Although the construction industry has made considerable progress in reducing the environmental impacts by waste diversion, the consumption rate of natural resources for construction material is still on the higher side. Hence, there needs to be a higher emphasis on reusing old building components in new construction without down-cycling them and retaining their original functions. Barriers involved in reusing recoverable building elements have multiple dimensions. Based on the current development of the industry, the solutions exist as isolated digital tools and focus only on one issue. This calls for a holistic approach to develop a solution which not only focuses on developing a technical tool but also focuses on embedding the solution in the process of reusing building elements. The main objective of this research is thus, to investigate the application of 'Systems thinking' as a holistic approach that focuses on developing a digital platform ecosystem based on the interventions amongst the actors and processes within the system. A serious gaming approach was used to simulate the system such that it can effectively derive its needs. Through the application of systems thinking and modelling approach to the problem, a set of six intervention strategies were derived. These strategies indicated that the digital platform ecosystem for enhancing reuse will fundamentally rely on these intervention strategies and thrive on reinforcing power from one side of the market to the other. By building on these intervention strategies, four propositions of change were given by comparing the existing scenario of the industry to the desired structure of a digital platform ecosystem. The research is concluded with the acknowledgement that implementation of the intervention strategies will require certain adaptations in the system. A conclusive, but not an exhaustive list of recommended adaptations in the design process and actor-network is provided to accompany the interventions strategies.

It is desirable to predict construction cost with a high level of accuracy in the early phase to compare the budgetary with feasibility determinations. Additionally, it is required to be as quick as possible. However, the accuracy of the cost estimation depends on the design details which are extremely limited in such an early phase, rendering numerous uncertainties and dynamics which are hard to control and foretell.

Currently, diverse approaches and techniques are being used and refined to reach the ultimate goal; the cost estimation is to accurately forecast the final cost of a project with no design details available. Generally, estimators’ experience plays a critical role here, and the availability of historical cost data is also crucial. The process is significantly dependent on an export-driven approach. However, decisions made by experts can be subjective and error-prone especially when the relationships between cost drivers and the target cost are not fully understood or even identified. Consequently, cost estimation to a fair level of accuracy is hardly possible to achieve manually within a restricted time. In recent years, civil engineering domain has begun to consider machine learning technique as an optimal approach in tackling the predictive problem through a data-driven approach. Adaptive Network-based Fuzzy Inference System (ANFIS) (a hybrid model of Artificial Neural Network and Fuzzy Inference System) is advantageous in managing uncertainties and representing knowledge. This research aims at investigating the applicability of using the ANFIS for cost estimation during the conceptual phase.