Skip to main content

Advertisement

SpringerLink
Log in

Prediction and modeling of mechanical properties of concrete modified with ceramic waste using artificial neural network and regression model

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Over two centuries, concrete has been crucial to building. Thus, eco-friendly concrete is being developed. Emulating these tangible traits has recently gained popularity. Ceramic waste concrete’s mechanical properties were modeled in this study. Ceramic waste percentages ranged from 5 to 20%. Compressive and tensile concrete strengths were modeled. To predict concrete hardness, regression modeling and artificial neural network (ANN) were used. Model performance was evaluated using prediction coefficients and root-mean-square error (RMSE). ANN models outperformed linear prediction with a coefficient for determination (R2) of 0.97. ANN models achieved root-mean-square errors (RMSEs) of 1.22 MPa, 1.21 MPa, and 1.022 MPa after 7, 14, and 28 days of retraining, respectively. Linear regression model showed RMSE values of 1.21, 1.32, and 1.27 MPa at 7, 14, and 28 days, respectively. In determining the compressive and tensile strength, the R2 was 0.70, meanwhile the ANN model achieved 0.87. Given its accuracy in predicting the strength qualities of ceramics cement and structural stiffness, the ANN model presents a promising tool for representing various types of concrete.

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
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Data availability

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

References

  1. Akojwar, S.G., Kshirsagar, P.: A novel probabilistic-PSO based learning algorithm for optimization of neural networks for benchmark problems. WSEAS trans. electron. 7, 79–84 (2016)

    Google Scholar 

  2. Kshirsagar P., Akojwar, S. Classification detection of neurological disorders using ICA AR as feature extractor. Int. J. Ser. Eng. Sci. IJSES, 1(1) 2015.

  3. Padmaja, M., Shitharth, S., Prasuna, K., et al.: Grow of artificial intelligence to challenge security in IoT application. Wirel. Perscommun. (2021). https://doi.org/10.1007/s11277-021-08725-4

    Article  Google Scholar 

  4. Kshirsagar, P., Akojwar, S., Bajaj, N.: A hybridised neural network and optimisation algorithms for prediction and classification of neurological disorders. Int. J. Biomed. Eng. Technol. 28(4), 307–321 (2020). https://doi.org/10.1504/IJBET.2018.095981

    Article  Google Scholar 

  5. Dilip, G., Guttula, S. Rajeyyagari, R. et al.: Artificial intelligence-based smart comrade robot for elders healthcare with strait rescue system. Journal of Healthcare Engineering, (2022)

  6. P. Kshirsagar, S. Akojwar, Novel approach for classification and prediction of non- linear chaotic databases, In: 2016 International conference on electrical, electronics, and optimization techniques (ICEEOT), pp. 514–518, doi: https://doi.org/10.1109/ICEEOT.2016.7755667. (2016)

  7. Kshirsagar, P.R., Manoharan, H., Al-Turjman, F., Kumar, K.: Design and testing of automatedsmoke monitoring sensors in vehicles”. IEEE Sens. J. 1, 1 (2020)

    Google Scholar 

  8. Manoharan, H., Teekaraman, Y., Kshirsagar, P.R., Sundaramurthy, S., Manoharan, A.: Examining the effect of aquaculture using sensor-based technology with machine learning algorithm. Aquac. Res. 51(11), 4748–4758 (2020)

    Article  Google Scholar 

  9. Jude, A.B., Singh, D., Islam, S., et al.: An artificial intelligence based predictive approach for smart waste management. Wireless PersCommun (2021). https://doi.org/10.1007/s11277-021-08803-7

    Article  Google Scholar 

  10. Dilip, G., Guttula, R., Rajeyyagari, S., Hemalatha, S., Pandey, R.R., Bora, A., Kshirsagar, P.R., Khanapurkar, M.M., Sundramurthy, V.P.: Artificial intelligence-based smart comrade robot for elders healthcare with strait rescue system. J. Healthc. Eng. (2022). https://doi.org/10.1155/2022/9904870

    Article  Google Scholar 

  11. Kshirsagar, P.R., Chippalkatti, P.P., Karve, S.M.: Performance optimization of neural network using GA incorporated PSO. J Adv Res Dyn Control Syst 10(4), 156–169 (2018)

    Google Scholar 

  12. Kshirsagar, P., Akojwar, S. Prediction of neurological disorders using optimized neural network. In: International conference on signal processing, communication, power and embedded system (SCOPES), (2016)

  13. Vijayakumar, P. et al.: Machine learning algorithm for improving the efficient of forgery detection. In: AIP Conference Proceedings, Vol 2393(1),https://doi.org/10.1063/5.0074086.

  14. Vijayakumar, P. et al: Network security using multi-layer neural network: AIP Conference Proceedings, Vol 2393(1), https://doi.org/10.1063/5.0074089.

