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Modeling and predicting the sensitivity of high-performance concrete compressive strength using machine learning methods

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A Correction to this article was published on 09 April 2023

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Abstract

Concrete compressive strength is the most important performance requirement in structural engineering when developing both traditional concrete and high-performance concrete (HPC) constructions (CCS). When using this parameter in time and cost–benefit analyses, it is much more important to forecast it correctly. Because of the complexities of the development processes, the compressive strength of concrete is highly sensitive to a variety of factors and notoriously difficult to estimate. In this study, datasets from the literature are used to model and forecast the sensitivity compressive strength of high-performance concrete. Researchers were able to examine the stability of this framework by comparing it to other models (such as Random forest, Decision tree, and Artificial neural network). For the concrete compressive strength of high-performance concrete, estimated 1030 data sets of nine input variables, such as cement, blast furnace slag, water, superplasticizer, fine aggregate, concrete age, and so on, are required. The most exciting discovery was that ANN models could be a useful tool for producing forecasts from laboratory and field data, as well as a precise and effective explicit formulation for various civil engineering applications where such assertions are required. Both of these outcomes may be related to ANN's ability to predict future events.

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Data availability

The data that support the findings of this study are available from the corresponding author, [Walaa Hussein AL Yamani], upon reasonable request.

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References

  • Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N. A., & Arshad, H. (2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4(11), e00938.

    Article  Google Scholar 

  • Agrawal, H., & Mavani, H. (2015). Student performance prediction using machine learning. International Journal of Engineering Research and Technology, 4(03), 111–113.

    Google Scholar 

  • Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Umar, A. M., Linus, O. U., Arshad, H., Kazaure, A. A., Gana, U., & Kiru, M. U. (2019). Comprehensive review of artificial neural network applications to pattern recognition. IEEE Access, 7, 158820–158846.

    Article  Google Scholar 

  • Ahmad, W., Ahmad, A., Ostrowski, K. A., Aslam, F., & Joyklad, P. (2021a). A scientometric review of waste material utilization in concrete for sustainable construction. Case Studies in Construction Materials. https://doi.org/10.1016/j.cscm.2021.e00683

    Article  Google Scholar 

  • Ahmad, W., Ahmad, A., Ostrowski, K. A., Aslam, F., Joyklad, P., & Zajdel, P. (2021b). Application of advanced machine learning approaches to predict the compressive strength of concrete containing supplementary cementitious materials. Materials, 14(19), 5762. https://doi.org/10.3390/ma14195762

    Article  Google Scholar 

  • Ahmad, W., Farooq, S. H., Usman, M., Khan, M., Ahmad, A., Aslam, F., Yousef, R. A., Abduljabbar, H. A., & Sufian, M. (2020). Effect of coconut fiber length and content on properties of high strength concrete. Materials, 13(5), 1075. https://doi.org/10.3390/ma13051075

    Article  Google Scholar 

  • Al-Shamiri, A. K., Kim, J. H., Yuan, T.-F., & Yoon, Y. S. (2019). Modeling the compressive strength of high-strength concrete: An extreme learning approach. Construction and Building Materials, 208, 204–219. https://doi.org/10.1016/j.conbuildmat.2019.02.165

    Article  Google Scholar 

  • Aprianti, S. E. (2017). A huge number of artificial waste material can be supplementary cementitious material (SCM) for concrete production—A review part II. Journal of Cleaner Production, 142, 4178–4194. https://doi.org/10.1016/j.jclepro.2015.12.115

    Article  Google Scholar 

  • Bhanja, S., & Sengupta, B. (2002). Investigations on the compressive strength of silica fume concrete using statistical methods. Cement and Concrete Research, 32(9), 1391–1394. https://doi.org/10.1016/s0008-8846(02)00787-1

    Article  Google Scholar 

  • Cheng, M.-Y., Chou, J.-S., Roy, A. F. V., & Wu, Y.-W. (2012). High-performance concrete compressive strength prediction using time-weighted evolutionary fuzzy support vector machines inference model. Automation in Construction, 28, 106–115. https://doi.org/10.1016/j.autcon.2012.07.004

    Article  Google Scholar 

  • Chou, J.-S., Chiu, C.-K., Farfoura, M., & Al-Taharwa, I. (2011). Optimizing the prediction accuracy of concrete compressive strength based on a comparison of data-mining techniques. Journal of Computing in Civil Engineering, 25(3), 242–253. https://doi.org/10.1061/(asce)cp.1943-5487.0000088

