Abstract
In the field of construction, the workability of concrete, specifically its ability to flow, is one of the most concerned parameters. In recent times, the integration of artificial intelligence (AI) has brought about a significant transformation in the construction industry, resulting in enhanced efficiency, precision, and innovation. Considering these aspects, the present study has been carried out on a large dataset comprising 1103 data points while taking the ten input parameters into account to predict the flow of concrete. In this regard, six distinct models such as multilayer perceptron (MLP), K-nearest neighbors (KNN), gradient boosting (GB), M5P regression, backpropagation neural networks (BPNN), and lasso regressor have been used to forecast the flow. Along with that, various visualization and evaluation techniques, including scatter plots, histograms, heatmap, coefficient of correlation, errors, SHAP, Taylor’s diagram, have been utilized to illustrate the data availability and performance of models. Based on the output of the study, it has been noticed that the KNN, M5P, and GB models demonstrated exceptional accuracy with negligible errors and high R-squared values (R2 ≤ 0.98), whereas other models encountered difficulties in achieving satisfactory performance. This study highlights the significance of water content, coarse aggregates, and fine aggregates as crucial factors that directly affect the flow characteristics of concrete.
This is a preview of subscription content, log in via an institution to check access.
We’re sorry, something doesn't seem to be working properly.
Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.
Data availability
The data used in the study are available to the corresponding author upon request.
References
Ahmad, M., Hu, J.-L., Ahmad, F., et al. (2021). Supervised learning methods for modeling concrete compressive strength prediction at high temperature. Materials, 14, 1983. https://doi.org/10.3390/ma14081983
Alhamzawi, R., & Ali, H. T. M. (2018). The Bayesian adaptive lasso regression. Mathematical Biosciences, 303, 75–82. https://doi.org/10.1016/J.MBS.2018.06.004
Azamathulla, G. A. A., Zakaria, N. A., & Guven, A. (2010). Genetic programming to predict bridge pier scour. Journal of Hydraulic Engineering, 136, 165–169. https://doi.org/10.1061/(ASCE)HY.1943-7900.0000133
Azamathulla, H., Haghiabi, A., & Parsaie, A. (2016). Prediction of side weir discharge coefficient by support vector machine technique. Water Science and Technology Water Supply, 16, 1002–1016. https://doi.org/10.2166/ws.2016.014
Azamathulla, H. M., & Jarrett, R. D. (2013). Use of gene-expression programming to estimate Manning’s roughness coefficient for high gradient streams. Water Resources Management, 27, 715–729. https://doi.org/10.1007/s11269-012-0211-1
Bassi, A., Manchanda, A., Singh, R., & Patel, M. (2023a). A comparative study of machine learning algorithms for the prediction of compressive strength of rice husk ash-based concrete. Natural Hazards, 118, 209–238. https://doi.org/10.1007/S11069-023-05998-9/METRICS
Bassi, A., Mir, A. A., Kumar, B., & Patel, M. (2023b). A comprehensive study of various regressions and deep learning approaches for the prediction of friction factor in mobile bed channels. Journal of Hydroinformatics. https://doi.org/10.2166/HYDRO.2023.246
Behnood, A., Behnood, V., & Materials, M. G. (2017). Prediction of the compressive strength of normal and high-performance concretes using M5P model tree algorithm. Construction and Building Materials, 142, 199.
Behnood, A., & Golafshani, E. M. (2022). Artificial intelligence to model the performance of concrete mixtures and elements: A review. Archives of Computational Methods in Engineering, 29, 1941–1964. https://doi.org/10.1007/S11831-021-09644-0/FIGURES/14
Bhaskar, S., Anwar Hossain, K. M., Lachemi, M., et al. (2017). Effect of self-healing on strength and durability of zeolite-immobilized bacterial cementitious mortar composites. Cement and Concrete Composites, 11, 151–162. https://doi.org/10.1016/j.cemconcomp.2017.05.013
Farooq, F., Amin, M. N., Khan, K., et al. (2020). A comparative study of random forest and genetic engineering programming for the prediction of compressive strength of high strength concrete (HSC). Applied Sciences, 10, 7330.
