Skip to main content
SpringerLink
Log in

Lightweight Bi-LSTM method for the prediction of mechanical properties of concrete

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Concrete is the predominant material in the construction industry, offering a wide range of mechanical attributes, including impressive compressive strength, excellent durability, robust plasticity, and a high elasticity modulus. Concrete properties prediction is crucial in designing components and also to improve their performance. High-complexity network-based baseline deep learning methods for predicting concrete may cause high computational time and high cost with the high-complexity network. To report this problem the proposed study aims to achieve better prediction of mechanical properties in the concrete using lightweight Bidirectional Long Short Term Memory (Bi-LSTM) which involves a weight pruning method and the parameters are tuned using Gridsearch Cross Validation (CV). The input variable is the grey image of the concrete microstructure, and the Representative Volume Element (RVE) provides the ground truth value to deep learning for the most efficient prediction of mechanical properties. Then the lightweight Bi-LSTM is trained, tested, and validated using 10-fold cross-validation after the parameter tuning. The Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2), which show the variability in the experimental score and the projected score, are used to quantify the mistakes of the suggested model’s prediction capability. According to experimental outcomes, the suggested lightweight Bi-LSTM approach outperforms the LSTM and Bi-LSTM approaches on the training and test datasets. Furthermore, compared to other models, the lightweight Bi-LSTM model runs much faster, making it more efficient for predictions.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data availability

Data will be shared on the reasonable request.

References

  1. Ziółkowski P, Niedostatkiewicz M (2019) Machine learning techniques in concrete mix design. Materials 12:1256. https://doi.org/10.3390/ma12081256

    Article  Google Scholar 

  2. Feng DC, Liu ZT, Wang XD, Chen Y, Chang JQ, Wei DF, Jiang ZM (2020) Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach. Constr Build Mater 10(230):117000

    Article  Google Scholar 

  3. Chopra P, Sharma RK, Kumar M (2015) Artificial neural networks for the prediction of compressive strength of concrete. Int J Appl Sci Eng 13(3):187–204

    Google Scholar 

  4. Chaabene WB, Flah M, Nehdi M (2020) Machine learning prediction of mechanical properties of concrete: Critical review. Constr Build Mater 260:1–18. https://doi.org/10.1016/j.conbuildmat.2020.119889

    Article  Google Scholar 

  5. Han Z, Zhao J, Leung H, Ma KF, Wang W (2019) A review of deep learning models for time series prediction. IEEE Sens J 21(6):7833–7848

    Article  Google Scholar 

  6. Basyigit C, Çomak B, Kılınçarslan Ş, Üncü I (2012) Assessment of concrete compressive strength by image processing technique. Constr Build Mater 37:526–532. https://doi.org/10.1016/j.conbuildmat.2012.07.055

    Article  Google Scholar 

  7. Shaswat K (2019) Hybrid-based deep belief network model for cement compressive strength prediction. Comput J 64(1):909–920. https://doi.org/10.1093/comjnl/bxaa197

    Article  Google Scholar 

  8. Gupta T, Sachdeva SN (2021) Recurrent neural network-based prediction of compressive and flexural strength of steel slag mixed concrete. Neural Comput Applic 33:6951–6963. https://doi.org/10.1007/s00521-020-05470-w

    Article  Google Scholar 

  9. Duan ZH, Kou SC, Poon CS (2013) Using artificial neural networks for predicting the elastic modulus of recycled aggregate concrete. Constr Build Mater 44:524–532, ISSN 0950–0618. https://doi.org/10.1016/j.conbuildmat.2013.02.064

  10. Singh P, Adebanjo A, Shafiq N et al (2023) Development of performance-based models for green concrete using multiple linear regression and artificial neural network. Int J Interact Des Manuf. https://doi.org/10.1007/s12008-023-01386-6

    Article  Google Scholar 

  11. Joshi DA et al (2023) Deep learning based concrete compressive Strength prediction model with hybrid meta-heuristic approach. Expert Syst Applic 233:120925. https://doi.org/10.1016/j.eswa.2023.120925

  12. Muliauwan H, Prayogo D, Harsono K (2020) Prediction of concrete compressive strength using artificial intelligence methods. J Phys: Conf Ser 1625:012018. https://doi.org/10.1088/1742-6596/1625/1/012018

    Article  Google Scholar 

  13. Gholami K, Ege F, Barzegar R (2023) Prediction of composite mechanical properties: integration of deep neural network methods and finite element analysis. J Compos Sci 7:54. https://doi.org/10.3390/jcs7020054

    Article  Google Scholar 

  14. BKA MAR, Ngamkhanong C, Wu Y, Kaewunruen S (2021) Recycled aggregates concrete compressive strength prediction using artificial neural networks (ANNs). Infrastructures 6(2):17

    Article  Google Scholar 

  15. Koya BP, Aneja S, Gupta R, Valeo C (2021) Comparative analysis of different machine learning algorithms to predict mechanical properties of concrete. Mech Adv Mater Struct. https://doi.org/10.1080/15376494.2021.1917021

  16. El Said B (2023) Predicting the non-linear response of composite materials using deep recurrent convolutional neural networks. Int J Solids Struct 276:112334

    Article  Google Scholar 

  17. Frankel AL, Jones RE, Alleman C, Templeton JA (2019) Predicting the mechanical response of oligocrystals with deep learning. Comput Mater Sci 169:109099

    Article  Google Scholar 

  18. Haghighi EM, Na S (2022) A single long short-term memory network for enhancing the prediction of path-dependent plasticity with material heterogeneity and anisotropy. arXiv preprint arXiv:2204.01466

