Obayashi Corporation
Location: Japan
iConnect member since: 2002

Obayashi is a major Japanese construction and engineering company with roots dating back to 1892. Headquartered in Tokyo, it is one of Japan's "big five" general contractors, operating across building, construction, civil engineering, real estate development, and infrastructure projects. Obayashi are known for delivering large-scale technically complex projects and has an extensive international presence. The company places strong emphasis on innovation, safety, and sustainability in the built environment.

Obayashi has been an iConnect member for over 20 years and has made extensive use of ConnectFlow in studies of the safe disposal of radioactive waste. This work includes research and modelling to support the interpretation of experiments at the Horonobe Underground Research Laboratory. 

SKB (Svensk Kärnbränslehantering Aktiebolag)
Location: Sweden
iConnect member since: 2002

The Swedish Nuclear Fuel and Waste Management Company (SKB), founded in 1972 and owned by the country's nuclear power producers, is responsible for the safe handling, transport, storage, and disposal of spent nuclear fuel and other radioactive waste. Headquarted in Solna outside of Stockholm, the company is internationally recognised for its long-term safety approach, particularly the KBS-3 geological disposal method. SKB plays a central role in Sweden's strategy for sustainable and responsible nuclear energy use.

SKB has been an iConnect member for over 20 years and has made extensive use of ConnectFlow. The software played a key role in the site selection process for a spent fuel geological disposal facility (GDF) in Sweden, contributing to the selection of the Forsmark site, as well as in the SR-Site safety assessment for that location. ConnectFlow has also been applied in studies of a future GDF for long-lived intermediate-level waste (SFL). In addition, the software has supported SKB’s broader research programme, including work on hydrogeochemical evolution and fracture conditioning which updates probabilistic discrete fracture network (DFN) models to better match observational data.