Linda Cusumano, Construction Management

The tender phase is a time-constrained and business-critical stage in which the contractor uses its technical expertise to interpret and transform project requirements into a building concept. The growing volume and complexity of requirements create a need for a more systematised and automated approach to requirement analysis and conceptual design. One established approach to achieving more systematic requirement management, practised in other industries, is systems engineering. This research explores how data-driven and artificial intelligence-supported methods can facilitate such an approach in construction, focusing on requirements analysis, verification data, and the design process.

The research is structured into three parts, each addressing one research question. The first part investigates how project requirements can be automatically extracted, digitised, and analysed to support specialists in tendering. A prototype for requirements analysis was developed and evaluated through a workshop and two surveys. The second part explores production data for requirements and quality verification. It includes an assessment of data standardisation, a survey of the current benefits of digital reporting, and an exploration of future possibilities for using digital production data for knowledge generation and verification. The third part addresses tender-phase conceptual design, applying set-based design and a genetic algorithm to reduce production costs and carbon emissions in building superstructures. Two requirement-driven design prototypes were developed and tested on a reference building to visualise trade-offs.

The results show that a data-informed and AI-supported tendering process can facilitate requirements analysis, enable benchmarking across projects, and generate insights from verification data. AI support can also enable parallel evaluation of design alternatives with visualised trade-offs. To further improve a systems engineering approach in construction, incorporating continuous requirements analysis, standardised verification data, traceable requirement-verification links, and a broader analysis of design alternatives is recommended.

The research contributes to a more systematised and AI-supported approach to tendering by demonstrating how digital tools can help contractors analyse extensive and complex requirements, utilise verification data from previous projects, and generate design proposals that are both cost- and carbon-informed. This opens new opportunities to make better-informed decisions, improve early-phase design quality, and increase competitiveness through data-driven workflows.