ABSTRACT
In contexts where empirical methods support decision-making, the need for organized information, often in a ‘Just in Time’ manner, is crucial. In such situations, machine learning (ML) and artificial intelligence (AI) based methods can play a significant role in offering meaningful solutions. Data must fulfil predefined criteria and be stored in structured databases. Quality of information and its organization are essential factors for predictive and decision-making algorithms. This paper will introduce essential database criteria and provide an example of data analysis for a case study to offer effective decision-making support for an underground construction project in an urban environment. In NATM (New Austrian Tunnelling Method) works, there is a heavy reliance on continuous monitoring and observation. Particularly in linear excavations, work cycles follow each other rapidly, often operating 24/7. The constant decision-making required for the progress of each daily cycle demands continually updated information that is readily available and presented in a clear and precise format for quick and rigorous interpretation.
