Abstract
The contributions of this study, which include enhanced predictive precision and the integration of supplementary admixtures, may be effectively used in real-world scenarios to optimize self-compacting concrete (SCC) mixes for particular applications. This technology exhibits the capacity to improve construction productivity, minimize material wastage, and foster the development of resilient and environmentally friendly infrastructures. This research examined radial basis function (RBF) network and extreme gradient boosting (XGB-based models for predicting the compressive strength (Cs) of SCC. A dataset was generated through the collection of experimental samples, which encompassed supplementary admixtures in addition to the conventional constituents of concrete comprised of lime powders, fly ash, granulated blast furnace slag, silica fume, steel slag powder, super-plasticizer, and viscosity-modifying admixtures. In the present study, two optimization algorithms named the northern Goshawk optimization algorithm (NGOA), and Henry gas solubility optimization (HGSO) were linked with RBF and XGB models (abbreviated as XGBNG, XGBHG, RBFNG, and RBFHG). The results indicate that each of the four models exhibits a notable degree of precision in their prediction methodologies for Cs. A considered comprehensive metric named OBJ showed that the XGBNG simulation received the slightest value at 0.8062, followed by XGBHG at 1.657, then RBFNG at 2.4891, and last RBFHG by 3.9131.
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References
Aghayari Hir M, Zaheri M, Rahimzadeh N (2022) Prediction of rural travel demand by spatial regression and artificial neural network methods (Tabriz County). Journal of Transportation Research 20(77):367–386, DOI: https://doi.org/10.22034/TRI.2022.312204.2970
Alabdullah AA, Iqbal M, Zahid M, Khan K, Amin MN, Jalal FE (2022) Prediction of rapid chloride penetration resistance of metakaolin based high strength concrete using light GBM and XGBoost models by incorporating SHAP analysis. Construction and Building Materials 345:128296, DOI: https://doi.org/10.1016/j.conbuildmat.2022.128296
Ali R, Muayad M, Mohammed AS, Asteris PG (2023) Analysis and prediction of the effect of Nanosilica on the compressive strength of concrete with different mix proportions and specimen sizes using various numerical approaches. Structural Concrete 24(3):4161–4184, DOI: https://doi.org/10.1002/suco.202200718
Ashrafian A, Taheri Amiri MJ, Masoumi P, Asadi-shiadeh M, Yaghoubichenari M, Mosavi A, Nabipour N (2020) Classification-based regression models for prediction of the mechanical properties of roller-compacted concrete pavement. Applied Sciences 10(11):3707, DOI: https://doi.org/10.3390/app10113707
Behar O, Khellaf A, Mohammedi K (2015) Comparison of solar radiation models and their validation under Algerian climate–The case of direct irradiance. Energy Conversion and Management 98:236–251, DOI: https://doi.org/10.1016/j.enconman.2015.03.067
Behnood A, Golafshani EM (2020) Machine learning study of the mechanical properties of concretes containing waste foundry sand. Construction and Building Materials 243:118152, DOI: https://doi.org/10.1016/j.conbuildmat.2020.118152
Benesty J, Chen J, Huang Y, Cohen I (2009) Pearson correlation coefficient. Noise Reduction in Speech Processing, Springer 1–4, DOI: https://doi.org/10.1007/978-3-642-00296-0_5
Chen T, Guestrin C (2016) Xgboost: A scalable tree boosting system. Proceedings of the 22nd acm Sigkdd International Conference on Knowledge Discovery and Data Mining 785–794, DOI: https://doi.org/10.1145/2939672.2939785