  15. Kshirsagar, P., Akojwar, S.: Optimization of BPNN parameters using PSO for EEG signals. In Proceedings of the international conference on communication and signal processing. (ICCASP 2016). (2016)

  16. Kshirsagar, P., Akojwar, S.: Hybrid heuristic optimization for benchmark datasets. Int. J. Comput. Appl. 146(7), 11–16 (2016)

    Google Scholar 

  17. Vijayakumar, P.: Artificial intelligence based algorithm to support disable person, In: AIP Conference Proceedings. Vol 2393(1), https://doi.org/10.1063/5.0074090.

  18. Kshirsagar, P., Akojwar, S.: Classification and prediction of epilepsy using FFBPNN with PSO. In IEEE international conference on communication networks. (2015)

  19. Kshirsagar, P., Balakrishnan, N., Yadav, A.D.: Modelling of optimised neural network forclassification and prediction of benchmark datasets. Comput. Methods Biomech. Biomed. Eng.: Imaging V. 8(4), 426–435 (2020)

    Google Scholar 

  20. Akojwar, S., Kshirsagar, P., Pai, V.: Feature extraction of EEG signals using wavelet and principal component analysis. In: National conference on research trends in electronics, computer science information technology and doctoral research meet, Feb 21st & 22nd. (2014)

  21. Vani et al,: Supervise the data security and performance in cloud using artificial intelligence”, In: AIP Conference Proceeding, Vol: 2393,pp 020094 (2022), https://doi.org/10.1063/5.0074225.

  22. Mohd Naved et al.: Artificial intelligence based women security and safety measure system, In: AIP conference proceedings. Vol: 2393, pp 020072 (2022). https://doi.org/10.1063/5.0074211

  23. Pravin Kshirsagar et.al. : Brain tumor classification and detection using neural network, (2016) DOI: https://doi.org/10.13140/RG.2.2.26169.72805.

  24. Pravin Kshirsagar, Sudhir Akojwar (2017), Classification of ECG-signals using artificial neural networks”, Researchgate.net

  25. Kshirsagar, P., Akojwar, S.: “classification of human emotions using EEG signals.” Int. J. Comput. Appl. 975, 8887 (2016)

    Google Scholar 

  26. A. Narasima Venkatesh: An approach for smart city applications using artificial intelligence, In: AIP Conference Proceedings, Vol 2393, pp 020068 (2022), https://doi.org/10.1063/5.0074166

  27. Pravin Kshirsagar, Sudhir Akojwar, Classification and prediction of epilepsy usingFFBPNN with PSO, IN: IEEE international conference on communication networks, (2015)

  28. Alterazi, H.A., Kshirsagar, P.R., Manoharan, H., Selvarajan, S., Alhebaishi, N., Srivastava, G., Lin, J.C.-W.: Prevention of cyber security with the internet of things using particle swarm optimization. Sensors 22(16), 6117 (2022). https://doi.org/10.3390/s22166117

    Article  Google Scholar 

  29. Alqahtani, A.S., Kshirsagar, P.R., Manoharan, H., Balachandran, P.K., Yogesh, C.K., Selvarajan, S.: Prophetic energy assessment with smart implements in hydroelectricity entities using artificial intelligence algorithm. Int. Trans. Electr. Energy Syst. (2022). https://doi.org/10.1155/2022/2376353

    Article  Google Scholar 

  30. Shitharth, S., Prasad, K.M., Sangeetha, K., Kshirsagar, P.R., Babu, T.S., Alhelou, H.H.: An EnrichedRPCO-BCNN mechanisms for attack detection and classification in SCADA systems. IEEE Access 9, 156297–156312 (2021)

    Article  Google Scholar 

  31. Manoharan, H., Haleem, S.L.A., Shitharth, S., et al.: A machine learning algorithm forclassification of mental tasks. Comput. Electr. Eng. 99, 107785 (2022)

    Article  Google Scholar 

  32. Hariprasath Manaoharan et al.: Autonomous robotic technology and conveyance for supply chain management using 5G standards”, DOI: https://doi.org/10.4018/978-1-7998-9640-1.ch02, (2022)

  33. Abdul Haleem, S.: Wireless sensor data acquisition and control monitoring model for internet of things applications. Scientific Programming, 9, https://doi.org/10.1155/2022/9099163. (2022)

  34. Kshirsagar, P.R., Manoharan, H., Selvarajan, S., Althubiti, S.A., Alenezi, F., Srivastava, G., Lin, J.-W.: A radical safety measure for identifying environmental changes using machine learning algorithms. Electronics 11(13), 1950 (2022). https://doi.org/10.3390/electronics11131950

    Article  Google Scholar 

  35. Sundaramurthy, S., Saravanabhavan, C., Kshirsagar, P. : Prediction and classification of rheumatoid arthritis using ensemble machine learning approaches. In: Proceedings of the 2020 International conference on decision aid sciences and application (DASA), Sakheer, Bahrain, 8–9, pp. 17–21 Nov 2020

