Abstract
Ultra-high-performance concrete (UHPC) is suitable for repairing and strengthening damaged normal strength concrete (NSC) structures due to its excellent qualities. However, a successful repair relies on whether the UHPC–NSC interface can offer a superb bonding performance under varying working conditions. Therefore, predicting the interface bond strength between substrate NSC and repair UHPC with sufficiently high accuracy has become essential for evaluating and maintaining NSC structures. This study utilized four different machine learning (ML) techniques, support vector machine (SVM), artificial neural network (ANN), multiple linear regression (MLR), and stepwise regression (SWR) to predict the UHPC–NSC interface bond strength. The ML models established the relationship between input variables and target bond strength and predicted the UHPC–NSC interface bond strength. Random search techniques were used to tune the selected algorithms hyperparameters, and the k-fold cross-validation technique was employed to ensure generalizability. Two datasets containing the UHPC–NSC bond strength test results from splitting-tensile and slant-shear tests were used to train and test the performance of the selected ML models. Results show that SVM and ANN models are more effective than the MLR and SWR models based on the two datasets. Besides, all the four ML models developed have better prediction accuracy than the empirical model given by the design codes. The correlation between the input variables and target bond strength was evaluated through partial dependence plots. The ML approach explored in this study has proven viable and effective in predicting UHPC–NSC bond strength and provided the basis for designing UHPC–NSC composite elements.
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References
Wu, Z., Shi, C., Khayat, K.H.: Investigation of mechanical properties and shrinkage of ultra-high performance concrete: Influence of steel fiber content and shape. Compos. Part B Eng. 174, 107021 (2019). Doi: https://doi.org/10.1016/j.compositesb.2019.107021
Amini Pishro, A., Feng, X., Ping, Y., Dengshi, H., Shirazinejad, R.S.: Comprehensive equation of local bond stress between UHPC and reinforcing steel bars. Constr. Build. Mater. 262, 119942 (2020). Doi: https://doi.org/10.1016/j.conbuildmat.2020.119942
Graybeal, B.A.: Material property characterization of ultra-high performance concrete. United States. Federal Highway Administration. Office of Infrastructure (2006)
Graybeal, B.; Tanesi, J.: Durability of an ultrahigh-performance concrete. J. Mater. Civ. Eng. 19, 848–854 (2007)
Shafieifar, M.; Farzad, M.; Azizinamini, A.: Experimental and numerical study on mechanical properties of Ultra High Performance Concrete (UHPC). Constr. Build. Mater. 156, 402–411 (2017). https://doi.org/10.1016/j.conbuildmat.2017.08.170
Farzad, M.; Shafieifar, M.; Azizinamini, A.: Retrofitting of bridge columns using UHPC. J. Bridg. Eng. 24, 1–13 (2019). https://doi.org/10.1061/(ASCE)BE.1943-5592.0001497
Aaleti, S.; Sritharan, S.: Quantifying bonding characteristics between UHPC and normal-strength concrete for bridge deck application. J. Bridg. Eng. 24, 1–13 (2019). https://doi.org/10.1061/(ASCE)BE.1943-5592.0001404
Farzad, M.; Shafieifar, M.; Azizinamini, A.: Experimental and numerical study on bond strength between conventional concrete and ultra high-performance concrete (UHPC). Eng. Struct. 186, 297–305 (2019). https://doi.org/10.1016/j.engstruct.2019.02.030
Tayeh, B.A.; Bakar, B.H.A.; Johari, M.A.M.; Voo, Y.L.: Mechanical and permeability properties of the interface between normal concrete substrate and ultra high performance fiber concrete overlay. Constr. Build. Mater. 36, 538–548 (2012)
Abo Sabah, S.H.; Hassan, M.H.; Muhamad Bunnori, N.; Megat Johari, M.A.: Bond strength of the interface between normal concrete substrate and GUSMRC repair material overlay. Constr. Build. Mater. 216, 261–271 (2019). https://doi.org/10.1016/j.conbuildmat.2019.04.270
