Similar articles

Abstract: Accurately estimating the interfacial bond capacity of the near-surface mounted (NSM) carbon fiber-reinforced polymer (CFRP) to concrete joint is a fundamental task in the strengthening and retrofit of existing reinforced concrete (RC) structures. The machine learning (ML) approach may provide an alternative to the commonly used semi-empirical or semi-analytical methods. Therefore, in this work we have developed a predictive model based on an artificial neural network (ANN) approach, i.e. using a back propagation neural network (BPNN), to map the complex data pattern obtained from an NSM CFRP to concrete joint. It involves a set of nine material and geometric input parameters and one output value. Moreover, by employing the neural interpretation diagram (NID) technique, the BPNN model becomes interpretable, as the influence of each input variable on the model can be tracked and quantified based on the connection weights of the neural network. An extensive database including 163 pull-out testing samples, collected from the authors’ research group and from published results in the literature, is used to train and verify the ANN. Our results show that the prediction given by the BPNN model agrees well with the experimental data and yields a coefficient of determination of 0.957 on the whole database. After removing one non-significant feature, the BPNN becomes even more computationally efficient and accurate. In addition, compared with the existed semi-analytical model, the ANN-based approach demonstrates a more accurate estimation. Therefore, the proposed ML method may be a promising alternative for predicting the bond strength of NSM CFRP to concrete joint for structural engineers.

应用可解释的人工神经网络预测表层嵌贴纤维增强复合材料板条与混凝土界面的粘结强度

[1]Abuodeh OR, Abdalla JA, Hawileh RA, 2020. Prediction of shear strength and behavior of RC beams strengthened with externally bonded FRP sheets using machine learning techniques. Composite Structures, 234:111698.

[2]Ali MSM, Oehlers DJ, Griffith MC, et al., 2008. Interfacial stress transfer of near surface-mounted FRP-to-concrete joints. Engineering Structures, 30(7):1861-1868.

[3]Al-Mahaidi R, Kalfat R, 2018. Rehabilitation of Concrete Structures with Fiber-reinforced Polymer. Butterworth-Heinemann, Oxford, UK.

[4]Beck MW, 2018. NeuralNetTools: visualization and analysis tools for neural networks. Journal of Statistical Software, 85(11).

[5]Bergstra J, Bengio Y, 2012. Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13(10):281-305.

[6]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.

[7]Ceroni F, 2010. Experimental performances of RC beams strengthened with FRP materials. Construction and Building Materials, 24(9):1547-1559.

[8]Chen C, Cheng LJ, 2016. Theoretical solution to fatigue bond stress distribution of NSM FRP reinforcement in concrete. Composites Part B: Engineering, 99:453-464.

[9]Chen YF, Ding DQ, Zhu CH, et al., 2019. Size- and edge-effect cohesive energy and shear strength between graphene, carbon nanotubes and nanofibers: continuum modeling and molecular dynamics simulations. Composite Structures, 208:150-167.

[10]de Lorenzis L, Teng JG, 2007. Near-surface mounted FRP reinforcement: an emerging technique for strengthening structures. Composites Part B: Engineering, 38(2):119-143.

[11]Garson GD, 1991. Interpreting neural-network connection weights. AI Expert, 6(4):46-51.

[12]Ghasemi H, Brighenti R, Zhuang XY, et al., 2014. Optimization of fiber distribution in fiber reinforced composite by using NURBS functions. Computational Materials Science, 83:463-473.

[13]Godoy C, I. Depina I, Thakur V, 2020. Application of machine learning to the identification of quick and highly sensitive clays from cone penetration tests. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 21(6):445-461. https://doi.org./10.1631/jzus.A1900556

[14]Hait P, Sil A, Choudhury S, 2020. Seismic damage assessment and prediction using artificial neural network of RC building considering irregularities. Journal of Structural Integrity and Maintenance, 5(1):51-69.

[15]Hoang ND, 2019. Estimating punching shear capacity of steel fibre reinforced concrete slabs using sequential piecewise multiple linear regression and artificial neural network. Measurement, 137:58-70.

[16]Ibrahim OM, 2013. A comparison of methods for assessing the relative importance of input variables in artificial neural networks. Journal of Applied Sciences Research, 9(11):5692-5700.

[17]Jung Y, 2018. Multiple predicting K-fold cross-validation for model selection. Journal of Nonparametric Statistics, 30(1):197-215.

[18]Karaci A, Yaprak H, Ozkaraca O, et al., 2019. Estimating the properties of ground-waste-brick mortars using DNN and ANN. CMES-Computer Modeling in Engineering & Sciences, 118(1):207-228.

[19]Mahal M, Täljsten B, Blanksvärd T, 2016. Experimental performance of RC beams strengthened with FRP materials under monotonic and fatigue loads. Construction and Building Materials, 122:126-139.

[20]Nielsen MA, 2015. Neural Networks and Deep Learning. Determination Press, New York, USA.

[21]Olden JD, Jackson DA, 2002a. A comparison of statistical approaches for modelling fish species distributions. Freshwater Biology, 47(10):1976-1995.

