Journal of the Chinese Ceramic Society, Volume. 53, Issue 1, 173(2025)

Progress on Multi-Scale Simulation on Tensile Cracking Behavior ofEngineered Cementitious Composites

LEI Dongyi1, WU Zhiying1, JIA Haoxuan1, YU Long1, WANG Bing1, TANG Jinhui2, LI Ying1, LI Yanlong3, and LIU Jiaping2
Author Affiliations
  • 1Department of Civil Engineering, Engineering Research Center of Concrete Technology under Marine Environment, Ministry of Education, Qingdao University of Technology, Qingdao 266520, Shandong, China
  • 2School of Materials Science and Engineering, Southeast University, Nanjing 211189, China
  • 3School of Water Resources and Hydropower, Xi′an University of Technology, Xi′an 710048, China
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    References(77)

    [1] [1] YUAN F, PAN J L, WU Y F. Numerical study on flexural behaviors of steel reinforced engineered cementitious composite (ECC) and ECC/concrete composite beams[J]. Sci China Technol Sci, 2014, 57(3): 637-645.

    [2] [2] FENG J, SUN W W, CHEN L, et al. Engineered Cementitious Composites using Chinese local ingredients: Material preparation and numerical investigation[J]. Case Stud Constr Mater, 2022, 16: e00852.

    [7] [7] KUNIEDA M, ROKUGO K. Recent progress on HPFRCC in Japan required performance and applications[J]. J Adv Concr Technol, 2006, 4(1): 19-33.

    [8] [8] LI V C, FISCHER G, LEPECH M D. Shotcreting with ECC[C]// Spritzbeton-Tagung, Alpbach, Austria, 2009: 1-16.

    [9] [9] LI V C. On engineered cementitious composites (ECC)[J]. J Adv Concr Technol, 2003, 1(3): 215-230.

    [10] [10] WANG P, QIAO G, ZHANG Y, et al. Molecular dynamics simulation study on interfacial shear strength between calcium-silicate-hydrate and polymer fibers[J]. Constr Build Mater, 2020, 257: 119557.

    [11] [11] ZHANG Y, QIAO P Z. A fully-discrete peridynamic modeling approach for tensile fracture of fiber-reinforced cementitious composites[J]. Eng Fract Mech, 2021, 242: 107454.

    [12] [12] GENCTURK B, ELNASHAI A S. Numerical modeling and analysis of ECC structures[J]. Mater Struct, 2013, 46(4): 663-682.

    [15] [15] LU Z Y, YU J, YAO J, et al. Experimental and molecular modeling of polyethylene fiber/cement interface strengthened by graphene oxide[J]. Cem Concr Compos, 2020, 112: 103676.

    [16] [16] JIN C R, BURATTI N, STACCHINI M, et al. Lattice discrete particle modeling of fiber reinforced concrete: Experiments and simulations[J]. Eur J Mech A, 2016, 57: 85-107.

    [17] [17] LUKOVI M, DONG H, AVIJA B, et al. Tailoring strain-hardening cementitious composite repair systems through numerical experimentation[J]. Cem Concr Compos, 2014, 53: 200-213.

    [18] [18] HOU D S, ZHANG W, WANG P, et al. Microscale peridynamic simulation of damage process of hydrated cement paste subjected to tension[J]. Constr Build Mater, 2019, 228: 117053.

    [19] [19] KHOSRAVANI M R, FRIEBERTSH&#x04D2USER K, WEINBERG K. On the use of peridynamics in fracture of ultra-high performance concrete[J]. Mech Res Commun, 2022, 123: 103899.

    [20] [20] ZHANG W, ZOU X S, WEI F Y, et al. Grafting SiO2 nanoparticles on polyvinyl alcohol fibers to enhance the interfacial bonding strength with cement[J]. Compos Part B Eng, 2019, 162: 500-507.

    [21] [21] MADENCI E, OTERKUS E. Peridynamic Theory and Its Applications[M]. New York, NY: Springer New York, 2014.

    [22] [22] ZHOU Y, HOU D S, GENG G Q, et al. Insights into the interfacial strengthening mechanisms of calcium-silicate-hydrate/polymer nanocomposites[J]. Phys Chem Chem Phys, 2018, 20(12): 8247-8266.

    [23] [23] NADERPOUR H, KHEYRODDIN A, AMIRI G G. Prediction of FRP-confined compressive strength of concrete using artificial neural networks[J]. Compos Struct, 2010, 92(12): 2817-2829.

    [25] [25] GEHLOT T, DAVE M, SOLANKI D. Neural network model to predict compressive strength of steel fiber reinforced concrete elements incorporating supplementary cementitious materials[J]. Mater Today Proc, 2022, 62: 6498-6506.