Valikhani, A.; Jahromi, A.J.; Mantawy, I.M.; Azizinamini, A.: Experimental evaluation of concrete-to-UHPC bond strength with correlation to surface roughness for repair application. Constr. Build. Mater. 238, 117753 (2020). https://doi.org/10.1016/j.conbuildmat.2019.117753
Zhang, Y., Zhang, C., Zhu, Y., Cao, J., Shao, X.: An experimental study: various influence factors affecting interfacial shear performance of UHPC-NSC. Constr. Build. Mater. 236, 117480 (2020)
Momayez, A., Ehsani, M.R., Ramezanianpour, A.A., Rajaie, H.: Comparison of methods for evaluating bond strength between concrete substrate and repair materials. Cem. Concr. Res. 35, 748–757 (2005). Doi: https://doi.org/10.1016/j.cemconres.2004.05.027
Ibrahim, A.; Farouk, B.; Haruna, S.I.: Evaluation of Bond Strength between Ultra-High-Performance Concrete and Normal Strength Concrete : An Overview. 32, 41–51 (2020)
Asteris, P.G.; Koopialipoor, M.; Armaghani, D.J.; Kotsonis, E.A.; Lourenço, P.B.: Prediction of cement-based mortars compressive strength using machine learning techniques. Neural Comput. Appl. (2021). https://doi.org/10.1007/s00521-021-06004-8
Gholampour, A.; Mansouri, I.; Kisi, O.; Ozbakkaloglu, T.: Evaluation of mechanical properties of concretes containing coarse recycled concrete aggregates using multivariate adaptive regression splines (MARS), M5 model tree (M5Tree), and least squares support vector regression (LSSVR) models. Neural Comput. Appl. 32, 295–308 (2020). https://doi.org/10.1007/s00521-018-3630-y
Gharehbaghi, S.; Yazdani, H.; Khatibinia, M.: Estimating inelastic seismic response of reinforced concrete frame structures using a wavelet support vector machine and an artificial neural network. Neural Comput. Appl. 32, 2975–2988 (2020). https://doi.org/10.1007/s00521-019-04075-2
Asteris, P.G., Skentou, A.D., Bardhan, A., Samui, P., Pilakoutas, K.: Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models. Cem. Concr. Res. 145, 106449 (2021). Doi: https://doi.org/10.1016/j.cemconres.2021.106449
Neves, A.C.; González, I.; Leander, J.; Karoumi, R.: Structural health monitoring of bridges: a model-free ANN-based approach to damage detection. J. Civ. Struct. Heal. Monit. 7, 689–702 (2017). https://doi.org/10.1007/s13349-017-0252-5
Cha, Y.-J., Choi, W., Büyüköztürk, O.: Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks. Comput. Civ. Infrastruct. Eng. 32, 361–378 (2017). Doi: https://doi.org/10.1111/mice.12263
Armaghani, D.J.; Mamou, A.; Maraveas, C.; Roussis, P.C.; Siorikis, V.G.; Skentou, A.D.; Asteris, P.G.: Predicting the unconfined compressive strength of granite using only two non-destructive test indexes. Geomech. Eng. 25, 317–330 (2021)
Apostolopoulou, M., Asteris, P.G., Armaghani, D.J., Douvika, M.G., Lourenço, P.B., Cavaleri, L., Bakolas, A., Moropoulou, A.: Mapping and holistic design of natural hydraulic lime mortars. Cem. Concr. Res. 136, 106167 (2020). Doi: https://doi.org/10.1016/j.cemconres.2020.106167
Shariati, M., Mafipour, M.S., Mehrabi, P., Bahadori, A., Zandi, Y., Salih, M.N., Nguyen, H., Dou, J., Song, X., Poi-Ngian, S.: Application of a Hybrid Artificial Neural Network-Particle Swarm Optimization (ANN-PSO) Model in Behavior Prediction of Channel Shear Connectors Embedded in Normal and High-Strength Concrete (2019)
Ngo, N.-T.; Le, H.A.; Pham, T.-P.-T.: Integration of support vector regression and grey wolf optimization for estimating the ultimate bearing capacity in concrete-filled steel tube columns. Neural Comput. Appl. (2021). https://doi.org/10.1007/s00521-020-05605-z
Le, T.-T.; Asteris, P.G.; Lemonis, M.E.: Prediction of axial load capacity of rectangular concrete-filled steel tube columns using machine learning techniques. Eng. Comput. (2021). https://doi.org/10.1007/s00366-021-01461-0
Su, M., Zhong, Q., Peng, H., Li, S.: Selected machine learning approaches for predicting the interfacial bond strength between FRPs and concrete. Constr. Build. Mater. 270, 121456 (2021). Doi: https://doi.org/10.1016/j.conbuildmat.2020.121456
