitoring information (CMI) can optimize the mitigation activities as an updating tool. In this paper, a framework for risk-based inspection and maintenance planning (RBI) is applied for OWT incorporating CMI, addressing this analysis to fatigue prone details in welded steel joints at jacket or tripod steel support structures for offshore wind turbines. The increase of turbulence in wind farms is taken into account by using a code-based turbulence model. Further, additional modes t integrate CMI in the RBI approach for optimal planning of inspection and maintenance. As part of the results, the life cycle reliabilities and inspection times are calculated, showing that earlier inspections are needed at in-wind farm sites. This is expected due to the wake turbulence increasing the wind load. With the integration of CMI by means Bayesian inference, a slightly change of first inspection times are coming up, influenced by the reduction of the uncertainty and harsher or milder external agents.


2018 ◽  
Vol 3 (3) ◽  
pp. 32 ◽  
Author(s):  
Shane Haladuick ◽  
Markus Dann

For engineering systems, decision analysis can be used to determine the optimal decision from a set of options via utility maximization. Applied to inspection and maintenance planning, decision analysis can determine the best inspection and maintenance plan to follow. Decision analysis is relatively straightforward for simple systems. However, for more complex systems with many components or defects, the set of all possible inspection and maintenance plans can be very large. This paper presents the use of a genetic algorithm to perform inspection and maintenance plan optimization for complex systems. The performance of the genetic algorithm is compared to optimization by exhaustive search. A numerical example of life cycle maintenance planning for a corroding pressure vessel is used to illustrate the method. Genetic algorithms are found to be an effective approach to reduce the computational demand of solving complex inspection and maintenance optimizations.


2005 ◽  
Vol 127 (3) ◽  
pp. 243-248 ◽  
Author(s):  
Michael Havbro Faber

In the present paper an introduction is initially given on the interpretation of uncertainty and probability in engineering decision analysis and it is explained how, in some cases, uncertainties may change type depending on the “scale” of the applied modeling and as a function of time. Thereafter it is attempted to identify and outline the generic character of different engineering decision problems and to categorize these as prior, posterior, and preposterior decision problems, in accordance with the Bayesian decision theory. Finally, input is given to an ongoing discussion concerning the correctness and consistency of uncertainty modeling applied in the most recent reliability updating analysis for structural requalification and inspection and maintenance planning. To this end an outline is given in regard to appropriate uncertainty treatment in the probabilistic modeling for different types of decision problems.


2021 ◽  
Vol 13 (4) ◽  
pp. 2051 ◽  
Author(s):  
Sakdirat Kaewunruen ◽  
Jessada Sresakoolchai ◽  
Wentao Ma ◽  
Olisa Phil-Ebosie

Over the past centuries, millions of bridge infrastructures have been constructed globally. Many of those bridges are ageing and exhibit significant potential risks. Frequent risk-based inspection and maintenance management of highway bridges is particularly essential for public safety. At present, most bridges rely on manual inspection methods for management. The efficiency is extremely low, causing the risk of bridge deterioration and defects to increase day by day, reducing the load-bearing capacity of bridges, and restricting the normal and safe use of them. At present, the applications of digital twins in the construction industry have gained significant momentum and the industry has gradually entered the information age. In order to obtain and share relevant information, engineers and decision makers have adopted digital twins over the entire life cycle of a project, but their applications are still limited to data sharing and visualization. This study has further demonstrated the unprecedented applications of digital twins to sustainability and vulnerability assessments, which can enable the next generation risk-based inspection and maintenance framework. This study adopts the data obtained from a constructor of Zhongcheng Village Bridge in Zhejiang Province, China as a case study. The applications of digital twins to bridge model establishment, information collection and sharing, data processing, inspection and maintenance planning have been highlighted. Then, the integration of “digital twins (or Building Information Modelling, BIM) + bridge risk inspection model” has been established, which will become a more effective information platform for all stakeholders to mitigate risks and uncertainties of exposure to extreme weather conditions over the entire life cycle.


2014 ◽  
Vol 58 (02) ◽  
pp. 106-116
Author(s):  
Wengang Mao

As a result of the wide use of high-tensile steel and the imperfect fabrication process in ship construction as well as some uncertainties in ship fatigue design, cracks could be initiated much earlier than expected. The presence of fatigue cracks greatly affects a ship's structural safety and serviceability. To ensure structural safety and perform reliable crack inspection and maintenance planning, it is important to know how fast cracks can grow in ship structures. In the current study, the principles of fracture mechanics are used for crack propagation analysis in ships. By taking into account the special properties of a ship's stress response, an efficient spectral method is proposed and validated for the prediction of crack propagation in ship structures. In this spectral method, structural stresses are assumed to be narrow-band Gaussian processes. Furthermore, for crack inspection and maintenance planning based on crack growth, it is essential to know the wave environments encountered in a ship's future operations. Therefore, a spatiotemporal statistical wave model based on both satellite and buoy measurements is briefly introduced. It is developed to generate wave environments along arbitrary ship routes. Finally, the deck longitudinal stiffener of a 2800TEU containership is used to demonstrate the application of the spectral method and the wave model for the prediction of crack propagation in ships. The route information and operating conditions are taken from full-scale measurements of this ship. In the case study, the scatter of crack propagation associated with the wave environments encountered is also investigated. The results from this investigation indicate possible potentials of crack inspection and maintenance optimization to enable more efficient ship operation.


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