    Article  Google Scholar 

  • Dalvand, A., & Ahmadi, M. (2021). Impact failure mechanism and mechanical characteristics of steel fiber reinforced self-compacting cementitious composites containing silica fume. Engineering Science and Technology, an International Journal, 24(3), 736–748. https://doi.org/10.1016/j.jestch.2020.12.016

    Article  Google Scholar 

  • Dao, D. V., Adeli, H., Ly, H.-B., Le, L. M., Le, V. M., Le, T.-T., & Pham, B. T. (2020). A sensitivity and robustness analysis of GPR and ann for high-performance concrete compressive strength prediction using a Monte Carlo simulation. Sustainability, 12(3), 830. https://doi.org/10.3390/su12030830

    Article  Google Scholar 

  • Demir, S., & Sahin, E. K. (2022). Comparison of tree-based machine learning algorithms for predicting liquefaction potential using canonical correlation forest, rotation forest, and random forest based on CPT data. Soil Dynamics and Earthquake Engineering, 154, 107130.

    Article  Google Scholar 

  • Du, X., Li, W., Ruan, S., & Li, L. (2020). CUS-heterogeneous ensemble-based financial distress prediction for imbalanced dataset with ensemble feature selection. Applied Soft Computing, 97, 106758.

  • Fee, C. E., Li, Z., & Peng, Q. (2022). Hidden Gems: Do market participants respond to performance expectations revealed in compensation disclosures? Journal of Accounting and Economics, 101519.

  • Gallagher, B., Rever, M., Loveland, D., Mundhenk, T. N., Beauchamp, B., Robertson, E., Jaman, G. G., Hiszpanski, A. M., & Han, T.Y.-J. (2020). Predicting compressive strength of consolidated molecular solids using computer vision and deep learning. Materials & Design, 190, 108541. https://doi.org/10.1016/j.matdes.2020.108541

    Article  Google Scholar 

  • Ghanbarpour, S., Mazaheripour, H., Mirmoradi, S. H., & Barari, A. (2010). The effect of type and volume fraction (VF) of steel fiber on the mechanical properties of self-compacting concrete. Journal of Engineering, Design and Technology, 8(3), 247–256. https://doi.org/10.1108/17260531011086144

    Article  Google Scholar 

  • Han, J., Kim, J., Park, S., Son, S., & Ryu, M. (2020). Seismic vulnerability assessment and mapping of Gyeongju, South Korea using frequency ratio, decision tree, and random forest. Sustainability, 12(18), 7787.

    Article  Google Scholar 

  • Hansen, S. G., Lauridsen, J. T., & Hoang, L. C. (2018). Experimental and statistical investigation of the compressive strength anisotropy in structural concrete. Cement and Concrete Research, 107, 304–316. https://doi.org/10.1016/j.cemconres.2018.02.011

    Article  Google Scholar 

  • Hasan, H. R., Rabby, A. S. A., Islam, M. T., & Hossain, S. A. (2019). Machine learning algorithm for student's performance prediction. In: 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (pp. 1–7). IEEE.

  • Hoang, N.-D., Pham, A.-D., Nguyen, Q.-L., & Pham, Q.-N. (2016). Estimating compressive strength of high performance concrete with gaussian process regression model. Advances in Civil Engineering, 2016, 1–8. https://doi.org/10.1155/2016/2861380

    Article  Google Scholar 

  • Ji, M., Liu, L., Du, R., & Buchroithner, M. F. (2019). A comparative study of texture and convolutional neural network features for detecting collapsed buildings after earthquakes using pre-and post-event satellite imagery. Remote Sensing, 11(10), 1202.

    Article  Google Scholar 

  • Kabir, M. A. B., Hasan, A. S., & Billah, A. M. (2021). Failure mode identification of column base plate connection using data-driven machine learning techniques. Engineering Structures, 240, 112389.

    Article  Google Scholar 

  • Kaloop, M. R., Kumar, D., Samui, P., Hu, J. W., & Kim, D. (2020). Compressive strength prediction of high-performance concrete using gradient tree boosting machine. Construction and Building Materials, 264, 120198. https://doi.org/10.1016/j.conbuildmat.2020.120198

    Article  Google Scholar 

  • Kamath, M. V., Prashanth, S., Kumar, M., & Tantri, A. (2022). Machine-learning-algorithm to predict the high-performance concrete compressive strength using multiple data. Journal of Engineering, Design and Technology. https://doi.org/10.1108/jedt-11-2021-0637

    Article  Google Scholar 

  • Kaveh, A., Gholipour, Y., & Rahami, H. (2008). Optimal design of transmission towers using genetic algorithm and neural networks. International Journal of Space Structures, 23(1), 1–19.