[22]Olden JD, Jackson DA, 2002b. Illuminating the black box: a randomization approach for understanding variable contributions in artificial neural networks. Ecological Modelling, 154(1-2):135-150.

[23]Olden JD, Joy MK, Death RG, 2004. An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecological Modelling, 178(3-4):389-397.

[24]Özesmi SL, Özesmi U, 1999. An artificial neural network approach to spatial habitat modelling with interspecific interaction. Ecological Modelling, 116(1):15-31.

[25]Padierna LC, Carpio M, Rojas A, et al., 2017. Hyper-parameter tuning for support vector machines by estimation of distribution algorithms. In: Melin P, Castillo O, Kacprzyk J (Eds.), Nature-Inspired Design of Hybrid Intelligent Systems. Springer, Cham, Germany, p.787-800.

[26]Pedregosa F, Varoquaux G, Gramfort A, et al., 2011. Scikit-learn: machine learning in Python. The Journal of Machine Learning Research, 12:2825-2830.

[27]Peng H, Liu Y, Cai CS, et al., 2019. Experimental investigation of bond between near-surface-mounted CFRP strips and concrete under freeze-thawing cycling. Journal of Aerospace Engineering, 32(1):04018125.

[28]Petersen RB, Masia MJ, Seracino R, 2010. In-plane shear behavior of masonry panels strengthened with NSM CFRP strips. II: finite-element model. Journal of Composites for Construction, 14(6):764-774.

[29]Raschka S, Mirjalili V, 2017. Python Machine Learning: Machine Learning and Deep Learning with Python, Scikit-Learn, and TensorFlow, 2nd Edition. Packt Publishing, Birmingham, UK.

[30]Rumelhart DE, Hinton GE, Williams RJ, 1986. Learning representations by back-propagating errors. Nature, 323(6088):533-536.

[31]Sadoun O, Merdas A, Douadi A, 2020. The bond and flexural strengthening of reinforced concrete elements strengthened with near surface mounted prestressing steel (PS) bars. Journal of Adhesion Science and Technology, 34(19):2120-2143.

[32]Seracino R, Jones NM, Ali SM, et al., 2007a. Bond strength of near-surface mounted FRP strip-to-concrete joints. Journal of Composites for Construction, 11(4):401-409.

[33]Seracino R, Saifulnaz MRR, Oehlers DJ, 2007b. Generic debonding resistance of EB and NSM plate-to-concrete joints. Journal of Composites for Construction, 11(1):62-70.

[34]Shishegaran A, Khalili MR, Karami B, et al., 2020. Computational predictions for estimating the maximum deflection of reinforced concrete panels subjected to the blast load. International Journal of Impact Engineering, 139:103527.

[35]Siu C, 2017. Day32: Variable Importance in ANNs. https://csiu.github.io/blog/update/2017/03/28/day32.html

[36]Su M, Peng H, Yuan M, et al., 2021a. Identification of the interfacial cohesive law parameters of FRP strips externally bonded to concrete using machine learning techniques. Engineering Fracture Mechanics, 247:107643.

[37]Su M, Zhong Q, Peng H, et al., 2021b. Selected machine learning approaches for predicting the interfacial bond strength between FRPs and concrete. Construction and Building Materials, 270: 121456.

[38]Vu DT, Hoang ND, 2016. Punching shear capacity estimation of FRP-reinforced concrete slabs using a hybrid machine learning approach. Structure and Infrastructure Engineering, 12(9):1153-1161.

[39]Wu YF, Zhou ZQ, Yang QD, et al., 2010. On shear bond strength of FRP-concrete structures. Engineering Structures, 32(3):897-905.

[40]Yang L, Qi CC, Lin XS, et al., 2019. Prediction of dynamic increase factor for steel fibre reinforced concrete using a hybrid artificial intelligence model. Engineering Structures, 189:309-318.

[41]Zhang C, Zhao JH, Rabczuk T, 2018. The interface strength and delamination of fiber-reinforced composites using a continuum modeling approach. Composites Part B: Engineering, 137:225-234.

[42]Zhang SS, 2012. Behaviour and Modelling of RC Beams Strengthened in Flexure with Near-surface Mounted FRP Strips. PhD Thesis, The Hong Kong Polytechnic University, Hong Kong, China.

[43]Zhang SS, Teng JG, Yu T, 2013. Bond-slip model for CFRP strips near-surface mounted to concrete. Engineering Structures, 56:945-953.

[44]Zhang SS, Teng JG, Yu T, 2014. Bond strength model for CFRP strips near-surface mounted to concrete. Journal of Composites for Construction, 18(3):A4014003.

[45]Zhang SS, Yu T, Chen GM, 2017. Reinforced concrete beams strengthened in flexure with near-surface mounted (NSM) CFRP strips: current status and research needs. Composites Part B: Engineering, 131:30-42.

[46]Zhu H, Wu G, Zhang L, et al., 2014. Experimental study on the fire resistance of RC beams strengthened with near-surface-mounted high-T_g BFRP bars. Composites Part B: Engineering, 60:680-687.