{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T20:22:32Z","timestamp":1784838152422,"version":"3.55.0"},"reference-count":68,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,3,24]],"date-time":"2025-03-24T00:00:00Z","timestamp":1742774400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,3,24]],"date-time":"2025-03-24T00:00:00Z","timestamp":1742774400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"TRR  research grant","award":["SASTRA\/SoCE\/PB\/TRR2022"],"award-info":[{"award-number":["SASTRA\/SoCE\/PB\/TRR2022"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Computing"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>High strength concrete (HSC) is undoubtedly the most advanced building materials available nowadays. Its production involves simple steps with a variety of additives, including cement, water, fine and coarse aggregates, fly ash (FA), and ground granulated blast furnace slag (GGBFS). Although the interactions between these materials do not strictly follow a mathematical formula, the amounts of these ingredients show a major impact on the compressive strength. The most often used mechanical property for quality monitoring in concrete is its compressive strength after 28 days. It is crucial to have a tool that can directly simulate these interactions prior to production and casting the specimen. Machine learning (ML) models have shown to remain an effective technique to predict the concrete compressive strength, yielding results that can be more reliable than conventional. For the experimental data, the XGBoost Regression (XGB) model is the most dependable, with a <jats:inline-formula>\n              <jats:alternatives>\n                <jats:tex-math>$$R^2$$<\/jats:tex-math>\n                <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:msup>\n                    <mml:mi>R<\/mml:mi>\n                    <mml:mn>2<\/mml:mn>\n                  <\/mml:msup>\n                <\/mml:math>\n              <\/jats:alternatives>\n            <\/jats:inline-formula> equal to 0.92, mean absolute error (MAE), and root mean squared error (RMSE) values of 2.92 <jats:italic>MPa<\/jats:italic> and 4.45 <jats:italic>MPa<\/jats:italic>. In addition, a comparison of particle swarm optimization was used to improve the relationship between input parameters and concrete compressive strength (CS). The study emphasises the accuracy with which machine learning approaches, specifically the XGB, can estimate the CS in building materials is higher than other models. It further provides researchers a swift and more reliable way to evaluate the effects of materials along with other factors on CS, eliminating the requirement for lengthy and expensive trial experiments.<\/jats:p>","DOI":"10.1007\/s10791-025-09517-1","type":"journal-article","created":{"date-parts":[[2025,3,24]],"date-time":"2025-03-24T03:31:09Z","timestamp":1742787069000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Prediction of ultimate strength for high strength concrete (HSC) using machine learning approaches - optimized by PSO technique"],"prefix":"10.1007","volume":"28","author":[{"given":"P.","family":"Ruba","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S.","family":"Aarthi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"P.","family":"Bhuvaneshwari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,3,24]]},"reference":[{"issue":"3","key":"9517_CR1","doi-asserted-by":"publisher","first-page":"302","DOI":"10.3390\/buildings12030302","volume":"12","author":"H Chen","year":"2022","unstructured":"Chen H, Li X, Wu Y, Zuo L, Lu M, Zhou Y. Compressive strength prediction of high-strength concrete using long short-term memory and machine learning algorithms. Buildings. 