Hoang, N.-D.; Tran, X.-L.; Nguyen, H.: Predicting ultimate bond strength of corroded reinforcement and surrounding concrete using a metaheuristic optimized least squares support vector regression model. Neural Comput. Appl. 32, 7289–7309 (2020). https://doi.org/10.1007/s00521-019-04258-x
Thakur, M.S.; Pandhiani, S.M.; Kashyap, V.; Upadhya, A.; Sihag, P.: Predicting Bond Strength of FRP Bars in Concrete Using Soft Computing Techniques. Arab. J. Sci. Eng. 46, 4951–4969 (2021). https://doi.org/10.1007/s13369-020-05314-8
Yan, F.; Lin, Z.; Wang, X.; Azarmi, F.; Sobolev, K.: Evaluation and prediction of bond strength of GFRP-bar reinforced concrete using artificial neural network optimized with genetic algorithm. (2016). https://doi.org/10.1016/j.compstruct.2016.11.068
Moghaddas, A.; Mostofinejad, D.: Empirical FRP-concrete bond strength model for externally bonded reinforcement on grooves. J. Compos. Constr. 23, 4018080 (2019)
LeCun, Y.; Bengio, Y.; Hinton, G.: Deep learning. Nature 521, 436–444 (2015). https://doi.org/10.1038/nature14539
Lourakis, M.I.A.: A brief description of the Levenberg-Marquardt algorithm implemented by levmar. Found. Res. Technol. 4, 1–6 (2005)
Carbonell Muñoz, M.A.; Harris, D.K.; Ahlborn, T.M.; Froster, D.C.: Bond performance between ultrahigh-performance concrete and normal-strength concrete. J. Mater. Civ. Eng. 26, 1–10 (2014). https://doi.org/10.1061/(ASCE)MT.1943-5533.0000890
Baharuddin, N.K.; Nazri, F.M.; Jaya, R.P.; Bakar, B.H.A.: Evaluation of bond strength between fire-damaged normal concrete substance and ultra-high-performance fiber-reinforced concrete as a repair material. World J. Eng. 13, 461–466 (2016). https://doi.org/10.1108/WJE-06-2016-0014
Jafarinejad, S.; Rabiee, A.; Shekarchi, M.: Experimental investigation on the bond strength between ultra high strength fiber reinforced cementitious mortar and conventional concrete. Constr. Build. Mater. 229, 116814 (2019). https://doi.org/10.1016/j.conbuildmat.2019.116814
Abo Sabah, S.H.; Zainal, N.L.; Muhamad Bunnori, N.; Megat Johari, M.A.; Hassan, M.H.: Interfacial behavior between normal substrate and green ultra-high-performance fiber-reinforced concrete under elevated temperatures. Struct. Concr. 20, 1896–1908 (2019). https://doi.org/10.1002/suco.201900152
Zhang, Y., Zhu, P., Liao, Z., Wang, L.: Interfacial bond properties between normal strength concrete substrate and ultra-high performance concrete as a repair material. Constr. Build. Mater. 235, 117431 (2020)
Hong, S.G., Kang, S.H.: Effect of surface preparation and curing method on bond strength between UHPC and normal strength concrete. IABSE Conf. Geneva 2015 Struct. Eng. Provid. Solut. Glob. Challenges Rep. 1537–1543 (2015). Doi: https://doi.org/10.2749/222137815818358925
Hoang, N.-D.: Estimating punching shear capacity of steel fibre reinforced concrete slabs using sequential piecewise multiple linear regression and artificial neural network. Meas. J. Int. Meas. Confed. 137, 58–70 (2019). https://doi.org/10.1016/j.measurement.2019.01.035
Ross, S.M.: Introduction to Probability and Statistics for Engineers and Scientists. Academic Press (2020)
Hong, P.; Mohsen, A.; Fei, Y.; Zhibin, L.: Time-frequency-based data-driven structural diagnosis and damage detection for cable-stayed bridges. J. Bridg. Eng. 23, 4018033 (2018). https://doi.org/10.1061/(ASCE)BE.1943-5592.0001199
Juszczyk, M.: On the search of models for early cost estimates of bridges: an SVM-based approach (2020)
Gill, M.K., Asefa, T., Kemblowski, M.W., McKee, M.: Soil moisture prediction using support vector machines1. Jawra J. Am. Water Resour. Assoc. 42, 1033–1046 (2006). Doi: https://doi.org/10.1111/j.1752-1688.2006.tb04512.x
Çevik, A., KURTOĞLU, A.E., Bilgehan, M., Gülşan, M.E., Albegmprli, H.M.: Support vector machines in structural engineering: a review. J. Civ. Eng. Manag. 21, 261–281 (2015). Doi: https://doi.org/10.3846/13923730.2015.1005021