  36. Oza, S. : IoT: the future for quality of services,” In: Proceedings of the ICCCE 2019, A. Kumar, S. Mozar, Eds., vol. 570, Springer, Singapore, December 2019, Lecture notes in electrical engineering. (2019)

  37. Khan, A.I.: Computational approach for detection of diabetes from ocular scans. Comput. Intell. Neurosci. (2022). https://doi.org/10.1155/2022/5066147

    Article  Google Scholar 

  38. Kshirsgar, P., More, V., Hendre, V., Chippalkatti, P., Paliwal, K. : IOT based baby incubator for clinic,” In: Proceedings of the ICCCE 2019, Kumar A., Mozar S., Eds., vol. 570, Springer, Singapore, August 2020, Lecture Notes in Electrical Engineering. (2020)

  39. Kshirsagar, P.R., Manoharan, H., Kasim, S., Khan, AsifIrshad, Alam, MdMottahir, Abushark, Y.B., Abera, W.: Expedite quantification of landslides using wireless sensors and artificial intelligence for data controlling practices. Comput. Intell. Neurosci. (2022). https://doi.org/10.1155/2022/3211512

    Article  Google Scholar 

  40. Arpit, D. Yadav: Deep learning approach for identification of students emotion. Journal of Xi&#39, an University of Architecture & Technology, Volume XII, Issue V, (2020).

  41. Kshirsgar, P., More, V., Hendre, V., Chippalkatti, P., Paliwal, K.: IOT based baby incubator for clinic. In: Kumar, A., Mozar, S. (eds.) ICCCE 2019. Lecture notes in electrical engineering, vol. 570. Springer, Singapore (2020). https://doi.org/10.1007/978-981-13-8715-9_42

    Chapter  Google Scholar 

  42. Rajkumar, A.: Artificial intelligence approach for breast cancer classification using machine learning classifiers (2021).

  43. Shitharth, S., Meshram, P., Kshirsagar, P.R., Manoharan, H., Tirth, V., Sundramurthy, V.P.: Impact of big data analysis on nanosensors for applied sciences using neural networks. J. Nanomater. 2021, 4927607 (2021)

    Article  Google Scholar 

  44. Velvizhi, V., Billewar, S.R., Londhe, G., Kshirsagar, P., Kumar, N.: Big data for time series and trend analysis of poly waste management in India. Mater. Today. Proc. 37(2021), 2607–2611 (2021). https://doi.org/10.1016/j.matpr.2020.08.507

    Article  Google Scholar 

  45. Mohammad Naushad : An Overview to various image compression techniques, international journal of applied information systems (IJAIS)–ISSN : 2249–0868, foundation of computer science fcs, new york, usa.

  46. Manoharan, H., Rambola, R.K., Kshirsagar, P.R., Chakrabarti, P., Alqahtani, J., Naveed, Q.N., Islam, S., Mekuriyaw, W.D.: Aerial separation and receiver arrangements on identifying lung syndromes using the artificial neural network. Comput. Intell. Neurosci. (2022). https://doi.org/10.1155/2022/7298903

    Article  Google Scholar 

  47. Kshirsagar, P., Manoharan, H.A.: “An operational collection strategy for monitoring smart waste management system using shortest path algorithm. J. Environ. Prot. Ecol. 22, 566–577 (2021)

    Google Scholar 

  48. Anusha, Anamdas, Sahithi Desani, Banala Manasa, Dendi Sindhu, Swathi, B., Raveendranadh Singh, B.: Heart disease prediction using machine learning algorithm. Complexity International. 25(2) (2021).

  49. Kshirsagar, P.: brain tumor classification and detection using neural network,” In: Proceedings of the 2013 fourth international conference on computing, communications and networking technologies (ICCCNT), pp. 83–88, IEEE, Tiruchengode, India, January, (2020)

  50. Albishry, N., Ghamdi, R.A., Almalawi, A., Khan, A.I., Kshirsagar, P.R.: An attribute extraction for automated malware attack classification and detection using soft computing techniques. Comput. Intell. Neurosci. (2022). https://doi.org/10.1155/2022/5061059

    Article  Google Scholar 

  51. Prabhu Kavin, B., Sagar Karki, S., Hemalatha, D.S., Vijayalakshmi, R., Thangamani, M., Abdul, H.S., Jose, D., Tirth, V., Kshirsagar, P.R., Adigo, A.G.: Machine learning-based secure data acquisition for fake accounts detection in future mobile communication networks. Wirel. Commun. Mobile Comput. (2022). https://doi.org/10.1155/2022/6356152

    Article  Google Scholar 

  52. Algaifi, H.A., Alqarni, A.S., Alyousef, R., Bakar, S.A., Ibrahim, M.H.W., Shahidan, S., Ibrahim, M., Salami, B.A.: Mathematical prediction of the compressive strength of bacterial concrete using gene expression programming. Ain Shams Eng. J. 12(4), 3629–3639 (2021). https://doi.org/10.1016/j.asej.2021.04.008

