AI-powered non-destructive testing for smart manufacturing of carbon-negative biopolymer-bound soil composite

Biopolymer-bound soil composite (BSC) is a materials technology that uses nature-based polymers to form carbon-negative building materials that are as strong as concrete. For large-scale applications of this material, a quality control system is necessary to minimize defects and avoid wastage. To meet this need, we developed a non-destructive vibration-based sensing system that assesses initial material quality, while still wet, before hardening occurs. We developed a set of artificial intelligence (AI) algorithms that use a set of physics-based features to aid in the smart manufacture of BSC. Our system allows for defect detection early on in the manufacturing process, so that the wet material can be salvaged when defects are identified. Another benefit is that our sensing system enables the monitoring of moisture loss as the material dries out and gains strength. Both of these functions are essential for large-scale deployment of this building material.

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Publication Details

Journal
Communications Engineering
Published
2026-02-25
DOI
https://doi.org/10.1038/s44172-026-00621-8
Primary Topic
Microbial Applications in Construction Materials
Type
article
Field-Weighted Citation Impact
0.00
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article

AI-powered non-destructive testing for smart manufacturing of carbon-negative biopolymer-bound soil composite

Michael D. Lepech, Andrew C. Lesh, Andreas Theissler, David J. Loftus et al.
Communications Engineering
Microbial Applications in Construction Materials
article

AI-powered non-destructive testing for smart manufacturing of carbon-negative biopolymer-bound soil composite

Michael D. Lepech, Andrew C. Lesh, Andreas Theissler, David J. Loftus, Barney H. Miao, Yiwen Dong
article en

Abstract

Biopolymer-bound soil composite (BSC) is a materials technology that uses nature-based polymers to form carbon-negative building materials that are as strong as concrete. For large-scale applications of this material, a quality control system is necessary to minimize defects and avoid wastage. To meet this need, we developed a non-destructive vibration-based sensing system that assesses initial material quality, while still wet, before hardening occurs. We developed a set of artificial intelligence (AI) algorithms that use a set of physics-based features to aid in the smart manufacture of BSC. Our system allows for defect detection early on in the manufacturing process, so that the wet material can be salvaged when defects are identified. Another benefit is that our sensing system enables the monitoring of moisture loss as the material dries out and gains strength. Both of these functions are essential for large-scale deployment of this building material.

Communications Engineering
Ames Research Center (US), University of Illinois Urbana-Champaign (US), Justus-Liebig-Universität Gießen (DE), Hochschule Aalen (DE), Stanford Medicine (US), Stanford University (US)
Zero hunger
Openalex Percentile: Top 51%
Microbial Applications in Construction Materials
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