Boser, B.E., Guyon, I.M., Vapnik, V.N.: A Training Algorithm for Optimal Margin Classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory. pp. 144–152. Association for Computing Machinery, New York, NY, USA (1992)
Iranmehr, A., Masnadi-Shirazi, H., Vasconcelos, N.: Cost-sensitive support vector machines. Neurocomputing. 343, 50–64 (2019). Doi: https://doi.org/10.1016/j.neucom.2018.11.099
Padierna, L.C., Carpio, M., Rojas, A., Puga, H., Baltazar, R., Fraire, H.: Hyper-parameter tuning for support vector machines by estimation of distribution algorithms. In: Nature-inspired design of hybrid intelligent systems. pp. 787–800. Springer (2017)
Gouda, S.G., Hussein, Z., Luo, S., Yuan, Q.: Model selection for accurate daily global solar radiation prediction in China. J. Clean. Prod. 221, 132–144 (2019). Doi: https://doi.org/10.1016/j.jclepro.2019.02.211
Bakay, M.S., Ağbulut, Ü.: Electricity production based forecasting of greenhouse gas emissions in Turkey with deep learning, support vector machine and artificial neural network algorithms. J. Clean. Prod. 285, 125324 (2021). Doi: https://doi.org/10.1016/j.jclepro.2020.125324
Zang, H., Cheng, L., Ding, T., Cheung, K.W., Wang, M., Wei, Z., Sun, G.: Application of functional deep belief network for estimating daily global solar radiation: A case study in China. Energy. 191, 116502 (2020). Doi: https://doi.org/10.1016/j.energy.2019.116502
Manju, S., Sandeep, M.: Prediction and performance assessment of global solar radiation in Indian cities: A comparison of satellite and surface measured data. J. Clean. Prod. 230, 116–128 (2019). Doi: https://doi.org/10.1016/j.jclepro.2019.05.108
Taylor, K.E.: Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res. Atmos. 106, 7183–7192 (2001)
AASHTO LRFD Bridge Design Specifications, U.S. Customary Units (7th Edition). (2014)
Code, M.: fib model Code for concrete structures. Ger. Wilhelm Ernst Sohn, Berlin (2010)
Hussein, H.H.; Walsh, K.K.; Sargand, S.M.; Steinberg, E.P.: Interfacial properties of ultrahigh-performance concrete and high-strength concrete bridge connections. J. Mater. Civ. Eng. 28, 4015208 (2016)
Zanotti, C., Randl, N.: Are concrete-concrete bond tests comparable? Cem. Concr. Compos. 99, 80–88 (2019). Doi: https://doi.org/10.1016/j.cemconcomp.2019.02.012
Espeche, A.D., León, J.: Estimation of bond strength envelopes for old-to-new concrete interfaces based on a cylinder splitting test. Constr. Build. Mater. 25, 1222–1235 (2011). Doi: https://doi.org/10.1016/j.conbuildmat.2010.09.032
Carol, I.; Prat, P.C.; López, C.M.: Normal/shear cracking model: application to discrete crack analysis. J. Eng. Mech. 123, 765–773 (1997)
Zhao, Q.; Hastie, T.: Causal Interpretations of black-box models. J. Bus. Econ. Stat. 39, 272–281 (2021). https://doi.org/10.1080/07350015.2019.1624293
Goldstein, A.; Kapelner, A.; Bleich, J.; Pitkin, E.: Peeking inside the black box: visualizing statistical learning with plots of individual conditional expectation. J. Comput. Graph. Stat. 24, 44–65 (2015). https://doi.org/10.1080/10618600.2014.907095
Asteris, P.G., Mokos, V.G.: Concrete compressive strength using artificial neural networks. Neural Comput. Appl. pp. 1–20 (2019)
Armaghani, D.J.; Asteris, P.G.: A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength. Neural Comput. Appl. 33, 4501–4532 (2021). https://doi.org/10.1007/s00521-020-05244-4
Zeng, J., Roussis, P.C., Mohammed, A.S., Maraveas, C., Fatemi, S.A., Armaghani, D.J., Asteris, P.G.: Prediction of Peak Particle Velocity Caused by Blasting through the Combinations of Boosted-CHAID and SVM Models with Various Kernels (2021)
Acknowledgements
This work was supported by the Hebei Transportation Science and Technology Project [grant number RW202011] and the Tianjin Transportation Science and Technology Development Plan Project [grant number 2019Bs1]. These supports are gratefully acknowledged. Any opinions, findings, conclusions, or recommendations expressed in this paper are those of the authors and do not necessarily reflect those of the sponsor.
