Buildability Supervisory Decision Support in Concrete 3D Printing Using Image Data
S. Senthilnathan and B. Raphael
Materials and Structures · Accepted for publication (2026). DOI pending.
Academic curriculum vitae
Dr. Shanmugaraj Senthilnathan
Postdoctoral Researcher, Chair of Materials and Methods in Digital Construction
Technical University of Munich, Munich, Germany
shanmugarajcode94@gmail.com
Research interests
Computer vision for construction process and quality monitoring; digital twins for concrete production and bridge structural monitoring; multimodal sensing and data-to-decision workflows; adaptive construction operations; construction management, productivity improvement, and process-waste reduction.
Academic appointment
Postdoctoral Researcher, Technical University of Munich
Chair of Materials and Methods in Digital Construction. Advisor: Prof. Dirk Lowke. Research on digital twins for concrete production, sensor-to-model integration for bridge structural monitoring, and multimodal characterisation of material and process state. Within TRR 277, work includes computer vision and laser-profile sensing for adaptive shotcrete deposition. These are ongoing research areas.
Education
Ph.D., Civil Engineering, IIT Madras (2020–2025)
Building Technology and Construction Management. Thesis: “Computer Vision-Based Quality Monitoring and Control of Concrete 3D Printed Elements.” Advisor: Prof. Benny Raphael. Defended 25 June 2025. CGPA: 8.64/10.
M.Tech., Civil Engineering, IIT Madras (2017–2019)
Building Technology and Construction Management. Thesis: “Computer Vision-Based Progress Monitoring of Linear Pipeline Laying Projects.” Advisor: Prof. Benny Raphael. CGPA: 9.30/10.
B.E., Civil Engineering, PSG College of Technology (2011–2015)
Thesis: “Performance of Plastic Fibre Wrapped Concrete Cylinders.” CGPA: 9.09/10.
Doctoral research
Process and quality monitoring in 3D concrete printing. Developed computer vision methods to quantify layer deformation and surface quality, assess buildability and extrudability, and support corrective process action. Concrete 3D printing provides the application context for a broader focus on process observability, quality, and reliability.
Selected publications
Predicting buildability using the surface texture of 3D printed concrete elements
S. Senthilnathan and B. Raphael
Journal of Architectural Engineering, ASCE
Printability assessment of concrete 3D printed elements with recycled fine aggregate
V. Haripan, S. Senthilnathan, M. Santhanam and B. Raphael
Construction and Building Materials
A review of concrete 3D printed structural members
B. Raphael, S. Senthilnathan, A. Patel and S. Bhat
Frontiers in Built Environment · Published January 2023
Using computer vision for monitoring the quality of 3D-printed concrete structures
S. Senthilnathan and B. Raphael
Sustainability, 14(23), 15682
Buildability assessment of 3D printed concrete elements through computer vision
Shanmugaraj Senthilnathan, Benny Raphael
41st ISARC, Lille, France, pp. 995–1002
Open time and extrudability performance analysis of 3D printed concrete with recycled concrete fine aggregates using rheological and computer vision techniques
Vislavath Haripan, Shanmugaraj Senthilnathan, Ravindra Gettu, Manu Santhanam, Benny Raphael
Digital Concrete 2024, Munich, Germany
Quality monitoring of concrete 3D printed elements using computer vision-based texture extraction technique
Shanmugaraj Senthilnathan, Benny Raphael
40th ISARC, Chennai, India, pp. 474–481
Teaching
Instructor / guest lecturer: introductory Python, machine learning, and deep learning model development for bachelor’s and master’s students.
Teaching assistant at IIT Madras: Machine Learning in Construction; Repair and Rehabilitation of Concrete Structures; Construction Contracts.
Teaching interests: construction project planning and management; lean and sustainable construction; BIM and digital fabrication; construction automation; computer vision and machine learning for civil engineering; concrete technology.
Industry experience
Water & Effluent Treatment Independent Company (IC), Larsen & Toubro Construction, India.
Assistant Planning Manager, L&T Construction (May 2019–May 2020)
Single point of responsibility for planning, monitoring, and resource management of an INR 1,200-crore EPC project. Maintained detailed construction schedules and coordinated directly with the client. Worked across client, supply chain, contract, and labour-management interfaces.
Senior Engineer, Project Coordination, L&T Construction (June 2015–July 2017)
Coordinated seven EPC projects across procurement, design, planning, monitoring, and project management. Experience included stakeholder management, contract management, design coordination, and supply chain management. Trained junior engineers and personally mentored two graduate trainees who progressed to senior engineer roles.
Honours, talks & co-curricular activities
- M.Tech. thesis recognised by L&T Construction as one of the three best master’s theses (2019).
- Invited talk: “Buildability Failure Prediction Using Computer Vision,” Loughborough University, UK.
- Journal reviewer: Automation in Construction; Journal of Building Engineering; Engineering Structures.
- Lead student coordinator, ISARC 2023, IIT Madras; Point Cloud Workshop coordinator.
- Head Liaison Officer, G20 Education Working Group meeting, Chennai (2023). Led a team of 30 liaison officers.
- Volunteer: ISARC 2022; Concrete 3D Printing Workshop; ISARC 2020 (online); IGLC 2018, IIT Madras.
Technical skills
Programming: Python, MATLAB, Octave, C, C++, VBA, VB.NET.
Machine learning & computer vision: TensorFlow, PyTorch, OpenCV, NumPy, Pandas, Pillow; object detection and instance / semantic segmentation.
Construction tools: AutoCAD, Revit, Navisworks, LumenRT, Primavera P6, MS Project.
Conferences & workshops
ISARC 2024, Lille; ISARC 2023, Chennai; TLC2 Week, IIT Madras (2023, 2024); Indian Lean Construction Conference, NICMAR Hyderabad (2023); Advances in 3D Concrete Printing Workshop (2023); NSM Machine Learning in Construction Workshop (2021); Academic Writing Workshop (2021); ChemPlus Data Science and Machine Learning Workshop (2020).