Ali Kazemian
Assistant Professor & J.W. “Billy” and Janice Maxey Guitreau Professor Louisiana State University
- Baton Rouge LA
Dr. Kazemian specializes in construction automation and robotic construction-scale 3D printing.
Media
Biography
He specializes in construction automation and robotic construction-scale 3D printing, with groundbreaking applications in low-income housing, coastal protection, disaster relief, and extraterrestrial construction. He earned both his PhD in Civil Engineering (2018) and a Master’s in Computer Science from the University of Southern California. Before joining LSU in 2020, he spent three years as a senior R&D engineer at a robotic construction company, gaining valuable industry experience. A recognized leader in his field, Dr. Kazemian was named a Kavli Fellow by the National Academy of Sciences in 2024 and received the “ASTM Young Professional in Additive Manufacturing” Award in 2023. His innovative research has garnered widespread attention, with features in The New York Times, MIT Technology Review, and on Fox44 TV.
Areas of Expertise
Research Focus
Construction-Scale 3D Printing & Robotic Construction
Dr. Kazemian’s research focuses on construction-scale 3D printing and robotic construction, advancing affordable housing on Earth and water-free concrete habitats for the Moon and Mars. He merges novel cementitious mix design with real-time sensing and computer-vision quality control to automate layer extrusion, cut costs, and boost resilience in hostile environments.
Accomplishments
Kavli Fellow, National Academy of Sciences
2024
Worley Professor of Excellence Award
2025
ASTM Young Professional in Additive Manufacturing Award
2023
Education
University of Southern California
Ph.D.
Civil Engineering
2018
University of Southern California
M.S.
Computer Science
2017
Amirkabir University of Technology
M.Sc.
Construction Engineering & Management
2012
Amirkabir University of Technology
B.Sc.
Civil Engineering
2010
Media Appearances
Maybe in Your Lifetime, People Will Live on the Moon and Then Mars
The New York Times
2023-10-01
His colleague Ali Kazemian is working with NASA on the printing material itself, focusing on a waterless concrete fashioned from simulated versions of the rock material that exists on the moon. Dr. Kazemian sees in the rich lunar minerals an even deeper potential than just concrete for 3-D printing: He sees resources that can be used extensively by those who stay behind on earth.
“People talk about humans living on the moon,” he said. “But there’s another likely scenario, too. At some point on earth we are going to run out of resources. So establishing mines and fully automated factories on the moon is a possibility too.”
The moon is just the beginning for this waterless concrete
MIT Technology Review online
2024-12-29
Building a home base on the moon will demand a steep supply of moon-based infrastructure: launch pads, shelter, and radiation blockers. But shipping Earth-based concrete to the lunar surface bears a hefty price tag. Sending just 1 kilogram (2.2 pounds) of material to the moon costs roughly $1.2 million, says Ali Kazemian, a robotic construction researcher at Louisiana State University (LSU). Instead, NASA hopes to create new materials from lunar soil and eventually adapt the same techniques for building on Mars.
NASA taps LSU for lunar construction research
Construction Dive online
2023-12-12
LSU Assistant Professor Ali Kazemian is working on the research project with two scientists from NASA Marshall Space Flight Center in Huntsville, Alabama: technical fellow Michael Fiske and Jennifer Edmunson, project manager and geologist. The group will primarily investigate the use of molten sulfur and moon dust to develop a 3D-printed, waterless concrete, per the release.
Articles
Automated Inspection in Robotic 3D Printing: In-Process Geometrical Measurements Using Structured Light Machine Vision
ASCE Journal of Computing in Civil Engineering2025
Automated geometrical inspection during the robotic deposition process remains a major challenge in the field of construction 3D printing. Existing methods are mainly designed for post-process inspection or fail to deliver accurate real-time measurements. This study presents an innovative measurement methodology leveraging structured light machine vision. The proposed system incorporates a 2D camera and a laser light pattern projected onto the extrudate, combined with a novel processing algorithm for continuous width and height measurements. The results show that the proposed algorithm successfully produces measurements for single-and multi-layer extrudates with submillimeter precision and a 0.2 s processing time across three different lighting conditions. Additionally, a baseline 2D vision measurement system without active lighting was implemented for comparative analysis. Overall, the findings of this study prove the superior accuracy and versatility of the proposed structured light machine vision methodology for real-time geometrical inspection compared to the existing methods.
