Journal of Structural and Construction Engineering

Journal of Structural and Construction Engineering

Modeling the ultimate strength of the bond between concrete and FRP using K-means clustering method and kriging method

Document Type : Original Article

Authors
Najmeh Zaermiri 1
Mohammad Reza Sohrabi 2
Yaser Moodi 3
1 Masters student,, Department of Civil Engineering, Shahid Nikbakht Faculty of Engineering, University of Sistan and Baluchestan, Zahedan, Iran
2 Associate Professor, Civil Engineering Department , Shahid Nikbakht Faculty of Engineering , University of Sistan and Baluchestan, Zahedan, Iran
3 Assistant Professor, Department of Civil Engineering, Sirjan University Of Technology, Sirjan, Iran
10.22065/jsce.2024.440615.3346
Abstract
Accurate estimation of the ultimate bond strength between concrete and FRP sheet is one of the important things that plays a significant role in the design of RC members strengthen with FRP sheet. Due to the difficulty and cost of experimental studies, the use of artificial intelligence methods is the best alternative for these approaches. Generally, to use artificial intelligence methods, the data is randomly divided into two groups of training and testing, which is based on the training data of the model developed and evaluated with the test data. The problem in using these methods is that the model may be trained based on a set of specific data, but it performs poorly during testing to predict data with different characteristics. In this study, a comprehensive set of data was first collected from past studies regarding the bond of FRP sheets to concrete. This collection includes 532 experimental specimens. Also, for the first time, the combined method of k-means clustering and kriging was used to predict the ultimate strength of FRP sheets and concrete. In this method, the input data to the model is first placed in several categories based on their average, and when these data are called to the estimation model, equal amounts of each category are called. In this study, dependent functions (6 models), regression degree (3 degrees) and the number of clusters (3 clusters) were considered different for analysis using the mentioned method, and among these 54 models, the best model was selected. The results show that the ultimate bond strength predicted using the developed model has a good agreement with the experimental results, and the models with linear dependent functions with one degree of regression and 6 and 5 clusters respectively with correlation coefficients, 0.938 and 0.921 have better performance than other models.
Keywords
Strengthening
FRP Sheet
Bond Strength
Clustering
Kriging