    [26] [26] KANG J G, BOLANDER J E. Event-based lattice modeling of strain-hardening cementitious composites[J]. Int J Fract, 2017, 206(2): 245-261.

    [27] [27] BOSHOFF W P, ADENDORFF C J. Effect of sustained tensile loading on SHCC crack widths[J]. Cem Concr Compos, 2013, 37: 119-125.

    [28] [28] KANG J, BOLANDER J E. Simulating crack width distributions in SHCC under tensile loading[C]//the VIII International conference on fracture mechanics of concrete and concrete structures, Toledo Spain, 2013: 10-14.

    [29] [29] SHALCHY F, RAHBAR N. Nanostructural characteristics and interfacial properties of polymer fibers in cement matrix[J]. ACS Appl Mater Interfaces, 2015, 7(31): 17278-17286.

    [30] [30] CHENG Z Q, HU Y Y, CHU L S, et al. Peridynamic modeling of engineered cementitious composite with fiber effects[J]. Eng Fract Mech, 2021, 245: 107601.

    [31] [31] KANG J G, KIM K, LIM Y M, et al. Modeling of fiber-reinforced cement composites: Discrete representation of fiber pullout[J]. Int J Solids Struct, 2014, 51(10): 1970-1979.

    [33] [33] YAO J K, HUANG S L, XU Y W, et al. Mix design of equal strength high volume fly ash concrete with artificial neural network[J]. Case Stud Constr Mater, 2023, 19: e02294.

    [36] [36] CAI X R, XU S L, FU B Q. A statistical micromechanical model of multiple cracking for ultra high toughness cementitious composites[J]. Eng Fract Mech, 2011, 78(6): 1091-1100.

    [37] [37] HUANG T, ZHANG Y X, YANG C H. Multiscale modelling of multiple-cracking tensile fracture behaviour of engineered cementitious composites[J]. Eng Fract Mech, 2016, 160: 52-66.

    [39] [39] HUANG H L, LUO J, PENG C H, et al. Interfacial bond between modified micro carbon fiber and high-strength cement paste in UHPC: Bond-slip tests and molecular dynamic simulation[J]. Cem Concr Compos, 2023, 142: 105168.

    [40] [40] HE S, QIU J S, LI J X, et al. Strain hardening ultra-high performance concrete (SHUHPC) incorporating CNF-coated polyethylene fibers[J]. Cem Concr Res, 2017, 98: 50-60.

    [41] [41] MEYER E E, ROSENBERG K J, ISRAELACHVILI J. Recent progress in understanding hydrophobic interactions[J]. Proc Natl Acad Sci USA, 2006, 103(43): 15739-15746.

    [42] [42] LU Z Y, YIN R, YAO J, et al. Experimental and molecular dynamic study on the interfacial strengthening mechanism of PE fiber/cement mortar using advanced oxidation processes[J]. Constr Build Mater, 2021, 309: 125144.

    [43] [43] ZHANG H, HUANG Y J, YANG Z J, et al. A discrete-continuum coupled finite element modelling approach for fibre reinforced concrete[J]. Cem Concr Res, 2018, 106: 130-143.

    [45] [45] KANG J G, BOLANDER J E. Multiscale modeling of strain-hardening cementitious composites[J]. Mech Res Commun, 2016, 78: 47-54.

    [46] [46] ZHANG J, LEUNG C K Y, GAO Y. Simulation of crack propagation of fiber reinforced cementitious composite under direct tension[J]. Eng Fract Mech, 2011, 78(12): 2439-2454.

    [47] [47] KRAHL P A, CARRAZEDO R, EL DEBS M K. Mechanical damage evolution in UHPFRC: Experimental and numerical investigation[J]. Eng Struct, 2018, 170: 63-77.

    [48] [48] SPAGNOLI A, YANG E H, LI V C. Micromechanical modelling of multiple fracture in Engineered Cementitious Composites[C]//the 17th Biennial European Conference on Fracture, Brno Czech Republic, 2008: 2407-2414.

    [49] [49] SPAGNOLI A. A micromechanical lattice model to describe the fracture behaviour of engineered cementitious composites[J]. Comput Mater Sci, 2009, 46(1): 7-14.

    [50] [50] KUNIEDA M, OGURA H, UEDA N, et al. Tensile fracture process of strain hardening cementitious composites by means of three-dimensional meso-scale analysis[J]. Cem Concr Compos, 2011, 33(9): 956-965.

    [52] [52] REDON C, LI V C, WU C, et al. Measuring and modifying interface properties of PVA fibers in ECC matrix[J]. J Mater Civ Eng, 2001, 13(6): 399-406.

