Analysis of Thin Carbon Reinforced Concrete Structures through Microtomography and Machine Learning
Abstract
:1. Introduction
2. Materials and Methods
2.1. Specimens
2.2. Microtomography
2.3. AI-Based Segmentation
2.3.1. Training Data
2.3.2. Augmentation
2.3.3. Training and Metrics
2.4. Roving Extraction
2.5. Multiscale Modeling
2.6. Scaled Boundary Isogeometric Analysis
2.7. Parameterized RVE and Definition of Characteristic Geometric Properties
2.7.1. Roving Dimensions and
2.7.2. In-Plane Dimensions
2.7.3. Shell Thickness
2.7.4. Concrete Cover
2.7.5. Concluding Assumptions
- Rovings are orthogonal to each other.
- Only one intersection of rovings is considered.
- Rovings are approximated as elliptical cylinders.
3. Results and Discussion
3.1. Microtomography
3.2. Deep Learning
- Weak offline augmentation:
- –
- Rotation around X, Y, and Z;
- Strong offline augmentation:
- –
- Rotation (around X, Y, and Z), resizing, flipping, tilting, squeezing, noise addition, blurring, sharpening, contrast manipulation, brightness manipulation;
- Online augmentation:
- –
- Offline: rotation around X and Y due to the difference in input height (128), width (128) and depth (64);
- –
- Online: rotation (around Z), resizing, flipping, tilting, squeezing, noise addition, blurring, sharpening, contrast manipulation, brightness manipulation.
3.2.1. Hardware and Training Duration
3.2.2. Training Results
3.3. Roving Extraction
3.4. Parameterized RVE and Definition of Characteristic Geometric Properties
3.4.1. Roving Dimensions and
3.4.2. In-Plane Dimensions
3.4.3. Shell Thickness
3.4.4. Concrete Cover
3.4.5. Example
3.4.6. Assumptions
- Orthogonality: From Figure 2c, it is clear that the rovings in warp and weft directions were not perfectly orthogonal to each other. However, the rotation of the extracted roving led to only small deviations in the derived geometric properties. Additionally, the distortion was introduced by the extrusion process. Using classical production methods, the distortion of the textile is less distinct. Thus, the assumption of orthogonal rovings is valid.
- A single roving intersection is sufficient for the RVE. This assumption is valid as long as manufacturing errors, which could lead to varying shell thicknesses or concrete covers, can be excluded. Ideally, the segmented area is greater than or equal to the RVE size in order to properly approximate the roving dimensions.
- Elliptical cross-sections are assumed for both rovings. Analysis of the cross-sectional images revealed that small height-to-width ratios should be considered for the roving. In the context of the presented linear-elastic tensile test, the shape approximation proved to be satisfactory.
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
CNN | Convolutional neural network |
CRC | Carbon reinforced concrete |
CT | Computed tomography |
LabMorTex | Laboratory Mortar Extruder |
NURBS | Non-uniform rational B-splines |
RVE | Representative volume element |
SBIGA | Scaled boundary isogeometric analysis |
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Roving | Fiber Strand Grid 1 | Fiber Strand Grid 2 | ||
---|---|---|---|---|
Height in mm | Width in mm | Height in mm | Width in mm | |
Warp direction | 0.55 | 1.41 | 1.41 | 2.13 |
Weft direction | 0.29 | 2.35 | 1.02 | 2.51 |
Strategy | Training Volumes | Duration (100 Epochs) |
1: No augmentation | 516 | 01 h:09 m |
2: Weak offline augmentation | 3658 | 08 h:06 m |
3: Strong offline augmentation | 21,776 | 38 h:50 m |
4: Online augmentation | 1262 | 45 h:43 m |
Strategy | DICE in % | Validation Loss | Validation Accuracy |
---|---|---|---|
1: No augmentation | 72.46 | 0.0493 | 98.88 |
2: Weak offline augmentation | 98.62 | 0.0354 | 99.21 |
3: Strong offline augmentation | 98.67 | 0.0239 | 99.34 |
4: Online augmentation | 97.44 | 0.0853 | 98.92 |
A in mm | in mm | in mm | ||
---|---|---|---|---|
warp direction | 0.83 | 1.49 | 0.71 | 0.48 |
weft direction | 0.71 | 2.45 | 0.37 | 0.15 |
Roving | Concrete | |
---|---|---|
Young’s modulus E in N/mm | 142,000 | 27,000 |
Poisson’s ratio | 0.35 | 0.2 |
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Wagner, F.; Mester, L.; Klinkel, S.; Maas, H.-G. Analysis of Thin Carbon Reinforced Concrete Structures through Microtomography and Machine Learning. Buildings 2023, 13, 2399. https://doi.org/10.3390/buildings13092399
Wagner F, Mester L, Klinkel S, Maas H-G. Analysis of Thin Carbon Reinforced Concrete Structures through Microtomography and Machine Learning. Buildings. 2023; 13(9):2399. https://doi.org/10.3390/buildings13092399
Chicago/Turabian StyleWagner, Franz, Leonie Mester, Sven Klinkel, and Hans-Gerd Maas. 2023. "Analysis of Thin Carbon Reinforced Concrete Structures through Microtomography and Machine Learning" Buildings 13, no. 9: 2399. https://doi.org/10.3390/buildings13092399
APA StyleWagner, F., Mester, L., Klinkel, S., & Maas, H. -G. (2023). Analysis of Thin Carbon Reinforced Concrete Structures through Microtomography and Machine Learning. Buildings, 13(9), 2399. https://doi.org/10.3390/buildings13092399