Subjects

[1]          Ueda, T. Dai, J. (2005). Interface bond between FRP sheets and concrete substrates: properties, numerical modeling and roles in member behaviour. Progress in Structural Engineering and Materials, 7(1), 27–43.
[2]          Oehlers, D.J. (2001). Development of design rules for retrofitting by adhesive bonding or bolting either FRP or steel plates to RC beams or slabs in bridges and buildings. Composites Part A: Applied Science and Manufacturing publishes, 32(9), 1345–155.
[3]          Sharma, S.K. Ali M.S.M. Goldar, D. Sikdar, P.K. (2006). Plate–concrete interfacial bond strength of FRP and metallic plated concrete specimens. Composites Part B: Engineering, 37(1), 54–63.
[4]          Yao, J. Teng, J.G. Chen, J.F. (2005). Experimental study on FRP-to-concrete bonded joints. Composites Part B: Engineering, 36(2), 99–113.
[5]          Ko, H. Matthys, S. Palmieri, A. Sato, Y. (2014). Development of a simplified bond stress-slip model for bonded FRP-concrete interfaces. Construction and Building Materials, 68, 142–157.
[6]          Pellegrino, C. Tinazzi, D. Modena, C. (2008). Experimental Study on Bond Behavior between Concrete and FRP Reinforcement. Journal of Composites for Construction, 12(2), 180–189.
[7]          Hosseini, A. Mostofinejad, D. (2014). Effective bond length of FRP-to-concrete adhesively-bonded joints: Experimental evaluation of existing models. International Journal of Adhesion and Adhesive, 48, 150–158.
[8]          Bilotta, A. Ceroni, F. Di Ludovico, M. Nigro, E. Pecce, M. Manfredi, G. (2011). Bond Efficiency of EBR and NSM FRP Systems for Strengthening Concrete Members. Journal of Composites for Construction, 15(5) 757–772.
[9]          Pham, H.B. Al-Mahaidi, R. (2007). Modelling of CFRP-concrete shear-lap tests. Construction and Building Materials, 21(4), 727–735.
[10]        Ueno, S. Toutanji, H. Vuddandam, R. (2015). Introduction of a Stress State Criterion to Predict Bond Strength between FRP and Concrete Substrate. Journal of Composites for Construction, 19(1), 04014024-1-11.
[11]        Wu ,Y.F. Jiang, C. (2013). Quantification of Bond-Slip Relationship for Externally Bonded FRP-to-Concrete Joints. Journal of Composites for Construction, 17(5), 673–686.
[12]        Dai, J.G. Sato, Y. Ueda, T. (2002). Improving the Load Transfer and Effective Bond Length for FRP Composites Bonded to Concrete. Proceedings of the Japan Concrete Institute, 24(1), 1423-1428.
[13]        Colombi, P. Fava, G. Poggi, C. (2014) End debonding of CFRP wraps and strips for the strengthening of concrete structures. Composite Structures, 111, 510–521.
[14]        Bilotta, A. Di Ludovico, M. Nigro, E. (2011). FRP-to-concrete interface debonding: Experimental calibration of a capacity model. Composites Part B: Engineering, 42(6), 1539–1553.
[15]        Moghaddas, A. Mostofinejad, D. Saljoughian, A. Ilia, E. (2021). An empirical FRP-concrete bond-slip model for externally-bonded reinforcement on grooves. Construction and Building Materials, 281, 122575-1-19.
[16]        Zhu, H. Wu, G. Shi, J. Liu, C. He, X. (2014). Digital image correlation measurement of the bond-slip relationship between fiber-reinforced polymer sheets and concrete substrate. Journal of Reinforced Plastics and Composites 33(17), 1590–1603.
[17]        Ming, Z. Ansari, F. (2004) BOND PROPERTIES OF FRP FABRICS AND CONCRETE JOINTS. 13th World Conference on Earthquake Engineering, Vancouver, 35.
[18]        Cho, D.Y. Park, S.K. Hong, S.N. (2011) BOND-SLIP BEHAVIOR OF CFRP PLATE-CONCRETE INTERFACE. Mechanics of Composite Materials, 47(5), 529–538.
[19]        Toutanji, H. Saxena, P. Zhao, L. Ooi, T. (2007). Prediction of Interfacial Bond Failure of FRP–Concrete Surface. Journal of Composites for Construction, 11(4), 427–436.
[20]        Heydari Mofrad, M. Mostofinejad, D. Hosseini, A. (2019). A generic non-linear bond-slip model for CFRP composites bonded to concrete substrate using EBR and EBROG techniques. Composite Structures, 220, 31–44.
