Hybrid model-driven and data-driven method for predicting concrete creep considering uncertainty quantification

Abstract

Reasonable prediction of concrete creep is the basis of studying long-term deflection of concrete structures. In this paper, a hybrid model-driven and data-driven (HMD) method for predicting concrete creep is proposed by using the sequence integration strategy. Then, a novel uncertainty prediction model (UPM) is developed considering uncertainty quantification. Finally, the effectiveness of the proposed method is validated by using the North-western University (NU) database of creep, and the effect of uncertainty on prediction results are also discussed. The analysis results show that the proposed HMD method outperforms the model-driven and three data-driven methods, including the genetic algorithm-back propagation neural network (GA-BPNN), particle swarm optimization-support vector regression (PSO-SVR) and convolutional neural network only method, in accuracy and time efficiency. The proposed UPM of concrete creep not only ensures relatively good prediction accuracy, but also quantifies the model and measurement uncertainties during the prediction process. Additionally, although incorporating measurement uncertainty into concrete creep prediction can improve the prediction performance of UPM, the prediction interval of the creep compliance is more sensitive to model uncertainty than to measurement uncertainty, and the mean contribution of variance attributed to the model uncertainty to the total variance is about 90%.

Graphical abstract

Keywords

concrete creep / uncertainty prediction / hybrid method / data-driven / model-driven / convolutional neural network

References

[1] Khalilzadehtabrizi S, Sadaghian H, Farzam M. Uncertainty of concrete strength in shear and flexural behavior of beams using lattice modeling. Frontiers of Structural and Civil Engineering, 2023, 17(2): 306–325

[2] Yang Y M, Peng J X, Cai C S, Tang H. Probabilistic analysis of corrosion initiation in existing reinforced concrete structures with imprecise random field. Structures, 2023, 52: 877–888

[3] Criel P, Reybrouck N, Caspeele R, Matthys S. Uncertainty quantification of creep in concrete by Taylor expansion. Engineering Structures, 2017, 153: 334–341

[4] Bischoff P H, Torres L. Rational approach for computing long-term deflection of reinforced concrete. ACI Structural Journal, 2021, 118: 215–224

[5] Yang Y M, Liu Z H, Tang H, Peng J X. Deflection-based failure probability analysis of low shrinkage-creep concrete structures in presence of non-stationary evolution of shrinkage and creep uncertainties. Construction and Building Materials, 2023, 376: 131077

[6] Wendling A, Sadhasivam K, Floyd R W. Creep and shrinkage of lightweight self-consolidating concrete for prestressed members. Construction and Building Materials, 2018, 167: 205–215

[7] Bažant Z P, Yu Q, Li G H. Excessive long-time deflections of prestressed box girders. II: Numerical analysis and lessons learned. Journal of Structural Engineering, 2012, 138(6): 687–696

[8] Chen P, Zhang G, Cao S J, Lv X Y, Shen B. Creep and post-creep mechanical properties of reinforced concrete columns. Journal of Building Engineering, 2023, 63: 105521

[9] Yang Y M, Peng J X, Cai C S, Zhou Y D, Wang L, Zhang J R. Time-dependent reliability assessment of aging structures considering stochastic resistance degradation process. Reliability Engineering & System Safety, 2022, 217: 108105

[10] Zheng Z H, Hu D, Liu P, Sha F, Liu L, Yu Z W. Considering the effect of the randomness of concrete strength and relative humidity on concrete creep. Structural Concrete, 2020, 22(5): 1–15

[11] Koh C G, Ang K K, Zhang L. Effects of repeated loading on creep deflection of reinforced concrete beams. Engineering Structures, 1997, 19(1): 2–18

[12] Bouziadi F, Boulekbache B, Tahenni T, Haddi A, Hamrat M, Amziane S. Finite element analysis of steady-state uniaxial basic creep of high-performance concrete. Journal of Building Engineering, 2022, 52: 104500

[13] Zhang H H, Xiao J Z, Tang Y X, Duan Z H, Poon C S. Long-term shrinkage and mechanical properties of fully recycled aggregate concrete: Testing and modelling. Cement and Concrete Composites, 2022, 130: 104527

