Self-compacting concrete (SCC) is commonly known for its high flowability and resistance to segregation. Using SCC in constructing large structural members where reinforcements are congested offers several benefits, including reduced project time and a better work environment due to the lack of vibration. However, the concern is the presumably higher pressure exerted on the formwork during casting.

This thesis presents the results of a study on the form pressure exerted by SCC, which included the literature review to evaluate existing theoretical design models, laboratory testing, and modelling. A laboratory setup was developed, including a 2-meter circular column instrumented with a wireless pressure system. Two types of SCC were tested: with and without ground granulated blast furnace slag (GGBFS). The pressure was recorded by novel pressure sensors attached to transmitters to send real-time data to the cloud. The system was equipped with a pressure membrane that was in direct contact with the concrete. Several material and environmental parameters were recorded before and during casting.

The collected data were used to assess the accuracy of the following models, including DIN1821 (2010), Khayat et al. (2009), Gardner et al. (2012), Teixeira et al. (2017), Beitzel (2010), Ovarlez and Roussel (2006), and Proske (2010).  Most models were conservative, calculating higher pressures than recorded. In the next step, machine learning methods were developed to monitor and predict the pressure during casting continuously. These models showed significantly higher accuracy and flexibility than the existing prediction models.