[21]        Mostofinejad, D. Mohammadi, M. (2020). Effect of Freeze–Thaw Cycles on FRP-Concrete Bond Strength in EBR and EBROG Systems. Journal of Composites for Construction, 24(3) 04020009-1-12.
[22]        Mostofinejad, D. Heydari Mofrad, M. Hosseini, A. Heydari Mofrad, H. (2018). Investigating the effects of concrete compressive strength, CFRP thickness and groove depth on CFRP-concrete bond strength of EBROG joints. Construction and Building Materials, 189, 323–337.
[23]        Hadigheh, S.A. Gravina, R.J. Setunge, S. (2015). Prediction of the bond-slip law in externally laminated concrete substrates by an analytical based nonlinear approach. Materials & Design, 66(Part A), 217–226.
[24]        Biolzi, L. Ghittoni, C. Fedele, R. Rosati, G. (2013). Experimental and theoretical issues in FRP-concrete bonding. Construction and Building Materials, 41, 182–190.
[25]        Hadigheh, S.A. Gravina, R.J. Setunge, S. Kim, S.J. (2013). Experimental study on the bondline behavior between concrete and FRP materials. From Materials to Structures: Advancement Through Innovation - Proceedings of the 22nd Australasian Conference on the Mechanics of Structures and Materials,  505–511.
[26]        Ghorbani, M. Mostofinejad, D. Hosseini, A. (2017). Experimental investigation into bond behavior of FRP-to-concrete under mixed-mode I/II loading. Construction and Building Materials, 132(9), 303–312.
[27]        Ali-Ahmad, M. Subramaniam, K. Ghosn, M. (2006). Experimental Investigation and Fracture Analysis of Debonding between Concrete and FRP Sheets. Journal of Engineering Mechanic,132(9), 914–923.
[28]        Barbieri, G. Biolzi, L. Bocciarelli, M. Cattaneo, S. (2016). Size and shape effect in the pull-out of FRP reinforcement from concrete. Composite Structures, 143, 395–417.
[29]        Ceroni, F. Pecce, M. (2010). Evaluation of Bond Strength in Concrete Elements Externally Reinforced with CFRP Sheets and Anchoring Devices. Journal of Composites for Construction, ;14(5), 521–530.
[30]        Subramaniam, KV. Carloni, C. Nobile, L. (2007). Width effect in the interface fracture during shear debonding of FRP sheets from concrete. Engineering Fracture Mechanic, 74(4), 578–94.
[31]        Chen, J.F. Teng, J.G. (2001), Anchorage Strength Models for FRP and Steel Plates Bonded to Concrete. Journal of Structural Engineering, 127(7), 784–791.
[32]        Khalifa, A. Gold, W.J. Nanni, A. M.I., A.A. (1998). Contribution of Externally Bonded FRP to Shear Capacity of RC Flexural Members. Journal of Composites for Construction, 2(4), 195–202.
[33]        Wu, Z. Islam, S.M. Said, H. (2009). A three-parameter bond strength model for FRP-concrete interface. Journal of Reinforced Plastics and Composites, 28(19), 2309–2323.
[34]        Dai, J. Ueda, T. Sato, Y. (2005). Development of the Nonlinear Bond Stress–Slip Model of Fiber Reinforced Plastics Sheet–Concrete Interfaces with a Simple Method. Journal of Composites for Construction, 9(1), 52–62.
[35]        Ahmadi, M. Naderpour, H. Fakharian, P. Eidgahee, D.R. (2021). Shear Capacity Prediction of FRP Reinforced Concrete Beams using Hybrid GMDH-GA. Journal of Structural and Construction Engineering, 8(6), 24–42.
[36]        Fathi, A. Peyman, F. (2021). Combining Neural Network Models to Prediction the Bond Strength of Glass FRP to Concrete Keywords: Bond strength of GFRP bar Structural concrete Artificial neural networks Combination of ensemble and single models MATLAB software. Journal of Structural and Construction Engineering, 8(41), 313–332.
[37]        Moodi, Y. Eslami, E. Mousavi, S.R. Dizangian, B. Mirshekar, H. (2022). Applying neural networks for estimating the compressive strength of confined circular concrete columns with FRP sheets. Journal of Structural and Construction Engineering, 9(5), 58–77. (In Persian)
[38]        Hoseini Vaez, S.R. Asaad Samani, A. Mobinipour, S.A. (2022). Dehghani E. Effect of Uncertainties in Design Variables on the Hysteresis Response of 2D Steel Moment-Resisting Frames. Practice Periodical on Structural Design and Construction, 27(4), 04022044-1-15.