Neuromorphic Imaging for In-process Monitoring of Concrete 3D Printing
ASCE Computing in Civil Engineering2025
In this work for the first time, we examine the suitability and pros/cons of using event-based neuromorphic imagers for in-process monitoring of concrete additive manufacturing. There is interest in incorporating in-process monitoring into 3D concrete additive manufacturing systems for the purpose of measuring/controlling geometry as well as for detecting/preventing defects, to enable a more robust large-scale fabrication process. A substantial challenge with in-process monitoring is reducing the volume of data generated; observing large prints for extended periods of time can require prohibitive amounts of memory, inhibiting the ease of transfer, storage, and real-time analysis. Neuromorphic imagers are fundamentally different from conventional imagers in that they only detect light intensity changes at the individual pixels, and report these changes as events. The result is a potential memory requirement reduction. This work represents a preliminary investigation into the use of event imagers for efficient and high-speed in-process monitoring of concrete additive manufacturing.
Machine learning approach to predict the early-age flexural strength of sensor-embedded 3D-printed structures
Progress in Additive Manufacturing2025
The absence of formwork in 3D-printed concrete, unlike conventional mold-cast concrete, introduces greater variability in curing conditions, posing significant challenges in accurately estimating the early-age mechanical strength. Therefore, common non-destructive techniques such as the maturity method fail to deliver a generalized predictive model for the mechanical strength of 3D-printed structures. In this study, multiple machine learning (ML) algorithms, including linear regression (LR), support vector regression (SVR), and artificial neural network (ANN), were developed to estimate the early-age flexural strength of 3D-printed beams under varying curing conditions, utilizing data collected from embedded sensors. Six input variables were employed for the ML models, including relative permittivity, internal temperature, and curing method.
Automated strength monitoring of 3D printed structures via embedded sensors
Automation in Construction2024
Estimating the early-age strength of 3D printed concrete is more challenging than that of conventional concrete due to the absence of formwork and increased variability in curing conditions. The common maturity method is ineffective for 3D printed structures since it fails to account for moisture content variations. This paper introduces a new approach using embedded sensors to continuously collect data on the electrical properties and temperature of 3D printed concrete, enabling accurate strength estimation under varying curing conditions. Empirical models based on electrical resistivity, internal temperature, and relative permittivity are developed and evaluated. The permittivity-based model can estimate the flexural strength of 3D printed specimens with at least 83% accuracy and a maximum root mean square error of 0.27 MPa under different curing conditions across three concrete grades.
LiDAR-based real-time geometrical inspection for large-scale additive manufacturing
Progress in Additive Manufacturing2024
This study proposes an automated real-time inspection methodology using 2D LiDAR for construction-scale additive manufacturing, aimed at prompt defect detection and preventing excessive layer deformations during the robotic fabrication process. Two new data processing algorithms are designed, implemented, and compared with benchmark data to conduct a comprehensive analysis. The proposed inspection module is integrated into the concrete printing machine, eliminating the need for an additional inspection robot—an important consideration for fabrication operations in outdoor and remote environments. Based on the quantitative data from a systematic experimental program, the proposed Variable Standard Deviation (VSD) algorithm continuously performs extrudate width and height measurements with an average error of less than 1.2 mm and maximum error values under 1.33 mm when data collection …
3D printed sulfur-regolith concrete performance evaluation for waterless extraterrestrial robotic construction
Automation in Construction2024
By leveraging the capabilities of construction 3D printing, building structures in harsh extraterrestrial environments is conceivable. Sulfur concrete is a waterless construction material that offers great potential to replace Portland cement concrete (PCC) in extraterrestrial construction. The shape stability of 3D printed Martian sulfur-regolith concrete (SRC) was found to benefit from a lower substrate layer temperature. However, this comes at the cost of flexural strength, resulting in up to 53% strength loss. The printed SRC specimens demonstrated a significantly faster strength development rate (gaining about 85% of the ultimate strength after only 12 h) compared to the printed PCC. The printed SRC specimens also outperformed the PCC specimens in vacuum conditions at higher temperatures.
Event Appearances
How to permanently live on the Moon
2024 | National Academy of Sciences Irvine, CA
Building on the Moon and Mars
2024 | LSU Science Café Baton Rouge, LA
Construction 3D Printing: Applications, Challenges, and Future Prospects (Invited Talk – selected as Darrell Elliott Lecture)
2021 | 31st Annual Louisiana Civil Engineering Conference New Orleans, LA
Research Grants
Planetary Robotic Construction on the Moon and Mars Using 3D Printed Waterless Concrete
NSF
2024-2026
Towards Sustainable Robotic Construction: Concrete 3D Printing with Quarry By-products and Low Portland Cement Content
Louisiana Board of Regents (BOR ITRS)
2023- 2026
ISRU-based Planetary Construction 3D Printing for Lunar and Martian Infrastructure Development: Process Optimization and Automated Quality Control
Louisiana Board of Regents
2022-2025
Patents
Intelligent and assistive motion capture interface to control robotic operations involving toolpath tracing
Provisional application # 63/765,924
Filed 2025
