Implementation of Soft Computing Techniques in Forecasting Compressive Strength and Permeability of Pervious Concrete Blended with Ground Granulated Blast-furnace Slag

Document Type : Regular Article

Authors

1 Ph.D. Student, Department of Civil Engineering, National Institute of Technology, Warangal, Telangana, India

2 Assistant Professor, Department of Civil Engineering, GMR Institute of Technology, Rajam, Andhra Pradesh India

3 B. Tech, Department of Civil Engineering, National Institute of Technology, Tiruchirappalli, Tamil Nadu, India

Abstract

Urban expansion and infrastructure development have exacerbated environmental issues by creating impermeable layers on the earth's surface, resulting in flash floods and reduced groundwater levels. These problems can be alleviated by using pervious concrete to enhance pavement drainage capacities. However, pervious concrete has limited applications due to its lower strength properties, which are attributed to its mix proportions featuring minimal fine aggregate quantities and an open-graded mix. This study examines the impact of incorporating Ground Granulated Blast-furnace Slag (GGBS) as a supplementary cementitious material in pervious concrete on its strength, drainage capabilities, and water absorption. Further, Artificial Neural Networks (ANN) were used to predict the mechanical and permeability properties of pervious concrete mixes with varying GGBS proportions. The study's results indicate that using GGBS as a 35% partial cement replacement with 10 mm aggregates significantly increases compressive and flexural strength by 28% and 20%, respectively. While permeability values were slightly reduced, they remained within acceptable limits for drainage properties. The developed ANN models outperformed the traditional MLR model, serving as a viable substitute logical tool for forecasting strength as well as permeability. Ultimately, adding GGBS to pervious concrete not only enhances strength but also contributes to environmentally friendly construction practices.

