For certain geotechnical structures, such as dikes and levees, the design quality is heavily reliant on the sampling quality. In addition, the extent of consequences on geotechnical design of using parameters from samples with a certain degree of disturbance in laboratory tests is unclear. While the available literature has been mainly focussed on disturbance experienced by clay samples, the same empirical findings are not always applicable to organic soils such as peat and organic clay. The global objective of this thesis was to gain an understanding of sampling disturbance in organic soils using both established and non-destructive experimental methods. In order to achieve this, the traditional Dutch Ackerman sampling technique was compared to that of a recently developed sampler, the Deltares Large Diameter Sampler (DLDS). Non-destructive methods and 1D consolidation parameters were used to quantify disturbance in organic soil samples. The results from existing databases showed that organic clay has a clear linear trend between w and RR and peat showed no definitive correlation, although the peat sampled by the DLDS had significantly higher water contents than the peats from the Ackerman sampler. Moreover, 37% of organic clay and peat Ackerman samples had an ∆e/e0 below 7%. Meanwhile, all the DLDS results from the Deltares laboratory had an ∆e/e0 of less than 4%. The use of this parameter is still questionable for peats, as the higher e0 found in DLDS peats is the cause for low disturbance measurements. However, the ∆e/e0 index may be used in conjunction with other destructive and non-destructive disturbance parameters. Furthermore, the trend showed it becomes increasingly difficult to produce high quality samples with increasing in-situ vertical stress. Using differences in shear wave velocity as a non-destructive disturbance determination technique, it was concluded that the increased shear wave velocity in Ackerman samples relative to the DLDS samples, although slight, was due to densification of the outermost volume of the sample being compressed by the sampling procedure. This effect was further observed in the other non-destructive method use in this investigation, CT scanning. Analysis of micro-CT scans showed that all specimens had higher greyvalue intensities at the perimeter than at the centre of the sample, however the observed effect was 7.4 times higher with the Ackerman specimens than with the DLDS specimens. The main recommendation is to continue research on the effectiveness of using non-destructive methods to quantify sample disturbance in peats. For the sampling industry, it is advised to increase the diameter of the Ackerman sampling tubes to between 70 and 120 mm when sampling in peat. In addition, in light of the lesser disturbance caused by the DLDS, it is advised to deploy it in shallow peats instead of the Ackerman sampler with the current inner diameter of 67 mm. Furthermore, it is recommended to deploy both types of samplers side-by-side in sampling projects in order to diminish the effect of site-specific variables.

In riverine environments under anaerobic conditions, methane and carbon dioxide are produced as a result of biological activity, causing degradation of organic matter. Under aerobic conditions, the bacteria present degrade the organic matter, whereby the concentration of dissolved oxygen may be lowered. Thus, issues experienced in the investigation area (the Port of Hamburg) are hindered construction operations, increased greenhouse gas emissions and the echo-sounding equipment used for sonic-depth finding for ships possibly showing an erroneous depth. The purpose of this investigation was to find out how gas generation and respiration relate to the basic sediment properties and what mathematical model with the highest accuracy can predict gas generation and respiration (separately), while maintaining within a given (precision) error (1%). Gas pressure was measured at the TU Delft for an incubation period of 100 days, which was later used to calculate the gas generation (mg C/g DW) with the use of the ideal gas law. Statistical methods used to analyze the data were: Pearson’s correlation coefficient, multiple regression analysis, adjusted coefficient of determination and error analysis. The results show that both gas generation and respiration have the highest Pearson’s correlation coefficient with TOC. Furthermore, in the multiple linear regression, gas generation had the highest coefficient of determination in a regression between TOC as the primary parameter and iron content (in solids) as the secondary parameter (푅2=0.91495). For respiration, it was displayed in a regression between TOC (as the primary parameter) and copper content in the solids (as the secondary parameter) (푅2=0.881). This concludes that organic matter degradation is driven by the quantity of organic matter. The residual sum of squares showed a decrease from the linear (and non-linear) model to the multiple linear regression model. The prob>|t| value (which determines the probability of error for the multiple linear regression) was much lower than 1% for all parameters in both the gas generation and respiration model, so it can be deduced that the variables are contributing to the model in a statistically significant way. Together with the previously mentioned highest coefficient of determination, the most accurate model for both gas generation and respiration found in this investigation was the multiple linear regression model, although the model for gas generation presented little difference to that of the simpler non-linear model. The exponential nature of the optimal fit for the data suggests that there is a threshold. In areas with low organic matter content, the organic matter present is much less degradable, falling into the “slow” pool category. It is recommended to investigate other mathematical models further. There is a possibility of a more accurate model (possibly a combination of a linear and non-linear model) for both gas generation and respiration which can model the parameters even better (higher coefficient of determination while still remaining within the permitted range of error). Furthermore, it is recommended to find out why the samples listed in tables 6 and 10 deviate more than accepted from the calculated value.

Blockchain consortia uphold value-addition through collaboration between organizations, by exploring, prototyping and innovating together. Blockchain technology presented itself as a new solution to the sharing of data proofs since 2008, with certain components such as smart contracts and privacy-enhancing components currently at different maturity levels. Blockchain consortia do not only navigate these technical factors, but also non-technical factors of decision-criteria are employed, and if development of a blockchain-based application fails, alternative data sharing technologies are explored. With the main research question ‘which guidelines can aid consortium managers in their decision-making process of developing blockchain-based applications?’, this study had the goal of contributing guidelines of decision-making processes, for aiding in the development of blockchain-applications in a rigorous manner. In order to tackle this problem, this research project performed case studies. This study makes use of a combined analytical framework, by merging the Resource-based View and Consortium Capabilities frameworks, the latter being a blockchain-specific framework. A multiple-case study approach is followed, by which four most-different cases of Dutch blockchain consortia are analysed. This is done by firstly scoping the consortium with desk research, followed by interviewing consortium managers, with interview questions based on the combined analytical framework. Thereafter, a hybrid coding approach, and thematic analysis are applied to investigate the challenges, potential benefits and risks in the decision-making process. The inductive part of the hybrid coding approach is the exploration of groups and factors of the deductively identified challenges, potential benefits and risks, which led to the creation of the Blockchain Consortia Guidelines, that presents guidelines on the inductively identified factors: Knowledge Transfer, Technology and use-case, Security, Funding and Economic Considerations, Organization and Vision. The guidelines were validated by interviewing managers of two additional blockchain cases, yielding a high level of recognition of the guidelines provided. Adaptation of certain guidelines was necessary, and implemented with the critique provided from partial recognition and non-recognition of guideline elements. This increased the external validity of the observed factors and decision-making guidelines. The scientific contribution of this research lies in providing top-level factors, the corresponding groups on the middle level, and guidelines on the bottom-level. The resulting factors and groups when compared to the existing literature (Consortium Capabilities framework), show that Knowledge Transfer, Funding and Economic Considerations, and Organizational, are additional aspects on the factor level that are given insufficient attention in the Consortium Capabilities framework. A limitation is that the interview data did not allow for yielding a (blockchain consortia-oriented) process-based framework as a final deliverable due to insufficient relationships between decision-making and factors in the combined analytical framework. Nonetheless, the rich qualitative data allowed for practical guidelines to be developed. Another limitation is a skew in the data towards potential benefits mentioned by managers, which is an indication that a self-serving bias, social-desirability bias, or a combination of these is present. Addressing these limitations is proposed when undertaking future research.