Title:
Predicting Ultrasonic Pulse Velocity for Concrete Health Monitoring
Author(s):
Mohammad Rahmati and Vahab Toufigh
Publication:
Materials Journal
Volume:
122
Issue:
5
Appears on pages(s):
13-26
Keywords:
concrete health monitoring; extreme gradient boosting (XGBoost); feature importance; machine learning (ML); support vector regression (SVR); ultrasonic pulse velocity (UPV)
DOI:
10.14359/51747869
Date:
9/1/2025
Abstract:
This study employs machine learning (ML) to predict ultrasonic pulse velocity (UPV) based on the mixture composition and curing conditions of concrete. A data set was compiled using 1495 experimental tests. Extreme gradient boosting (XGBoost) and support vector regression (SVR) were applied to predict UPV in both direct and surface transmissions. The Monte Carlo approach was used to assess model performance under input fluctuations. Feature-
importance analyses, including the SHapley Additive exPlanation (SHAP), were conducted to evaluate the influence of input variables on wave propagation velocity in concrete. Based on the results, XGBoost outperformed SVR in predicting both direct and surface UPV. The accuracy of the XGBoost model was reflected in average R2 values of 0.8724 and 0.9088 for direct and surface UPV, respectively. For the SVR algorithm, R2 values were 0.8362 and 0.8465 for direct and surface UPV, respectively. In contrast, linear regression exhibited poor performance, with average R2 values of 0.6856 and 0.6801 for direct and surface UPV. Among the input features, curing pressure had the greatest impact on UPV, followed by cement content. Water content and concrete age also demonstrated high importance. In contrast, sulfite in fine aggregates and the type of coarse aggregates were the least influential variables. Overall, the findings indicate that ML approaches can reliably predict UPV in healthy concrete, offering a useful step toward more precise health monitoring through the detection of UPV deviations caused by potential damage.
Related References:
1. Khoury, G. A., “Passive Fire Protection of Concrete Structures,” Proceedings of the Institution of Civil Engineers. Structures and Buildings, V. 161, No. 3, 2008, pp. 135-145. doi: 10.1680/stbu.2008.161.3.135
2. Rahmati, M., and Toufigh, V., “Evaluation of Geopolymer Concrete at High Temperatures: An Experimental Study Using Machine Learning,” Journal of Cleaner Production, V. 372, 2022, p. 133608. doi: 10.1016/j.jclepro.2022.133608
3. Ranjkesh Rashteh Roudi, M.; Hosseini, A.; Ranjkesh, M.; Pan, Z.; and Korayem, A. H., “Dispersion Stability and the Reaction Mechanism of Boron Nitride Nanosheets in a Cementitious Alkaline Environment: An Experimental and Computational Study,” ACS Applied Nano Materials, V. 7, No. 11, 2024, pp. 12592-12604. doi: 10.1021/acsanm.4c01082
4. Roudi, M. R. R.; Ranjkesh, M.; Korayem, A. H.; and Shahsavary, R., “Review of Boron Nitride Nanosheet-Based Composites for Construction Applications,” ACS Applied Nano Materials, V. 5, No. 12, 2022, pp. 17356-17372. doi: 10.1021/acsanm.2c03200
5. Rashidi, Y.; Roudi, M. R. R.; Korayem, A. H.; and Shamsaei, E., “Investigation of Ultrasonication Energy Effect on Workability, Mechanical Properties and Pore Structure of Halloysite Nanotube Reinforced Cement Mortars,” Construction and Building Materials, V. 304, 2021, p. 124610. doi: 10.1016/j.conbuildmat.2021.124610
6. Nazari, A., and Toufigh, V., “Effects of Elevated Temperatures and Re-Curing on Concrete Containing Rice Husk Ash,” Construction and Building Materials, V. 439, 2024, p. 137277. doi: 10.1016/j.conbuildmat.2024.137277
7. Nematzadeh, M.; Nazari, A.; and Tayebi, M., “Post-Fire Impact Behavior and Durability of Steel Fiber-Reinforced Concrete Containing Blended Cement–Zeolite and Recycled Nylon Granules as Partial Aggregate Replacement,” Archives of Civil and Mechanical Engineering, V. 22, No. 1, 2021, p. 5. doi: 10.1007/s43452-021-00324-1