[14] CEB-FIP. CEB-FIP Model Code 1990: Design Code. London: Thomas Telford, 1994

[15] ACI-209. Prediction of Creep, Shrinkage, and Temperature Effects in Concrete Structures. Farmington Hills, MI: American Concrete Institute, 1992

[16] JTG3362-2018. Specifications for Design of Highway Reinforced Concrete and Prestressed Concrete Bridges and Culverts. Beijing: China People’s Communications Press, 2018 (in Chinese)

[17] AASHTO. Standard Specifications for Highway Bridges. 17th ed. Washington, D.C.: AASHTO, 2002

[18] Bažant Z P. RILEM draft recommendation: TC-242-MDC multi-decade creep and shrinkage of concrete: Material model and structural analysis. Materials and Structures, 2015, 48(4): 753–770

[19] Yu P, Li R Q, Bie D P, Yao X M, Liu X C, Duan Y H. A coupled creep and damage model of concrete considering rate effect. Journal of Building Engineering, 2022, 45: 103621

[20] Tapeh A T G, Naser M Z. Artificial intelligence, machine learning, and deep learning in structural engineering: A scientometrics review of trends and best practices. Archives of Computational Methods in Engineering, 2023, 30(1): 115–159

[21] Akinosho T D, Oyedele L O, Bilal M, Ajayi A O, Delgado M D, Akinade O O, Ahmed A A. Deep learning in the construction industry: A review of present status and future innovations. Journal of Building Engineering, 2020, 32: 101827

[22] Dogan A, Birant D. Machine learning and data mining in manufacturing. Expert Systems with Applications, 2021, 166: 114060

[23] Bal L, Buyle-Bodin F. Artificial neural network for predicting creep of concrete. Neural Computing & Applications, 2014, 25(6): 1359–1367

[24] Gandomi A H, Sajedi S, Kiani B, Huang Q. Genetic programming for experimental big data mining: A case study on concrete creep formulation. Automation in Construction, 2016, 70: 89–97

[25] Hodhod O A, Said T E, Ataya A M. Prediction of creep in concrete using genetic programming hybridized with ANN. Computers and Concrete, 2018, 21(5): 513–523

[26] Li K, Long Y P, Wang H, Wang Y F. Modeling and sensitivity analysis of concrete creep with machine learning methods. Journal of Materials in Civil Engineering, 2021, 33(8): 04021206

[27] Liang M F, Chang Z, Wan Z, Gan Y D, Schlangen E, Savija B. Interpretable ensemble-machine-learning models for predicting creep behavior of concrete. Cement and Concrete Composites, 2022, 125: 104295

[28] Zhu J S, Wang Y L. Convolutional neural networks for predicting creep and shrinkage of concrete. Construction and Building Materials, 2021, 306: 124868

[29] Karthikeyan J, Upadhyay A, Bhandari N M. Artificial neural network for predicting creep and shrinkage of high performance concrete. Journal of Advanced Concrete Technology, 2008, 6(1): 135–142

[30] Gedam B A, Bhandari N, Upadhyay A. An apt material model for drying shrinkage and specific creep of HPC using artificial neural network. Structural Engineering and Mechanics, 2014, 52(1): 97–113

[31] Liu Y Z, Li Y, Mu J L, Li H W, Shen J L. Modeling and analysis of creep in concrete containing supplementary cementitious materials based on machine learning. Construction and Building Materials, 2023, 392: 131911

[32] Feng J P, Zhang H W, Gao K, Liao Y C, Gao W, Wu G. Efficient creep prediction of recycled aggregate concrete via machine learning algorithms. Construction and Building Materials, 2022, 360: 129497

[33] Feng J P, Zhang H W, Gao K, Liao Y C, Yang J, Wu G. A machine learning and game theory-based approach for predicting creep behavior of recycled aggregate concrete. Case Studies in Construction Materials, 2022, 17: e01653

