Estimating flexural strength of precast deck joints using Monte Carlo Model Averaging of non-fine-tuned machine learning models

Abstract

The bending capacity of the precast decks is greatly dependent on the flexural strength exhibited by the joints between them. However, due to the complexity and diversity of this system, precise predictive models are currently unavailable. This study introduces an effective and precise methodology for assessing flexural strength using Monte Carlo Model Averaging (MCMA), a statistical technique that combines the strengths of model averaging (MA) and Monte Carlo simulation. To construct the MCMA model, input variables were derived by analyzing the experimental results, and a database of 433 bending test specimens was compiled. The MCMA model incorporated four different machine learning models, namely decision tree (DT), linear regression (LR), adaptive boosting (AdaBoost), and multilayer perceptron (MLP). Comparative analyses revealed that the MCMA model outperformed baseline models (DT, AdaBoost, LR, and MLP) across all employed metrics. The impact of three different categories on flexural capacity was explored through boxplot analysis. Furthermore, a comparison between the MCMA model and the strut and tie model highlighted the superior performance of the MCMA model. The impact of input variables on the flexural strength prediction was further examined through Shapley Additive exPlanations based feature importance and global interpretation, as well as parametric study.

Graphical abstract

Keywords

precast deck joint / flexural strength / machine learning / model averaging / Monte Carlo method / parameter tuning

References

[1] He Z Q, Ma Z J, Chapman C E, Liu Z. Longitudinal joints with accelerated construction features in decked bulb-tee girder bridges: Strut-and-tie model and design guidelines. Journal of Bridge Engineering, 2013, 18(5): 372–379

[2] Haber Z B, Graybeal B A. Lap-spliced rebar connections with UHPC closures. Journal of Bridge Engineering, 2018, 23(6): 04018028

[3] Ma Z G, Chaudhury S, Millam J L, Hulse J L. Field test and 3D FE modeling of decked bulb-tee bridges. Journal of Bridge Engineering, 2007, 12(3): 306–314

[4] Ong K C G, Hao J B, Paramasivam P. Flexural behavior of precast joints with horizontal loop connections. ACI Structural Journal, 2006, 103(5): 664–671

[5] Ryu H K, Kim Y J, Chang S P. Experimental study on static and fatigue strength of loop joints. Engineering Structures, 2007, 29(2): 145–162

[6] Li L, Ma Z, Griffey M E, Oesterle R G. Improved longitudinal joint details in decked bulb tees for accelerated bridge construction: Concept development. Journal of Bridge Engineering, 2010, 15(3): 327–336

[7] Joergensen H B, Hoang L C. Tests and limit analysis of loop connections between precast concrete elements loaded in tension. Engineering Structures, 2013, 52: 558–569

[8] Hwang HH, Yeo IS, Cho KH, Park SY. Evaluation of flexural strength for UHPC deck joints with lap-spliced reinforced steel bar. Journal of Korea Institute for Structural Maintenance and Inspection, 2011, 15: 92–99

[9] Casanova M, Clauson C, Ebrahimpour A, Mashal M. High-early strength concrete with polypropylene fibers as cost-effective alternative for field-cast connections of precast elements in accelerated bridge construction. Journal of Materials in Civil Engineering, 2019, 31(11): 04019266

[10] Deng E F, Zhang Z, Zhang C X, Tang Y, Wang W, Du Z J, Gao J P. Experimental study on flexural behavior of UHPC wet joint in prefabricated multi-girder bridge. Engineering Structures, 2023, 275: 115314

[11] Di J, Han B, Qin F. Investigation of U-bar joints between precast bridge decks loaded in combined bending and shear. Structures, 2020, 27: 37–45

[12] Ong K C G, Hao J B, Paramasivam P. A strut-and-tie model for ultimate loads of precast concrete joints with loop connections in tension. Construction & Building Materials, 2006, 20(3): 169–176

[13] Zhang J, Guan Z, Liang L, Ling X. Experimental study on longitudinal joints with accelerated construction features in precast multibox girder bridges. Journal of Bridge Engineering, 2018, 23(1): 04017116

[14] Qi J, Cheng Z, Wang J, Zhu Y, Li W. Full-scale testing on the flexural behavior of an innovative dovetail UHPC joint of composite bridges. Structural Engineering and Mechanics, 2020, 75(1): 49–57

[15] Li L, Jiang Z. Flexural behavior and strut-and-tie model of joints with headed bar details connecting precast members. Perspectives in Science, 2016, 7: 253–260