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Appendix: The Dataset Source
Appendix: The Dataset Source
Table Splitting-tensile dataset
Reference | NSC Fc (MPa) | UHPC Age (days) | UHPC Curing Method | NSC Surface Treatment | NSC Moisture condition | Bond Strength (MPa) |
---|---|---|---|---|---|---|
Tayeh et al. [9] | 45 | 3 | 2 | 1 | 2 | 1.81 |
45 | 3 | 2 | 1 | 2 | 1.55 | |
45 | 3 | 2 | 1 | 2 | 1.66 | |
45 | 3 | 2 | 6 | 2 | 3.44 | |
45 | 3 | 2 | 6 | 2 | 2.73 | |
45 | 3 | 2 | 6 | 2 | 3.3 | |
45 | 3 | 2 | 3 | 2 | 2.49 | |
45 | 3 | 2 | 3 | 2 | 2.82 | |
45 | 3 | 2 | 3 | 2 | 2.07 | |
45 | 3 | 2 | 5 | 2 | 3.35 | |
45 | 3 | 2 | 5 | 2 | 3.84 | |
45 | 3 | 2 | 5 | 2 | 3.46 | |
45 | 3 | 2 | 2 | 2 | 2.25 | |
45 | 3 | 2 | 2 | 2 | 2.1 | |
45 | 3 | 2 | 2 | 2 | 2.62 | |
45 | 7 | 2 | 1 | 2 | 1.87 | |
45 | 7 | 2 | 1 | 2 | 1.95 | |
45 | 7 | 2 | 1 | 2 | 1.65 | |
45 | 7 | 2 | 6 | 2 | 3.07 | |
45 | 7 | 2 | 6 | 2 | 2.87 | |
45 | 7 | 2 | 6 | 2 | 3.4 | |
45 | 7 | 2 | 3 | 2 | 2.35 | |
45 | 7 | 2 | 3 | 2 | 1.94 | |
45 | 7 | 2 | 3 | 2 | 2.54 | |
45 | 7 | 2 | 5 | 2 | 3.31 | |
45 | 7 | 2 | 5 | 2 | 4.16 | |
45 | 7 | 2 | 5 | 2 | 3.13 | |
45 | 7 | 2 | 2 | 2 | 1.99 | |
45 | 7 | 2 | 2 | 2 | 2.59 | |
45 | 7 | 2 | 2 | 2 | 2.42 | |
45 | 28 | 2 | 1 | 2 | 1.87 | |
45 | 28 | 2 | 1 | 2 | 1.68 | |
45 | 28 | 2 | 1 | 2 | 2 | |
45 | 28 | 2 | 6 | 2 | 3.14 | |
45 | 28 | 2 | 6 | 2 | 3.2 | |
45 | 28 | 2 | 6 | 2 | 3.06 | |
45 | 28 | 2 | 3 | 2 | 2.55 | |
45 | 28 | 2 | 3 | 2 | 2.84 | |
45 | 28 | 2 | 3 | 2 | 2.4 | |
45 | 28 | 2 | 5 | 2 | 3.86 | |
45 | 28 | 2 | 5 | 2 | 4.09 | |
45 | 28 | 2 | 5 | 2 | 3.43 | |
45 | 28 | 2 | 2 | 2 | 3.17 |
Reference | NSC Fc (MPa) | UHPC Age (days) | UHPC Curing Method | NSC Surface Treatment | NSC Moisture condition | Bond Strength (MPa) |
---|---|---|---|---|---|---|
45 | 28 | 2 | 2 | 2 | 2.76 | |
45 | 28 | 2 | 2 | 2 | 2.96 | |
Carbonell et al. [33] | 54.3 | 186 | 1 | 5 | 1 | 3.7 |
53.7 | 186 | 1 | 5 | 1 | 4.2 | |
59.4 | 298 | 1 | 5 | 1 | 4 | |
59.4 | 298 | 1 | 5 | 1 | 4.3 | |
54.3 | 186 | 1 | 2 | 1 | 4.1 | |
53.7 | 186 | 1 | 2 | 1 | 4.2 | |
59.4 | 298 | 1 | 2 | 1 | 2.8 | |
59.4 | 298 | 1 | 2 | 1 | 3.9 | |
54.3 | 186 | 1 | 1 | 1 | 3.6 | |
53.7 | 186 | 1 | 1 | 1 | 4.2 | |
59.4 | 298 | 1 | 1 | 1 | 3.3 | |
59.4 | 298 | 1 | 1 | 1 | 4.2 | |
54.3 | 186 | 1 | 3 | 1 | 4.1 | |
53.7 | 186 | 1 | 3 | 1 | 4.5 | |
59.4 | 298 | 1 | 3 | 1 | 4.1 | |
59.4 | 298 | 1 | 3 | 1 | 4.5 | |
54.3 | 186 | 1 | 6 | 1 | 4.8 | |
53.7 | 186 | 1 | 6 | 1 | 5.7 | |
59.4 | 298 | 1 | 6 | 1 | 5.9 | |
59.4 | 298 | 1 | 6 | 1 | 6.6 | |
Hong and Kang [38] | 19.3 | 7 | 1 | 1 | 2 | 0.96 |
