Use of Nondestructive Testing of Ultrasound and Artificial Neural Networks to Estimate Compressive Strength of Concrete
Abstract
:1. Introduction
2. Research Significance
3. Materials and Methods
- −
- Portland Cement CPII-F 32 with filler (90–94% clinker and 10–6% lime filler), with a compressive strength class of 32 MPa and a bulk density of 3110 kg/m3.
- −
- Coarse aggregates (CA) with maximum sizes of 12.5, 16.0 and 19.0 mm and a bulk density of 2684 kg/m3 (Table 2 shows the granulometric results).
- −
- Fine aggregates (FA) with a bulk density of 2620 kg/m3 (Table 2 shows the granulometric results). The sand used was acquired in the Metropolitan Region of Recife, and all the tests were carried out in the Laboratory of Construction Materials of the Catholic University of Pernambuco—TECOMAT, Recife, Brazil.
- −
- Additive: poly-functional super-plasticizer (TEC-PAST-100P) with a bulk density of 1135 kg/m3.
- −
- Metakaolin: metakaolin is a highly reactive pozzolan, consisting basically of silica (SiO2)- and alumina (Al2O3)-based compounds in the amorphous phase, which combine with the calcium hydroxide—Ca(OH)2—which significantly improves many features of most cement-based products.
- −
- Water.
4. Results
- (a)
- Factors resulting directly from concrete properties, such as the (1) aggregate sizing, grading, type and content; (2) cement type; (3) water/cement ratio; (4) admixtures; and (5) age of the concrete.
- (b)
- Other factors, such as the (1) transducer contact, (2) temperature of the concrete, (3) moisture and curing conditions for the concrete, (4) path length, (5) size and shape of the specimens, (6) level of stress, and (7) presence of reinforcing steel.
4.1. Relationship between Compressive Strength and UPV
4.2. Influence of Metakaolin and Aggregate on Concrete Compressive Strength
4.3. Artificial Neural Network Modelling
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Mix | Cement (kg/m3) | Sand (kg/m3) | Coarse Aggregate (kg/m3) | Additive (kg/m3) | Metakaolin (kg/m3) | Water (kg/m3) | w/c | |||
---|---|---|---|---|---|---|---|---|---|---|
Gravel 19 | Gravel 16 | Gravel 25 | Gravel 12 | |||||||
1 | 471.6 | 581.5 | 768.8 | 330.1 | 0.0 | 0.0 | 2.4 | 0.0 | 217.0 | 0.46 |
2 | 451.5 | 586.0 | 774.6 | 332.7 | 0.0 | 0.0 | 2.4 | 23.9 | 218.6 | 0.48 |
3 | 431.0 | 590.5 | 780.6 | 335.2 | 0.0 | 0.0 | 2.4 | 47.9 | 220.3 | 0.51 |
4 | 471.6 | 581.5 | 0.0 | 330.1 | 768.8 | 0.0 | 2.4 | 0.0 | 217.0 | 0.46 |
5 | 451.5 | 586.0 | 0.0 | 332.7 | 774.6 | 0.0 | 2.4 | 23.9 | 218.6 | 0.48 |
6 | 431.0 | 590.5 | 0.0 | 335.2 | 780.6 | 0.0 | 2.4 | 47.9 | 220.3 | 0.51 |
7 | 471.6 | 581.5 | 0.0 | 0.0 | 768.8 | 330.1 | 2.4 | 0.0 | 217.0 | 0.46 |
8 | 451.5 | 586.0 | 0.0 | 0.0 | 774.6 | 332.7 | 2.4 | 23.9 | 218.6 | 0.48 |
9 | 431.0 | 590.5 | 0.0 | 0.0 | 780.6 | 335.2 | 2.4 | 47.9 | 220.3 | 0.51 |
Sieves (mm) | Cumulative Mass Retained (%) | ||||
---|---|---|---|---|---|
Gravel 19 | Gravel 16 | Gravel 25 | Gravel 12 | Sand (FA) | |
25.0 | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% |
19.0 | 75.1% | 0.0% | 51.9% | 0.0% | 0.0% |
16.0 | 89.2% | 2.5% | 86.3% | 0.0% | 0.0% |
12.5 | 95.9% | 59.3% | 94.1% | 0.0% | 0.0% |
9.5 | 99.5% | 71.9% | 97.3% | 88.3% | 0.0% |
6.3 | 100.0% | 97.1% | 99.2% | 98.9% | 0.0% |
4.75 | 100.0% | 100.0% | 100.0% | 100.0% | 1.9% |