8. Yazdani, F.; Sadeghi, H.; AliPanahi, P.; Gholami, M.; and Leung, A. K., “Evaluation of Plant Growth and Spacing Effects on Bioengineered Slopes Subjected to Rainfall,” Biogeotechnics, V. 2, No. 2, 2024, p. 100080. doi: 10.1016/j.bgtech.2024.100080
9. Rucka, M., and Wilde, K., “Ultrasound Monitoring for Evaluation of Damage in Reinforced Concrete,” Bulletin of the Polish Academy of Sciences. Technical Sciences, V. 63, No. 1, 2015, pp. 65-75. doi: 10.1515/bpasts-2015-0008
10. Ma, G., and Du, Q., “Structural Health Evaluation of the Prestressed Concrete Using Advanced Acoustic Emission (AE) Parameters,” Construction and Building Materials, V. 250, 2020, p. 118860. doi: 10.1016/j.conbuildmat.2020.118860
11. Özdal, M.; Karakoç, M. B.; and Özcan, A., “Investigation of the Properties of Two Different Slag-Based Geopolymer Concretes Exposed to Freeze–Thaw Cycles,” Structural Concrete, V. 22, 2021, pp. E332-E340. doi: 10.1002/suco.201900441
12. Rahmati, M.; Toufigh, V.; and Keyvan, K., “Monitoring of Crack Healing in Geopolymer Concrete Using a Nonlinear Ultrasound Approach in Phase-Space Domain,” Ultrasonics, V. 134, 2023, p. 107095. doi: 10.1016/j.ultras.2023.107095
13. Hwang, E.; Kim, G.; Choe, G.; Yoon, M.; Gucunski, N.; and Nam, J., “Evaluation of Concrete Degradation Depending on Heating Conditions by Ultrasonic Pulse Velocity,” Construction and Building Materials, V. 171, 2018, pp. 511-520. doi: 10.1016/j.conbuildmat.2018.03.178
14. Camara, L. A.; Wons, M.; Esteves, I. C. A.; and Medeiros-Junior, R. A., “Monitoring the Self-Healing of Concrete from the Ultrasonic Pulse Velocity,” Journal of Composites Science, V. 3, No. 1, 2019, p. 16. doi: 10.3390/jcs3010016
15. Güneyli, H.; Karahan, S.; Güneyli, A.; and Yapιcι, N., “Water Content and Temperature Effect on Ultrasonic Pulse Velocity of Concrete,” Russian Journal of Nondestructive Testing, V. 53, No. 2, 2017, pp. 159-166. doi: 10.1134/S1061830917020024
16. Haddad, K.; Haddad, O.; Aggoun, S.; and Kaci, S., “Correlation between the Porosity and Ultrasonic Pulse Velocity of Recycled Aggregate Concrete at Different Saturation Levels,” Canadian Journal of Civil Engineering, V. 44, No. 11, 2017, pp. 911-917. doi: 10.1139/cjce-2016-0449
17. Güçlüer, K., “Investigation of the Effects of Aggregate Textural Properties on Compressive Strength (CS) and Ultrasonic Pulse Velocity (UPV) of Concrete,” Journal of Building Engineering, V. 27, 2020, p. 100949. doi: 10.1016/j.jobe.2019.100949
18. Haach, V. G., and Ramirez, F. C., “Qualitative Assessment of Concrete by Ultrasound Tomography,” Construction and Building Materials, V. 119, 2016, pp. 61-70. doi: 10.1016/j.conbuildmat.2016.05.056
19. Whitehurst, E. A., Evaluation of Concrete Properties from Sonic Tests, American Concrete Institute Monograph No. 2, Farmington Hills, MI, 1966, 27 pp.
20. Carino, N. J., “Nondestructive Test Methods,” Concrete Construction Engineering Handbook, second edition, 2008, pp. 879-952.
21. Khademi, F.; Akbari, M.; and Nikoo, M., “Displacement Determination of Concrete Reinforcement Building Using Data-Driven Models,” International Journal of Sustainable Built Environment, V. 6, No. 2, 2017, pp. 400-411. doi: 10.1016/j.ijsbe.2017.07.002
22. Nguyen, T. T.; Pham Duy, H.; Pham Thanh, T.; and Vu, H. H., “Compressive Strength Evaluation of Fiber-Reinforced High-Strength Self-Compacting Concrete with Artificial Intelligence,” Advances in Civil Engineering, V. 2020, No. 1, 2020, p. 3012139. doi: 10.1155/2020/3012139
23. Houssein, E. H.; Abd Elaziz, M.; Oliva, D.; and Abualigah, L., Integrating Meta-Heuristics and Machine Learning for Real-World Optimization Problems, Springer Nature, 2022.