    Article  Google Scholar 

  53. Kollu, P.K., Kumar, K., Kshirsagar, P.R., Islam, S., Naveed, Q.N., Hussain, M.R., Sundramurthy, V.P.: Development of advanced artificial intelligence and IoT automation in the crisis of COVID-19 detection. J. Healthc. Eng. (2022). https://doi.org/10.1155/2022/1987917

    Article  Google Scholar 

  54. Berlin, M.A., Upadhayaya, N., Alghatani, A., Tirth, V., Islam, S., Murali, K., Kshirsagar, P.R., Hung, B.T., Chakrabarti, P., Dadheech, P.: “Novel hybrid artificial intelligence based algorithm to determine the effects of air pollution on human electroencephalogram signals. J. Environ. Prot. Ecol. 22(5), 1825–1835 (2021)

    Google Scholar 

  55. Brekailo, F., Pereira, E., Pereira, E., Farias, M.M., Medeiros-Junior, R.A.: Red ceramic and concrete waste as replacement of Portland cement: Microstructure aspect of eco-mortar in external sulfate attack. Clean. Mater 3, 100034 (2022). https://doi.org/10.1016/j.clema.2021.100034

    Article  Google Scholar 

  56. Abul Hasan, M., Raghuveer, K., Pandey, P.S., Kumar, A., Bora, A., Deepa Jose, P.R., Kshirsagar, B.T., Hung, P.C., Khanapurkar, M.M.: Internet of things and its applications in Industry 4.0 for smart waste management. J. Environ. Prot. Ecol. 22(6), 2368–2378 (2021)

    Google Scholar 

  57. Hemalatha, S., Pravin, R. Kshirsagar, Hariprasath Manoharan, Vasantha Gowri, N., Vani, Sana Qaiyum A., Vijayakumar, Vineet Tirth, P., Sulaima Lebbe Abdul Haleem, Prasun Chakrabarti and Dawit Mamiru Teressa: Novel link establishment communication scheme against selfish attack using node reward with trust level evaluation algorithm in MANET. Wireless Communications and Mobile Computing, (2022)

  58. Cladera, A., Marí, A., Ribas, C.: Mechanical model for the shear strength prediction of corrosion-damagedreinforced concrete slender and non-slender beams. Eng. Struct. 247, 113163 (2021). https://doi.org/10.1016/j.engstruct.2021.113163

    Article  Google Scholar 

  59. Hemalatha, S., Pravin R. Kshirsagar, Hariprasath Manoharan, Vasantha Gowri, N., Vani, Sana Qaiyum, A., Vijayakumar, Vineet Tirth, P., Sulaima Lebbe Abdul Haleem, Prasun Chakrabarti and Dawit Mamiru Teressa: Novel link establishment communication scheme against selfish attack using node reward with trust level evaluation algorithm in MANET”, Wireless Communications and Mobile Computing, 2022

  60. Kshirsagar, P.R., Yadav, A.D., Joshi, K.A., Chippalkatti, P., Nerkar, R.Y.: Classification and detection of brain tumor by using GLCM Texture feature and ANFIS. J. Res. Image Signal Proc 5, 15–31 (2020)

    Google Scholar 

  61. Ekanayake, I.U., Meddage, D.P.P., Rathnayake, U.: A novel approach to explain the black-box nature of machine learning in compressive strength predictions of concrete using Shapley additive explanations (SHAP). Case Stud. Constr. Mater. 16(January), e01059 (2022). https://doi.org/10.1016/j.cscm.2022.e01059

    Article  Google Scholar 

  62. Pravin Kshirsagar, Sudhir Akojwar: Prediction of neurological disorders using optimized neural network, In: Proceeding of international conference on signal processing, communication, power and embedded system (2016)

  63. Ahmad, A., Ahmad, W., Aslam, F., Joyklad, P.: Compressive strength prediction of fly ash-based geopolymer concrete via advanced machine learning techniques. Case Stud. Constr. Mater 16, e00840 (2022). https://doi.org/10.1016/j.cscm.2021.e00840

    Article  Google Scholar 

  64. Ikumi, T., Galeote, E., Pujadas, P., de la Fuente, A., López-Carreño, R.D.: Neural network-aided prediction of post-cracking tensile strength of fibre-reinforced concrete. Comput. Struct. 256, 106640 (2021). https://doi.org/10.1016/j.compstruc.2021.106640

    Article  Google Scholar 

  65. Pravin, R. Kshirsagar et al : Machine learning algorithm for leaf disease detection, In: AIP Conference Proceedings, Vol 2393(1), https://doi.org/10.1063/5.0074122.