[16] Vella J P, Vollum R L, Jackson A. Investigation of headed bar joints between precast concrete panels. Engineering Structures, 2017, 138: 351–366

[17] Truong G T, Choi K K, Nguyen T H, Kim C S. Prediction of shear strength of RC deep beams using XGBoost regression with Bayesian optimization. European Journal of Environmental and Civil Engineering, 2023, 27(14): 4046–4066

[18] Truong G T, Choi K K, Kim C S. Implementation of boosting algorithms for prediction of punching shear strength of RC column footings. Structures, 2022, 46: 521–538

[19] Truong G T, Hwang H J, Kim C S. Assessment of punching shear strength of FRP-RC slab-column connections using machine learning algorithms. Engineering Structures, 2022, 255: 113898

[20] Karami B, Shishegaran A, Taghavizade H, Rabczuk T. Presenting innovative ensemble model for prediction of the load carrying capacity of composite castellated steel beam under fire. Structures, 2021, 33: 4031–4052

[21] Shishegarana A. Khalili M R, Karami B, Rabczuk T, Shishegaran A. Computational predictions for estimating the maximum deflection of reinforced concrete panels subjected to the blast load. International Journal of Impact Engineering, 2020, 139: 103527

[22] Hu J, Ji X. A novel prediction model construction and result interpretation method for slope deformation of deep excavated expansive soil canals. Expert Systems with Applications, 2024, 236: 121326

[23] Kim T, Kwon O S, Song J. Deep learning-based response spectrum analysis method for building structures. Earthquake Engineering & Structural Dynamics, 2024, 53(4): 1638–1655

[24] Shishegaran A, Saeedi M, Mirvalad S, Korayem A H. Computational predictions for estimating the performance of flexural and compressive strength of epoxy resin-based artificial stones. Engineering with Computers, 2023, 39(1): 347–372

[25] Malekloo A, Ozer E, AlHamaydeh M, Girolami M. Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights. Structural Health Monitoring, 2022, 21(4): 1906–1955

[26] Ye X J, Cao Y J, Liu A R, Wang X W, Zhao Y H, Hu N. Parallel convolutional neural network toward high efficiency and robust structural damage identification. Structural Health Monitoring, 2023, 22(6): 3805–3826

[27] Nobahari M, Ghasemi M R, Shabakhty N. A fast and robust method for damage detection of truss structures. Applied Mathematical Modelling, 2019, 68: 368–382

[28] Chalouhi E K, Gonzalez I, Gentile C, Karoumi R. Damage detection in railway bridges using Machine Learning: Application to a historic structure. Procedia Engineering, 2017, 199: 1931–1936

[29] Guo X Y, Fang S E. Structural parameter identification using physics-informed neural networks. Measurement, 2023, 220: 113334

[30] Liu D, Bao Y, Li H. Machine learning-based stochastic subspace identification method for structural modal parameters. Engineering Structures, 2023, 274: 115178

[31] Anitescu C, Atroshchenko E, Alajlan N, Rabczuk T. Artificial neural network methods for the solution of second order boundary value problems. Computers, Materials & Continua, 2019, 59(1): 345–359

[32] Samaniegoc E, Anitescu C, Goswami S, Nguyen-Thanh V M, Guo H, Hamdia K, Zhuang X, Rabczuk T. An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications. Computer Methods in Applied Mechanics and Engineering, 2020, 362: 112790

[33] Nguyen-Thanh V M, Anitescu C, Alajlan N, Rabczuk T, Zhuang X. Parametric deep energy approach for elasticity accounting for strain gradient effects. Computer Methods in Applied Mechanics and Engineering, 2021, 386: 114096

[34] Kang M C, Yoo D Y, Gupta R. Machine learning-based prediction for compressive and flexural strengths of steel fiber-reinforced concrete. Construction & Building Materials, 2021, 266: 121117

[35] Nguyen H, Vu T, Vo T P, Thai H T. Efficient machine learning models for prediction of concrete strengths. Construction & Building Materials, 2021, 266: 120950

[36] Rahman J, Ahmed K S, Khan N I, Islam K, Mangalathu S. Data-driven shear strength prediction of steel fiber reinforced concrete beams using machine learning approach. Engineering Structures, 2021, 233: 111743

[37] Bogaert P, Taghizadeh-Mehrjardi R, Hamzehpour N. Model averaging of machine learning algorithms for digital soil mapping: A minimum variance framework. Geoderma, 2023, 437: 116604

[38] Drachal K. Dynamic model averaging in economics and finance with FDMA: A package for R. Signals, 2020, 1(1): 47–99

