A Review on Geometry and Surface Inspection in 3D Concrete Printing

2025-03-10

AI Summary

To address the low geometric accuracy and inconsistent surface quality inherent in extrusion- and jet-based 3D concrete printing (3DCP), this study establishes the first full-cycle quality control framework tailored to architectural additive manufacturing. The framework spans four sequential phases—during printing, inter-layer, pre-assembly, and post-assembly—and integrates four complementary sensing modalities: laser scanning, photogrammetry, structured light, and infrared thermography. We propose environment-adaptive modeling and automated sensor trajectory planning algorithms, and formalize a closed-loop methodology encompassing data acquisition, processing, evaluation, and feedback. The work systematically identifies four critical research gaps and clarifies cross-domain technology transfer pathways. By unifying multi-sensor metrology with adaptive process control, this framework provides both theoretical foundations and practical paradigms for high-precision, verifiable intelligent construction.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: 3D Computer Vision
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content

Abstract

Given the substantial growth in the use of additive manufacturing in construction (AMC), it is necessary to ensure the quality of printed specimens which can be much more complex than conventionally manufactured parts. This study explores the various aspects of geometry and surface quality control for 3D concrete printing (3DCP), with a particular emphasis on deposition-based methods, namely extrusion and shotcrete 3D printing (SC3DP). A comprehensive overview of existing quality control (QC) methods and strategies is provided and preceded by an in-depth discussion. Four categories of data capture technologies are investigated and their advantages and limitations in the context of AMC are discussed. Additionally, the effects of environmental conditions and objects' properties on data capture are also analyzed. The study extends to automated data capture planning methods for different sensors. Furthermore, various quality control strategies are explored across different stages of the fabrication cycle of the printed object including: (i) During printing, (ii) Layer-wise, (iii) Preassembly, and (iv) Assembly. In addition to reviewing the methods already applied in AMC, we also address various research gaps and future trends and highlight potential methodologies from adjacent domains that could be transferred to AMC.

Problem

• Ensuring quality in 3D concrete printing (3DCP) geometry and surface inspection.
• Exploring quality control methods for deposition-based 3DCP techniques.
• Analyzing data capture technologies and environmental impacts on 3DCP quality.

Innovation

• Explores geometry and surface quality control in 3DCP.
• Investigates four data capture technologies for AMC.
• Extends to automated data capture planning methods.