In the field of urban development few challenges are as tough as the housing problem. The cause for this is the rapid expansion of urban populations combined with slow construction processes. The Open Design Systems (Odesys) methodology is a preference-based design methodology for creating and evaluating designs. For this it uses a-priori method of optimization, where the preferences of stakeholders for each project objective are determined in the beginning of the process. Designs are thereafter created based on these preferences. Central to the success of preference-based design is the attitude of stakeholders towards using the methodology, which in other words is acceptance for this methodology. How can acceptance be evaluated? To do this the Technology Acceptance Model (TAM) was used. The system design features in the TAM were categorized in the “Use”, “Functionality” and the “Presentation”. Modifications to the Odesys methodology were made in these categories to see the effect on the stakeholder acceptability using the TAM. This research attempted to raise the acceptability for the Odesys methodology with the use of 2 case studies. The conclusion was that for the “Use” category the acceptability was dependent on the project design phase and the involvement of stakeholders. For the “Functionality”, the result reliability and the running time were important factors. The “Presentation” category consisted of the interface and the distinction between group and individual sessions. Modifications to these were developed and tested. This resulted in an increased perceived usefulness and a perceived ease-of-use , which according to the TAM results in an increased acceptance.

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.

The construction and infrastructure industry has witnessed an increase in the need for an optimized mitigation strategy to combat schedule and cost overruns amid the rise in market competitiveness and more strict timelines and budgets. Moreover, projects nowadays are more complex in terms their scope and requirements which ultimately drives more innovative and efficient solutions to enhance project risk mitigation. Typically, Monte Carlo (MC) simulations with different combinations of mitigation measures are performed until a random set of measures is chosen to satisfy the targeted budget. Several attempts aimed at optimizing around different objective functions to obtain more efficient strategies that would demonstrate the dynamics faced on the construction sites(Safaeian et al., 2022). The main flaw of all attempts were the absence of the goal-oriented control behavior of the project manager who would only opt to the optimal mitigation strategy given a risk event and cost overrun. The closest attempt was demonstrated in the objective functions of MitC developed to keep projects within schedule given a set of optimized mitigation strategies (Kammouh et al., 2021). However, the aforementioned tool solely addresses the selection of mitigation measures in the case of project delays affecting a strict delivery date with a trade-off restricted to one criterion: cost. The development in this document demonstrates a tool with broader functionalities that aims at selecting an optimized strategy to keep the project within budget yet with further additional features that extend its usefulness to a multi-criteria approach that involves time delays, environmental impacts as well as noise disturbance. The usefulness of the Mitigation controller for cost is demonstrated using a real case of a construction project in the Netherlands with the analysis performed on the go yielding significant negative impact savings relative to current approaches used to maintain the project’s budget.

Construction projects are notorious for going over budget. It is important for a project manager to have as much grip on the financial situation of the project as possible. In order to deal with the uncertainty that is prevalent in a construction project, a project manager should be able to steer project cost back towards budget.
To provide project managers with clear insight on the mitigation strategy that would be advised to be used. An adaptation of the mitigation controller (Kammouh et al, 2021) was developed in order to provide insight into mitigating project cost, as opposed to the original focus on project planning.
By adding goal focussed optimisation to a Monte Carlo Simulation, the human element of the corrective behaviour that is shown by a project manager is taken into account. This removes the fundamental modelling error that is present within traditional probabilistic approaches.
Through a real-life case from an infrastructural project in the centre of the Netherlands, its usefulness was shown. The Cost Mitigation Controller identified the most effective mitigation strategy for the projects current situation. The results from this study were evaluated using an experiment that was done with practitioners that work in project management roles. As a result it was shown that the Cost Mitigation Controller outperformed all project managers that were involved in the experiment. The probability of staying within budget for the Cost Mitigation Controller (74.6%) was around 4% higher than those of the three top performing project managers (70.3%).