The Architecture, Engineering & Construction (AEC) industry has been traditionally fragmented, with a lot of disciplines specialising in different aspects of a project and encapsulating different knowledge areas. This research project, entitled “Knowledge Model for the Parametric Design of Concrete Viaducts”, investigates the development of a knowledge model around common, prefabricated concrete viaducts and its translation into an integrated, parametric platform and communication medium between architects and structural engineers. This model can act as a basis of discussion between the architect, the engineer, as well as the client and support a fast decision making process especially in the first steps of design. The main fields of interest for this project are Building Information Modelling (BIM) and parametric/associative design. BIM has revolutionised the industry by providing the practitioners with concepts and tools towards an integrated, object-oriented process. However, the current BIM technologies are often characterised by limited design flexibility. Parametric/associative design promotes the idea of designing structures, as systems of parameters which can generate numerous alternatives. This paradigm facilitates the development of complex geometries, enhances automation and supports optimisation algorithms. In this research, the advantages and limitations of the aforementioned fields, as well as a number of other concepts, such as Object-Oriented Programming, Extreme Programming and Model-Based Systems Engineering are investigated and the results are employed in the development of the model. The main parameters of viaduct design are determined and structured in a knowledge model considering their limitations and typical values. Moreover, the generic geometry of the components and their interaction is considered. This knowledge model starts with a strict top-down UML class diagram and gradually evolves into a parametric platform for viaduct design on Dynamo. Due to the complexity of the parametric script, a Python-based User Interface is created to increase the usability of the platform. The parametric model is seamlessly connected to Robot, a structural analysis software, through a Python script, in order to derive instant structural analysis results. The geometry of the deck is transferred to Robot, the structural analysis software, along with the properties of the beams, the support conditions and the loads of Load Model 1. A linear structural analysis is automatically conducted and the engineer can directly assess the results in Robot. The link between the applications ensures that both disciplines are working on the same design and merges their different knowledge areas into one model. The number of parameters considered in this research is 82, and they are divided in 3 categories according to the level to which the designer can influence them on the platform. As an outcome of the standardisation process and the restriction of the scope of the research, 51.2% of those parameters require input from the designer, while in the case of a symmetrical design (the same components are used on both sides of the viaduct) this percentage drops to 36.6%. This underlines the potential of such an integrated platform, since automation can significantly reduce the design time and both architects and engineers can focus more on the creative parts of the design and doing more design iterations. Important issues for the development of the platform are the validation of the results, in order to gain the trust of the user, as well as the limitations of the current parametric platforms. The Recommendations for further research include the immediate changes that can be implemented to the algorithm, such as the integration of cost analysis and the increase of the level of detail, as well as ways to unlock the full potential of this platform by considering cloud-based solutions and taking advantage of cutting edge technologies like machine learning.

The construction sector contributes to a significant portion of global energy consumption, CO2 emissions and the use of material resources. As climate change escalates with rapidly increasing temperatures, more extreme weather patterns and rising sea levels, an urge to reduce the environmental impact is evident.

A discrepancy is visible between early structural design and the required information for performing an environmental assessment. The first phase of design is an excellent time to steer the design to be more sustainable. However, today's procedures, involving LCA, only provides feedback at a later phase. This is due to the required highly detailed information for performing this complex and time consuming assessment. An assessment tool that supports the conceptual design would help to reduce the environmental impact.

The research project aims to show the feasibility of sustainability feedback using analytical methods on stored building data in early design. It provides LCA feedback in the conceptual design phase. A proof-of-concept is developed on industrial warehouses showcasing the functionality of the framework. Lacking the required building data and LCA scoring, a fully automated script is created with the parametric plugin Grasshopper, to generate the warehouse data. Due to time constrains and performance limitations, the scope was limited to the structural system of beams, columns, purlins and braces. The script randomly iterates over its parameter domain, automatically changing the geometry, performing a structural optimisation and calculating the LCA. This assessment is limited to a cradle-to-gate system boundary. Data is automatically written to a web-hosted database on the Packhunt.io platform of White Lioness technologies. The feedback is based on retrieving data from the database, applying a performance based algorithm, and finding the parameter that decreases the LCA score the most. Results are visualised to the user.

The result is the Interactive Design Assist (IDA) prototype that suggests parameters to the engineer in reducing the LCA score. This information provides guidance in the conceptual design phase on structural design of industrial warehouses. It demonstrates the large potential of reusing 'knowledge' inside the design process to improve building designs.

The objective of this research is to investigate the possibilities of creating an algorithmic design for structural glass covers, to create an innovative methodology with a broad spectrum of potential applications. The methodology should stimulate the easy use of glass as a structural material. It should also contribute to the bridging of the gap between architects and structural engineers and take advantage of the potential of parametric design for architectural and structural purposes.