24. Zhang, J.; Huang, Y.; Wang, Y.; and Ma, G., “Multi-Objective Optimization of Concrete Mixture Proportions Using Machine Learning and Metaheuristic Algorithms,” Construction and Building Materials, V. 253, 2020, p. 119208. doi: 10.1016/j.conbuildmat.2020.119208
25. Mishra, P.; Al Khatib, A. M. G.; Yadav, S.; Ray, S.; Lama, A.; Kumari, B.; Sharma, D.; and Yadav, R., “Modeling and Forecasting Rainfall Patterns in India: A Time Series Analysis with XGBoost Algorithm,” Environmental Earth Sciences, V. 83, No. 6, 2024, p. 163. doi: 10.1007/s12665-024-11481-w
26. Soegianto, L. M.; Hinandra, A. T.; Suri, P. A.; and Fajar, M., “Comparison of Model Performance on Housing Business Using Linear Regression, Random Forest Regressor, SVR, and Neural Network,” Procedia Computer Science, V. 245, 2024, pp. 1139-1145. doi: 10.1016/j.procs.2024.10.343
27. el Mahdi Safhi, A.; Dabiri, H.; Soliman, A.; and Khayat, K. H., “Prediction of Self-Consolidating Concrete Properties Using XGBoost Machine Learning Algorithm: Rheological Properties,” Powder Technology, V. 438, 2024, p. 119623. doi: 10.1016/j.powtec.2024.119623
28. Singh, P. K., and Rajhans, P., “Experimental Investigation and SVR Model to Predict the Mechanical Properties of RAC by Enhancing the Characteristic of RCA Using Surface Treatment Method Along with Modified Mixing Approach,” Construction and Building Materials, V. 393, 2023, p. 132032. doi: 10.1016/j.conbuildmat.2023.132032
29. Ashrafian, A.; Taheri Amiri, M. J.; Rezaie-Balf, M.; Ozbakkaloglu, T.; and Lotfi-Omran, O., “Prediction of Compressive Strength and Ultrasonic Pulse Velocity of Fiber Reinforced Concrete Incorporating Nano Silica Using Heuristic Regression Methods,” Construction and Building Materials, V. 190, 2018, pp. 479-494. doi: 10.1016/j.conbuildmat.2018.09.047
30. Yousif, S. T., and Abdullah, S. M., “Artificial Neural Network Model for Predicting Compressive Strength of Concrete,” Tikrit Journal of Engineering Sciences, V. 16, No. 3, 2009, pp. 55-66. doi: 10.25130/tjes.16.3.05
31. Silva, F. A. N.; Delgado, J. M. P. Q.; Cavalcanti, R. S.; Azevedo, A. C.; Guimarães, A. S.; and Lima, A. G. B., “Use of Nondestructive Testing of Ultrasound and Artificial Neural Networks to Estimate Compressive Strength of Concrete,” Buildings, V. 11, No. 2, 2021, p. 44. doi: 10.3390/buildings11020044
32. Na, U. J.; Park, T. W.; Feng, M. Q.; and Chung, L., “Neuro-Fuzzy Application for Concrete Strength Prediction Using Combined Non-Destructive Tests,” Magazine of Concrete Research, V. 61, No. 4, 2009, pp. 245-256. doi: 10.1680/macr.2007.00127
33. Poorarbabi, A.; Ghasemi, M.; and Moghaddam, M. A., “Concrete Compressive Strength Prediction Using Non-Destructive Tests through Response Surface Methodology,” Ain Shams Engineering Journal, V. 11, No. 4, 2020, pp. 939-949. doi: 10.1016/j.asej.2020.02.009
34. Domingo, R., and Hirose, S., “Correlation between Concrete Strength and Combined Nondestructive Tests for Concrete Using High-Early Strength Cement,” The Sixth Regional Symposium on Infrastructure Development, 2009, pp. 12-13.