2.36 | 100.0% | 100.0% | 100.0% | 100.0% | 14.6% |
1.18 | 100.0% | 100.0% | 100.0% | 100.0% | 29.1% |
0.6 | 100.0% | 100.0% | 100.0% | 100.0% | 44.2% |
0.3 | 100.0% | 100.0% | 100.0% | 100.0% | 64.9% |
0.15 | 100.0% | 100.0% | 100.0% | 100.0% | 83.6% |
0.075 | 100.0% | 100.0% | 100.0% | 100.0% | 93.6% |
<0.075 | 100.0% | 6.43% | 100.0% | 100.0% | 100.0% |
Fineness modulus (−) | 6.97 | 6.88 | 7.52 | 6.61 | 2.38 |
Mixture | Slump (cm) | Metakaolin (%) | Compressive Strength (MPa) | Ultrasonic Pulse Velocity, UPV (km/s) | ||||
---|---|---|---|---|---|---|---|---|
7 days | 28 days | 60 days | 7 days | 28 days | 60 days | |||
1 | 12 | 0 | 34.77 | 36.55 | 41.00 | 4.23 | 4.30 | 4.36 |
2 | 19 | 5 | 34.96 | 37.27 | 43.90 | 4.28 | 4.34 | 4.45 |
3 | 10 | 10 | 39.18 | 44.86 | 45.25 | 4.36 | 4.47 | 4.50 |
4 | 20 | 0 | 32.24 | 35.86 | 37.00 | 4.22 | 4.33 | 4.36 |
5 | 18 | 5 | 35.33 | 35.95 | 40.35 | 4.27 | 4.28 | 4.38 |
6 | 10 | 10 | 37.61 | 42.67 | 44.26 | 4.36 | 4.45 | 4.47 |
7 | 18 | 0 | 30.47 | 34.00 | 35.56 | 4.18 | 4.28 | 4.30 |
8 | 18 | 5 | 33.60 | 35.95 | 36.47 | 4.24 | 4.30 | 4.32 |
9 | 17 | 10 | 36.33 | 40.09 | 41.15 | 4.33 | 4.39 | 4.43 |
w/c | ag/c | Age (days) | mk/c (%) | V (m/s) | Compressive Strength (MPa) | |
---|---|---|---|---|---|---|
Lab Tests | ANN Model | |||||
0.511 | 2.589 | 60 | 10 | 4.31 | 33.18 | 36.50 |
0.460 | 2.330 | 60 | - | 4.37 | 40.51 | 40.33 |
0.511 | 2.589 | 60 | 10 | 4.33 | 38.33 | 39.36 |
0.511 | 2.589 | 60 | 10 | 4.47 | 43.53 | 45.02 |
0.484 | 2.453 | 7 | 5 | 4.28 | 34.96 | 36.21 |
0.511 | 2.589 | 28 | 10 | 4.47 | 44.82 | 44.13 |
0.484 | 2.453 | 28 | 5 | 4.35 | 37.77 | 38.14 |
0.460 | 2.330 | 7 | - | 4.22 | 32.24 | 34.73 |
0.460 | 2.330 | 28 | - | 4.40 | 39.61 | 40.23 |
0.511 | 2.589 | 28 | 10 | 4.25 | 35.33 | 36.29 |
0.484 | 2.453 | 60 | 5 | 4.28 | 32.09 | 35.41 |
0.460 | 2.330 | 28 | - | 4.33 | 35.31 | 37.95 |
0.460 | 2.33 | 7 | - | 4.25 | 35.18 | 35.10 |
0.511 | 2.589 | 28 | 10 | 4.46 | 42.29 | 44.01 |
0.484 | 2.453 | 28 | 5 | 4.26 | 37.48 | 36.30 |
0.511 | 2.589 | 60 | 10 | 4.52 | 45.81 | 45.39 |
0.511 | 2.589 | 7 | 10 | 4.49 | 43.27 | 43.62 |
0.460 | 2.330 | 28 | - | 4.40 | 39.61 | 40.23 |
0.460 | 2.330 | 7 | - | 4.28 | 34.87 | 35.41 |
0.484 | 2.453 | 28 | 5 | 4.31 | 38.22 | 37.69 |
Input Node | Contribution (%) |
---|---|
w/c | 26.0 |
ag/c | 22.9 |
t | 5.6 |
mk/c | 26.3 |
V | 19.2 |
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Silva, F.A.N.; Delgado, J.M.P.Q.; Cavalcanti, R.S.; Azevedo, A.C.; Guimarães, A.S.; Lima, A.G.B. Use of Nondestructive Testing of Ultrasound and Artificial Neural Networks to Estimate Compressive Strength of Concrete. Buildings 2021, 11, 44. https://doi.org/10.3390/buildings11020044
Silva FAN, Delgado JMPQ, Cavalcanti RS, Azevedo AC, Guimarães AS, Lima AGB. Use of Nondestructive Testing of Ultrasound and Artificial Neural Networks to Estimate Compressive Strength of Concrete. Buildings. 2021; 11(2):44. https://doi.org/10.3390/buildings11020044
Chicago/Turabian StyleSilva, Fernando A. N., João M. P. Q. Delgado, Rosely S. Cavalcanti, António C. Azevedo, Ana S. Guimarães, and Antonio G. B. Lima. 2021. "Use of Nondestructive Testing of Ultrasound and Artificial Neural Networks to Estimate Compressive Strength of Concrete" Buildings 11, no. 2: 44. https://doi.org/10.3390/buildings11020044