Impacts of climate change on optimal mixture design of blended concrete considering carbonation and chloride ingress

Xiao-Yong WANG

Front. Struct. Civ. Eng. ›› 2020, Vol. 14 ›› Issue (2) : 473-486.

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PDF(1254 KB)
Front. Struct. Civ. Eng. ›› 2020, Vol. 14 ›› Issue (2) : 473-486. DOI: 10.1007/s11709-020-0608-5
RESEARCH ARTICLE
RESEARCH ARTICLE

Impacts of climate change on optimal mixture design of blended concrete considering carbonation and chloride ingress

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Abstract

Many studies on the mixture design of fly ash and slag ternary blended concrete have been conducted. However, these previous studies did not consider the effects of climate change, such as acceleration in the deterioration of durability, on mixture design. This study presents a procedure for the optimal mixture design of ternary blended concrete considering climate change and durability. First, the costs of CO2 emissions and material are calculated based on the concrete mixture and unit prices. Total cost is equal to the sum of material cost and CO2 emissions cost, and is set as the objective function of the optimization. Second, strength, slump, carbonation, and chloride ingress models are used to evaluate concrete properties. The effect of different climate change scenarios on carbonation and chloride ingress is considered. A genetic algorithm is used to find the optimal mixture considering various constraints. Third, illustrative examples are shown for mixture design of ternary blended concrete. The analysis results show that for ternary blended concrete exposed to an atmospheric environment, a rich mix is necessary to meet the challenge of climate change, and for ternary blended concrete exposed to a marine environment, the impact of climate change on mixture design is marginal.

Keywords

ternary blended concrete / climate change / optimal mixture design / carbonation / chloride ingress

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Xiao-Yong WANG. Impacts of climate change on optimal mixture design of blended concrete considering carbonation and chloride ingress. Front. Struct. Civ. Eng., 2020, 14(2): 473‒486 https://doi.org/10.1007/s11709-020-0608-5