19.3 | 7 | 1 | 6 | 2 | 1.12 | |
19.3 | 7 | 1 | 6 | 2 | 1.51 | |
19.3 | 7 | 1 | 4 | 2 | 1.49 | |
Baharuddin et al. [34] | 40 | 28 | 1 | 1 | 2 | 0.75 |
40 | 28 | 1 | 1 | 2 | 0.5 | |
40 | 28 | 1 | 1 | 2 | 0.75 | |
40 | 28 | 1 | 1 | 2 | 0.75 | |
40 | 28 | 1 | 1 | 2 | 0.5 | |
40 | 28 | 1 | 1 | 1 | 2.75 | |
40 | 28 | 1 | 1 | 1 | 3.25 | |
40 | 28 | 1 | 1 | 1 | 2.25 | |
40 | 28 | 1 | 1 | 1 | 2.75 | |
40 | 28 | 1 | 1 | 1 | 2.25 | |
40 | 28 | 1 | 1 | 3 | 2.25 | |
40 | 28 | 1 | 1 | 3 | 2 | |
40 | 28 | 1 | 1 | 3 | 2.25 | |
40 | 28 | 1 | 1 | 3 | 2.5 | |
40 | 28 | 1 | 1 | 3 | 2.25 | |
Abo Sabah et al. [10] | 39.9 | 7 | 2 | 6 | 2 | 5 |
39.9 | 7 | 2 | 6 | 2 | 4.41 | |
39.9 | 7 | 2 | 6 | 2 | 4.09 |
Reference | NSC Fc (MPa) | UHPC Age (days) | UHPC Curing Method | NSC Surface Treatment | NSC Moisture condition | Bond Strength (MPa) |
---|---|---|---|---|---|---|
49.2 | 28 | 2 | 6 | 2 | 6.45 | |
49.2 | 28 | 2 | 6 | 2 | 6.63 | |
49.2 | 28 | 2 | 6 | 2 | 6.8 | |
50.76 | 90 | 2 | 6 | 2 | 6.73 | |
50.76 | 90 | 2 | 6 | 2 | 7.15 | |
50.76 | 90 | 2 | 6 | 2 | 6.97 | |
39.9 | 7 | 2 | 5 | 2 | 5.09 | |
39.9 | 7 | 2 | 5 | 2 | 4.93 | |
39.9 | 7 | 2 | 5 | 2 | 4.72 | |
49.2 | 28 | 2 | 5 | 2 | 8.15 | |
49.2 | 28 | 2 | 5 | 2 | 8.41 | |
49.2 | 28 | 2 | 5 | 2 | 8.56 | |
50.76 | 90 | 2 | 5 | 2 | 8.21 | |
50.76 | 90 | 2 | 5 | 2 | 8.53 | |
50.76 | 90 | 2 | 5 | 2 | 8.85 | |
Saddam et al. [36] | 39.9 | 7 | 2 | 5 | 2 | 4.91 |
49.2 | 28 | 2 | 5 | 2 | 8.37 | |
51.16 | 90 | 2 | 5 | 2 | 8.53 | |
39.9 | 7 | 2 | 6 | 2 | 4.5 | |
49.2 | 28 | 2 | 6 | 2 | 6.63 | |
51.16 | 90 | 2 | 6 | 2 | 6.95 | |
Zhang et al. [37] | 53 | 3 | 1 | 2 | 1 | 2.69 |
53 | 7 | 1 | 2 | 1 | 2.98 | |
53 | 28 | 1 | 2 | 1 | 3.11 | |
53 | 7 | 1 | 4 | 1 | 3.83 | |
53 | 7 | 1 | 4 | 1 | 4.08 | |
53 | 3 | 1 | 4 | 1 | 3.38 | |
53 | 7 | 1 | 4 | 1 | 3.82 | |
53 | 28 | 1 | 4 | 1 | 3.85 | |
53 | 90 | 1 | 4 | 1 | 3.72 | |
53 | 180 | 1 | 4 | 1 | 3.8 | |
53 | 28 | 1 | 1 | 1 | 2.77 | |
53 | 28 | 1 | 5 | 1 | 3.7 | |
53 | 28 | 1 | 5 | 1 | 3.73 | |
53 | 28 | 1 | 5 | 1 | 3.65 | |
53 | 28 | 1 | 2 | 3 | 2.74 | |
53 | 28 | 1 | 2 | 2 | 2.2 | |
53 | 28 | 1 | 4 | 3 | 3.44 | |
53 | 28 | 1 | 4 | 1 | 3.02 | |
53 | 28 | 2 | 1 | 1 | 2.94 | |
53 | 28 | 2 | 1 | 1 | 2.24 | |
53 | 28 | 2 | 2 | 1 | 3.19 | |
53 | 28 | 2 | 2 | 1 | 2.37 | |
53 | 28 | 2 | 4 | 1 | 3.92 | |
53 | 28 | 2 | 4 | 1 | 3.22 | |
42.2 | 28 | 1 | 4 | 1 | 3.51 | |
31.9 | 28 | 1 | 4 | 1 | 2.6 |
Slant-shear dataset
References | NSC Fc (MPa) | UHPC Age (days) | UHPC Curing Method | NSC Surface Treatment | NSC Moisture condition | Bond Strength (MPa) |