Gupta, H., Narain, H., Rastogi, B. K., & Mohan, I. (1969). A study of the Koyna earthquake of December 10, 1967. Bulletin of the Seismological Society of America, 59, 1149–1162. https://doi.org/10.1785/BSSA0590031149
Haque, M. A., Chen, B., Kashem, A., et al. (2023). Hybrid intelligence models for compressive strength prediction of MPC composites and parametric analysis with SHAP algorithm. Materials Today Communications, 35, 105547. https://doi.org/10.1016/j.mtcomm.2023.105547
Jiang, Y., Li, H., & Zhou, Y. (2022). Compressive strength prediction of fly ash concrete using machine learning techniques. Buildings. https://doi.org/10.3390/buildings12050690
Kaveh, A. (2016). Advances in metaheuristic algorithms for optimal design of structures, second edition. Advances in Metaheuristic Algorithms for Optimal Design of Structures. https://doi.org/10.1007/978-3-319-46173-1/COVER
Kaveh, A., Eskandari, A., & Movasat, M. (2023). Buckling resistance prediction of high-strength steel columns using metaheuristic-trained artificial neural networks. Structures, 56, 104853. https://doi.org/10.1016/J.ISTRUC.2023.07.043
Kaveh, A., & Khavaninzadeh, N. (2023). Efficient training of two ANNs using four meta-heuristic algorithms for predicting the FRP strength. Structures, 52, 256–272. https://doi.org/10.1016/J.ISTRUC.2023.03.178
Kumar, P. M., Lokesh, S., Varatharajan, R., et al. (2018). Cloud and IoT based disease prediction and diagnosis system for healthcare using fuzzy neural classifier. Future Generation Computer Systems, 86, 527–534. https://doi.org/10.1016/J.FUTURE.2018.04.036
Kumar, R., & Panchal, T. V. R. (2023). Enhancing chloride concentration prediction in marine concrete using conjugate gradient optimized backpropagation neural network. Asian Journal of Civil Engineering. https://doi.org/10.1007/s42107-023-00801-3
Kumar, R., Singh, R., & Patel, M. (2023). Effect of metakaolin on mechanical characteristics of the mortar and concrete: A critical review. Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2023.07.262
Malami, S. I., Musa, A. A., Haruna, S. I., et al. (2021). Implementation of soft-computing models for prediction of flexural strength of pervious concrete hybridized with rice husk ash and calcium carbide waste. Model Earth System and Environment. https://doi.org/10.1007/s40808-021-01195-4
Onyari, E., & Ilunga, F. (2013). Application of MLP neural network and M5P model tree in predicting streamflow: A case study of Luvuvhu catchment, South Africa. International Journal of Innovation, Management and Technology. https://doi.org/10.7763/IJIMT.2013.V4.347
Parathi, S., Nagarajan, P., & Pallikkara, S. A. (2021). Ecofriendly geopolymer concrete: A comprehensive review. Clean Technologies and Environmental Policy, 23, 1701–1713. https://doi.org/10.1007/s10098-021-02085-0
Patel, M., Deshpande, V., & Kumar, B. (2015). Geomorphology Turbulent characteristics and evolution of sheet fl ow in an alluvial channel with downward seepage. Geomorphology, 248, 161–171. https://doi.org/10.1016/j.geomorph.2015.07.042
Patel, M., & Kumar, B. (2017). Catena flow and bedform dynamics in an alluvial channel with downward seepage. CATENA, 158, 219–234. https://doi.org/10.1016/j.catena.2017.07.009
Phung, R. (2019). A high-accuracy model average ensemble of convolutional neural networks for classification of cloud image patches on small datasets. Applied Sciences, 9, 4500. https://doi.org/10.3390/app9214500
Shahani, N. M., Kamran, M., Zheng, X., et al. (2021). Application of gradient boosting machine learning algorithms to predict uniaxial compressive strength of soft sedimentary rocks at Thar coalfield. Advances in Civil Engineering. https://doi.org/10.1155/2021/2565488
Shang, M., Li, H., Ahmad, A., et al. (2022). Predicting the mechanical properties of RCA-based concrete using supervised machine learning algorithms. Materials, 15, 647. https://doi.org/10.3390/ma15020647
Shen, Z., Deifalla, A. F., Kamiński, P., & Dyczko, A. (2022). Compressive strength evaluation of ultra-high-strength concrete by machine learning. Materials, 15, 3523. https://doi.org/10.3390/ma15103523
Singh, B., Ishwarya, G., Gupta, M., & Bhattacharyya, S. K. (2015). Geopolymer concrete: A review of some recent developments. Construction and Building Materials, 85, 78–90. https://doi.org/10.1016/J.CONBUILDMAT.2015.03.036
Singh, R., Chaturvedi, V., Chaurasiya, A. K., & Patel, M. (2021). Utilization of industrial waste in concrete mixes—A review. RILEM Bookseries, 29, 77–97. https://doi.org/10.1007/978-3-030-51485-3_7/COVER