    [54] [54] ARAIN M F, WANG M X, CHEN J Y, et al. Experimental and numerical study on tensile behavior of surface modified PVA fiber reinforced strain-hardening cementitious composites (PVA-SHCC)[J]. Constr Build Mater, 2019, 217: 403-415.

    [55] [55] PARK J W, CHOO B, BOLANDER J E, et al. Investigation of high strain rate effects on strain-hardening cementitious composites using Voronoi-cell lattice models[J]. Cem Concr Compos, 2024, 147: 105408.

    [56] [56] RANJBARIAN M, MECHTCHERINE V, ZHANG Z Y, et al. Locking Front Model for pull-out behaviour of PVA microfibre embedded in cementitious matrix[J]. Cem Concr Compos, 2019, 103: 318-330.

    [58] [58] ZHU B R, PAN J L, ZHANG M Z, et al. Predicting the strain-hardening behaviour of polyethylene fibre reinforced engineered cementitious composites accounting for fibre-matrix interaction[J]. Cem Concr Compos, 2022, 134: 104770.

    [59] [59] KESNER K, BILLINGTON S L. Tension, compression and cyclic testing of engineered cementitious composite materials[J]. Mceer, 2004(7): 107.

    [60] [60] LU C, LEUNG C K Y. Theoretical evaluation of fiber orientation and its effects on mechanical properties in engineered cementitious composites (ECC) with various thicknesses[J]. Cem Concr Res, 2017, 95: 240-246.

    [61] [61] WU C, YAO J, CHEN M K, et al. Theoretical modification of the laboratory-determined tensile stress-strain relationship of strain hardening cementitious composites (SHCCs) for large-scale specimens[J]. Constr Build Mater, 2022, 326: 126879.

    [62] [62] ZHAO D P, WANG C J, LI K, et al. An experimental and analytical study on a damage constitutive model of engineered cementitious composites under uniaxial tension[J]. Materials, 2022, 15(17): 6063.

    [63] [63] SHAHBEYK S, HOSSEINI M, YAGHOOBI M. Mesoscale finite element prediction of concrete failure[J]. Comput Mater Sci, 2011, 50(7): 1973-1990.

    [64] [64] SUKUMAR N, CHOPP D L, MOS N, et al. Modeling holes and inclusions by level sets in the extended finite-element method[J]. Comput Meth Appl Mech Eng, 2001, 190(46-47): 6183-6200.

    [65] [65] SILLING S A. Reformulation of elasticity theory for discontinuities and long-range forces[J]. J Mech Phys Solids, 2000, 48(1): 175-209.

    [66] [66] SILLING S A, LEHOUCQ R B. Convergence of peridynamics to classical elasticity theory[J]. J Elast, 2008, 93(1): 13-37.

    [67] [67] WU P, YANG F, CHEN Z G, et al. Stochastically homogenized peridynamic model for dynamic fracture analysis of concrete[J]. Eng Fract Mech, 2021, 253: 107863.

    [68] [68] YANG D, DONG W, LIU X F, et al. Investigation on mode-I crack propagation in concrete using bond-based peridynamics with a new damage model[J]. Eng Fract Mech, 2018, 199: 567-581.

    [69] [69] LI W J, GUO L. Meso-fracture simulation of cracking process in concrete incorporating three-phase characteristics by peridynamic method[J]. Constr Build Mater, 2018, 161: 665-675.

    [70] [70] HUANG D, ZHANG Q, QIAO P Z. Damage and progressive failure of concrete structures using non-local peridynamic modeling[J]. Sci China Technol Sci, 2011, 54(3): 591-596.

    [71] [71] HOU D S, ZHANG W, GE Z, et al. Experimentally validated peridynamic fracture modelling of mortar at the meso-scale[J]. Constr Build Mater, 2021, 267: 120939.

    [72] [72] YAN X F, LI W J, ZHANG R, et al. A three-dimensional meso-scale approach to the fracture analysis of ultrahigh performance concrete based on micropolar peridynamics[J]. Constr Build Mater, 2023, 382: 131303.

    [73] [73] GU X, LI X, XIA X Z, et al. A robust peridynamic computational framework for predicting mechanical properties of porous quasi-brittle materials[J]. Compos Struct, 2023, 303: 116245.

    [74] [74] GOLAFSHANI E M, RAHAI A, SEBT M H, et al. Prediction of bond strength of spliced steel bars in concrete using artificial neural network and fuzzy logic[J]. Constr Build Mater, 2012, 36: 411-418.

    [75] [75] YAN F, LIN Z B. New strategy for anchorage reliability assessment of GFRP bars to concrete using hybrid artificial neural network with genetic algorithm[J]. Compos Part B Eng, 2016, 92: 420-433.