[34] Rong X, Liu Y B, Chen P, Lv X Y, Shen C, Yao B Q. Prediction of creep of recycled aggregate concrete using back-propagation neural network and support vector machine. Structural Concrete, 2023, 24(2): 2229–2244

[35] Sadowski Ł, Nikoo M, Shariq M, Joker E, Czarnecki S. The nature-inspired metaheuristic method for predicting the creep strain of green concrete containing ground granulated blast furnace slag. Materials, 2019, 12(2): 293

[36] Yang Z Y, Zhu H, Zhang B, Dong Z Q, Wu P. Short-term creep behaviors of seawater sea-sand coral aggregate concrete: An experimental study with Rheological model and neural network. Construction and Building Materials, 2023, 363: 129786

[37] Bouras Y, Li L. Utilisation of machine learning techniques to model creep behaviour of low-carbon concretes. Buildings, 2023, 13(9): 2252

[38] Bouras Y, Li L. Prediction of high-temperature creep in concrete using supervised machine learning algorithms. Construction and Building Materials, 2023, 400: 132828

[39] Xia Z Y, Quek S T, Li A Q, Li J H, Duan M J. Hybrid approach to seismic reliability assessment of engineering structures. Engineering Structures, 2017, 153: 665–673

[40] Bažant Z P, Wendner R, Boumakis G, Hubler M. Statistical comparisons of creep and shrinkage prediction models using RILEM and NU-ITI databases. ACI Materials Journal, 2015, 112(1): 829–832

[41] Han B, Xie H B, Zhang D J, Ma X. Sensitivity analysis of creep models considering correlation. Materials and Structures, 2016, 49(10): 4217–4227

[42] Keitel H, Dimmig-Osburg A. Uncertainty and sensitivity analysis of creep models for uncorrelated and correlated input parameters. Engineering Structures, 2010, 32(11): 3758–3767

[43] Jin S S, Cha S L, Jung H J. Improvement of concrete creep prediction with probabilistic forecasting method under model uncertainty. Construction and Building Materials, 2018, 184: 617–633

[44] Guo T, Chen Z H, Liu T, Han D Z. Time-dependent reliability of strengthened PSC box-girder bridge using phased and incremental static analyses. Engineering Structures, 2016, 117: 358–371

[45] Al-Manaseer A, Lam J P. Statistical evaluation of shrinkage and creep models. ACI Materials Journal, 2005, 102(3): 170–176

[46] Liu H, Zhang Y F. Bridge condition rating data modeling using deep learning algorithm. Structure and Infrastructure Engineering, 2020, 16(10): 1447–1460

[47] Bolandi H, Li X Y, Salem T, Boddeti V N, Lajnef N. Bridging finite element and deep learning: High-resolution stress distribution prediction in structural components. Frontiers of Structural and Civil Engineering, 2022, 16(11): 1365–1377

[48] Chiaia B, Marasco G, Aiello S. Deep convolutional neural network for multi-level non-invasive tunnel lining assessment. Frontiers of Structural and Civil Engineering, 2022, 16(2): 214–223

[49] Jing Y, Zhang L, Hao W, Huang L M. Numerical study of a CNN-based model for regional wave prediction. Ocean Engineering, 2022, 255: 111400

[50] Wang Q, Li F, Tang Y, Xu Y. Integrating model-driven and data-driven methods for power system frequency stability assessment and control. IEEE Transactions on Power Systems, 2019, 34(6): 4557–4568

[51] He J J, Huang M, Wang W, Wang S H, Guan X F. An asymptotic stochastic response surface approach to reliability assessment under multi-source heterogeneous uncertainties. Reliability Engineering & System Safety, 2021, 215: 107804

[52] Hamdia K M, Zhuang X Y, He P F, Rabczuk T. Fracture toughness of polymeric particle nanocomposites: Evaluation of models performance using Bayesian method. Composites Science and Technology, 2016, 126: 122–129

[53] JiW Q. Research on data-driven remaining useful life prediction method based on deep learnning and uncertainty quantification. Thesis for the Master’s Degree. Hefei: University of Science and Technology of China, 2020 (in Chinese)