Singh, R., & Patel, M. (2022a). Effective utilization of rice straw in value-added by-products: A systematic review of state of art and future perspectives. Biomass and Bioenergy, 159, 106411. https://doi.org/10.1016/J.BIOMBIOE.2022.106411
Singh, R., & Patel, M. (2022b). Strength and durability performance of rice straw ash-based concrete: An approach for the valorization of agriculture waste. International Journal of Environmental Science and Technology. https://doi.org/10.1007/s13762-022-04554-5
Singh, R., & Patel, M. (2023). Experimental and machine learning approaches to investigate the application of sugarcane bagasse ash as a partial replacement of fine aggregate for concrete production. Journal of Building Engineering, 76, 107168. https://doi.org/10.1016/J.JOBE.2023.107168
Singh, R., Patel, M., & Sohal, K. S. (2022). The potential use of waste paper sludge for sustainable production of concrete—A review. Lecture Notes in Civil Engineering, 172, 365–374. https://doi.org/10.1007/978-981-16-4396-5_33/COVER
Sodhi, A. K., Bhanot, N., Singh, R., & Alkahtani, M. (2022). Effect of integrating industrial and agricultural wastes on concrete performance with and without microbial activity. Environmental Science and Pollution Research, 29, 86092–86108. https://doi.org/10.1007/s11356-021-16445-2
Tabassum, T., & Mir, A. A. (2023). A review of 3d printing technology-the future of sustainable construction. Materials Today: Proceedings. https://doi.org/10.1016/J.MATPR.2023.08.013
Taye, J., Barman, J., Patel, M., & Kumar, B. (2019). Turbulent characteristics of sinuous river bend. ISH J Hydraul Eng, 00, 1–8. https://doi.org/10.1080/09715010.2019.1629843
Taylor, K. E. (2001). Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research: Atmospheres, 106, 7183–7192. https://doi.org/10.1029/2000JD900719
Thakarya, V. M., & Patel, M. (2023). Revolutionizing concrete: A study of geopolymer concrete with metakaolin and G.G.B.S inclusion. Materials Today: Proceedings. https://doi.org/10.1016/J.MATPR.2023.07.033
Tipu, R. K., & Suman, B. V. (2023a). Enhancing prediction accuracy of workability and compressive strength of high-performance concrete through extended dataset and improved machine learning models. Asian Journal of Civil Engineering. https://doi.org/10.1007/s42107-023-00768-1
Tipu, R. K., & Suman, B. V. (2023b). Development of a hybrid stacked machine learning model for predicting compressive strength of high-performance concrete. Asian Journal of Civil Engineering. https://doi.org/10.1007/s42107-023-00689-z
Wadhawan, S., Bassi, A., Singh, R., & Patel, M. (2023). Prediction of compressive strength for fly ash-based concrete: Critical comparison of machine learning algorithms. Journal of Soft Computing in Civil Engineering, 7, 68–110.
Yucel, M., & Namlı, E. (2020). High performance concrete (HPC) compressive strength prediction with advanced machine learning methods. Artificial Intelligence Machine Learning Applications. https://doi.org/10.4018/978-1-7998-0301-0.ch007
Zhang, M.-L., & Zhou, Z.-H. (2007). ML-KNN: A lazy learning approach to multi-label learning. Pattern Recognit, 40, 2038–2048. https://doi.org/10.1016/j.patcog.2006.12.019
Zhang, X., Akber, M. Z., & Zheng, W. (2021). Prediction of seven-day compressive strength of field concrete. Construction and Building Materials, 305, 124604. https://doi.org/10.1016/j.conbuildmat.2021.124604
Funding
This research did not receive any specific grant from the public, commercial, or not-for-profit funding agencies.
Author information
Authors and Affiliations
Contributions
All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by [RS], [AAM], [MP], [AR] and [RKT]. The first draft of the manuscript was written by [RKT], [AAM] and [RK]. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no known competing interests or personal relationships that could have appeared to influence the work reported in this article. The authors have no competing interests to declare that are relevant to the content of this article.
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.
About this article
Cite this article
Kumar, R., Rathore, A., Singh, R. et al. Prognosis of flow of fly ash and blast furnace slag-based concrete: leveraging advanced machine learning algorithms. Asian J Civ Eng 25, 2483–2497 (2024). https://doi.org/10.1007/s42107-023-00922-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s42107-023-00922-9