35. Zhang, F.; Pang, K.; Li, J.; Liu, Q.; Du, J.; Xiao, H.; Guo, B.; and Zhang, J., “Ultrasonic Pulse Velocity as a Non-Destructive Measure for the Projectile Impact Resistance of Cementitious Composites Across a Wide Range of Mix Compositions,” Journal of Building Engineering, V. 94, 2024, p. 109875. doi: 10.1016/j.jobe.2024.109875
36. Hedjazi, S., and Castillo, D., “Relationships among Compressive Strength and UPV of Concrete Reinforced with Different Types of Fibers,” Heliyon, V. 6, No. 3, 2020, p. e03646. doi: 10.1016/j.heliyon.2020.e03646
37. Said, A. M. I., and Ali, B. A. H., “Assessment of Concrete Compressive Strength by Ultrasonic Non-Destructive Test,” E3S Web of Conferences, V. 318, 2021, p. 3004. doi: 10.1051/e3sconf/202131803004
38. Ali, B. A. H., “Assessment of Concrete Compressive Strength by Ultrasonic Non-Destructive Test,” MSc thesis, University of Baghdad, Baghdad, Iraq, 2008.
39. Li, Y.; Gou, J.; and Fan, Z., “Particle Swarm Optimization-Based Extreme Gradient Boosting for Concrete Strength Prediction,” Proceedings of 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2019, 2019, pp. 982-986. doi: 10.1109/IAEAC47372.2019.8997825
40. Chen, T., and Guestrin, C., “XGBoost: A Scalable Tree Boosting System,” Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug. 13-17, 2016, pp. 785-794. doi: 10.1145/2939672.2939785
41. Friedman, J. H., “Greedy Function Approximation: A Gradient Boosting Machine,” Annals of Statistics, V. 29, No. 5, 2001, pp. 1189-1232. doi: 10.1214/aos/1013203451
42. Nguyen-Sy, T.; Wakim, J.; To, Q. D.; Vu, M. N.; Nguyen, T. D.; and Nguyen, T. T., “Predicting the Compressive Strength of Concrete from its Compositions and Age Using the Extreme Gradient Boosting Method,” Construction and Building Materials, V. 260, 2020, p. 119757. doi: 10.1016/j.conbuildmat.2020.119757
43. Wan, Z.; Xu, Y.; and Šavija, B., “On the Use of Machine Learning Models for Prediction of Compressive Strength of Concrete: Influence of Dimensionality Reduction on the Model Performance,” Materials, V. 14, No. 4, 2021, pp. 1-23. doi: 10.3390/ma14040713
44. Dong, W.; Huang, Y.; Lehane, B.; and Ma, G., “XGBoost Algorithm-Based Prediction of Concrete Electrical Resistivity for Structural Health Monitoring,” Automation in Construction, V. 114, 2020, p. 103155. doi: 10.1016/j.autcon.2020.103155
45. Vapnik, V.; Golowich, S. E.; and Smola, A., “Support Vector Method for Function Approximation, Regression Estimation, and Signal Processing,” Advances in Neural Information Processing Systems 9 (NIPS 1996), 1997, pp. 281-287.
46. Alam, M. S.; Sultana, N.; and Hossain, S. M. Z., “Bayesian Optimization Algorithm Based Support Vector Regression Analysis for Estimation of Shear Capacity of FRP Reinforced Concrete Members,” Applied Soft Computing, V. 105, 2021, p. 107281. doi: 10.1016/j.asoc.2021.107281
47. Tang, F.; Wu, Y.; and Zhou, Y., “Hybridizing Grid Search and Support Vector Regression to Predict the Compressive Strength of Fly Ash Concrete,” Advances in Civil Engineering, V. 2022, No. 1, 2022, p. 3601914. doi: 10.1155/2022/3601914
48. Mozumder, R. A.; Roy, B.; and Laskar, A. I., “Support Vector Regression Approach to Predict the Strength of FRP Confined Concrete,” Arabian Journal for Science and Engineering, V. 42, No. 3, 2017, pp. 1129-1146. doi: 10.1007/s13369-016-2340-y
49. Li, L.; Zheng, W.; and Wang, Y., “Prediction of Moment Redistribution in Statically Indeterminate Reinforced Concrete Structures Using Artificial Neural Network and Support Vector Regression,” Applied Sciences, V. 9, No. 1, 2018, p. 28. doi: 10.3390/app9010028
50. Singh, D., and Singh, B., “Investigating the Impact of Data Normalization on Classification Performance,” Applied Soft Computing, V. 97, 2020, p. 105524. doi: 10.1016/j.asoc.2019.105524
51. Laurensia, Y.; Young, J. C.; and Suryadibrata, A., “Early Detection of Diabetic Retinopathy Cases Using Pre-Trained EfficientNet and XGBoost,” International Journal of Advances in Soft Computing & Its Applications, V. 12, No. 3, 2020, pp. 101-111.