[39]        Aramesh, S. Fakharian, P. (2022). New Models for Determining Concrete Elastic Modulus Considering Variation in Values of Compressive Strength. Civil Infrastructure Researches, 8(1), 171–183. (In Persian)
[40]        Hendouyan, M.A. Hoseini Vaez S.R. Haji Mazdarani, M.J. (2022). Investigating the Lateral Deformation Capacity of Reinforced Concrete Columns by Soft Computing. Journal of Concrete Structures and Materials, 7(2), 37–61.
[41]        Ghorbani, A. Maleki, H. Naderpour, H. Khatami, S.M.H. (2024). Advancing Compressive Strength Prediction in Self-Compacting Concrete via Soft Computing: A Robust Modeling Approach. Journal of Soft Computing in Civil Engineering, 8(1), 126–140.
[42]        Goutham, D.R. Krishnaiah, A.J. (2024). Prediction of Unconfined Compressive Strength of Expansive Soil Amended with Bagasse Ash and Lime Using Artificial Neural Network. Journal of Soft Computing in Civil Engineering, 8(1), 33–54.
[43]        Abdellahi, M. Heidari, J. Bahmanpour, M. (2014). A new predictive model for the bond strength of FRP-to-concrete composite joints. Structural Concrete, ;15(4), 509–521.
[44]        Haddad, R. Haddad, M. (2021). Predicting fiber-reinforced polymer–concrete bond strength using artificial neural networks: A comparative analysis study. Structural Concrete, 22(1), 38–49.
[45]        Chen, S.Z. Zhang, S.Y. Han, W.S. Wu, G. (2021). Ensemble learning based approach for FRP-concrete bond strength prediction. Construction and Building Materials, 302, 124230.
[46]        Baghaei, K.A. Hadigheh, S.A. (2021). Durability assessment of FRP-to-concrete bonded connections under moisture condition using data-driven machine learning-based approaches. Composite Structures, 114576 (In Press).
[47]        Mashrei, M.A. Seracino, R. Rahman, M.S. (2013). Application of artificial neural networks to predict the bond strength of FRP-to-concrete joints. Construction and Building Materials, ;40, 812–821.
[48]        Abuodeh, O.R. Abdalla, J.A. Hawileh, R.A. (2020). Prediction of shear strength and behavior of RC beams strengthened with externally bonded FRP sheets using machine learning techniques. Composite Structures, 234, 111698.
[49]        Sharifi-Nik, K. (2017). Simulation-based Reliability Analysis using Kriging Method‎. Masters, University of Sistan and Baluchestan. (In Persian)
[50]        Almpanidis, G. Kotropoulos, C. Pitas, I. (2007). Combining text and link analysis for focused crawling—An application for vertical search engines. Information System, 32(6), 886–908.
[51]        Erisoglu, M. Calis, N. Sakallioglu, S. (2011). A new algorithm for initial cluster centers in k-means algorithm. Pattern Recognition Letter, 32(14), 1701–1705.
[52]        Sacks, J. Welch, W.J. Mitchell, T.J. Wynn, H.P. (1989). Design and analysis of computer experiments. Statistical Science, 4(4), 409–423.
[53]        Jiang, Z. Li, T. Min, W. Qi, Z. Rao, Y. (2017). Fuzzy c-means clustering based on weights and gene expression programming. Pattern Recognition Letter, 90, 1–7.
[54]        Ben Seghier, M.E.A. Keshtegar, B. Tee, K.F. Zayed, T. Abbassi, R. Trung, N.T. (2020) Prediction of maximum pitting corrosion depth in oil and gas pipelines. Engineering Failure Analysis, 112, 104505.
[55]        Ben Seghier, M.E.A. Corriea, J.A.F.O. Jafari-Asl, J. Malekjafarian, A. Plevris, V. Trung, N.T. (2021) On the modeling of the annual corrosion rate in main cables of suspension bridges using combined soft computing model and a novel nature-inspired algorithm. Neural Computing and Applications, 33, 15969-15985.
[56]        Mai, S.H. Ben Seghier, M.E.A. Nguyen, P.L. Jafari-Asl, J. Thai, D.K. (2022). A hybrid model for predicting the axial compression capacity of square concrete-filled steel tubular columns. Engineering with Computers, 38, 1205–1222.
Journal of Structural and Construction Engineering
Volume 11, Issue 11 - Serial Number 88
February 2025
Pages 198-219

  • Receive Date 12 February 2024
  • Revise Date 10 May 2024
  • Accept Date 08 June 2024