Pranav Chougule
Publications

Published Research


Peer-reviewed publications, conference proceedings, and technical contributions from my research in robotic NDE, intelligent sensing, and infrastructure monitoring.

Conference ProceedingPublished16 April 2026

Laser-based monitoring of robotic carbon dioxide injection in fresh additively manufactured concrete

Pranav Chougule, David Abraham, Yaghoob (Amir) Farnam, and Arvin Ebrahimkhanlou

Proc. SPIE 13952, Digital Twins, AI, and NDE for Industry Applications and Energy Systems 2026, 139520L. Event: SPIE Smart Structures + Nondestructive Evaluation, 2026, Vancouver, BC, Canada.

Citation

Pranav Chougule, David Abraham, Yaghoob (Amir) Farnam, and Arvin Ebrahimkhanlou "Laser-based monitoring of robotic carbon dioxide injection in fresh additively manufactured concrete", Proc. SPIE 13952, Digital Twins, AI, and NDE for Industry Applications and Energy Systems 2026, 139520L (16 April 2026); https://doi.org/10.1117/12.3093819

Abstract

This study focuses on monitoring a novel process for injecting CO₂ gas into fresh 3D-printed concrete. The construction sector is responsible for nearly 8% of global carbon emissions, and embodied carbon in concrete is a major contributor. CO₂ sequestration into fresh cementitious material presents a promising solution. Mineral carbonation of fresh cementitious paste offers permanent in-situ CO₂ storage, and extrusion-based 3D concrete printing (3DCP) provides a uniquely favorable geometry for robotic inter-layer injection between printing passes. The authors have developed a robotic system and a laser-based monitoring pipeline for gas injection into fresh concrete. The system integrates a 6-DOF collaborative robotic arm, a syringe-based gas actuator, and a Keyence line laser profilometer on a motorized stage under a shared coordinate frame. Experiments cover 30 penetrations in fresh mortar under a 3×3×3 full factorial parameter design varying injection depth, flow rate, and volume. The pipeline detected a measurable surface depression in 28 of 30 injections. Injection depth was the dominant driver of surface response, with mean depression depth increasing from 1.63 mm at 10 mm depth to 3.52 mm at 35 mm depth. A significant depth-by-speed interaction was also identified: fast injection produces substantially deeper surface depressions at shallow depths, but this effect diminishes at greater injection depths. This validated pipeline establishes the foundation for future optimization of CO₂ injection parameters in 3D-printed cementitious layers.

Ongoing Research

Additional manuscripts are in preparation, covering vision-language model interpretation of UPV signals, Gaussian Splatting for multi-visit NDE scene registration, and robotic acoustic emission source localisation in reinforced concrete structures. This page will be updated as submissions are accepted.