Gaining insights into anticipated future expenses is essential for both the planning and construction phases of a project. A significant factor in future costs, particularly over extended periods, is the variability of material and labor prices. the change in costs over time, known as ’cost escalation,’ has been the focus of numerous research efforts. These studies primarily aim to forecast the Construction Cost Index (CCI), a composite index representing a standardized array of materials and labor typical in construction projects. This research presents a novel approach to forecast cost escalations, tailored to individual construction projects, addressing the shortcomings of predictions of a generic Construction Cost Index (CCI). Traditional CCI predictions, while providing some foresight, are limited by their generic nature and often overlook the specific material and labor variations within different projects. Additionally, current research generally fails to account for the uncertainty in the forecasts of the change in the material price and the estimate of the construction cost. In response to these challenges, this research pivots around the following research question: How can cost escalation for various types of construction projects be predicted, accounting for uncertainties in final construction costs and the forecast? Addressing this question involves selecting distinct indices for various project resources, such as steel, concrete, and labor, and combining these predicted indices in line with each resource’s cost. To achieve this, the study evaluates several time-series forecasting models, namely the vector error correction model (VECM), the vector autoregression (VAR) model, and the Holt-Winters model. For each model an automatic forecasting process is created, which automatically checks the suitability of the model, select the appropriate variables (in case of a multivariate model) and selects parameters. These models are evaluated for their accuracy across different time-frames and forecasting horizons. The Holt-Winters model, in particular, showed promise in providing reliable confidence intervals and point forecasts. The study’s testing phase revealed varying degrees of success, as volatile indices like copper and steel proved to be challenging, whereas forecasts for less volatile indices achieved higher accuracy. This outcome suggests room for improvement in refining these forecasting methods. The project-specific forecasts, including cost uncertainties, are developed by inputting cost estimates associated to the material price at current day value. The cost estimates input includes material and labor costs and their uncertainties. The uncertainties are represented through a three v point estimate (optimistic, pessimistic, and most likely values). This input is then transformed into a PERT distribution which is a transformation of the Beta distribution. The final stage involves combining the PDFs of each project activities cost (considering the cost of each specific resource) with the monthly forecast PDFs in a Monte Carlo simulation, providing a detailed cost distribution histogram of total cost and the individual resources cost. The tool’s functionality was demonstrated through a case study on a highway construction project. In this demonstration, specific project data, including material and labor cost estimates, were inputted into a hybrid web and Excel interface. This setup facilitates visualization and manipulation of project information. The tool processes these inputs via the AFP and Monte Carlo simulation, yielding comprehensive outputs such as histograms and statistical properties of the output. This is visible for total project costs and resource-specific escalations. This demonstration effectively showcased the tool’s capability to offer detailed insights into cost escalation, addressing the variability and uncertainty in construction projects. It underscored the tool’s alignment with the study’s objective of providing nuanced, project-specific cost escalation forecasts, moving beyond traditional CCI predictions. In conclusion, this study introduces a tool that caters to specific project resources, timelines, and uncertainties in cost escalations. While current limitations prevent its immediate practical application, this proof-of-concept lays the groundwork for future improvements. Focus of future research should be on refining accuracy and comparing the tool’s forecasts with escalation of historic projects to establish more robust insight into the actual escalation of projects.

Extreme value analyses (EVA) are often used to determine the frequency of extreme events. The length of the available observations is an important aspect when performing EVA. It is generally known that more available data results in better estimates with less uncertainties. The main objective of this research report was to assess what the influence of the length of the observations is when inferring rare events. This was done by first analyzing the sensitivity of inferred return levels from synthetic data from three known distributions. Also, three case studies were analyzed to observe the sensitivity of inferred return levels and return periods from the observations. The results of the analyses were that a larger sample size generally leads to a higher confidence in the estimates of inferred return levels from synthetic data. However, there will always remain some uncertainty associated with the estimates. The confidence in the inferred return levels from observations also generally increase for an increasing sample size. However, this can not always be observed and other aspects can be more dominant than an increasing sample size. No correlation could be observed between the sample size and the inferred return periods. The conclusion of the research was that, while there is a positive correlation between the sample size and the confidence in the estimates, there will always remain some uncertainties. It is therefore important to always communicate the uncertainties associated with estimates.

Monitoring fatigue damage is complex because ships are designed to crack after 20 years, and typically cracks will only occur after a long time. A novel fatigue damage sensor has been invented to indicate damage in corresponding aluminium welded joints. Fatigue damage is accelerated by introducing a stress concentration in the sensor w.r.t. the stress in the structure. New in this fatigue damage sensor compared to sensors invented in the past is the fact that this sensor is both in geometry and fatigue damage mechanisms similar to aluminium welded joints. Based on the time it takes for the sensor to crack, a prediction can be made of the expected lifetime of corresponding aluminium welded joints. This prediction is based on the in the VOMAS joint industry project developed Total Stress Concept in which fatigue resistance of all aluminum welded joints is combined in a single SN curve. It was found that with the total stress concept, resistance of welded joints is significantly higher than used in current fatigue design methods. Since the fatigue resistance increases with the total stress concept it is also investigated what the effects of implementing the total stress fatigue resistance curves at Damen would be on the fatigue design of high speed craft.