Dawei Y, Bing Z, Bingbing G, Xibo G, Razzaghzadeh B (2023) Predicting the CPT-based pile set-up parameters using HHO-RF and PSO-RF hybrid models. Structural Engineering and Mechanics 86(5)673–686, DOI: https://doi.org/10.12989/sem.2023.86.5.673
Dehghani M, Hubálovský Š, Trojovský P (2021) Northern goshawk optimization: A new swarm-based algorithm for solving optimization problems. Ieee Access 9:162059–162080, DOI: https://doi.org/10.1109/ACCESS.2021.3133286
Emamian SA, Eskandari-Naddaf H (2019) Effect of porosity on predicting compressive and flexural strength of cement mortar containing micro and nano-silica by ANN and GEP. Construction and Building Materials 218:8–27
Eskandari-Naddaf H, Kazemi R (2017) ANN prediction of cement mortar compressive strength, influence of cement strength class. Construction and Building Materials 138:1–11, DOI: https://doi.org/10.1016/j.conbuildmat.2017.01.132
Esmaeili-Falak M, Benemaran RS (2023) Ensemble deep learning-based models to predict the resilient modulus of modified base materials subjected to wet-dry cycles. Geomechanics and Engineering 32(6):583–600, DOI: https://doi.org/10.12989/gae.2023.32.6.583
Esmaeili-Falak M, Katebi H, Vadiati M, Adamowski J (2019) Predicting triaxial compressive strength and Young’s modulus of frozen sand using artificial intelligence methods. Journal of Cold Regions Engineering 33(3):4019007, DOI: https://doi.org/10.1061/(ASCE)CR.1943-5495.0000188
Figueroa JD, Fout T, Plasynski S, McIlvried H, Srivastava RD (2008) Advances in CO2 capture technology—The U.S. department of energy’s carbon sequestration program. International Journal of Greenhouse Gas Control 2(1):9–20, DOI: https://doi.org/10.1016/S1750-5836(07)00094-1
Ghafoori N, Najimi M, Sobhani J, Aqel MA (2013) Predicting rapid chloride permeability of self-consolidating concrete: a comparative study on statistical and neural network models. Construction and Building Materials 44:381–390, DOI: https://doi.org/10.1016/j.conbuildmat.2013.03.039
Gogineni A, Panday IK, Kumar P, Paswan RK (2023) Predicting compressive strength of concrete with fly ash and admixture using XGBoost: A comparative study of machine learning algorithms. Asian Journal of Civil Engineering 1–14, DOI: https://doi.org/10.1007/s42107-023-00804-0
Golafshani EM, Behnood A, Arashpour M (2020) Predicting the compressive strength of normal and high-performance concretes using ANN and ANFIS hybridized with grey wolf optimizer. Construction and Building Materials 232:117266, DOI: https://doi.org/10.1016/j.conbuildmat.2019.117266
Gueymard CA (2014) A review of validation methodologies and statistical performance indicators for modeled solar radiation data: Towards a better bankability of solar projects. Renewable and Sustainable Energy Reviews 39:1024–1034, DOI: https://doi.org/10.1016/j.rser.2014.07.117
Hammoudi A, Moussaceb K, Belebchouche C, Dahmoune F (2019) Comparison of artificial neural network (ANN) and response surface methodology (RSM) prediction in compressive strength of recycled concrete aggregates. Construction and Building Materials 209:425–436, DOI: https://doi.org/10.1016/j.conbuildmat.2019.03.119
Hashim FA, Houssein EH, Mabrouk MS, Al-Atabany W, Mirjalili S (2019) Henry gas solubility optimization: A novel physics-based algorithm. Future Generation Computer Systems 101:646–667, DOI: https://doi.org/10.1016/j.future.2019.07.015
Ibrahim AK, Dhahir HY, Mohammed AS, Omar HA, Sedo AH (2023) The effectiveness of surrogate models in predicting the long-term behavior of varying compressive strength ranges of recycled concrete aggregate for a variety of shapes and sizes of specimens. Archives of Civil and Mechanical Engineering 23(1):61, DOI: https://doi.org/10.1007/s43452-022-00595-2