[39] Dormann C F, Calabrese J M, Guillera-Arroita G, Matechou E, Bahn V, Bartoń K, Beale C M, Ciuti S, Elith J, Gerstner K, Guelat J, Keil P, Lahoz-Monfort J J, Pollock L J, Reineking B, Roberts D R, Schröder B, Thuiller W, Warton D I, Wintle B A, Wood S N, Wüest R O, Hartig F. Model averaging in ecology: A review of Bayesian, information-theoretic, and tactical approaches for predictive inference. Ecological Monographs, 2018, 88(4): 485–504

[40] Parkinson D, Liddle A R. Bayesian model averaging in astrophysics: A review. Statistical Analysis and Data Mining, 2013, 6(1): 3–14

[41] Tassi C R N, Borner A, Triebel R. Monte Carlo averaging for uncertainty estmation in neural networks. Journal of Physics: Conference Series, 2023, 2506(1): 012004

[42] AttanayakeUAktanH. Reflective Cracking Between Precast Prestressed Box Girders. Technical Report WHRP 0092-14-01. 2017

[43] Shim C, Lee C D, Ji S W. Crack control of precast deck loop joint using high strength concrete. Advances in Concrete Construction, 2018, 6(5): 527–543

[44] Jung K, Park S, Kim S, Kim B, Cho K. A study on the flexural performance of UHPC precast deck-joint interface by the exposure of steel fiber. Engineering, 2014, 6(13): 1000–1006

[45] Abokifa M, Moustafa M A. Experimental behavior of precast bridge deck systems with non-proprietary UHPC transverse field joints. Materials, 2021, 14(22): 6964

[46] Villalba-Herrero S, Casas J R. New structural joint by rebar looping applied to staged box girder bridge construction. Static tests. Structural Concrete, 2016, 17(5): 824–835

[47] Soliman A A, Heard W F, Williams B A, Ranade R. Effects of the tensile properties of UHPC on the bond behavior. Construction & Building Materials, 2023, 392: 131990

[48] Alavi-FardM. Bond characteristics of high strength concrete. Dissertation for the Doctoral Degree. Newfoundland: Memorial University of Newfoundland, 1999

[49] Jia J F, Ren Z D, Bai Y L, Li B, Sun Y G, Zhang Z X, Zhang J X. Tensile behavior of UHPC wet joints for precast bridge deck panels. Engineering Structures, 2023, 282: 115826

[50] SamuelL. Experimental investigation of precast bridge deck joints with U-bar and headed bar joint details. Thesis for the Master’s Degree. Knoxville: University of Tennessee, 2019

[51] Shah Y I, Hu Z, Yin B S, Li X. Flexural performance analysis of UHPC wet joint of prefabricated. Arabian Journal for Science and Engineering, 2021, 46(11): 11253–11266

[52] ShinHDH COhI GKimT KByun. Structural behavior of precast concrete deck with ribbed loop joints in a composite bridge. Smart Structures and Systems, 2016, 17(4): 559–576

[53] Zhang Z, Zhang Y, Zhu P. Flexural behavior of precast RC deck panels with cast-in-place UHPFRC connection. Coatings, 2022, 12(8): 1183

[54] Nguyen Q H, Ly H B, Ho S L, Al-Ansari N, Le H V, Tran V Q, Prakash I, Pham B T. Influence of data splitting on performance of machine learning models in prediction of shear strength of soil. Mathematical Problems in Engineering, 2021, 2021: 4832864

[55] Tran V L, Kim J K. Ensemble machine learning-based models for estimating the transfer length of strands in PSC beams. Expert Systems with Applications, 2023, 221: 119768

[56] Rácz A, Bajusz D, Héberger K. Effect of dataset size and train/test split ratios in QSAR/QSPR multiclass classification. Molecules, 2021, 26(4): 1111

[57] Wang X, Liu Y, Chen A, Ruan X. Flexural capacity assessment of precast deck joints based on deep forest. Structures, 2022, 41: 270–286

[58] Shishegaran A, Varaee H, Rabczuk T, Shishegaran G. High correlated variables creator machine: Prediction of the compressive strength of concrete. Computers & Structures, 2021, 247: 106479

[59] Naghsh M A, Shishegaran A, Karami B, Rabczuk T, Shishegaran A, Taghavizadeh H, Moradi M. shishegaran A, Taghavizadeh H, Moradi M. An innovative model for predicting the displacement and rotation of column-tree moment connection under fire. Frontiers of Structural and Civil Engineering, 2021, 15(1): 194–212

RIGHTS & PERMISSIONS

Higher Education Press