Offshore wind is expected to be one of the important contributors to the energy transition towards a more renewable and sustainable energy future. This can be clearly seen from the amount of investments over the past years as well as from the substantial upcoming offshore wind projects in the years to come. Many technological implementation challenges have already been addressed, but the number of new challenges will continue to increase. Especially, as the industry continues moving further offshore with larger wind turbines and as the existing offshore wind farms will approach the end of their service lives. Therefore, the need for improved asset management modelling over the entire service life from design towards decommissioning will continue increasing to support better data driven decision making under uncertainty. For this and in particular for the construction management of offshore wind assets, in this thesis new models and methods have been developed to support this enhanced decision making. These decisions are subject to various types of risks and uncertainties, varying from environmental uncertainties, supply chain disruptions and stochasticity of construction activities’ duration. Therefore, these should be properly taken into account in construction management models using performance and/or expert data from past construction projects. In this thesis two types of data availability have been distinguished: (i) where sufficient relevant performance data is available and (ii) where relevant past performance data is rather limited. In the first case, statistical methods are used, such as Copula functions to model the dependence between metocean variables and Bayesian Networks to model the dependence between subsequent construction activities. In the second case, expert knowledge and data are used to quantify the uncertainty using a mathematical aggregation method for expert judgments (i.e. Cooke’s classical modelling). The different methods have been applied to several test cases to investigate the associated cost and time impact. As a result of this research, different tools and an open-source software were developed. These also can be used in different fields of application using this proper mathematical expert judgment aggregation modelling. Finally, it can be concluded that the state-of the art developments within this thesis substantially contribute to decision making under uncertainty, so that construction management strategies are optimized and thereby the offshore wind energy assets life cycle value is maximized.@en

This is an update to PII: S2352711018300608 and S2352711019302419 In this paper, we present three main improvements of ANDURIL and its python version ANDURYL. First the MATLAB version ANDURIL is brought to the Python version standard by implementing (i) user defined quantiles and (ii) the possibility to deal with missing values. Second, the computational engines of both ANDURIL and ANDURYL were significantly improved making calculation time lower and improving further accuracy. Finally a standalone Graphical User Interface is presented which we believe will make the software more accessible to practitioners of Cooke’s method.@en

Offshore asset construction is a complex and costly process that is subject to various uncertainties within the entire supply chain. Hence, both the construction management optimization and the reduction of deployment expenditures should be supported by automated decision support models which include proper representations of predominant uncertainties. One of these is the supply disruption risk that is often ignored in existing models. Therefore, this article proposes a methodology to properly take this construction risk into account. An algorithm to model this risk was developed and a study was conducted to obtain the required probability distributions of disruption delays using real data and expert judgments for an offshore wind farm construction application. The simulation of a realistic test case with an appropriately modified stochastic simulation tool showed that it is important to consider this risk in order to make optimal decisions for different offshore wind farm construction strategies.

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Installation and maintenance operations of offshore assets are impacted by local environmental conditions such as wave height and period, wind speed and current velocity. These parameters are substantially of influence for the asset planning (time and costs) given the uncertainty of operational windows. In this article, a method is proposed to construct realistic time series of the aforementioned dependent conditions using a vine copulas approach. This method makes it possible to obtain a large number of realizations of these conditions at a certain location. It is shown that the operational windows remain persistent with the original limited dataset. Moreover, this method enables the incorporation of environmental uncertainties in the operational planning processes. To illustrate the value of this method, an application regarding replacement maintenance of a tidal energy infrastructure is examined. For this purpose, the maintenance activities are represented as a semi-Markov decision process. For every synthetic environmental time series, the algorithm finds the optimal set of decisions and the corresponding maintenance plans, including replacement costs and revenue losses. It is shown that the proposed method is effective in replacement maintenance decision making, while taking into account the environmental uncertainties.@en