[54] Gal Y, Ghahramani Z. Dropout as a Bayesian approximation: Representing model uncertainty in deep learning. In: Proceedings of the 33rd International Conference on Machine Learning. New York, NY: PMLR, 2016, 48: 1050–1059

[55] Cao L X, Zhang H Y, Meng Z, Wang X P. A parallel GRU with dual-stage attention mechanism model integrating uncertainty quantification for probabilistic RUL prediction of wind turbine bearings. Reliability Engineering & System Safety, 2023, 235: 109197

[56] Zhu R, Chen Y, Peng W W, Ye Z S. Bayesian deep-learning for RUL prediction: An active learning perspective. Reliability Engineering & System Safety, 2022, 228: 108758

[57] Baraldi P, Compare M, Sauco S, Zio E. Ensemble neural network-based particle filtering for prognostics. Mechanical Systems and Signal Processing, 2013, 41(1–2): 288–300

[58] Wang J Z, Zhou Y L, Jiang H. A novel interval forecasting system based on multi-objective optimization and hybrid data reconstruct strategy. Expert Systems with Applications, 2023, 217: 119539

[59] Chong A, Augenbroe G, Yan D. Occupancy data at different spatial resolutions: Building energy performance and model calibration. Applied Energy, 2021, 286: 116492

[60] ChenY X. Prediction and analysis of concrete shrinkage and creep behavior based on LSTM deep learning theory. Thesis for the Master’s Degree. Chengdu: Xihua University, 2021 (in Chinese)

[61] Taffese W Z, Espinosa-Leal L. Prediction of chloride resistance level of concrete using machine learning for durability and service life assessment of building structures. Journal of Building Engineering, 2022, 60: 105146

[62] Deng L, Xu S P, Wang W, Xiang C. Uniaxial stress identification of steel components based on one dimensional-CNN and ultrasonic method. Measurement, 2022, 194: 110868

[63] Botchkarev A. A new typology design of performance metrics to measure errors in machine learning regression algorithms. Interdisciplinary Journal of Information, Knowledge, and Management, 2019, 14: 45–76

[64] Naser M Z, Alavi A H. Error metrics and performance fitness indicators for artificial intelligence and machine learning in engineering and sciences. Architecture, Structures and Construction, 2023, 3: 499–517

[65] Tran V Q, Mai H V T, Nguyen T A, Ly H B. Assessment of different machine learning techniques in predicting the compressive strength of self-compacting concrete. Frontiers of Structural and Civil Engineering, 2022, 16(7): 928–945

[66] Ly H B, Nguyen T A, Pham B T, Nguyen M H. A hybrid machine learning model to estimate self-compacting concrete compressive strength. Frontiers of Structural and Civil Engineering, 2022, 16(8): 990–1002

[67] Guo B C, Lin X S, Wu Y F, Zhang L H. Machine learning-driven evaluation and optimisation of compression yielded FRP-reinforced concrete beam with T section. Engineering Structures, 2023, 275: 115240

[68] Mai H V T, Nguyen M H, Trinh S H, Ly H B. Optimization of machine learning models for predicting the compressive strength of fiber-reinforced self-compacting concrete. Frontiers of Structural and Civil Engineering, 2023, 17(2): 284–305

[69] Seghier M E A B, Keshtegar B, Tee K F, Zayed T, Abbassi R, Trung N T. Prediction of maximum pitting corrosion depth in oil and gas pipelines. Engineering Failure Analysis, 2020, 112: 104505

[70] KawasumiMKasaharaKKuriyamaT. Creep of Concrete at Elevated Temperatures, Part 3: The Influence of Ages at Loading and Water/Cement Ratios. CRIEPI Report No. 382008. 1982

[71] Hansen T C, Mattock A H. Influence of size and shape of member on the shrinkage and creep of concrete. ACI Materials Journal, 1966, 63: 267–290

[72] de LarrardF. Formulation and properties of ultra-high performance concretes. Paris: Central Laboratory of Bridges and Roads, 1988 (in French)

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