Skip to main content
SpringerLink
Log in

Optimizing Sustainable Construction Materials with Machine Learning Algorithms: Predicting Compressive Strength of Concrete Composites

  • Conference paper
  • First Online:
Recent Advances in Civil Engineering for Sustainable Communities (IACESD 2023)

Part of the book series: Lecture Notes in Civil Engineering ((LNCE,volume 459))

  • 36 Accesses

Abstract

This research paper presents a study on predicting the compressive strength (CS) of Limestone and Calcined Clay-based concrete composites using machine learning algorithms. Limestone and Calcined Clay are promising materials to replace Ordinary Portland Cement, with the potential benefits of significantly reduced carbon dioxide emissions and lower production costs. In this study, three Ensemble Machine Learning (EML) models, Gradient Boosting, Random Forest, and AdaBoost, were employed to develop predictive models for the compressive strength of the concrete composite. The models were trained using 80% of the data and tested with the remaining data. The results showed that the developed models effectively predicted the compressive strength of concrete composite with high accuracy and consistency. The findings of this research can provide valuable insights into the development of sustainable construction materials and the use of machine learning techniques in predicting the strength of concrete composites. The assessment of model efficiency revealed that the Gradient Boosting model emerged as the optimal choice for achieving accurate CS predictions, demonstrating a superior Correlation Coefficient (R2) alongside diminished values of Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The Random Forest model was deemed inferior with lower R2 and higher RMSE and MAE values.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Zhang D, Jaworska B, Zhu H, Dahlquist K, Li VC (2020) Engineered cementitious composites (ECC) with limestone calcined clay cement (LC3). Cem Concr Compos 114:103766. https://doi.org/10.1016/j.cemconcomp.2020.103766

    Article  Google Scholar 

  2. Rashad AM (2013) Metakaolin as cementitious material: history, scours, production and composition—a comprehensive overview. Constr Build Mater 41:303–318. https://doi.org/10.1016/j.conbuildmat.2012.12.001

    Article  Google Scholar 

  3. Sanchez S, Cancio Y, Martirena F (2016) Expanding boundaries - low carbon cement: a sustainable way to meet growing demand in Cuba. In: Sanchez S, Cancio Y, Martirena F, Sanchez IR, Scrivener K, Habert G (eds) vdf Hochschulverlag AG an der ETH Zürich. https://doi.org/10.3218/3774-6_68

  4. Scrivener KL, John VM, Gartner EM (2018) Eco-efficient cements: potential economically viable solutions for a low-CO2 cement-based materials industry. Cem Concr Res 114:2–26. https://doi.org/10.1016/j.cemconres.2018.03.015

    Article  Google Scholar 

  5. Odhiambo VO, Scheinherrová L, Abuodha SO, Mwero JN, Marangu JM (2022) Effects of alternate wet and dry conditions on the mechanical and physical performance of limestone calcined clay cement mortars immersed in sodium sulfate media. Materials 15(24):8935. https://doi.org/10.3390/ma15248935

    Article  Google Scholar 

  6. Salimbahrami SR, Shakeri R (2021) Experimental investigation and comparative machine-learning prediction of compressive strength of recycled aggregate concrete. Soft Comput 25(2):919–932. https://doi.org/10.1007/s00500-021-05571-1

    Article  Google Scholar 

  7. Erdal HI (2013) Two-level and hybrid ensembles of decision trees for high performance concrete compressive strength prediction. Eng Appl Artif Intell 26(7):1689–1697. https://doi.org/10.1016/j.engappai.2013.03.014

    Article  Google Scholar 

  8. Shamim Ansari S, Muhammad Ibrahim S, Danish Hasan S (2023) Conventional and ensemble machine learning models to predict the compressive strength of fly ash based geopolymer concrete. Mater Today Proc: S2214785323022952. https://doi.org/10.1016/j.matpr.2023.04.393

  9. Mughees A, Sharma A, Ansari SS, Ibrahim SM (2023) Prediction of the compressive strength of nano-titanium based concrete composites using machine learning. Mater Today Proc: S2214785323016085. https://doi.org/10.1016/j.matpr.2023.03.540