This is an update to PII: S2352711018300608. In this paper, we discuss ANDURYL, which is a Python-based open source successor of the MATLAB toolbox ANDURIL. The output of ANDURYL is in good agreement with the results obtained from ANDURIL and EXCALIBUR. Additional features available in ANDURYL, and not available in its predecessors, are discussed.@en

The optimal moment at which maintenance activities should be performed on structures with long service-life to guarantee the required quality of service is hard to define, due to uncertainties in their deterioration processes. Most of the developed methods and concepts use historical data to predict the deterioration process with deterministic values as a result. Some researchers recognise that probabilistic deterioration models are required for life-cycle models but in practice, however, historical data are often scarce. Moreover, the available data often only inform about a short period of time, while maintenance strategies, technologies, materials and external circumstances change over time. Therefore, the required probabilistic deterioration models cannot be retrieved and remain unproven in life-cycle modelling so far. Hence, this article introduces an expert judgement based Condition Over Time Assessment method that quantifies the uncertainty regarding the period that is required for structural assets to deteriorate to a given condition. The proposed method utilises Cooke’s classical model, which makes use of knowledge and experience of experts, who are weighed according to their performance in judging uncertainty, to assess this period. A bridge-based experiment shows that the proposed method has the potential to provide a means to effectively plan maintenance.@en

Ageing public infrastructure assets necessitate economic replacement analysis. A common replacement problem concerns an existing asset challenged by a replacement option. Classic techniques obtained from the domain of engineering economics are the mainstream approach to replacement optimization in practice. However, the validity of these classic techniques is built on the assumption that life cycle cash flows of a replacement option are repetitive. Differential inflation undermines this assumption and therefore more advanced replacement optimization techniques are required under these circumstances. These techniques are found in the domain of operations research and require linear or dynamic programming (LP/DP). Since LP/DP techniques are complex and time-consuming, the current study develops an alternative model for replacement optimizations under differential inflation. This approach builds on the classic capitalized equivalent replacement technique. The alternative model is validated by comparison with a DP model showing to be equally accurate for a case with characteristics that apply to many infrastructure assets.@en

The Classical model (or Cooke’s model) for elicitation and combination of expert judgments has been used in science and engineering since at least the early 1990’s. The most widely used program for applications of this model is EXCALIBUR. However, its code is not available for practitioners, which limits the accessibility and potential of the method. In this paper, we discuss a MATLAB toolbox (ANDURIL) intended to fill in this gap. The software has been tested in a recent real-life application reproducing the results of EXCALIBUR. We discuss different advantages for the users from having the developed source code available for practice.@en

Installation of Offshore Wind Farms (OWF) is subject to various uncertainties which concern environmental conditions, failures and availability of components. These should be taken into account when estimating the duration in order to efficiently plan the construction. This paper presents a methodology to include
possible disturbances on the supply of cable, in order to extend a developed decision support tool for the cable installation. A realistic test case was simulated for two scenarios, when uncertainty regarding the availability of the cable is neglected and when it is modelled using expert assessments. It was found that the proposed methodology can help professionals and/or researchers in investigating cost-effective alternatives concerning the assets that are used in the installation. Concluding, it is suggested to apply this methodology to the supply chain of the entire OWF installation as well as use structured expert judgement in order to improve the quality of resulting estimates.@en

There are numerous uncertainties that impact offshore operations. However, environmental uncertainties concerning variables such as wave height and wind speed are crucial because these may affect installation and maintenance operations with potential delays and financial consequences. In order to include these uncertainties into the duration estimation, adequate tools should be developed to simulate an installation scenario for a large number of historical environmental data. Data regarding environmental time series are usually scarce and limited, therefore they should be modelled. Since the environmental variables are in reality dependent, we propose a probabilistic method for their construction using copulas. To demonstrate the effectiveness of this method compared to the cases where observed or independently constructed environmental time series are used, a realistic cable installation scenario for an offshore wind farm was simulated. It was found that the proposed method should be followed to acquire more reliable and accurate estimation of the installation's duration.

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