2022;12(3):302.","journal-title":"Buildings"},{"issue":"8","key":"9517_CR2","doi-asserted-by":"publisher","first-page":"1227","DOI":"10.1016\/S0008-8846(01)00553-1","volume":"31","author":"M Mbessa","year":"2001","unstructured":"Mbessa M, P\u00e9ra J. Durability of high-strength concrete in ammonium sulfate solution. Cem Concr Res. 2001;31(8):1227\u201331.","journal-title":"Cem Concr Res"},{"issue":"12","key":"9517_CR3","doi-asserted-by":"publisher","first-page":"1875","DOI":"10.1016\/S0008-8846(99)00152-0","volume":"29","author":"W Ji-Zong","year":"1999","unstructured":"Ji-Zong W, Hong-Guang N, Jin-Yun H. The application of automatic acquisition of knowledge to mix design of concrete. Cem Concr Res. 1999;29(12):1875\u201380.","journal-title":"Cem Concr Res"},{"key":"9517_CR4","unstructured":"Neville AM, et al. Properties of Concrete . Longman London, ??? 1995."},{"key":"9517_CR5","first-page":"1","volume":"26","author":"S Shah","year":"1993","unstructured":"Shah S. Recent trends in the science and technology of concrete. Concrete Technol. 1993;26:1\u201318.","journal-title":"Concrete Technol"},{"key":"9517_CR6","unstructured":"Bache HH. Densified cement ultra-fine particle-based materials 1981."},{"key":"9517_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.jobe.2020.101998","volume":"35","author":"KL Chung","year":"2021","unstructured":"Chung KL, Wang L, Ghannam M, Guan M, Luo J. Prediction of concrete compressive strength based on early-age effective conductivity measurement. J Buil Eng. 2021;35: 101998.","journal-title":"J Buil Eng"},{"key":"9517_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.conbuildmat.2020.118581","volume":"247","author":"KT Nguyen","year":"2020","unstructured":"Nguyen KT, Nguyen QD, Le TA, Shin J, Lee K. Analyzing the compressive strength of green fly ash based geopolymer concrete using experiment and machine learning approaches. Constr Build Mater. 2020;247: 118581.","journal-title":"Constr Build Mater"},{"key":"9517_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.cemconcomp.2020.103863","volume":"115","author":"E Gomaa","year":"2021","unstructured":"Gomaa E, Han T, ElGawady M, Huang J, Kumar A. Machine learning to predict properties of fresh and hardened alkali-activated concrete. Cement Concr Compos. 2021;115: 103863.","journal-title":"Cement Concr Compos"},{"key":"9517_CR10","doi-asserted-by":"crossref","unstructured":"Chiew FH. Prediction of blast furnace slag concrete compressive strength using artificial neural networks and multiple regression analysis. In: 2019 International Conference on Computer and Drone Applications (IConDA), pp. 54\u201358;2019. IEEE.","DOI":"10.1109\/IConDA47345.2019.9034920"},{"key":"9517_CR11","unstructured":"Kosmatka SH, Panarese W, Allen G, Cumming S. Design and Control of Concrete Mixtures. Canadian Portland Cement Association [CPCA], ??? 1991."},{"issue":"1","key":"9517_CR12","first-page":"23","volume":"56","author":"M Ozturan","year":"2008","unstructured":"Ozturan M, Kutlu B, Ozturan T. Comparison of concrete strength prediction techniques with artificial neural network approach. Build Res J. 2008;56(1):23\u201336.","journal-title":"Build Res J"},{"key":"9517_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.conbuildmat.2020.121117","volume":"266","author":"M-C Kang","year":"2021","unstructured":"Kang M-C, Yoo D-Y, Gupta R. Machine learning-based prediction for compressive and flexural strengths of steel fiber-reinforced concrete. Constr Build Mater. 2021;266: 121117.","journal-title":"Constr Build Mater"},{"key":"9517_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.conbuildmat.2020.118271","volume":"244","author":"T Han","year":"2020","unstructured":"Han T, Siddique A, Khayat K, Huang J, Kumar A. An ensemble machine learning approach for prediction and optimization of modulus of elasticity of recycled aggregate concrete. Constr Build Mater. 