52. Van Dao, D.; Ly, H. B.; Trinh, S. H.; Le, T. T.; and Pham, B. T., “Artificial Intelligence Approaches for Prediction of Compressive Strength of Geopolymer Concrete,” Materials, V. 12, No. 6, 2019, p. 983. doi: 10.3390/ma12060983
53. Lundberg, S., “A Unified Approach to Interpreting Model Predictions,” Advances in Neural Information Processing Systems 30 (NIPS 2017), 2017.
54. Yilmaz, T.; Ercikdi, B.; Karaman, K.; and Külekçi, G., “Assessment of Strength Properties of Cemented Paste Backfill by Ultrasonic Pulse Velocity Test,” Ultrasonics, V. 54, No. 5, 2014, pp. 1386-1394. doi: 10.1016/j.ultras.2014.02.012
55. Çakir, Ö., and Aköz, F., “Effect of Curing Conditions on the Mortars with and without GGBFS,” Construction and Building Materials, V. 22, No. 3, 2008, pp. 308-314. doi: 10.1016/j.conbuildmat.2006.08.013
56. Mannan, M. A.; Basri, H. B.; Zain, M. F. M.; and Islam, M. N., “Effect of Curing Conditions on the Properties of OPS-Concrete,” Building and Environment, V. 37, No. 11, 2002, pp. 1167-1171. doi: 10.1016/S0360-1323(01)00078-6
57. Shen, P.; Lu, L.; He, Y.; Wang, F.; and Hu, S., “The Effect of Curing Regimes on the Mechanical Properties, Nano-Mechanical Properties and Microstructure of Ultra-High Performance Concrete,” Cement and Concrete Research, V. 118, 2019, pp. 1-13. doi: 10.1016/j.cemconres.2019.01.004
58. Liu, B.; Luo, G.; and Xie, Y., “Effect of Curing Conditions on the Permeability of Concrete with High Volume Mineral Admixtures,” Construction and Building Materials, V. 167, 2018, pp. 359-371. doi: 10.1016/j.conbuildmat.2018.01.190
59. Yazıcı, H.; Deniz, E.; and Baradan, B., “The Effect of Autoclave Pressure, Temperature and Duration Time on Mechanical Properties of Reactive Powder Concrete,” Construction and Building Materials, V. 42, 2013, pp. 53-63. doi: 10.1016/j.conbuildmat.2013.01.003
60. Chen, Y.-L.; Chang, J.-E.; Lai, Y.-C.; and Chou, M.-I. M., “A Comprehensive Study on the Production of Autoclaved Aerated Concrete: Effects of Silica-Lime-Cement Composition and Autoclaving Conditions,” Construction and Building Materials, V. 153, 2017, pp. 622-629. doi: 10.1016/j.conbuildmat.2017.07.116
61. Chen, T.; Gao, X.; and Ren, M., “Effects of Autoclave Curing and Fly Ash on Mechanical Properties of Ultra-High Performance Concrete,” Construction and Building Materials, V. 158, 2018, pp. 864-872. doi: 10.1016/j.conbuildmat.2017.10.074
62. Solís-Carcaño, R., and Moreno, E. I., “Evaluation of Concrete Made with Crushed Limestone Aggregate Based on Ultrasonic Pulse Velocity,” Construction and Building Materials, V. 22, No. 6, 2008, pp. 1225-1231. doi: 10.1016/j.conbuildmat.2007.01.014
63. Hwang, C. L., and Shen, D. H., “The Effects of Blast-Furnace Slag and Fly Ash on the Hydration of Portland Cement,” Cement and Concrete Research, V. 21, No. 4, 1991, pp. 410-425. doi: 10.1016/0008-8846(91)90090-5
64. Bakharev, T.; Sanjayan, J. G.; and Cheng, Y. B., “Effect of Elevated Temperature Curing on Properties of Alkali-Activated Slag Concrete,” Cement and Concrete Research, V. 29, No. 10, 1999, pp. 1619-1625. doi: 10.1016/S0008-8846(99)00143-X
65. Alawad, O. A.; Alhozaimy, A.; Jaafar, M. S.; Aziz, F. N. A.; and Al-Negheimish, A., “Effect of Autoclave Curing on the Microstructure of Blended Cement Mixture Incorporating Ground Dune Sand and Ground Granulated Blast Furnace Slag,” International Journal of Concrete Structures and Materials, V. 9, No. 3, 2015, pp. 381-390. doi: 10.1007/s40069-015-0104-9