    [76] [76] YAN F, LIN Z B, WANG X Y, et al. Evaluation and prediction of bond strength of GFRP-bar reinforced concrete using artificial neural network optimized with genetic algorithm[J]. Compos Struct, 2017, 161: 441-452.

    [77] [77] CHANDWANI V, AGRAWAL V, NAGAR R. Modeling slump of ready mix concrete using genetic algorithms assisted training of artificial neural networks[J]. Expert Syst Appl, 2015, 42(2): 885-893.

    [78] [78] SHI L, LIN S T K, LU Y, et al. Artificial neural network based mechanical and electrical property prediction of engineered cementitious composites[J]. Constr Build Mater, 2018, 174: 667-674.

    [79] [79] SHANMUGASUNDARAM N, PRAVEENKUMAR S, GAYATHIRI K, et al. Prediction on compressive strength of engineered cementitious composites using machine learning approach[J]. Constr Build Mater, 2022, 342: 127933.

    [80] [80] NASIR UDDIN M, LI L Z, AHMED A, et al. Prediction of PVA fiber effect in engineered composite cement (ECC) by artificial neural network (ANN)[J]. Mater Today Proc, 2022, 65: 537-542.

    [81] [81] YU J, WENG Y W, YU J T, et al. Generative AI for performance-based design of engineered cementitious composite[J]. Compos Part B Eng, 2023, 266: 110993.

    [82] [82] JI T, LIN T W, LIN X J. A concrete mix proportion design algorithm based on artificial neural networks[J]. Cem Concr Res, 2006, 36(7): 1399-1408.

    [84] [84] AIKGEN M, ULA M, ALYAMA K E. Using an artificial neural network to predict mix compositions of steel fiber-reinforced concrete[J]. Arab J Sci Eng, 2015, 40(2): 407-419.

    [85] [85] HOSSAIN K M A, GLADSON L R, ANWAR M S. Modeling shear strength of medium- to ultra-high-strength steel fiber-reinforced concrete beams using artificial neural network[J]. Neural Comput Appl, 2017, 28(1): 1119-1130.

    [86] [86] IKUMI T, GALEOTE E, PUJADAS P, et al. Neural network-aided prediction of post-cracking tensile strength of fibre-reinforced concrete[J]. Comput Struct, 2021, 256: 106640.

    [87] [87] LIU F Y, DING W Q, QIAO Y F, et al. Compressive behavior of hybrid steel-polyvinyl alcohol fiber-reinforced concrete containing fly ash and slag powder: Experiments and an artificial neural network model[J]. J Zhejiang Univ SCIENCE A, 2021, 22(9): 721-735.

    [88] [88] MAHESH R R, SATHYAN D. Modelling the hardened properties of steel fiber reinforced concrete using ANN[J]. Mater Today Proc, 2022, 49: 2081-2089.

    [89] [89] RAHMAN S K, AL-AMERI R. Structural assessment of Basalt FRP reinforced self-compacting geopolymer concrete using artificial neural network (ANN) modelling[J]. Constr Build Mater, 2023, 397: 132464.

    [90] [90] HEMMATIAN A, JALALI M, NADERPOUR H, et al. Machine learning prediction of fiber pull-out and bond-slip in fiber-reinforced cementitious composites[J]. J Build Eng, 2023, 63: 105474.

    [91] [91] TSAI M L, HUANG C W, CHANG S W. Theory-inspired machine learning for stress-strain curve prediction of short fiber-reinforced composites with unseen design space[J]. Extreme Mech Lett, 2023, 65: 102097.

    [92] [92] YUAN Z, WANG L N, JI X. Prediction of concrete compressive strength: Research on hybrid models genetic based algorithms and ANFIS[J]. Adv Eng Softw, 2014, 67: 156-163.

    [93] [93] SU M, PENG H, LI S F. Application of an interpretable artificial neural network to predict the interface strength of a near-surface mounted fiber-reinforced polymer to concrete joint[J]. J Zhejiang Univ Science A, 2021, 22(6): 427-440.

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    LEI Dongyi, WU Zhiying, JIA Haoxuan, YU Long, WANG Bing, TANG Jinhui, LI Ying, LI Yanlong, LIU Jiaping. Progress on Multi-Scale Simulation on Tensile Cracking Behavior ofEngineered Cementitious Composites[J]. Journal of the Chinese Ceramic Society, 2025, 53(1): 173

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    Paper Information

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    Received: Jul. 19, 2024

    Accepted: Jan. 10, 2025

    Published Online: Jan. 10, 2025

    The Author Email:

    DOI:10.14062/j.issn.0454-5648.20240474

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