Utilizing artificial neural networks to anticipate early-age thermal parameters in concrete piers

  • Hoàng Việt Hải

    Faculty of Civil Engineering, University of Transport and Communications, 3 Cau Giay, Lang Thuong, Dong Da, Hanoi, Vietnam
  • Đỗ Anh Tú

    Faculty of Civil Engineering, University of Transport and Communications, 3 Cau Giay, Lang Thuong, Dong Da, Hanoi, Vietnam
  • Phạm Đức Thọ

    Faculty of Civil Engineering, Hanoi University of Mining and Geology, No.18 Vien Street, Duc Thang Ward, Bac Tu Liem District, Ha Noi, Vietnam
Email: hoangviethai@utc.edu.vn
Từ khóa: Mass concrete pier, maximum temperature, temperature difference, artificial neural network (ANN), thermal crack.

Tóm tắt

Recently, researches have been used Artificial Neural Network (ANN) to predict the early-age thermal cracking of rectangle piers. But ANN has not resulted for different types of concrete piers. This article presents an evaluation of the early-age thermal characteristics of mass concrete piers with four distinct cross-sectional shapes. A finite element (FE) model was employed to estimate the maximum temperature, thermal stress, and cracking potential of the concrete pier at its early age. To investigate the impact of various pier geometries on the thermal cracking potential, different pier geometries were considered. In this study, an ANN model was utilized to predict the maximum temperature and decrease the risk of cracking in mass concrete piers at early age. The database of thermal mass concrete piers used in this study comprises 128 results obtained from the FE model. The results of the analysis indicate that the ANN model can predict early-age thermal parameters, and cracking risk in early-age concrete piers with good accuracy and help to the designer to choose the appropriate size in minimizing cracks on the pier concrete.

Tài liệu tham khảo

[1]. ACI, 116R-90, Cement and Concrete Terminology, Technical Documents, 2000.
[2]. ACI, 207.1 R-05 Guide to Mass Concrete, 2005.
[3]. T.A. Do et al., Effects of Thermal Conductivity of Soil on Temperature Development and Cracking in Mass Concrete Footings, Journal of Testing and Evaluation, 43 (2015) 20140026. https://doi.org/10.1520/JTE20140026
[4]. T.A. Do et al., Evaluation of heat of hydration, temperature evolution and thermal cracking risk in high-strength concrete at early ages, Case Studies in Thermal Engineering, 21 (2020) 100658. https://doi.org/10.1016/j.csite.2020.100658
[5]. T.A. Do, Influence of Footing Dimensions on Early-Age Temperature Development and Cracking in Concrete Footings, Journal of Bridge Engineering, 20 (2015) 06014007. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000690
[6]. Institute, J.C., Guidelines for control of cracking of mass concrete, 2016.
[7]. I. Maruyama, P. Lura, Properties of early-age concrete relevant to cracking in massive concrete, Cement and Concrete Research, 123 (2019) 105770. https://doi.org/10.1016/j.cemconres.2019.05.015
[8]. A. Smolana et al., Early age cracking risk in a massive concrete foundation slab: Comparison of analytical and numerical prediction models with on-site measurements, Construction and Building Materials, 301 (2021) 124135. https://doi.org/10.1016/j.conbuildmat.2021.124135
[9]. V.T. Luu et al, Research on thermal cracking control in mass concrete by using cooling pile system, Journal of Science and Technology in Civil Engineering, 13 (2019) 99-107. https://doi.org/10.31814/stce.nuce2019-13(3V)-11(in Vietnamese)
[10]. M. Rasul, A. Hosoda, Prediction of occurrence of thermal cracking of RC abutments using artificial neural networks., 65A (2019) 560-568. https://doi.org/10.11532/structcivil.65A.560
[11]. Y. Sargam et al., Predicting thermal performance of a mass concrete foundation – A field monitoring case study, Case Studies in Construction Materials, 11 (2019) e00289. https://doi.org/10.1016/j.cscm.2019.e00289
[12]. Y. Sargam et al, Machine learning based prediction model for thermal conductivity of concrete, Journal of Building Engineering, 34 (2021) 101956. https://doi.org/10.1016/j.jobe.2020.101956
[13]. S. Bhokha, S.O. Ogunlana, Application of artificial neural network to forecast construction duration of buildings at the predesign stage, Engineering, Construction and Architectural Management, 6 (1999) 133-144. https://doi.org/10.1108/eb021106
[14]. H. B. Ly et al., Compressive strength prediction of recycled aggregate concrete by artificial neural network, Transport and Communications Science Journal, 72 (2021) 369-383. https://doi.org/10.47869/tcsj.72.3.11(in Vietnamese)
[15]. T.A Do et al, Forecasting thermal cracking risk in early-age concrete bridge piers using artificial neural net-works, Journal of Science and Technology in Civil engineering, 16 (2022) 139-150. https://doi.org/10.31814/stce.huce(nuce)2022-16(5V)-12 (in Vietnamese)
[16]. A. Ghajar, Y. Cengel, Heat and Mass Transfer - Fundamentals and Applications, 6th Edition, McGraw-Hill Education, New York, NY, 2020, 2021.
[17]. Z. Zhao, et al., Creep and thermal cracking of ultra-high volume fly ash mass concrete at early age, Cement and Concrete Composites, 99 (2019) 191-202. https://doi.org/10.1016/j.cemconcomp.2019.02.018
[18]. MIDAS, Heat of Hydration – Analysis Manual. MIDAS Information Technology, 2011.
[19]. N.K. Ho, C.C. Vu, Analysis of temperature field and thermal stress in mass concrete using FEA method, Journal of Science and Technology in Civil engineering, 6 (2012) 17-27. (in Vietnamese)
[20]. ACI, 318-19, Building Code Requirements for Structural Concrete (ACI 318-14) and Commentary, 2014.
[21]. Mathworks, Global Optimization Toolbox: User's Guide (r2015b), 2015.

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