---|---|---|---|---|---|---|
Tayeh et al. [9] | 45 | 3 | 2 | 1 | 2 | 9.68 |
45 | 3 | 2 | 1 | 2 | 7.86 | |
45 | 3 | 2 | 1 | 2 | 7.01 | |
45 | 3 | 2 | 6 | 2 | 14.25 | |
45 | 3 | 2 | 6 | 2 | 14.7 | |
45 | 3 | 2 | 6 | 2 | 12.67 | |
45 | 3 | 2 | 3 | 2 | 11.78 | |
45 | 3 | 2 | 3 | 2 | 9.72 | |
45 | 3 | 2 | 3 | 2 | 11.59 | |
45 | 3 | 2 | 6 | 2 | 16.8 | |
45 | 3 | 2 | 6 | 2 | 17.57 | |
45 | 3 | 2 | 6 | 2 | 17.18 | |
45 | 3 | 2 | 6 | 2 | 11.3 | |
45 | 3 | 2 | 6 | 2 | 12.32 | |
45 | 3 | 2 | 6 | 2 | 10.69 | |
45 | 7 | 2 | 1 | 2 | 7.21 | |
45 | 7 | 2 | 1 | 2 | 8.52 | |
45 | 7 | 2 | 1 | 2 | 9.69 | |
45 | 7 | 2 | 6 | 2 | 14.83 | |
45 | 7 | 2 | 6 | 2 | 14.61 | |
45 | 7 | 2 | 6 | 2 | 12.23 | |
45 | 7 | 2 | 3 | 2 | 12.1 | |
45 | 7 | 2 | 3 | 2 | 10.12 | |
45 | 7 | 2 | 3 | 2 | 11.09 | |
45 | 7 | 2 | 6 | 2 | 16.13 | |
45 | 7 | 2 | 6 | 2 | 18.5 | |
45 | 7 | 2 | 6 | 2 | 16.88 | |
45 | 7 | 2 | 6 | 2 | 10.49 | |
45 | 7 | 2 | 6 | 2 | 13.17 | |
45 | 7 | 2 | 6 | 2 | 11.3 | |
45 | 28 | 2 | 1 | 2 | 7.38 | |
45 | 28 | 2 | 1 | 2 | 10.45 | |
45 | 28 | 2 | 1 | 2 | 8.22 | |
45 | 28 | 2 | 6 | 2 | 14.74 | |
45 | 28 | 2 | 6 | 2 | 12.34 | |
45 | 28 | 2 | 6 | 2 | 14.69 | |
45 | 28 | 2 | 3 | 2 | 11.11 | |
45 | 28 | 2 | 3 | 2 | 12.48 | |
45 | 28 | 2 | 3 | 2 | 13.22 | |
45 | 28 | 2 | 6 | 2 | 17.08 | |
45 | 28 | 2 | 6 | 2 | 18.15 | |
45 | 28 | 2 | 6 | 2 | 18.19 | |
45 | 28 | 2 | 6 | 2 | 14.13 | |
45 | 28 | 2 | 6 | 2 | 10.67 | |
45 | 28 | 2 | 6 | 2 | 13.46 | |
Carbonell et al. [33] | 44.5 | 8 | 1 | 6 | 1 | 16.1 |
44.5 | 8 | 1 | 6 | 1 | 12.1 | |
55.9 | 8 | 1 | 6 | 1 | 21.7 |
References | NSC Fc (MPa) | UHPC Age (days) | UHPC Curing Method | NSC Surface Treatment | NSC Moisture condition | Bond Strength (MPa) |
---|---|---|---|---|---|---|
55.9 | 8 | 1 | 6 | 1 | 14.6 | |
45.6 | 8 | 1 | 6 | 1 | 17 | |
45.6 | 8 | 1 | 6 | 1 | 12.2 | |
44.5 | 8 | 1 | 6 | 1 | 17.5 | |
44.5 | 8 | 1 | 6 | 1 | 11.3 | |
56.8 | 3 | 1 | 6 | 1 | 17.8 | |
55.9 | 3 | 1 | 6 | 1 | 15.3 | |
54.5 | 3 | 1 | 6 | 1 | 9.4 | |
50.2 | 3 | 1 | 6 | 1 | 9.7 | |
50.2 | 3 | 1 | 6 | 1 | 16.7 | |
55.9 | 2 | 1 | 6 | 1 | 5.9 | |
55.9 | 2 | 1 | 6 | 1 | 3.4 | |
45.6 | 2 | 1 | 6 | 1 | 5.2 | |
Baharuddin et al. [34] | 40 | 28 | 1 | 1 | 2 | 1.35 |
40 | 28 | 1 | 1 | 2 | 1.19 | |
40 | 28 | 1 | 1 | 2 | 2.71 | |
40 | 28 | 1 | 1 | 2 | 2.23 | |
40 | 28 | 1 | 1 | 2 | 2.39 | |
40 | 28 | 1 | 1 | 1 | 3.18 | |
40 | 28 | 1 | 1 | 1 | 3.34 | |
40 | 28 | 1 | 1 | 1 | 3.5 | |
40 | 28 | 1 | 1 | 1 | 3.02 | |
40 | 28 | 1 | 1 | 1 | 3.18 | |
40 | 28 | 1 | 1 | 3 | 2.55 | |
40 | 28 | 1 | 1 | 3 | 2.55 | |
40 | 28 | 1 | 1 | 3 | 2.23 | |