  66. Algarni, S., Tirth, V., Alqahtani, T., Kshirsagar, P.R., Debtera, B.: Scrutiny of solar water heating system employing supercritical fluid. Math. Probl. Eng. (2022). https://doi.org/10.1155/2022/6752289

    Article  Google Scholar 

  67. Iqbal, M., Elbaz, K., Zhang, D., Hu, L., Jalal, F.E.: Prediction of residual tensile strength of glass fiber reinforced polymer bars in harsh alkaline concrete environment using fuzzy metaheuristic models. J. Ocean Eng. Sci. (2022). https://doi.org/10.1016/j.joes.2022.03.011

    Article  Google Scholar 

  68. Kshirsagar, P.R., Jagannadham, D.B., Alqahtani, H., Naveed, Q.N., Islam, S., Thangamani, M., Dejene, M.: Human intelligence analysis through perception of AI in teaching and learning. Comput. Intell. Neurosci. (2022). https://doi.org/10.1155/2022/9160727

    Article  Google Scholar 

  69. Kshirsagar, P.R., Hariprasath Manoharan, V., Nagaraju, S., Alqahtani, H., Noorulhasan, Q., Saiful Islam, M., Thangamani, VarshaSahni, Gosu Adigo, A.: Accrual and dismemberment of brain tumours using fuzzy interface and grey textures for image disproportion. Comput. Intell. Neurosci. (2022). https://doi.org/10.1155/2022/2609387

    Article  Google Scholar 

  70. Meena, R.V., Jain, J.K., Chouhan, H.S., Beniwal, A.S.: Use of waste ceramics to produce sustainable concrete: a review. Clean. Mater. 4(January), 100085 (2022). https://doi.org/10.1016/j.clema.2022.100085

    Article  Google Scholar 

  71. Kshirsagar, P.R., Manoharan, H., Selvarajan, S., Alterazi, H.A., Singh, D., Lee, H.-N.: Perception exploration on robustness syndromes with pre-processing entities using machine learning algorithm. Front. Public Health 10, 893989 (2022). https://doi.org/10.3389/fpubh.2022.893989

    Article  Google Scholar 

  72. Kshirsagar, P.R., Manoharan, H., Shitharth, S., Alshareef, A.M., Singh, D., Lee, H.-N.: Probabilistic framework allocation on underwater vehicular systems using hydrophone sensor networks. Water 14, 1292 (2022). https://doi.org/10.3390/w14081292

    Article  Google Scholar 

  73. Narendar Singh, D., Murugamani, C., Kshirsagar, P.R., VineetTirth, S.I., Qaiyum, S., Suneela, B., Duhayyim, M.A., Waji, Y.A.: IOT based smart wastewater treatment model for industry 4.0 using artificial intelligence. Sci. Program. (2022). https://doi.org/10.1155/2022/5134013

    Article  Google Scholar 

  74. Murad, Y.Z., Hunifat, R., AL-Bodour, W.: Interior reinforced concrete beam-to-column joints subjected to cyclic loading: shear strength prediction using gene expression programming. Case. Stud. Constr. Mater. 13, e00432 (2020). https://doi.org/10.1016/j.cscm.2020.e00432

    Article  Google Scholar 

  75. Kshirsagar, P.R., Manoharan, H., Shitharth, S., Alshareef, A.M., Albishry, N., Balachandran, P.K.: Deep learning approaches for prognosis of automated skin disease. Life 12, 426 (2022). https://doi.org/10.3390/life12030426

    Article  Google Scholar 

  76. Naser, A.H., Badr, A.H., Henedy, S.N., Ostrowski, K.A., Imran, H.: Application of multivariate adaptive regression splines (MARS) approach in prediction of compressive strength of eco-friendly. Case Stud Constr Mater 17, e01262 (2022)

    Google Scholar 

  77. Chandan, R. R., Kshirsagar, P. R., Manoharan, H. et al., Substantial phase exploration for intuiting COVID using form expedient with variance sensor. International Journal of Computers Communications Control. 17(3), (2022)

  78. Murugamani, C., Shitharth, S., Hemalatha, S., Kshirsagar, P.R., Riyazuddin, K., Naveed, Q.N., Islam, S., Ali, S.P.M., Batu, A.: Machine learning technique for precision agriculture applications in 5G-based internet of things. Wirel Commun Mobile Comput (2022). https://doi.org/10.1155/2022/6534238

    Article  Google Scholar 

  79. Kshirsagar, P.R., Manoharan, H., Alterazi, H.A., Alhebaishi, N., Osama, B.J., Rabie, S.S.: Construal attacks on wireless data storage applications and unraveling using machine learning algorithm. J Sens (2022). https://doi.org/10.1155/2022/9386989

    Article  Google Scholar 

  80. Concrete. Case studies in construction materials, 17(March), e01262. https://doi.org/10.1016/j.cscm.2022.e01262

  81. Kshirsagar, P.R.: Covid heuristic analysis using machine learning. AIP Conf. Proc. 2393, 020077 (2022). https://doi.org/10.1063/5.0074120