    Article  Google Scholar 

  • Kaveh, A., & Khalegi, A. (1998). Prediction of strength for concrete specimens using artificial neural networks. Advances in Engineering Computational Technology, 165–171.

  • Kaveh, A., & Servati, H. (2001). Design of double layer grids using backpropagation neural networks. Computers & Structures, 79(17), 1561–1568.

    Article  Google Scholar 

  • Khan, M., & Ali, M. (2019). Improvement in concrete behavior with fly ash, silica-fume and coconut fibres. Construction and Building Materials, 203, 174–187. https://doi.org/10.1016/j.conbuildmat.2019.01.103

    Article  Google Scholar 

  • Kim, T., Davis, J. M., Ley, M. T., Kang, S., & Amrollahi, P. (2018). Fly ash particle characterization for predicting concrete compressive strength. Construction and Building Materials, 165, 560–571. https://doi.org/10.1016/j.conbuildmat.2018.01.059

    Article  Google Scholar 

  • Kosmatka, S. H., Panarese, W. C., & Kerkhoff, B. (2002). Design and control of concrete mixtures, (Vol. 5420, pp. 60077–61083). Skokie: Portland Cement Association.

    Google Scholar 

  • Li, J., Wu, Z., Shi, C., Yuan, Q., & Zhang, Z. (2020). Durability of ultra-high performance concrete—A review. Construction and Building Materials, 255, 119296. https://doi.org/10.1016/j.conbuildmat.2020.119296

    Article  Google Scholar 

  • Liu, Q.-f, Iqbal, M. F., Yang, J., Lu, X.-y, Zhang, P., & Rauf, M. (2021). Prediction of chloride diffusivity in concrete using artificial neural network: Modelling and performance evaluation. Construction and Building Materials, 268, 121082. https://doi.org/10.1016/j.conbuildmat.2020.121082

    Article  Google Scholar 

  • Liu, T., Nafees, A., khan, S., Javed, M. F., Aslam, F., Alabduljabbar, H., Xiong, J.-J., Ijaz Khan, M., & Malik, M. Y. (2022). Comparative study of mechanical properties between irradiated and regular plastic waste as a replacement of cement and fine aggregate for manufacturing of Green Concrete. Ain Shams Engineering Journal, 13(2), 101563. https://doi.org/10.1016/j.asej.2021.08.006

    Article  Google Scholar 

  • Mangi, S. A., Wan Ibrahim, M. H., Jamaluddin, N., Arshad, M. F., & Shahidan, S. (2019). Performances of concrete containing coal bottom ash with different fineness as a supplementary cementitious material exposed to seawater. Engineering Science and Technology, an International Journal, 22(3), 929–938. https://doi.org/10.1016/j.jestch.2019.01.011

    Article  Google Scholar 

  • Mansour, M. Y., Dicleli, M., Lee, J. Y., & Zhang, J. (2004). Predicting the shear strength of reinforced concrete beams using artificial neural networks. Engineering Structures, 26(6), 781–799. https://doi.org/10.1016/j.engstruct.2004.01.011

    Article  Google Scholar 

  • Marcelino, P., de Lurdes Antunes, M., Fortunato, E., & Gomes, M. C. (2021). Machine learning approach for pavement performance prediction. International Journal of Pavement Engineering, 22(3), 341–354.

    Article  Google Scholar 

  • Naderpour, H., & Mirrashid, M. (2020). Estimating the compressive strength of eco-friendly concrete incorporating recycled coarse aggregate using neuro-fuzzy approach. Journal of Cleaner Production, 265, 121886. https://doi.org/10.1016/j.jclepro.2020.121886

    Article  Google Scholar 

  • Nemirovsky, D., Arkose, T., Markovic, N., Nemirovsky, M., Unsal, O., & Cristal, A. (2017). A machine learning approach for performance prediction and scheduling on heterogeneous CPUs. In: 2017 29th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD) (pp. 121–128). IEEE.