Kandiri A, Golafshani EM, Behnood A (2020) Estimation of the compressive strength of concretes containing ground granulated blast furnace slag using hybridized multi-objective ANN and salp swarm algorithm. Construction and Building Materials 248:118676, DOI: https://doi.org/10.1016/j.conbuildmat.2020.118676
Khorsheed MS, Al-Thubaity AO (2013) Comparative evaluation of text classification techniques using a large diverse Arabic dataset. Language Resources and Evaluation 47(2):513–538, DOI: https://doi.org/10.1007/s10579-013-9221-8
Kooshkaki A, Eskandari-Naddaf H (2019) Effect of porosity on predicting compressive and flexural strength of cement mortar containing micro and nano-silica by multi-objective ANN modeling. Construction and Building Materials 212:176–191, DOI: https://doi.org/10.1016/j.conbuildmat.2019.03.243
Leema N, Nehemiah HK, Kannan A (2016) Neural network classifier optimization using differential evolution with global information and back propagation algorithm for clinical datasets. Applied Soft Computing 49:834–844
Liang R, Bayrami B (2023) Estimation of frost durability of recycled aggregate concrete by hybridized Random Forests algorithms. Steel and Composite Structures 49(1):91–107, DOI: https://doi.org/10.12989/scs.2023.49.1.091
Li Y, Li H, Shen J (2022) The study of effect of carbon nanotubes on the compressive strength of cement-based materials based on machine learning. Construction and Building Materials 358:129435, DOI: https://doi.org/10.1016/j.conbuildmat.2022.129435
Mahmood W, Mohammed A (2022) Performance of ANN and M5P-tree to forecast the compressive strength of hand-mix cement-grouted sands modified with polymer using ASTM and BS standards and evaluate the outcomes using SI with OBJ assessments. Neural Computing and Applications 34(17):15031–15051, DOI: https://doi.org/10.1007/s00521-022-07349-4
Mahmood W, Mohammed AS, Asteris PG, Ahmed H (2022) Testing and modeling the gradually applying compressive stress to measuring the strain of self-compacted cement paste using Vipulanandan Pq model. Journal of Testing and Evaluation 50(3):1604–1621, DOI: https://doi.org/10.1520/JTE20210219
Mai HVT, Nguyen MH, Ly HB (2023) Development of machine learning methods to predict the compressive strength of fiber-reinforced self-compacting concrete and sensitivity analysis. Construction and Building Materials 130339, DOI: https://doi.org/10.1016/j.conbuildmat.2023.130339
Mayet AM, Al-Qahtani AA, Qaisi RMA, Ahmad I, Alhashim HH, Eftekhari-Zadeh E (2022) Developing a model based on the radial basis function to predict the compressive strength of concrete containing fly ash. Buildings 12(10):1743, DOI: https://doi.org/10.3390/buildings12101743
Mohammed A, Burhan L, Ghafor K, Sarwar W, Mahmood W (2021) Artificial neural network (ANN), M5P-tree, and regression analyses to predict the early age compression strength of concrete modified with DBC-21 and VK-98 polymers. Neural Computing and Applications 33(13):7851–7873, DOI: https://doi.org/10.1007/s00521-020-05525-y
Moodi Y, Ghasemi M, Mousavi SR (2022) Estimating the compressive strength of rectangular fiber reinforced polymer–confined columns using multilayer perceptron, radial basis function, and support vector regression methods. Journal of Reinforced Plastics and Composites 41(3–4):130–146, DOI: https://doi.org/10.1177/07316844211050168
Nayak DK, Verma G, Dimri A, Kumar R, Kumar V (2023) Predicting the Twenty-eight day compressive strength of OPC-and PPC-prepared concrete through hybrid GA-XGB model. Practice Periodical on Structural Design and Construction 28(3):4023020, DOI: https://doi.org/10.1061/PPSCFX.SCENG-131
Nguyen N-H, Abellán-García J, Lee S, Garcia-Castano E, Vo TP (2022) Efficient estimating compressive strength of ultra-high performance concrete using XGBoost model. Journal of Building Engineering 52:104302, DOI: https://doi.org/10.1016/j.jobe.2022.104302
Pang M, Yan G, Li J, Zhou M (2023) Use of RBF model in GOA and MPA optimizers to estimate the compressive strength of concrete in the HPC model. Journal of Applied Science and Engineering 26(10):1427–1439, DOI: https://doi.org/10.6180/jase.202310_26(10).0008
Parsajoo M, Mohammed AS, Yagiz S, Armaghani DJ, Khandelwal M (2021) An evolutionary adaptive neuro-fuzzy inference system for estimating field penetration index of tunnel boring machine in rock mass. Journal of Rock Mechanics and Geotechnical Engineering 13(6):1290–1299, DOI: https://doi.org/10.1016/j.jrmge.2021.05.010
Pazouki G, Golafshani EM, Behnood A (2021) Predicting the compressive strength of self-compacting concrete containing Class F fly ash using metaheuristic radial basis function neural network. Structural Concrete 23(2):1191–1213, DOI: https://doi.org/10.1002/suco.202000047
Piro NS, Mohammed AS, Hamad SM (2022a) The impact of GGBS and ferrous on the flow of electrical current and compressive strength of concrete. Construction and Building Materials 349:128639, DOI: https://doi.org/10.1016/j.conbuildmat.2022.128639
Piro NS, Mohammed A, Hamad SM, Kurda R (2023) Artificial neural networks (ANN), MARS, and adaptive network-based fuzzy inference system (ANFIS) to predict the stress at the failure of concrete with waste steel slag coarse aggregate replacement. Neural Computing and Applications 35(18):13293–13319, DOI: https://doi.org/10.1007/s00521-023-08439-7
Piro NS, Mohammed A, Hamad SM, Kurda R (2022b) Electrical resistivity-Compressive strength predictions for normal strength concrete with waste steel slag as a coarse aggregate replacement using various analytical models. Construction and Building Materials 327:127008, DOI: https://doi.org/10.1016/j.conbuildmat.2022.127008
Qaidi S, Al-Kamaki YSS, Al-Mahaidi R, Mohammed AS, Ahmed HU, Zaid O, Althoey F, Ahmad J, Isleem HF, Bennetts I (2022) Investigation of the effectiveness of CFRP strengthening of concrete made with recycled waste PET fine plastic aggregate. PLoS One 17(7): e0269664, DOI: https://doi.org/10.1371/journal.pone.0269664
Rochelle GT (2009) Amine scrubbing for CO2 capture. Science 325(5948):1652–1654, DOI: https://doi.org/10.1126/science.1176731
Sarkhani Benemaran R (2023) Application of extreme gradient boosting method for evaluating the properties of episodic failure of borehole breakout. Geoenergy Science and Engineering 211837, DOI: https://doi.org/10.1016/j.geoen.2023.211837
Shen Z, Deifalla AF, Kamiński P, Dyczko A (2022) Compressive strength evaluation of ultra-high-strength concrete by machine learning. Materials 15(10):3523, DOI: https://doi.org/10.3390/ma15103523
Shi X, Yu X, Esmaeili-Falak M (2023) Improved arithmetic optimization algorithm and its application to carbon fiber reinforced polymer-steel bond strength estimation. Composite Structures 306:116599, DOI: https://doi.org/10.1016/j.compstruct.2022.116599
Staudinger J, Roberts PV (1996) A critical review of Henry’s law constants for environmental applications. Critical Reviews in Environmental Science and Technology 26(3):205–297, DOI: https://doi.org/10.1080/10643389609388492
Todhunter A, Crowley M, Sartipi F, Jegendran K (2019) Use of the byproducts of post-combustion carbon capture in concrete production: Australian case study. Journal of Construction Materials
Uddin MN, Li L-Z, Deng B-Y, Ye J (2023) Interpretable XGBoost-SHAP machine learning technique to predict the compressive strength of environment-friendly rice husk ash concrete. Innovative Infrastructure Solutions 8(5):147, DOI: https://doi.org/10.1007/s41062-023-01122-9
Ullah I, Liu K, Yamamoto T, Al Mamlook RE, Jamal A (2022) A comparative performance of machine learning algorithm to predict electric vehicles energy consumption: A path towards sustainability. Energy & Environment 33(8):1583–1612, DOI: https://doi.org/10.1177/0958305X211044998
Wang Y, Zhao L, Otto A, Robinius M, Stolten D (2017) A review of post-combustion CO2 capture technologies from coal-fired power plants. Energy Procedia 114:650–665, DOI: https://doi.org/10.1016/j.egypro.2017.03.1209
Yeh I-C (2006) Analysis of strength of concrete using design of experiments and neural networks. Journal of Materials in Civil Engineering 18(4):597–604, DOI: https://doi.org/10.1061/(ASCE)0899-1561(2006)18:4(597)
Yeh I-C (1999) Design of high-performance concrete mixture using neural networks and nonlinear programming. Journal of Computing in Civil Engineering 13(1):36–42, DOI: https://doi.org/10.1061/(ASCE)0887-3801(1999)13:1(36)
Yeh I-C (1998a) Modeling concrete strength with augment-neuron networks. Journal of Materials in Civil Engineering, 10(4):263–268, DOI: https://doi.org/10.1061/(ASCE)0899-1561(1998)10:4(263)
Yeh I-C (1998b) Modeling of strength of high-performance concrete using artificial neural networks. Cement and Concrete Research 28(12):1797–1808, DOI: https://doi.org/10.1016/S0008-8846(98)00165-3
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
Yu C, Koopialipoor M, Murlidhar BR, Mohammed AS, Armaghani DJ, Mohamad ET, Wang Z (2021) Optimal ELM-Harris Hawks optimization and ELM-Grasshopper optimization models to forecast peak particle velocity resulting from mine blasting. Natural Resources Research 30:2647–2662, DOI: https://doi.org/10.1007/s11053-021-09826-4
Zaji AH, Bonakdari H (2014) Performance evaluation of two different neural network and particle swarm optimization methods for prediction of discharge capacity of modified triangular side weirs. Flow Measurement and Instrumentation 40:149–156, DOI: https://doi.org/10.1016/j.flowmeasinst.2014.10.002
Zhang J, Huang Y, Ma G, Sun J, Nener B (2020) A metaheuristicoptimized multi-output model for predicting multiple properties of pervious concrete. Construction and Building Materials 249:118803, DOI: https://doi.org/10.1016/j.conbuildmat.2020.118803
Zhao J, Wu T, Li J, Shi L (2023) Incorporation of radial basis function with Gorilla Troops Optimization and Moth-Flame Optimization to predict the compressive strength of high-performance concrete. Multiscale and Multidisciplinary Modeling, Experiments and Design 1–14, DOI: https://doi.org/10.1007/s41939-023-00169-6
Zhu W, Huang L, Mao L, Esmaeili-Falak M (2022a) Predicting the uniaxial compressive strength of oil palm shell lightweight aggregate concrete using artificial intelligence-based algorithms. Structural Concrete 23(6):3631–3650, DOI: https://doi.org/10.1002/suco.202100656
Zhu Y, Huang L, Zhang Z, Bayrami B (2022b) Estimation of splitting tensile strength of modified recycled aggregate concrete using hybrid algorithms. Steel and Composite Structures 44(3):389–406, DOI: https://doi.org/10.12989/scs.2022.44.3.389
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This study was supported by the National Natural Science Foundation of China (Grant No. 51508270).
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Bian, J., Huo, R., Zhong, Y. et al. XGB-Northern Goshawk Optimization: Predicting the Compressive Strength of Self-Compacting Concrete. KSCE J Civ Eng 28, 1423–1439 (2024). https://doi.org/10.1007/s12205-024-1647-6
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DOI: https://doi.org/10.1007/s12205-024-1647-6