    Article  Google Scholar 

  82. Sathawane, N. K., Kshirsagar, P.: Prediction and analysis of ecgsignal behavior using soft computing. International Journal of Research in Engineering & Technology

  83. Negm, A.A., El Nemr, A., Elgabbas, F., Khalaf, M.A.: High and normal strength concrete using grounded vitrified clay pipe (GVCP). Clean. Mater. 5(June), 100107 (2022). https://doi.org/10.1016/j.clema.2022.100107

    Article  Google Scholar 

  84. Kshirsagar, P.R., Kumar, N., Almulihi, A.H., Alassery, F., Khan, A.I., Islam, S., Rothe, J.P., Jagannadham, D.B., Dekeba, K.: Artificial intelligence-based robotic technique for reusable waste materials. Comput. Intell. Neurosci. 2(5), 9 (2022). https://doi.org/10.1155/2022/20734823

    Article  Google Scholar 

  85. Oyejobi, D.O., Jameel, M., Sulong, N.H.R., Raji, S.A., Ibrahim, H.A.: Prediction of optimumcompressive strength of light-weight concrete containing Nigerian palm kernel shells. J. K. Saud Univ.: Eng. Sci. 32(5), 303–309 (2020). https://doi.org/10.1016/j.jksues.2019.04.001

    Article  Google Scholar 

  86. Murugamani, C., Sahoo, S.K., Kshirsagar, P.R., Prathap, B.R., Islam, S., Noorulhasan Naveed, Q., Hussain, M.R., Hung, B.T., Teressa, D.M.: Wireless communication for robotic process automation using machine learning technique. Wirel .Commun. Mobile Comput. (2022). https://doi.org/10.1155/2022/4723138

    Article  Google Scholar 

  87. Kshirsagar, P.R., Manaoharan, H., Tirth, V., Islam, S., Srivastava, S., Sahni, V., Thangamani, M., Khanapurkar, M.M., Sundramurthy, V.P.: Implementation of whale optimization for budding healthiness of fishes with preprocessing approach. J. Healthc. Eng. (2022). https://doi.org/10.1155/2022/2345600

    Article  Google Scholar 

  88. Kshirsagar, P.R., Manoharan, H., Meshram, P., Alqahtani, J., Naveed, Q.N., Islam, S., Abebe, T.G.: Recognition of diabetic retinopathy with ground truth segmentation using fundus images and neural network algorithm. Comput. Intell. Neurosci. (2022). https://doi.org/10.1155/2022/8356081

    Article  Google Scholar 

  89. Shitharth, S., Kshirsagar, P.R., Balachandran, P.K., Alyoubi, K.H., Khadidos, A.O.: AnInnovative perceptual pigeon galvanized optimization (PPGO) based likelihood naïve Bayes (LNB)classification approach for network intrusion detection system. IEEE Access 10, 46424–46441 (2022). https://doi.org/10.1109/ACCESS.2022.3171660

    Article  Google Scholar 

  90. Poorarbabi, A., Ghasemi, M., AzhdaryMoghaddam, M.: Concrete compressive strength prediction usingnon-destructive tests through response surface methodology. Ain Shams Eng J 11(4), 939–949 (2020). https://doi.org/10.1016/j.asej.2020.02.009

    Article  Google Scholar 

  91. Kshirsagar, P. Akojwar, S.: Hybrid heuristic optimization for benchmark datasets. International Journal of Computer Application. 146(7) (2016)

  92. Kshirsagar, P., Akojwar, S.: Novel approach for classification and predict ion of non-linear chaotic databases, In: International conference on electrical, electronics, and optimization techniques, March (2016)

  93. Kshirsagar, P. Akojwar, S., Prediction of neurological disorders using optimized neural network, In the proceeding of international conference on signal processing, communication, power and embedded system, (2016).

  94. Akojwar, S., Kshirsagar, P.: A novel probabilistic-PSO based learning algorithm for optimization of neural networks for benchmark problems In: WSEAS International conference on Neural Network-2016, Rome, Italy.

  95. Ray, S., Haque, M., Rahman, M.M., Sakib, M.N., Al Rakib, K.: Experimental investigation and SVM-based prediction of compressive and splitting tensile strength of ceramic waste aggregate concrete. J. K. Saud Univ.: Eng. Sci. (2021). https://doi.org/10.1016/j.jksues.2021.08.010

    Article  Google Scholar 

  96. Akojwar, S., Kshirsagar, P.: Performance evolution of optimization techniques for M at hematical benchmark functions, WSEAS International conference on Neural Network-2016, Rome, Italy.

  97. Kshirsagar, P., Akojwar, S., Classification& detection of neurological disorders using ICA &. AR as feature extractor, International Journal Series in Engineering Science (IJSES), 1(1), 2015.