66. Tan, K., and Zhu, J., “Influences of Steam and Autoclave Curing on the Strength and Chloride Permeability of High Strength Concrete,” Materials and Structures, V. 50, No. 1, 2017, pp. 1-9. doi: 10.1617/s11527-016-0913-6
67. Ludwig, N. C., and Pence, S. A., “Properties of Portland Cement Pastes Cured at Elevated Temperatures and Pressures,” ACI Journal Proceedings, V. 52, No. 2, Feb. 1956, pp. 673-687. doi: 10.14359/11624
68. Alhozaimy, A.; Jaafar, M. S.; Al-Negheimish, A.; Abdullah, A.; Taufiq-Yap, Y. H.; Noorzaei, J.; and Alawad, O. A., “Properties of High Strength Concrete Using White and Dune Sands under Normal and Autoclaved Curing,” Construction and Building Materials, V. 27, No. 1, 2012, pp. 218-222. doi: 10.1016/j.conbuildmat.2011.07.057
69. Hamidian, M.; Shariati, M.; Arabnejad, M. M. K.; and Sinaei, H., “Assessment of High Strength and Light Weight Aggregate Concrete Properties Using Ultrasonic Pulse Velocity Technique,” International Journal of Physical Sciences, V. 6, No. 22, 2011, pp. 5261-5266.
70. Jain, A.; Kathuria, A.; Kumar, A.; Verma, Y.; and Murari, K., “Combined Use of Non-Destructive Tests for Assessment of Strength of Concrete in Structure,” Procedia Engineering, V. 54, 2013, pp. 241-251. doi: 10.1016/j.proeng.2013.03.022
71. Estévez, E.; Martín, D. A.; Argiz, C.; and Sanjuán, M. Á., “Ultrasonic Pulse Velocity—Compressive Strength Relationship for Portland Cement Mortars Cured at Different Conditions,” Crystals, V. 10, No. 2, 2020, p. 133. doi: 10.3390/cryst10020133
72. Abo-Qudais, S. A., “Effect of Concrete Mixing Parameters on Propagation of Ultrasonic Waves,” Construction and Building Materials, V. 19, No. 4, 2005, pp. 257-263. doi: 10.1016/j.conbuildmat.2004.07.022
73. Neville, A., “The Confused World of Sulfate Attack on Concrete,” Cement and Concrete Research, V. 34, No. 8, 2004, pp. 1275-1296. doi: 10.1016/j.cemconres.2004.04.004
74. Naser, M. H., and Zainab, J. K., “Studying the Effect of Internal Sulfates on Normal and Lightweight Concrete,” IOP Conference Series. Materials Science and Engineering, V. 888, No. 1, 2020, p. 012054. doi: 10.1088/1757-899X/888/1/012054
75. Al-Rawi, R. S., and Abdul-Latif, A. M., “Compatibility of Sulphate Contents in Concrete Ingredients,” Fourth Scientific Conference, College of Engineering, University of Baghdad, Baghdad, Iraq, 1998.
76. Shooshpasha, I.; Hasanzadeh, A.; and Kharun, M., “Effect of Silica Fume on the Ultrasonic Pulse Velocity of Cemented Sand,” Journal of Physics: Conference Series, V. 1687, No. 1, 2020, p. 012017. doi: 10.1088/1742-6596/1687/1/012017
77. Macfarlane, J., “A Review on Use of Metakaolin in Concrete,” Engineering Science and Technology: An International Journal, V. 3, 2013, pp. 2250-3498.
78. Selcuk, S., and Tang, P., “A Metaheuristic-Guided Machine Learning Approach for Concrete Strength Prediction with High Mix Design Variability Using Ultrasonic Pulse Velocity Data,” Developments in the Built Environment, V. 15, 2023, p. 100220. doi: 10.1016/j.dibe.2023.100220
79. Singh, T.; Singh, B.; Bansal, S.; and Saggu, K., “Prediction of Ultrasonic Pulse Velocity of Concrete,” Applications of Computational Intelligence in Concrete Technology, CRC Press, Boca Raton, FL, 2022, pp. 235-251.