Keywords

Main Subjects

References

[1]     Ahmad W, Ahmad A, Ostrowski KA, Aslam F, Joyklad P. A scientometric review of waste material utilization in concrete for sustainable construction. Case Stud Constr Mater 2021;15:e00683. https://doi.org/10.1016/j.cscm.2021.e00683.
[2]     Sarkar M, Adak D, Tamang A, Chattopadhyay B, Mandal S. Genetically-enriched microbe-facilitated self-healing concrete – a sustainable material for a new generation of construction technology. RSC Adv 2015;5:105363–71. https://doi.org/10.1039/C5RA20858K.
[3]     Zhong R, Leng Z, Poon C. Research and application of pervious concrete as a sustainable pavement material: A state-of-the-art and state-of-the-practice review. Constr Build Mater 2018;183:544–53. https://doi.org/10.1016/j.conbuildmat.2018.06.131.
[4]     Liew KM, Sojobi AO, Zhang LW. Green concrete: Prospects and challenges. Constr Build Mater 2017;156:1063–95. https://doi.org/10.1016/j.conbuildmat.2017.09.008.
[5]     Elango KS, Gopi R, Saravanakumar R, Rajeshkumar V, Vivek D, Raman SV. Properties of pervious concrete – A state of the art review. Mater Today Proc 2021;45:2422–5. https://doi.org/10.1016/j.matpr.2020.10.839.
[6]     Bonicelli A, Arguelles GM, Pumarejo LGF. Improving Pervious Concrete Pavements for Achieving More Sustainable Urban Roads. Procedia Eng 2016;161:1568–73. https://doi.org/10.1016/j.proeng.2016.08.628.
[7]     Lu J-X, Yan X, He P, Poon CS. Sustainable design of pervious concrete using waste glass and recycled concrete aggregate. J Clean Prod 2019;234:1102–12. https://doi.org/10.1016/j.jclepro.2019.06.260.
[8]     Alireza Joshaghani, Alireza Moazenian, Richard Abubakar Shuaibu. Experimental Study on the Use of Trass as a Supplementary Cementitious Material in Pervious Concrete. J Environ Sci Eng A 2017;6. https://doi.org/10.17265/2162-5298/2017.01.005.
[9]     Adamu M, Ayeni KO, Haruna SI, Ibrahim Mansour YE-H, Haruna S. Durability performance of pervious concrete containing rice husk ash and calcium carbide: A response surface methodology approach. Case Stud Constr Mater 2021;14:e00547. https://doi.org/10.1016/j.cscm.2021.e00547.
[10]   Ahmad J, Martínez-García R, Szelag M, De-Prado-Gil J, Marzouki R, Alqurashi M, et al. Effects of Steel Fibers (SF) and Ground Granulated Blast Furnace Slag (GGBS) on Recycled Aggregate Concrete. Materials (Basel) 2021;14:7497. https://doi.org/10.3390/ma14247497.
[11]   Sun Z, Lin X, Vollpracht A. Pervious concrete made of alkali activated slag and geopolymers. Constr Build Mater 2018;189:797–803. https://doi.org/10.1016/j.conbuildmat.2018.09.067.
[12]   An Q, Pan H, Zhao Q, Du S, Wang D. Strength development and microstructure of recycled gypsum-soda residue-GGBS based geopolymer. Constr Build Mater 2022;331:127312. https://doi.org/10.1016/j.conbuildmat.2022.127312.
[13]   El-Hassan H, Kianmehr P. Pervious concrete pavement incorporating GGBS to alleviate pavement runoff and improve urban sustainability. Road Mater Pavement Des 2018;19:167–81. https://doi.org/10.1080/14680629.2016.1251957.
[14]   Almasaeid HH, Salman DG. Application of Artificial Neural Network to Predict the Properties of Permeable Concrete. Civ Eng Archit 2022;10:2290–305. https://doi.org/10.13189/cea.2022.100605.
[15]   Adewumi AA, Owolabi TO, Alade IO, Olatunji SO. Estimation of physical, mechanical and hydrological properties of permeable concrete using computational intelligence approach. Appl Soft Comput 2016;42:342–50. https://doi.org/10.1016/j.asoc.2016.02.009.
[16]   Shanmuganathan S. Artificial Neural Network Modelling: An Introduction, 2016, p. 1–14. https://doi.org/10.1007/978-3-319-28495-8_1.
[17]   Suri RS, Jain AK, Kapoor NR, Kumar A, Arora HC, Kumar K, et al. Air Quality Prediction - A Study Using Neural Network Based Approach. J Soft Comput Civ Eng 2023;7:93–113. https://doi.org/10.22115/scce.2022.352017.1488.
[18]   Kapoor NR, Kumar A, Kumar A, Kumar A, Kumar K. Transmission Probability of SARS-CoV-2 in Office Environment Using Artificial Neural Network. IEEE Access 2022;10:121204–29. https://doi.org/10.1109/ACCESS.2022.3222795.
[19]   Kapoor NR, Kumar A, Kumar A, Kumar A, Mohammed MA, Kumar K, et al. Machine Learning-Based CO2 Prediction for Office Room: A Pilot Study. Wirel Commun Mob Comput 2022;2022:1–16. https://doi.org/10.1155/2022/9404807.
[20]   Wadhawan S, Bassi A, Singh R, Patel M. Prediction of Compressive Strength for Fly Ash-Based Concrete: Critical Comparison of Machine Learning Algorithms. J Soft Comput Civ Eng 2023;7:68–110. https://doi.org/10.22115/scce.2023.353183.1493.
[21]   Arora HC, Kumar S, Kontoni D-PN, Kumar A, Sharma M, Kapoor NR, et al. Axial Capacity of FRP-Reinforced Concrete Columns: Computational Intelligence-Based Prognosis for Sustainable Structures. Buildings 2022;12:2137. https://doi.org/10.3390/buildings12122137.
[22]   Kumar A, Arora HC, Kapoor NR, Kumar K, Hadzima-Nyarko M, Radu D. Machine learning intelligence to assess the shear capacity of corroded reinforced concrete beams. Sci Rep 2023;13:2857. https://doi.org/10.1038/s41598-023-30037-9.
[23]   al-Swaidani AM, Khwies WT. Applicability of Artificial Neural Networks to Predict Mechanical and Permeability Properties of Volcanic Scoria-Based Concrete. Adv Civ Eng 2018;2018:5207962. https://doi.org/10.1155/2018/5207962.
[24]   Baykasoglu A, Dereli T, Tanış S. Prediction of cement strength using soft computing techniques. Cem Concr Res 2004;34:2083–90. https://doi.org/10.1016/j.cemconres.2004.03.028.
[25]   Kumar BS, Srikanth K. A study on properties of pervious concrete with high-volume usage of supplementary cementitious materials as substitutes for cement. Asian J Civ Eng 2023. https://doi.org/10.1007/s42107-023-00619-z.
[26]   Golafshani EM, Behnood A. Predicting the mechanical properties of sustainable concrete containing waste foundry sand using multi-objective ANN approach. Constr Build Mater 2021;291:123314. https://doi.org/https://doi.org/10.1016/j.conbuildmat.2021.123314.
[27]   Behnood A, Golafshani EM. Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves. J Clean Prod 2018;202:54–64. https://doi.org/https://doi.org/10.1016/j.jclepro.2018.08.065.
[28]   Kumar BS, Chowdary V. Use of artificial neural networks to assess train horn noise at a railway level crossing in India. Environ Monit Assess 2023;195:426. https://doi.org/10.1007/s10661-023-11021-2.
[29]   Debnath M, Sarma AK, Mahanta C. Development of ANN Model for Simulation of the Runoff as Affected by Climatic Factors on the Jamuna River, Assam, India. Water Energy Manag. India, Cham: Springer International Publishing; 2021, p. 127–39. https://doi.org/10.1007/978-3-030-66683-5_6.
[30]   Bureau of Indian Standards (BIS). Method of physical tests for hydraulic cement: Determination of fineness by dry sieving. IS 4031 Part 1, New Delhi 1996:Reaffirmed in 2005.
[31]   IRC 44. Guidelines for Cement Concrete Mix Design for Pavements. Indian Roads Congr 2017:1–60.
[32]   IS 516. Method of Tests for Strength of Concrete. Bur Indian Stand 1959:1–30.
[33]   IS 5816-1999. Indian standard Splitting tensile strength of concrete- method of test. Bur Indian Stand 1999:1–14.
[34]   ACI Committee 522: 522R-10: Report on Pervious Concrete. 2010.
[35]   Siddique R, Aggarwal Y, Aggarwal P, Kadri E-H, Bennacer R. Strength, durability, and micro-structural properties of concrete made with used-foundry sand (UFS). Constr Build Mater 2011;25:1916–25. https://doi.org/10.1016/j.conbuildmat.2010.11.065.
[36]   Wild S, Khatib JM, Jones A. Relative strength, pozzolanic activity and cement hydration in superplasticised metakaolin concrete. Cem Concr Res 1996;26:1537–44. https://doi.org/10.1016/0008-8846(96)00148-2.
[37]   Zhong R, Wille K. Compression response of normal and high strength pervious concrete. Constr Build Mater 2016;109:177–87. https://doi.org/10.1016/j.conbuildmat.2016.01.051.
[38]   Chithra S, Kumar SRRS, Chinnaraju K, Alfin Ashmita F. A comparative study on the compressive strength prediction models for High Performance Concrete containing nano silica and copper slag using regression analysis and Artificial Neural Networks. Constr Build Mater 2016;114:528–35. https://doi.org/10.1016/j.conbuildmat.2016.03.214.
[39]   García Á, Anjos O, Iglesias C, Pereira H, Martínez J, Taboada J. Prediction of mechanical strength of cork under compression using machine learning techniques. Mater Des 2015;82:304–11. https://doi.org/10.1016/j.matdes.2015.03.038.
[40]   Dibakor Boruah, Pintu Kumar Thakur, Dipal Baruah. Artificial Neural Network based Modelling of Internal Combustion Engine Performance. Int J Eng Res 2016;V5. https://doi.org/10.17577/IJERTV5IS030924.

Files

Cited by

History

  • Receive Date: 27 August 2022
  • Revise Date: 09 June 2023
  • Accept Date: 10 June 2023

Share

How to cite

Statistics