References

[1]
Yoon I S, Çopuroğlu O, Park K B. Effect of global climatic change on carbonation progress of concrete. Atmospheric Environment, 2007, 41(34): 7274–7285
CrossRef Google scholar
[2]
Oh B H, Jang S Y. Prediction of diffusivity of concrete based on simple analytic equations. Cement and Concrete Research, 2004, 34(3): 463–480
CrossRef Google scholar
[3]
Celik K, Meral C, Petek Gursel A, Mehta P K, Horvath A, Monteiro P J M. Mechanical properties, durability, and life-cycle assessment of self-consolidating concrete mixtures made with blended Portland cements containing fly ash and limestone powder. Cement and Concrete Composites, 2015, 56: 59–72
CrossRef Google scholar
[4]
Tae S H, Baek C H, Shin S W. Life cycle CO2 evaluation on reinforced concrete structures with high-strength concrete. Environmental Impact Assessment Review, 2011, 31(3): 253–260
CrossRef Google scholar
[5]
Rivera F, Martínez P, Castro J, Lopez M. Massive volume fly-ash concrete: A more sustainable material with fly ash replacing cement and aggregates. Cement and Concrete Composites, 2015, 63: 104–112
CrossRef Google scholar
[6]
Tait M W, Cheung W M. A comparative cradle-to-gate life cycle assessment of three concrete mix designs. International Journal of Life Cycle Assessment, 2016, 21(6): 847–860
CrossRef Google scholar
[7]
Yang K H, Lee K H, Song J K, Gong M H. Properties and sustainability of alkali-activated slag foamed concrete. Journal of Cleaner Production, 2014, 68: 226–233
CrossRef Google scholar
[8]
Wang J J, Wang Y F, Sun Y W, Tingley D D, Zhang Y R. Life cycle sustainability assessment of fly ash concrete structures. Renewable & Sustainable Energy Reviews, 2017, 80: 1162–1174
CrossRef Google scholar
[9]
Lee B Y, Kim J H, Kim J K. Optimum concrete mixture proportion based on a database considering regional characteristics. Journal of Computing in Civil Engineering, 2009, 23(5): 258–265
CrossRef Google scholar
[10]
Lee J H, Yoon Y S. Modified harmony search algorithm and neural networks for concrete mix proportion design. Journal of Computing in Civil Engineering, 2009, 23(1): 57–61
CrossRef Google scholar
[11]
Kim T H, Tae S H, Suk S J, Ford G, Yang K H. An optimization system for concrete life cycle cost and related CO2 emissions. Sustainability, 2016, 8(4): 361–379
CrossRef Google scholar
[12]
Sebaaly H, Varma S, Maina J W. Optimizing asphalt mix design process using artificial neural network and genetic algorithm. Construction & Building Materials, 2018, 168: 660–670
CrossRef Google scholar
[13]
Tapali J D, Demis S, Papadakis V G. Sustainable concrete mix design for a target strength and service life. Computers and Concrete, 2013, 12(6): 755–774
CrossRef Google scholar
[14]
Yeh I C. Computer-aided design for optimum concrete mixtures. Cement and Concrete Composites, 2007, 29(3): 193–202
CrossRef Google scholar
[15]
Park H S, Kwon B K, Shin Y A, Kim Y S, Hong T H, Choi S W. Cost and CO2 emission optimization of steel reinforced concrete columns in high-rise buildings. Energies, 2013, 6(11): 5609–5624
CrossRef Google scholar
[16]
Papadakis V G. Effect of supplementary cementing materials on concrete resistance against carbonation and chloride ingress. Cement and Concrete Research, 2000, 30(2): 291–299
CrossRef Google scholar
[17]
Papadakis V G, Tsimas S. Supplementary cementing materials in concrete Part I: Efficiency and design. Cement and Concrete Research, 2002, 32(10): 1525–1532
CrossRef Google scholar
[18]
Thomas M D A, Bentz E C. Life-365TM Service Life Prediction Model. Version 2.2.1. Toronto: Life-365 Consortium, 2014
[19]
Kwon S J, Na U J, Park S S, Jung S H. Service life prediction of concrete wharves with early-aged crack: Probabilistic approach for chloride diffusion. Structural Safety, 2009, 31(1): 75–83
CrossRef Google scholar
[20]
Kim P. MATLAB Deep Learning: With Machine Learning, Neural Networks and Artificial Intelligence. Berkeley: Apress, 2017
[21]
Gheibi M, Karrabi M, Shakerian M, Mirahmadi M. Life cycle assessment of concrete production with a focus on air pollutants and the desired risk parameters using genetic algorithm. Journal of Environmental Health Science & Engineering, 2018, 16(1): 89–98
CrossRef Google scholar
[22]
European Committee for Standardization. Eurocode 2: Design of Concrete Structures. London: British Standards Institution, 2006
[23]
Intergovernmental Panel on Climate Change (IPCC). Climate Change 2014 Synthesis Report. IPCC: Geneva, 2014
[24]
Abdelkader B, El-Hadj K, Karim E. Efficiency of granulated blast furnace slag replacement of cement according to the equivalent binder concept. Cement and Concrete Composites, 2010, 32(3): 226–231
CrossRef Google scholar
[25]
Chandwani V, Agrawal V, Nagar R. Modeling slump of ready mix concrete using genetic algorithms assisted training of Artificial Neural Networks. Expert Systems with Applications, 2015, 42(2): 885–893
CrossRef Google scholar
[26]
Belalia Douma O, Boukhatem B, Ghrici M, Tagnit-Hamou A. Prediction of properties of self-compacting concrete containing fly ash using artificial neural network. Neural Computing & Applications, 2017, 28(S1): 707–718
CrossRef Google scholar
[27]
Demis S, Efstathiou M P, Papadakis V G. Computer-aided modeling of concrete service life. Cement and Concrete Composites, 2014, 47: 9–18
CrossRef Google scholar
[28]
Bucher R, Diederich P, Escadeillas G, Cyr M. Service life of metakaolin-based concrete exposed to carbonation: Comparison with blended cement containing fly ash, blast furnace slag and limestone filler. Cement and Concrete Research, 2017, 99: 18–29
CrossRef Google scholar
[29]
Stewart M G, Wang X M, Nguyen M N. Climate change impact and risks of concrete infrastructure deterioration. Engineering Structures, 2011, 33(4): 1326–1337
CrossRef Google scholar
[30]
Wang Y Z, Wu L J, Wang Y C, Li Q M, Xiao Z. Prediction model of long-term chloride diffusion into plain concrete considering the effect of the heterogeneity of materials exposed to marine tidal zone. Construction & Building Materials, 2018, 159: 297–315
CrossRef Google scholar

Acknowledgments

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (No. 2015R1A5A1037548) and an NRF Grant (NRF-2020R1A2C4002093). This study was supported by a 2018 Research grant (POINT) from Kangwon National University.

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2020 Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature
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