Ranjith Kuttantharappel Soman

Dr. Ranjith Kuttantharappel Soman



Contact Data

Tel.: +41 44 633 62 87

ORCID: 0000-0003-3967-9121


ETH Zürich
Ranjith Kuttantharappel Soman
Innovatives und Industrial. Bauen
HIL  F 21.2
Stefano-Franscini-Platz 5
8093 Zürich

Organisations Lecturer at the Department of Civil, Environmental and Geomatic Engineering
Research Field

As an academic, Ranjith is passionate about applying semantic web technologies, systems thinking, and artificial intelligence in civil infrastructure and is keen to educate the next generation of infrastructure professionals on the importance of embedding information technology within the scope of the traditional functions of existing core professional service. In addition, he aspires to have an academic career to research and drive innovation for a step-change in infrastructure sustainability.In particular, his research interests are:


• Circular economy in the built environment
• Industrialized construction
• Extended reality in the built environment
• Construction robotics and informatics
• Systems engineering
• Lean construction

Curriculum Vitae (CV)

Ranjith is a post-doctoral researcher in the Circular Future Cities research module in the Future Cities Lab Global programme and at the Chair of Innovative and Industrial Construction at ETH Zurich.

Curriculum Vitae (CV) as PDFDownload
Additional Information

Trained as a civil engineer, he received his Master’s by research in Building Technology and Construction Management from the Indian Institute of Technology Madras. After his Master’s, he worked as a project assistant in the Building Automation Lab at IIT Madras to coordinate the development of a robotic system for self-assembly of columns and beams. He then moved to the Centre for Systems Engineering and Innovation at Imperial College London, where he did a PhD which focused on knowledge codification, semantic-web technologies, and reinforcement learning to improve construction productivity in an infrastructure context.


During his PhD, he developed a novel Linked-data based Constraint-Checking (LDCC) to identify possible blockers for smooth construction execution by deriving insights and from a large amount of heterogeneous disconnected datasets. He also spent an exchange semester at the Alan Turing Institute to combine LDCC with reinforcement learning to predict and generate conflict-free construction plans for infrastructure projects. After his PhD, he spent a year as a post-doctoral researcher at the same centre to implement and disseminate his research output to the industry through an InnovateUK funded project.

Publications inResearch Collection – Publication platform of ETH Zurich
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