The early design phases of construction projects have a major impact on the success of the projects in terms of cost, construction time, global warming potential and other aspects. However, detailed information on the ongoing design is often lacking in these early project phases. This gap can be filled by using information and data of existing infrastructure. The historic data from past projects is collected, processed, analyzed, and evaluated systematically so that it is made into ready input for design. This is the evidence-based design (EBD) assistant’s approach to support design at the early project stages. A database of past footbridge designs provided by the engineering design firm schlaich bergermann partner (sbp) is used as the knowledge database to investigate and validate the potential of using historic data for evidence-based design to improve the design of future projects. Data collection was done manually involving digitizing past project data which will be stored in the digital twin for construction. The collected data was cleaned, filtered, and clustered using data analytics methods. Data processing, analyses and evaluation was done using machine learning approaches. The study found that (i) historic data/information is valuable input for design, (ii) historic data/information provides good predictions for performance indicator values (PI-values), and (iii) machine learning models can be used to evaluate and compare accuracies of PI-values. The very limited size of the database and scanty data was a major challenge in the implementation of the approach. However, we recommend that organizations consider growing such knowledge databases to enrich evidence-based design.