  19. Chen H, Li X, Wu Y, Zuo L, Lu M, Zhou Y (2022) Compressive strength prediction of high-strength concrete using long short-term memory and machine learning algorithms. Buildings 12(3):302

    Article  Google Scholar 

  20. Li F, He A, Song Y et al (2023) Deep learning for predictive mechanical properties of hot-rolled strip in complex manufacturing systems. Int J Miner Metall Mater 30:1093–1103. https://doi.org/10.1007/s12613-022-2536-y

    Article  Google Scholar 

  21. Yan H, Ouyang H (2018) Financial time series prediction based on deep learning. Wireless Pers Commun 102:683–700

    Article  Google Scholar 

  22. Drugan WJ, Willis JR (1996) A micromechanics-based nonlocal constitutive equation and estimates of representative volume element size for elastic composites. J Mech Phys Solids 44(4):497–524

    Article  MathSciNet  Google Scholar 

  23. Ye S, Li B, Li Q, Zhao H-P, Feng X-Q (2019) Deep neural network method for predicting the mechanical properties of composites. Appl Phys Lett 115(16):161901. https://doi.org/10.1063/1.5124529

    Article  Google Scholar 

  24. Ye F, Wang H (2017) A simple Python code for computing effective properties of 2D and 3D representative volume element under periodic boundary conditions. arXiv preprint arXiv:1703.03930

  25. Yang L, Zhao Q (2020) A novel PPA method for fluid pipeline leak detection based on OPELM and bidirectional LSTM. IEEE Access 8:107185–107199

    Article  Google Scholar 

  26. Putri AR, Anyanwu GO, Maharani MP, Lee JM, Kim DS (2021) Compressed Neural Network for Thermal Array-Based Fall Detection System on Embedded AI. In 2021 International Conference on Information and Communication Technology Convergence (ICTC) (pp. 1754–1757). IEEE

  27. Amar M, Benzerzour M, Zentar R, Abriak NE (2022) Prediction of the compressive strength of waste-based concretes using artificial neural network. Materials 15(20):7045

    Article  Google Scholar 

  28. Keshtegar B, Bagheri M, Mundher Z (2019) Shear strength of steel fi ber-uncon fi ned reinforced concrete beam simulation: application of novel intelligent model. 212:230–242.https://doi.org/10.1016/j.compstruct.2019.01.004

  29. Sarveghadi M, Gandomi AH, Bolandi H, Alavi AH (2015) Development of prediction models for shear strength of SFRCB using a machine learning approach. Neural Comput Appl 31:2085–2094. https://doi.org/10.1007/s00521-015-1997-6

    Article  Google Scholar 

  30. Al-Musawi AA, Alwanas AAH, Salih SQ, Ali ZH, Tran MT, Yaseen ZM (2018) Shear strength of SFRCB without stirrups simulation: implementation of hybrid artificial intelligence model. Eng Comput. https://doi.org/10.1007/s00366-018-0681-8

    Article  Google Scholar 

  31. Vu D, Hoang N (2016) Punching shear capacity estimation of FRP-reinforced concrete slabs using a hybrid machine learning approach. Struct Infrastruct Eng 12:1153–1161. https://doi.org/10.1080/15732479.2015.1086386

    Article  Google Scholar 

  32. Xu J, Zhao X, Yu Y, Xie T, Yang G, Xue J (2019) Parametric sensitivity analysis and modelling of mechanical properties of normal- and high-strength recycled aggregate concrete using grey theory, multiple nonlinear regression and artificial neural networks. Constr Build Mater 211:479–491. https://doi.org/10.1016/j.conbuildmat.2019.03.234

    Article  Google Scholar 

  33. Abu Yaman M, Elaty MA, Taman M (2017) Predicting the ingredients of self compacting concrete using artificial neural network. Alex Eng J 56:523–532

    Article  Google Scholar 

Download references

Acknowledgements

I confirm that all authors listed on the title page have contributed significantly to the work, have read the manuscript, attest to the validity and legitimacy of the data and its interpretation, and agree to its submission.

Funding

Funding information is not applicable because no funding was received.

Author information

Authors and Affiliations

  1. Department of Civil Engineering, VSVN Polytechnic College, Virudhunagar, Tamil Nadu, 626001, India

    M. Prem Anand

  2. Department of Civil Engineering, Annamalai Polytechnic College, Chettinad, Tamil Nadu, 630102, India

    M. Anand

  3. Department of Civil Engineering, Middle East College, Muscat, 124, Sultanate of Oman

    M. Adams Joe

  4. Department of Civil Engineering, Rohini College of Engineering and Technology, Anjugramam, Tamil Nadu, 629401, India

    J. Sahaya Ruben

Authors
  1. M. Prem Anand
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Anand
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. Adams Joe
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. J. Sahaya Ruben
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. Prem Anand.

Ethics declarations

Competing interests

The authors declare that they have no conflict of interest. I confirm that I have read, understand and agreed to the submission guidelines, policies and submission declaration of the journal. I confirm that the paper now submitted is not copied or plagiarized version of some other published work. I understand that submission of false or incorrect information/undertaking would invite appropriate penal actions as per norms/rules of the journal.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix A

Appendix A

Figures 6, 7 and 8

Fig. 6
figure 6

RMSE and Loss curve for the proposed method

Full size image
Fig. 7
figure 7

MSE of the proposed model

Full size image
Fig. 8
figure 8

Regression analysis

Full size image

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Prem Anand, M., Anand, M., Adams Joe, M. et al. Lightweight Bi-LSTM method for the prediction of mechanical properties of concrete. Multimed Tools Appl 83, 54863–54884 (2024). https://doi.org/10.1007/s11042-023-17796-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-17796-3

Keywords

Search

Navigation