  10. Kang M-C, Yoo D-Y, Gupta R (2021) Machine learning-based prediction for compressive and flexural strengths of steel fiber-reinforced concrete. Constr Build Mater 266:121117. https://doi.org/10.1016/j.conbuildmat.2020.121117

    Article  Google Scholar 

  11. Nguyen QD, Afroz S, Zhang Y, Kim T, Li W, Castel A (2022) Autogenous and total shrinkage of limestone calcined clay cement (LC3) concretes. Constr Build Mater 314:125720. https://doi.org/10.1016/j.conbuildmat.2021.125720

    Article  Google Scholar 

  12. Zolfagharnasab A, Ramezanianpour AA, Bahman-Zadeh F (2021) Investigating the potential of low-grade calcined clays to produce durable LC3 binders against chloride ions attack. Constr Build Mater 303:124541. https://doi.org/10.1016/j.conbuildmat.2021.124541

    Article  Google Scholar 

  13. Zhu H, Zhang D, Wang T, Wu H, Li VC (2020) Mechanical and self-healing behavior of low carbon engineered cementitious composites reinforced with PP-fibers. Constr Build Mater 259:119805. https://doi.org/10.1016/j.conbuildmat.2020.119805

    Article  Google Scholar 

  14. Wang X-Y (2021) Evaluation of the properties of cement–calcined Hwangtoh clay–limestone ternary blends using a kinetic hydration model. Constr Build Mater 303:124596. https://doi.org/10.1016/j.conbuildmat.2021.124596

    Article  Google Scholar 

  15. Liu J et al (2022) Effects of w/b ratio, fly ash, limestone calcined clay, seawater and sea-sand on workability, mechanical properties, drying shrinkage behavior and micro-structural characteristics of concrete. Constr Build Mater 321:126333. https://doi.org/10.1016/j.conbuildmat.2022.126333

    Article  Google Scholar 

  16. Du H, Pang SD (2020) High-performance concrete incorporating calcined kaolin clay and limestone as cement substitute. Constr Build Mater 264:120152. https://doi.org/10.1016/j.conbuildmat.2020.120152

    Article  Google Scholar 

  17. Chen Y, He S, Zhang Y, Wan Z, Çopuroğlu O, Schlangen E (2021) 3D printing of calcined clay-limestone-based cementitious materials. Cem Concr Res 149:106553. https://doi.org/10.1016/j.cemconres.2021.106553

    Article  Google Scholar 

  18. Ruan Y, Jamil T, Hu C, Gautam BP, Yu J (2022) Microstructure and mechanical properties of sustainable cementitious materials with ultra-high substitution level of calcined clay and limestone powder. Constr Build Mater 314:125416. https://doi.org/10.1016/j.conbuildmat.2021.125416

    Article  Google Scholar 

  19. Nguyen QD, Kim T, Castel A (2020) Mitigation of alkali-silica reaction by limestone calcined clay cement (LC3). Cem Concr Res 137:106176. https://doi.org/10.1016/j.cemconres.2020.106176

    Article  Google Scholar 

  20. Wang L et al (2021) On the use of limestone calcined clay cement (LC3) in high-strength strain-hardening cement-based composites (HS-SHCC). Cem Concr Res 144:106421. https://doi.org/10.1016/j.cemconres.2021.106421

    Article  Google Scholar 

  21. Frías M, Rodríguez O, Vegas I, Vigil R (2008) Properties of calcined clay waste and its influence on blended cement behavior. J Am Ceram Soc 91(4):1226–1230. https://doi.org/10.1111/j.1551-2916.2008.02289.x

    Article  Google Scholar 

  22. Zunino F, Scrivener K (2021) The reaction between metakaolin and limestone and its effect in porosity refinement and mechanical properties. Cem Concr Res 140:106307. https://doi.org/10.1016/j.cemconres.2020.106307

    Article  Google Scholar 

  23. Nguyen QD, Khan MSH, Castel A (2018) Engineering properties of limestone calcined clay concrete. J Adv Concr Technol 16(8):343–357. https://doi.org/10.3151/jact.16.343

    Article  Google Scholar 

  24. Shi Z et al (2019) Sulfate resistance of calcined clay—limestone—Portland cements. Cem Concr Res 116:238–251. https://doi.org/10.1016/j.cemconres.2018.11.003