  • Roeslin, S., Ma, Q., Chigullapally, P., Wicker, J., & Wotherspoon, L. (2022). Development of a seismic loss prediction model for residential buildings using machine learning–Christchurch, New Zealand. Natural Hazards and Earth System Sciences Discussions, 1–31

  • Shahmansouri, A. A., Yazdani, M., Ghanbari, S., Akbarzadeh Bengar, H., Jafari, A., & Farrokh Ghatte, H. (2021). Artificial neural network model to predict the compressive strength of eco-friendly geopolymer concrete incorporating silica fume and natural zeolite. Journal of Cleaner Production, 279, 123697. https://doi.org/10.1016/j.jclepro.2020.123697

    Article  Google Scholar 

  • Sun, H., Burton, H. V., & Huang, H. (2021). Machine learning applications for building structural design and performance assessment: State-of-the-art review. Journal of Building Engineering, 33, 101816.

    Article  Google Scholar 

  • Tenza-Abril, A. J., Villacampa, Y., Solak, A. M., & Baeza-Brotons, F. (2018). Prediction and sensitivity analysis of compressive strength in segregated lightweight concrete based on artificial neural network using ultrasonic pulse velocity. Construction and Building Materials, 189, 1173–1183. https://doi.org/10.1016/j.conbuildmat.2018.09.096

    Article  Google Scholar 

  • Todorov, Z., Efnusheva, D., & Nikolic, T. (2021). FPGA implementation of computer network security protection with Machine Learning. In: 2021 IEEE 32nd International Conference on Microelectronics (MIEL). https://doi.org/10.1109/miel52794.2021.9569201

  • Wang, J., & Hu, J. (2015). A robust combination approach for short-term wind speed forecasting and analysis—Combination of the ARIMA (autoregressive integrated moving average), Elm (Extreme Learning Machine), SVM (Support Vector Machine) and LSSVM (least square SVM) forecasts using a GPR (gaussian process regression) model. Energy, 93, 41–56. https://doi.org/10.1016/j.energy.2015.08.045

    Article  Google Scholar 

  • Warmdt, L., Užik, M., & Löcher, M. (2018). Financial signaling with open market share repurchases and private redemptions (No. 93). Working Paper.

  • Xiao, M., Guo, Y., Yang, X., Li, G., & Kholaif, M. M. N. H. K. (2022). The effect of controlling person’s illegalities on stock price returns: Evidence from Elman neural network model. PLoS ONE, 17(4), e0266741.

    Article  Google Scholar 

  • Xu, Y., Ahmad, W., Ahmad, A., Ostrowski, K. A., Dudek, M., Aslam, F., & Joyklad, P. (2021). Computation of high-performance concrete compressive strength using standalone and ensembled Machine Learning Techniques. Materials, 14(22), 7034. https://doi.org/10.3390/ma14227034

    Article  Google Scholar 

  • Yeh, I.-C. (1998). Modeling of strength of high-performance concrete using artificial neural networks. Cement and Concrete Research, 28(12), 1797–1808. https://doi.org/10.1016/s0008-8846(98)00165-3

    Article  Google Scholar 

  • Yeh, I. C. (2003). Prediction of strength of fly ash and slag concrete by the use of artificial neural networks. Journal of the Chinese Institute of Civil and Hydraulic Engineering, 15(4), 659–663.

    Google Scholar 

  • Young, B. A., Hall, A., Pilon, L., Gupta, P., & Sant, G. (2019). Can the compressive strength of concrete be estimated from knowledge of the mixture proportions: New insights from statistical analysis and machine learning methods? Cement and Concrete Research, 115, 379–388. https://doi.org/10.1016/j.cemconres.2018.09.006

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Civil Engineering, Faculty of Engineering, Al-Balqa Applied University, Salt, 19117, Jordan

    Walaa Hussein Al Yamani & Dalin Mohammad Ghunimat

  2. Department of Civil Engineering, Faculty of Engineering, University Putra Malaysia, Selangor City, Malaysia

    Majdi Mowafaq Bisharah

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  1. Walaa Hussein Al Yamani
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  2. Dalin Mohammad Ghunimat
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Contributions

AB and BC conceived of the presented idea. AB developed the theory and performed the computations. CD and DE verified the analytical methods. BC encouraged AB to investigate [a specific aspect] and supervised the findings of this work. All authors discussed the results and contributed to the final manuscript.

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Correspondence to Walaa Hussein Al Yamani.

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Al Yamani, W.H., Ghunimat, D.M. & Bisharah, M.M. Modeling and predicting the sensitivity of high-performance concrete compressive strength using machine learning methods. Asian J Civ Eng 24, 1943–1955 (2023). https://doi.org/10.1007/s42107-023-00614-4

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