40 | 28 | 1 | 1 | 3 | 2.71 | |
40 | 28 | 1 | 1 | 3 | 2.23 | |
Abo Sabah et al. [10] | 39.9 | 7 | 2 | 6 | 2 | 27.1 |
39.9 | 7 | 2 | 6 | 2 | 23.6 | |
39.9 | 7 | 2 | 6 | 2 | 19.1 | |
49.2 | 28 | 2 | 6 | 2 | 23.6 | |
49.2 | 28 | 2 | 6 | 2 | 19.7 | |
49.2 | 28 | 2 | 6 | 2 | 28.4 | |
50.76 | 90 | 2 | 6 | 2 | 28.4 | |
50.76 | 90 | 2 | 6 | 2 | 19.8 | |
50.76 | 90 | 2 | 6 | 2 | 29.7 | |
39.9 | 7 | 2 | 6 | 2 | 38.2 | |
39.9 | 7 | 2 | 6 | 2 | 29.5 | |
39.9 | 7 | 2 | 6 | 2 | 36.1 | |
49.2 | 28 | 2 | 6 | 2 | 28.5 | |
49.2 | 28 | 2 | 6 | 2 | 37.3 | |
49.2 | 28 | 2 | 6 | 2 | 39.7 | |
50.76 | 90 | 2 | 6 | 2 | 31 | |
50.76 | 90 | 2 | 6 | 2 | 41.2 | |
50.76 | 90 | 2 | 6 | 2 | 36.2 | |
Jafarinejad et al.[35] | 51 | 28 | 1 | 1 | 2 | 11.5 |
51 | 28 | 1 | 6 | 2 | 13.5 | |
57.4 | 28 | 1 | 6 | 2 | 19.6 | |
57.4 | 28 | 1 | 6 | 2 | 29.4 |
References | NSC Fc (MPa) | UHPC Age (days) | UHPC Curing Method | NSC Surface Treatment | NSC Moisture condition | Bond Strength (MPa) |
---|---|---|---|---|---|---|
53.1 | 3 | 1 | 6 | 2 | 26.5 | |
53.1 | 7 | 1 | 6 | 2 | 28.6 | |
53.1 | 28 | 1 | 6 | 2 | 28.2 | |
Saddam et al. [36] | 39.9 | 7 | 2 | 6 | 2 | 34.6 |
49.2 | 28 | 2 | 6 | 2 | 35.2 | |
51.16 | 90 | 2 | 6 | 2 | 36.13 | |
39.9 | 7 | 2 | 6 | 2 | 23.27 | |
49.2 | 28 | 2 | 6 | 2 | 23.9 | |
51.16 | 90 | 2 | 6 | 2 | 25.9 | |
Zhang et al. [37] | 53 | 3 | 1 | 6 | 1 | 14.35 |
53 | 7 | 1 | 6 | 1 | 15.5.9 | |
53 | 28 | 1 | 6 | 1 | 16.09 | |
53 | 7 | 1 | 6 | 1 | 18.97 | |
53 | 7 | 1 | 6 | 1 | 20.22 | |
53 | 3 | 1 | 6 | 1 | 18.17 | |
53 | 7 | 1 | 6 | 1 | 19.85 | |
53 | 28 | 1 | 6 | 1 | 21.34 | |
53 | 90 | 1 | 6 | 1 | 21.62 | |
53 | 180 | 1 | 6 | 1 | 20.68 | |
53 | 28 | 1 | 1 | 1 | 12.06 | |
53 | 28 | 1 | 6 | 1 | 21.29 | |
53 | 28 | 1 | 6 | 1 | 21.74 | |
53 | 28 | 1 | 6 | 1 | 21.99 | |
53 | 28 | 1 | 6 | 3 | 14.68 | |
53 | 28 | 1 | 6 | 2 | 10.15 | |
53 | 28 | 1 | 6 | 3 | 20.39 | |
53 | 28 | 1 | 6 | 1 | 16.64 | |
53 | 28 | 2 | 1 | 1 | 11.4 | |
53 | 28 | 2 | 1 | 1 | 10.96 | |
53 | 28 | 2 | 6 | 1 | 15.57 | |
53 | 28 | 2 | 6 | 1 | 13.21 | |
53 | 28 | 2 | 6 | 1 | 21.81 | |
53 | 28 | 2 | 6 | 1 | 16.88 | |
42.2 | 28 | 1 | 6 | 1 | 17.93 | |
31.9 | 28 | 1 | 6 | 1 | 13.76 |
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Farouk, A.I.B., Jinsong, Z. Prediction of Interface Bond Strength Between Ultra-High-Performance Concrete (UHPC) and Normal Strength Concrete (NSC) Using a Machine Learning Approach. Arab J Sci Eng 47, 5337–5363 (2022). https://doi.org/10.1007/s13369-021-06433-6
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DOI: https://doi.org/10.1007/s13369-021-06433-6