    Article  Google Scholar 

  25. Nguyen QD, Afroz S, Castel A (2020) Influence of calcined clay reactivity on the mechanical properties and chloride diffusion resistance of limestone calcined clay cement (LC3) concrete. J Mar Sci Eng 8(5):301. https://doi.org/10.3390/jmse8050301

    Article  Google Scholar 

  26. Maraghechi H, Avet F, Wong H, Kamyab H, Scrivener K (2018) Performance of limestone calcined clay cement (LC3) with various kaolinite contents with respect to chloride transport. Mater Struct 51(5):125. https://doi.org/10.1617/s11527-018-1255-3

    Article  Google Scholar 

  27. Scrivener K, Martirena F, Bishnoi S, Maity S (2018) Calcined clay limestone cements (LC3). Cem Concr Res 114:49–56. https://doi.org/10.1016/j.cemconres.2017.08.017

    Article  Google Scholar 

  28. Hanein T et al (2022) Clay calcination technology: state-of-the-art review by the RILEM TC 282-CCL. Mater Struct 55(1):3. https://doi.org/10.1617/s11527-021-01807-6

    Article  Google Scholar 

  29. Zhang W, Wu C, Zhong H, Li Y, Wang L (2021) Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization. Geosci Front 12(1):469–477. https://doi.org/10.1016/j.gsf.2020.03.007

    Article  Google Scholar 

  30. Natekin A, Knoll A (2013) Gradient boosting machines, a tutorial. Front Neurorobot 7. https://doi.org/10.3389/fnbot.2013.00021

  31. Friedman JH (2002) Stochastic gradient boosting. Comput Stat Data Anal 38(4):367–378. https://doi.org/10.1016/S0167-9473(01)00065-2

    Article  MathSciNet  Google Scholar 

  32. Schapire RE (2013) Explaining AdaBoost. In: Schölkopf B, Luo Z, Vovk V (eds) Empirical inference. Springer, Berlin, Heidelberg, pp 37–52. https://doi.org/10.1007/978-3-642-41136-6_5

  33. Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning. In: Springer series in statistics. Springer, New York. https://doi.org/10.1007/978-0-387-84858-7

  34. Breiman L (2001) Random forests. Mach Learn 45(1):5–32. https://doi.org/10.1023/A:1010933404324

    Article  Google Scholar 

  35. Ziegler A, König IR (2014) Mining data with random forests: current options for real-world applications: mining data with random forests. Wiley Interdiscip Rev Data Min Knowl Discov 4(1):55–63. https://doi.org/10.1002/widm.1114

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Civil Engineering, ZHCET, Aligarh Muslim University, Aligarh, UP, India

    Toaha Mohammad, Saad Shamim Ansari, Syed Muhammad Ibrahim & Abdul Baqi

Authors
  1. Toaha Mohammad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Saad Shamim Ansari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Syed Muhammad Ibrahim
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Abdul Baqi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Toaha Mohammad .

Editor information

Editors and Affiliations

  1. Amrita Vishwa Vidyapeetham, Kollam, Kerala, India

    N. Vinod Chandra Menon

  2. Department of Civil Engineering, National Institute of Technology, Mangalore, Karnataka, India

    Sreevalsa Kolathayar

  3. Department of Civil Engineering, University of Aveiro, Aveiro, Portugal

    Hugo Rodrigues

  4. Department of Civil Engineering, Jyothy Institute of Technology, Bangalore, Karnataka, India

    K. S. Sreekeshava

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mohammad, T., Ansari, S.S., Ibrahim, S.M., Baqi, A. (2024). Optimizing Sustainable Construction Materials with Machine Learning Algorithms: Predicting Compressive Strength of Concrete Composites. In: Menon, N.V.C., Kolathayar, S., Rodrigues, H., Sreekeshava, K.S. (eds) Recent Advances in Civil Engineering for Sustainable Communities. IACESD 2023. Lecture Notes in Civil Engineering, vol 459. Springer, Singapore. https://doi.org/10.1007/978-981-97-0072-1_9

Download citation

  • DOI: https://doi.org/10.1007/978-981-97-0072-1_9

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-0071-4

  • Online ISBN: 978-981-97-0072-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation