Abstract
Soft computing methods were used in this research to design and model the compressive strength of high-performance concrete (HPC) with silica fume. Box–Behnken design-based response surface methodology (RSM) was used to develop 29 HPC mixes with a target compressive strength of 80 ± 10 MPa. Cement (450–500 kg/m3), aggregates (1500–1700 kg/m3), silica fume (SF) (20–45% weight of cement), and water–binder (w/b) ratio of (0.24–0.32) were provided as input factors; while, the compressive strength at 7 and 28 days were analyzed as responses. Datasets for the artificial neural network (ANN) prediction were generated from 87 experimental observations from the compressive strength test. Performance indicators such as p-value, coefficient of determination (R2), and mean square error (MSE) were used to assess the models. Results demonstrated that RSM worked relatively well in projecting compressive strength with model p-values < 0.05 and R2 values of 0.913 and 0.892 for compressive strength at 7 and 28 days, respectively. In addition, RSM performed better in detecting the synergistic effects of the variables on the responses. On the other hand, ANN best generalized the relationship between independent and dependent variables considering the low MSE of 12.32 and 14.60, and high R2 values of 0.912 and 0.946 for compressive strength at 7 and 28 days, respectively. Model equations were developed to predict the compressive strength of silica-based HPC after 7 and 28 days. It is considered that adopting components from both approaches could help the design process for developing consistent mixes of HPC with supplementary cementitious materials (SCMs).
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The datasets generated during and/or analyzed during the current study are not publicly available but are available from the corresponding author upon request.
References
Adebanjo AU, Dahunsi BIO, Labiran JO (2021) Effects of concrete grades on strength characteristics of metakaolin modified recycled aggregate concrete. Nigerian J Technol Dev 18:. https://doi.org/10.4314/njtd.v18i3.3
Adebanjo A, Kareem M, Olawuyi O, et al (2022) Effects of waste steel fibres on the mechanical properties of modified self compacting concrete. J Eng Stud Res 28:7–16. https://doi.org/10.29081/jesr.v28i2.001
Adeyanju DO, Kareem MA, Awogboro OO, Adebanjo AU (2021) Compressive strength and water absorption of concrete with palm kernel shell ash as partial replacement for cement. J Civ Eng Technol 3:
Afroughsabet V, Biolzi L, Ozbakkaloglu T (2017) Influence of double hooked-end steel fibers and slag on mechanical and durability properties of high performance recycled aggregate concrete. Compos Struct 181:273–284. https://doi.org/10.1016/j.compstruct.2017.08.086
Aitcin P-Claude, Stern BB (1998) High-performance concrete. E & FN Spon
Akhnoukh AK (2020) Accelerated bridge construction projects using high performance concrete. Case Studies in Construction Materials 12:. https://doi.org/10.1016/j.cscm.2019.e00313
Alaneme GU, Mbadike EM, Iro UI et al (2021) Adaptive neuro-fuzzy inference system prediction model for the mechanical behaviour of rice husk ash and periwinkle shell concrete blend for sustainable construction. Asian J Civ Eng. https://doi.org/10.1007/s42107-021-00357-0
Amar M, Benzerzour M, Zentar R, Abriak NE (2022) Prediction of the compressive strength of waste-based concretes using artificial neural network. Materials. https://doi.org/10.3390/ma15207045
Ayub T, Shafiq N, Nuruddin MF (2014b) Effect of chopped basalt fibers on the mechanical properties and microstructure of high performance fiber reinforced concrete. Adv Mater Sci Eng. https://doi.org/10.1155/2014/587686
Ayub T, Shafiq N, Khan SU, Nuruddin MF (2014a) Flexural Behaviour of High Performance Basalt Fibre Reinforced Concrete Beams: 3D Nonlinear Finite Element Analysis
Ayub T, Shafiq N, Nuruddin MF (2014c) Mechanical properties of high-performance concrete reinforced with basalt fibers. In: Procedia Engineering. Elsevier Ltd, pp 131–139
Bal Beşikçi E, Arslan O, Turan O, Ölçer AI (2016) An artificial neural network based decision support system for energy efficient ship operations. Comput Oper Res 66:393–401. https://doi.org/10.1016/j.cor.2015.04.004
Chen H, Li X, Wu Y et al (2022) Compressive strength prediction of high-strength concrete using long short-term memory and machine learning algorithms. Buildings. https://doi.org/10.3390/buildings12030302
Chen X, Zhang Y, Ge P (2023) Prediction of concrete strength using response surface function modified depth neural network. PLoS ONE 18:e0285746. https://doi.org/10.1371/journal.pone.0285746
Dey A, Miyani G, Sil A (2020) Application of artificial neural network (ANN) for estimating reliable service life of reinforced concrete (RC) structure bookkeeping factors responsible for deterioration mechanism. Soft Comput. https://doi.org/10.1007/s00500-019-04042-y
Du H, Pang SD (2020) High-performance concrete incorporating calcined kaolin clay and limestone as cement substitute. Constr Build Mater. https://doi.org/10.1016/j.conbuildmat.2020.120152
Fang B, Hu Z, Shi T, et al (2023) Research progress on the properties and applications of magnesium phosphate cement. Ceram Int 49
Garg C, Namdeo A, Singhal A, et al (2022) Adaptive Fuzzy Logic Models for the Prediction of Compressive Strength of Sustainable Concrete. In: Lecture Notes in Networks and Systems
Gholampour A, Mansouri I, Kisi O, Ozbakkaloglu T (2020) Evaluation of mechanical properties of concretes containing coarse recycled concrete aggregates using multivariate adaptive regression splines (MARS), M5 model tree (M5Tree), and least squares support vector regression (LSSVR) models. Neural Comput Appl. https://doi.org/10.1007/s00521-018-3630-y
Gonzalez-Corominas A, Etxeberria M (2014) Properties of high performance concrete made with recycled fine ceramic and coarse mixed aggregates. Constr Build Mater 68:618–626. https://doi.org/10.1016/j.conbuildmat.2014.07.016
Hadji T, Guettala S, Quéneudec M (2021) Mix design of high performance concrete with different mineral additions. World J Eng 18:767–779. https://doi.org/10.1108/WJE-12-2020-0650
Hassooni S, Ethaib S (2020) Evaluation the effect of reuse sewage sludge and sewage sludge ash on concrete for cement replacement. Journal of Engineering Science and Technology 15:
Hossain SKS, Mathur L, Roy PK (2018) Rice husk/rice husk ash as an alternative source of silica in ceramics: a review. Journal of Asian Ceramic Societies 6
Hossain MU, Liu JC, Xuan D, et al (2022) Designing sustainable concrete mixes with potentially alternative binder systems: Multicriteria decision making process. J Build Eng https://doi.org/10.1016/j.jobe.2021.103587
Ismail FI, Abbas YM, Shafiq N et al (2021) Investigation of the impact of graphene nanoplatelets (Gnp) on the bond stress of high-performance concrete using pullout testing. Materials. https://doi.org/10.3390/ma14227054
Kareem MA, Akintonde BB, Adesoye JS et al (2023) Influence of cashew leaf ash as partial replacement for cement on the properties of fresh and hardened concrete. Cleaner Waste Systems 4:100063. https://doi.org/10.1016/j.clwas.2022.100063
Krystek M, Pakulski D, Patroniak V et al (2019) High-Performance Graphene-Based Cementitious Composites. Adv Sci. https://doi.org/10.1002/advs.201801195
Kumar R, Shafiq N, Kumar A, Jhatial AA (2021) Investigating embodied carbon, mechanical properties, and durability of high-performance concrete using ternary and quaternary blends of metakaolin, nano-silica, and fly ash. Environ Sci Pollut Res 28:49074–49088. https://doi.org/10.1007/s11356-021-13918-2
Kumar CV, Sargunan K, Vasa JSSK et al (2022) Applying ANN – PSO algorithm to maximize the compressive strength and split tensile strength of blended self curing concrete on the impact of supplementary cementitious materials. Int J Interact Des Manuf. https://doi.org/10.1007/s12008-022-00907-z
Kumar V, Kutty SRM, Abd Razak SN et al (2023) Exploring the untapped potentials of oily sludge ash blended with fly ash for geopolymer binder via waste valorisation approach. J Hazardous Mater Lett. https://doi.org/10.1016/j.hazl.2023.100076
de Larrard F, Sedran T Mixture-proportioning of high-performance concrete
Latif SD (2021) Concrete compressive strength prediction modeling utilizing deep learning long short-term memory algorithm for a sustainable environment. Environ Scie Pollution Res. https://doi.org/10.1007/s11356-021-12877-y
Lawal AI, Idris MA (2020) An artificial neural network-based mathematical model for the prediction of blast-induced ground vibrations. Int J Environ Stud 77:318–334. https://doi.org/10.1080/00207233.2019.1662186
Leong LY, Hew TS, Ooi KB, Wei J (2020) Predicting mobile wallet resistance: A two-staged structural equation modeling-artificial neural network approach. Int J Inf Manage. https://doi.org/10.1016/j.ijinfomgt.2019.102047
Li QF, Song ZM (2022) High-performance concrete strength prediction based on ensemble learning. Constr Build Mater. https://doi.org/10.1016/j.conbuildmat.2022.126694
Li B, Gao A, Li Y et al (2023a) Effect of silica fume content on the mechanical strengths, compressive stress–strain behavior and microstructures of geopolymeric recycled aggregate concrete. Constr Build Mater. https://doi.org/10.1016/j.conbuildmat.2023.131417
Li D, Tang Z, Kang Q et al (2023b) Machine Learning-Based Method for Predicting Compressive Strength of Concrete. Processes. https://doi.org/10.3390/pr11020390
Mahmoodi-Babolan N, Heydari A, Nematollahzadeh A (2019) Removal of methylene blue via bioinspired catecholamine/starch superadsorbent and the efficiency prediction by response surface methodology and artificial neural network-particle swarm optimization. Bioresour Technol. https://doi.org/10.1016/j.biortech.2019.122084
Mermerdaş K, Arbili MM (2015) Explicit formulation of drying and autogenous shrinkage of concretes with binary and ternary blends of silica fume and fly ash. Constr Build Mater. https://doi.org/10.1016/j.conbuildmat.2015.07.074
Moayedi H, Eghtesad A, Khajehzadeh M, et al (2022) Optimized ANNs for predicting compressive strength of high-performance concrete. Steel and Composite Structures 44:. https://doi.org/10.12989/scs.2022.44.6.867
Muhammad Adnan Ikram R, Dai HL, mirshekari chargari M, et al (2022) Prediction of the FRP reinforced concrete beam shear capacity by using ELM-CRFOA. Measurement (Lond) 205:. https://doi.org/10.1016/j.measurement.2022.112230
Murugesan T, Vidjeapriya R, Bahurudeen A (2020) Sugarcane bagasse ash-blended concrete for effective resource utilization between sugar and construction industries. Sugar Tech. https://doi.org/10.1007/s12355-020-00794-2
Neville A, Tcin P-CA (1998) High performance concrete-An overview
Nuruddin MF, Khan SU, Shafiq N (2014) Effect of calcined kaolin on the mechanical properties of high-strength concrete as cement replacing material. In: Applied Mechanics and Materials. Trans Tech Publications Ltd, pp 375–380
Olawale SO, Kareem MA, Muritala HT et al (2021a) Utilization of Iron Filings as Partial Replacements for Sand in Self-Compacting Concrete. Tanzania J Sci 47:906–916. https://doi.org/10.4314/tjs.v47i3.3
Olawale SOA, Kareem MA, Ojo OY et al (2021b) Strength characteristics of m40 grade concrete using waste pet as replacement for sand. Nigerian J Technol Dev. https://doi.org/10.4314/njtd.v18i3.5
Olawuyi BJ, Babafemi AJ, Boshoff WP (2021) Early-age and long-term strength development of high-performance concrete with SAP. Constr Build Mater. https://doi.org/10.1016/j.conbuildmat.2020.121798
Olonade KA, Akindahunsi AA, Ajagbe WO, et al (2023) Pretreatment of recycle aggregates
Öztürk OB, Başar E (2022) Multiple linear regression analysis and artificial neural networks based decision support system for energy efficiency in shipping. Ocean Eng. https://doi.org/10.1016/j.oceaneng.2021.110209
Pham AD, Ngo NT, Nguyen QT, Truong NS (2020) Hybrid machine learning for predicting strength of sustainable concrete. Soft Comput. https://doi.org/10.1007/s00500-020-04848-1
Sabet FA, Libre NA, Shekarchi M (2013) Mechanical and durability properties of self consolidating high performance concrete incorporating natural zeolite, silica fume and fly ash. Constr Build Mater 44:175–184. https://doi.org/10.1016/j.conbuildmat.2013.02.069
Sadowski Ł, Piechówka-Mielnik M, Widziszowski T et al (2019) Hybrid ultrasonic-neural prediction of the compressive strength of environmentally friendly concrete screeds with high volume of waste quartz mineral dust. J Clean Prod. https://doi.org/10.1016/j.jclepro.2018.12.059