  98. Kshirsagar, P., Akojwar, S.: Classification and prediction of epilepsy using FFBPNN with PSO, In: IEEE international conference on communication networks, 2015

  99. Kshirsagar, P., Akojwar, S.: Chaotic time series prediction using correlation dimension and adaptive neuro-fuzzy inference system. International Journal of Engineering Research and General Science. 3(5), (2015)

  100. Zheng, Z., Tian, C., Wei, X., Zeng, C.: Numerical investigation and ANN-based prediction oncompressive strength and size effect using the concrete mesoscale concretization model. Case Stud. Constr. Mater. 16(March), e01056 (2022). https://doi.org/10.1016/j.cscm.2022.e01056

    Article  Google Scholar 

  101. Sudhir Akojwar, Pravin Kshirsagar, Vijetalaxmi Pai: Feature extraction of EEG signals using wavelet and principal component analysis, In: national conference on research trends in electronics, computer science & information technology and doctoral research meet, Feb 21st & 22nd, 2014.

  102. Ray, S., Rahman, M.M., Haque, M., Hasan, M.W., Alam, M.M.: Performance evaluation of SVM and GBM in predicting compressive and splitting tensile strength of concrete prepared with ceramic waste and nylon fiber. J. K. Saud Univ.: Eng Sci. (2021). https://doi.org/10.1016/j.jksues.2021.02.009

    Article  Google Scholar 

  103. Venkatesh, A. Narasima, Bhati, Parulkumari, Agarwal, Shradha, Maitri, Kshirsagar, Pravin, R.: Employee association, commitment and habituation in the time of COVID-19: Imputation for human resource management. Psychology and education 2021, Available at SSRN: https://ssrn.com/abstract=3886475. Accessed on 14 July 2021

  104. Deshmukh, V. Kshirsagar, P.: Intelligent vehicle navigation using Fuzzy Logic, National Conference on innovative paradigms in engineering & technology. In: Proceedings published by International Journal of Computer Applications® (IJCA), pp. 13–16, (2013).

  105. Tafhim, M. O., Kshirsagar, P. R.: A review on EMG Signal classification for neurological disorder using neural network: In International conference on Advances in Engineering & Technology. (ICAET-2014), pp. 21–23. 2014.

  106. Ridha, M.M.S., Sarsam, K.F., Al-Shaarbaf, I.A.S.: Experimental study and shear strength prediction for reactive powder concrete beams. Case Stud. Constr. Mater. 8(March), 434–446 (2018). https://doi.org/10.1016/j.cscm.2018.03.002

    Article  Google Scholar 

  107. Dubey, Ankur C., Kshirsagar P.: Feature extraction of EEG signals by auto- regression. International Journal on Recent and Innovation Trends in Computing and Communication 3(2): 090–092.

  108. Kshirsagar, P., Salodkar, A., Bhaiswar, R.: Generic approach in automation and sensors for enhanced efficiency. Int. J. Emerg. Technol. Adv. Eng. 2(3), 152–156 (2012)

    Google Scholar 

  109. Yasmin, M.: Compressive strength prediction for concrete modified with nanomaterials. Case Stud. Constr. Mater. 15(July), e00660 (2021). https://doi.org/10.1016/j.cscm.2021.e00660

    Article  Google Scholar 

  110. Koteswara Chari, K., Chinna Babu, M.: Classification of diabetes using random forest with feature selection algorithm. Int. J. Innov. Technol. Explor. Eng. (IJITEE) 9(1), 1295–1300 (2019)

    Article  Google Scholar 

  111. Pravin Kshirsagar, Chaotic time series prediction using correlation dimension and adaptive neuro-fuzzy inference system. International Journal of Engineering Research and General Science. 3(5), 2015

  112. Dravyakar, Saurabh, P., Pravin Kshirsagar. Hybrid approach for feature extraction and chaotic time series prediction using ANFIS Model (2015).

  113. Younis, M.O., Amin, M., Tahwia, A.M.: Durability and mechanical characteristics of sustainable self-curing concrete utilizing crushed ceramic and brick wastes. Case Stud. Constr. Mater. 17(June), e01251 (2022). https://doi.org/10.1016/j.cscm.2022.e01251

    Article  Google Scholar 

  114. Yogeswari, Y., Mounika, M., Dharani, M., Bhanu Prakash, C. H., Pravin R. Kshirsagar. A Case study on smart weather forecasting using machine learning. Complexity International 25(2) (2021).

