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Runtime-based metaheuristic prediction of the compressive strength of net-zero traditional concrete mixed with BFS, FA, SP considering multiple curing regimes

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Abstract

Toward carbon neutrality concrete technology (TCNCT) is the main topic in the net-zero construction industry, and designing a net-zero concrete optimized to meet the required near-zero carbon footprint specifications is one of the major goals of ecofriendly concrete technology. Several concrete mix-design methods were developed based on optimizing certain factor such as the unit weight of the concrete or the specific surface area of the aggregates. However, these available mix-design methods are limited to certain types of cement and aggregates while the construction industry today is looking for more advanced, sustainable, and ecofriendly (net-zero) materials such as blast furnace slag (BFS), fly ash (FA), and superplasticizer (SP). The aim of this study is to develop a predictive model for the compressive strength of certain net-zero concrete mixes at different ages considering the previously mentioned “towards carbon neutrality” industrial waste materials using the novel superspeed metaheuristic predictive techniques. A database of 1133 records of net-zero concrete mixes was collected from literature and studied to be used in training and testing the predictive models. The performance metrics used in this exercise are mean absolute error (MAE), mean squared error (MSE), and R2 score. The Automl tools were very handy as they presented the fastest models with the best performances also. The AutoSklearn models were presented for both 5 min and 10 min execution time. At the 5-min limit execution time, a performance of R2 equals 91.8%, with MAE of 3.395% and MSE of 23.224% was achieved in 262 s (4.37 min). At the 10-min set execution time, a performance of R2 equals 92.2%, with MAE of 3.197% and MSE of 22.224% was achieved in 597 s (9.95 min). The AutoGluon (TabularPredictor) was also deployed to model the CCS from the global database of nine features with no set time limits. This was executed in 785 s (13.08 min) and produced a model performance with R2 of 92.6%, MAE of 2.820%, and MSE of 20.918%. Though the execution time was higher than those of AutoSklearn models set for 5- and 10-min run time limits, it produced a better performance. Finally, the SVR and the RFR were deployed to produce models for the compressive strength of the hybrid concrete, and the following performances were recorded; run time of 6081 s (101.35 min), R2 of 86.6%, MAE of 3.901%, and MSE of 38.158% and runtime of 6442 s (107.37 min), R2 of 90.5%, MAE of 3.404%, and MSE of 27.105%, respectively. It can be learned that excessive waiting time was used by the SVR and the RFR compared to the AutoSklearn set at 5 and 10 min and the TabularPredictor, which is known as the AutoGluon. In addition to the less time advantage exhibited by the AutoML techniques, they also produced the best performance, which also outclassed previous studies that used the ANN to produce less performance models even at higher execution times. Overall, the results showed that BFS has a major impact on the overall performance of the ecofriendly concrete strength and it is a good sustainable and ecofriendly replacement for cement than FA.

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Authors and Affiliations

  1. Department of Civil Engineering, Michael Okpara University of Agriculture, Umudike, Umuahia, 440109, Nigeria

    Kennedy C. Onyelowe

  2. Department of Civil Engineering, School of Engineering, University of the Peloponnese, 26334, Patras, Greece

    Kennedy C. Onyelowe & Denise-Penelope N. Kontoni

  3. School of Science and Technology, Hellenic Open University, 26335, Patras, Greece

    Denise-Penelope N. Kontoni

  4. Department of Artificial Intelligence and Data Science, Vishnu Institute of Technology, Vishnupur, Bhimavaram, Andhra Pradesh, India

    Sita Rama Murty Pilla

  5. Civil Engineering Department, Faculty of Engineering, Al-Balqa Applied University, Salt, Jordan

    Shadi Hanandeh

  6. Department of Civil Engineering, Faculty of Engineering, Future University in Egypt, New Cairo, Egypt

    Ahmed M. Ebid

  7. School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran

    Mehrdad Razzaghian Ghadikolaee

  8. Department of Civil Engineering, University of Agriculture and Environmental Sciences, Umuagwo, Nigeria

    Liberty U. Stephen

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  1. Kennedy C. Onyelowe
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  2. Denise-Penelope N. Kontoni
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Contributions

KCO conceptualized, KCO, D-PNK, SRMP, SH, AME, MRG and LUS wrote the main manuscript text, KCO, D-PNK, AME, and SRMP prepared and validated the figures. All authors reviewed the manuscript.

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Correspondence to Kennedy C. Onyelowe.

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Onyelowe, K.C., Kontoni, DP.N., Pilla, S.R.M. et al. Runtime-based metaheuristic prediction of the compressive strength of net-zero traditional concrete mixed with BFS, FA, SP considering multiple curing regimes. Asian J Civ Eng 25, 1241–1253 (2024). https://doi.org/10.1007/s42107-023-00839-3

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