Shafiq N, Nuruddin MF, Khan SU, Ayub T (2015) Calcined kaolin as cement replacing material and its use in high strength concrete. Constr Build Mater 81:313–323. https://doi.org/10.1016/j.conbuildmat.2015.02.050
Shekari AH, Razzaghi MS (2011) Influence of nano particles on durability and mechanical properties of high performance concrete. In: Procedia Engineering. pp 3036–3041
Singh P, Adebanjo A, Shafiq N et al (2023) Development of performance-based models for green concrete using multiple linear regression and artificial neural network. Int J Interactive Design Manuf (IJIDeM). https://doi.org/10.1007/s12008-023-01386-6
Smarzewski P (2019) Influence of silica fume on mechanical and fracture properties of high performance concrete. In: Procedia Structural Integrity. Elsevier B.V., pp 5–12
Usman A, Hartadi Sutanto M, Bin Napiah M, Shehu Aliyu Yaro N (2021) Response Surface Methodology Optimization in Asphalt Mixtures: A Review. In: Response Surface Methodology in Engineering Science
Vejmelková E, Keppert M, Rovnaníková P et al (2012) Properties of high performance concrete containing fine-ground ceramics as supplementary cementitious material. Cem Concr Compos 34:55–61. https://doi.org/10.1016/j.cemconcomp.2011.09.018
Wang D, Zhou X, Meng Y, Chen Z (2017) Durability of concrete containing fly ash and silica fume against combined freezing-thawing and sulfate attack. Constr Build Mater. https://doi.org/10.1016/j.conbuildmat.2017.04.172
Xincheng P, Jixin D, Milestone N (2012) Super-high-strength high performance concrete
Yaseen ZM, Deo RC, Hilal A et al (2018) Predicting compressive strength of lightweight foamed concrete using extreme learning machine model. Adv Eng Softw. https://doi.org/10.1016/j.advengsoft.2017.09.004
Yu C, Yao W (2017) Robust linear regression: A review and comparison. Commun Stat Simul Comput 46
Zaitri R, Bederina M, Bouziani T et al (2014) Development of high performances concrete based on the addition of grinded dune sand and limestone rock using the mixture design modelling approach. Constr Build Mater 60:8–16. https://doi.org/10.1016/j.conbuildmat.2014.02.062
Zhang Z, Li W, Yang J (2021) Analysis of stochastic process to model safety risk in construction industry. J Civ Eng Manag. https://doi.org/10.3846/jcem.2021.14108
Zheng Y, Zhang Y, Zhuo J et al (2023) Mesoscale synergistic effect mechanism of aggregate grading and specimen size on compressive strength of concrete with large aggregate size. Constr Build Mater. https://doi.org/10.1016/j.conbuildmat.2023.130346
Zhutovsky S, Kovler K (2012) Effect of internal curing on durability-related properties of high performance concrete. Cem Concr Res 42:20–26. https://doi.org/10.1016/j.cemconres.2011.07.012
Zhutovsky S, Kovler K (2017) Influence of water to cement ratio on the efficiency of internal curing of high-performance concrete. Constr Build Mater 144:311–316. https://doi.org/10.1016/j.conbuildmat.2017.03.203
Acknowledgements
This research was funded by Petroleum Technology Development Fund (PTDF) Nigeria Authors also like to thank Staff of the Concrete Laboratory of Universiti Teknologi PETRONAS (Mdm. Suhaila Bt. Meor Hussin, Mdm. Norhayama Bt. Ramli, Mdm. Rj. Intan Shafinaz Bt Rj M Noor) for providing the technical assistance to conduct the laboratory activities.
Funding
This work was supported by Petroleum Technology Development Fund (PTDF), Grant Number/PTDF/ED/OSS/PHD/AUA/1743/20, Nigeria Adebanjo Abiola has received scholarship and research support from PTDF for a PhD in Civil Engineering at the Universiti Teknologi PETRONAS Malaysia.
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All authors contributed to the study's conception and design. AUA, VK, SN AbR and ASA performed material preparation, data collection, and analysis. The first draft of the manuscript was read by NS, SAF, and PS and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Adebanjo, A.U., Shafiq, N., Razak, S.N.A. et al. Design and modeling the compressive strength of high-performance concrete with silica fume: a soft computing approach. Soft Comput 28, 6059–6083 (2024). https://doi.org/10.1007/s00500-023-09414-z
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DOI: https://doi.org/10.1007/s00500-023-09414-z