  115. Indira, D.N., Ganiya, R.K., Ashok Babu, P., Xavier, A., Kavisankar, L., Hemalatha, S., Senthilkumar, V., Kavitha, T., Rajaram, A., Annam, K., Yeshitla, A.: Improved artificial neural network with state order dataset estimation for brain cancer cell diagnosis. BioMed. Res. Int. (2022). https://doi.org/10.1155/2022/7799812

    Article  Google Scholar 

  116. Kalaivani, K., Kshirsagarr, P. R., Sirisha Devi, J., Bandela, S. R., Colak, I., Nageswara Rao, J., Rajaram, A.: Prediction of biomedical signals using deep learning techniques. Journal of Intelligent & Fuzzy Systems, (Preprint), 1–14, (2023)

  117. Zegardło, B.: Heat-resistant concretes containing waste carbon fibers from the sailing industry and recycled ceramic aggregates. Case Stud. Constr. Mater. (2022). https://doi.org/10.1016/j.cscm.2022.e01084

    Article  Google Scholar 

  118. Iftikhar, B., Alih, S.C., Vafaei, M., Javed, M.F., Rehman, M.F., Abdullaev, S.S., Hassan, A.M.: Predicting compressive strength of eco-friendly plastic sand paver blocks using gene expression and artificial intelligence programming. Sci. Rep. 13(1), 12149 (2023). https://doi.org/10.1038/s41598-023-39349-2

    Article  Google Scholar 

  119. Chen, Z., Amin, M.N., Iftikhar, B., Ahmad, W., Althoey, F., Alsharari, F.: Predictive modelling for the acid resistance of cement-based composites modified with eggshell and glass waste for sustainable and resilient building materials. J. Build. Eng. 76, 107325 (2023). https://doi.org/10.1016/j.jobe.2023.107325

    Article  Google Scholar 

  120. Iftikhar, B., Alih, S.C., Vafaei, M., Javed, M.F., Ali, M., Gamil, Y., Rehman, M.F.: A machine learning-based genetic programming approach for the sustainable production of plastic sand paver blocks. J. Mater. Res. Technol. 25, 5705–5719 (2023). https://doi.org/10.1016/j.jmrt.2023.07.034

    Article  Google Scholar 

  121. Zou, B., Wang, Y., Amin, M.N., Iftikhar, B., Khan, K., Ali, M., Althoey, F.: Artificial intelligence-based optimized models for predicting the slump and compressive strength of sustainable alkali-derived concrete. Constr. Build. Mater. 409, 134092 (2023). https://doi.org/10.1016/j.conbuildmat.2023.134092

    Article  Google Scholar 

  122. Chen, Z., Iftikhar, B., Ahmad, A., Dodo, Y., Abuhussain, M.A., Althoey, F., Sufian, M.: Strength evaluation of eco-friendly waste-derived self-compacting concrete via interpretable genetic-based machine learning models. Mater. Today Commun. 37, 107356 (2023). https://doi.org/10.1016/j.mtcomm.2023.107356

    Article  Google Scholar 

  123. Qureshi, H.J., Alyami, M., Nawaz, R., Hakeem, I.Y., Aslam, F., Iftikhar, B., Gamil, Y.: Prediction of compressive strength of two-stage (preplaced aggregate) concrete using gene expression programming and random forest. Case Stud. Constr. Mater. 19, e02581 (2023). https://doi.org/10.1016/j.cscm.2023.e02581

    Article  Google Scholar 

Download references

Acknowledgements

There is no acknowledgement involved in this work.

Funding

No funding is involved in this work.

Author information

Authors and Affiliations

  1. Department of Electronics & Telecommunication Engineering, J D College of Engineering & Management, Nagpur, India

    Pravin R. Kshirsagar

  2. Department of Computer Science, CHRIST (Deemed to Be University), Delhi-NCR, Ghaziabad, Uttar Pradesh, India

    Kamal Upreti

  3. School of Computing Science & Engineering, VIT Bhopal University, Bhopal-Indore Highway, Bhopal, Madhya Pradesh, India

    Virendra Singh Kushwah

  4. MIT College of Management, MIT ADT University, Loni Kalbhor, Pune, India

    Sheela Hundekari

  5. Department of Computer Science and Engineering, ABES Engineering College, Ghaziabad, India

    Dhyanendra Jain & Amit Kumar Pandey

  6. Department of Computer Science and Engineering, Dr. Akhilesh Das Gupta Institute of Engineering and Technology, New Delhi, India

    Jyoti Parashar

Authors
  1. Pravin R. Kshirsagar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kamal Upreti
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Virendra Singh Kushwah
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sheela Hundekari
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dhyanendra Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Amit Kumar Pandey
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jyoti Parashar
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors are contributed equally to this work.

Corresponding author

Correspondence to Kamal Upreti.

Ethics declarations

Conflict of interest

Conflict of interest is not applicable in this work.

Ethics approval and consent to participate

No participation of humans takes place in this implementation process.

Human and animal rights

No violation of human and animal rights is involved.

Additional information

Publisher's Note

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

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

Kshirsagar, P.R., Upreti, K., Kushwah, V.S. et al. Prediction and modeling of mechanical properties of concrete modified with ceramic waste using artificial neural network and regression model. SIViP (2024). https://doi.org/10.1007/s11760-024-03142-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11760-024-03142-z

Keywords

Search

Navigation