2020;244: 118271.","journal-title":"Constr Build Mater"},{"key":"9517_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.cemconres.2021.106449","volume":"145","author":"PG Asteris","year":"2021","unstructured":"Asteris PG, Skentou AD, Bardhan A, Samui P, Pilakoutas K. Predicting concrete compressive strength using hybrid Ensembling of surrogate machine learning models. Cem Concr Res. 2021;145: 106449.","journal-title":"Cem Concr Res"},{"issue":"3","key":"9517_CR16","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1007\/s42979-021-00592-x","volume":"2","author":"IH Sarker","year":"2021","unstructured":"Sarker IH. Machine learning: Algorithms, real-world applications and research directions. SN Comput Sci. 2021;2(3):160.","journal-title":"SN Comput Sci"},{"issue":"Suppl 2","key":"9517_CR17","doi-asserted-by":"publisher","first-page":"1655","DOI":"10.1007\/s00366-021-01284-z","volume":"38","author":"MM Hameed","year":"2022","unstructured":"Hameed MM, AlOmar MK, Baniya WJ, AlSaadi MA. Prediction of high-strength concrete: high-order response surface methodology modeling approach. Eng Comput. 2022;38(Suppl 2):1655\u201368.","journal-title":"Eng Comput"},{"issue":"19","key":"9517_CR18","doi-asserted-by":"publisher","first-page":"13089","DOI":"10.1007\/s00521-021-06004-8","volume":"33","author":"PG Asteris","year":"2021","unstructured":"Asteris PG, Koopialipoor M, Armaghani DJ, Kotsonis EA, Louren\u00e7o PB. Prediction of cement-based mortars compressive strength using machine learning techniques. Neural Comput Appl. 2021;33(19):13089\u2013121.","journal-title":"Neural Comput Appl"},{"issue":"15","key":"9517_CR19","doi-asserted-by":"publisher","first-page":"11807","DOI":"10.1007\/s00521-019-04663-2","volume":"32","author":"PG Asteris","year":"2020","unstructured":"Asteris PG, Mokos VG. Concrete compressive strength using artificial neural networks. Neural Comput Appl. 2020;32(15):11807\u201326.","journal-title":"Neural Comput Appl"},{"key":"9517_CR20","doi-asserted-by":"publisher","first-page":"1145591","DOI":"10.3389\/fbuil.2023.1145591","volume":"9","author":"Y Gamil","year":"2023","unstructured":"Gamil Y. Machine learning in concrete technology: a review of current researches, trends, and applications. Front Built Environ. 2023;9:1145591.","journal-title":"Front Built Environ"},{"key":"9517_CR21","doi-asserted-by":"publisher","first-page":"104","DOI":"10.56947\/amcs.v18.218","volume":"18","author":"S Kamolov","year":"2023","unstructured":"Kamolov S. Machine learning methods in analysis of concrete: a state-of-the-art review. Ann Mathe Comput Sci. 2023;18:104\u201315.","journal-title":"Ann Mathe Comput Sci"},{"key":"9517_CR22","doi-asserted-by":"publisher","first-page":"1853","DOI":"10.7717\/peerj-cs.1853","volume":"10","author":"SI Hassan","year":"2024","unstructured":"Hassan SI, Syed SA, Ali SW, Zahid H, Tariq S, Alam MM, et al. Systematic literature review on the application of machine learning for the prediction of properties of different types of concrete. Peer J Comput Sci. 2024;10:1853.","journal-title":"Peer J Comput Sci"},{"issue":"1","key":"9517_CR23","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/S0958-9465(00)00071-8","volume":"23","author":"B Bharatkumar","year":"2001","unstructured":"Bharatkumar B, Narayanan R, Raghuprasad B, Ramachandramurthy D. Mix proportioning of high performance concrete. Cement Concr Compos. 2001;23(1):71\u201380.","journal-title":"Cement Concr Compos"},{"key":"9517_CR24","doi-asserted-by":"publisher","first-page":"1533","DOI":"10.1007\/s00521-015-1952-6","volume":"27","author":"ZM Yaseen","year":"2016","unstructured":"Yaseen ZM, El-Shafie A, Afan HA, Hameed M, Mohtar WHMW, Hussain A. RBFNN versus FFNN for daily river flow forecasting at Johor river, Malaysia. Neural Comput Appl. 2016;27:1533\u201342.","journal-title":"Neural Comput Appl"},{"issue":"10","key":"9517_CR25","doi-asserted-by":"publisher","first-page":"1525","DOI":"10.1016\/S0008-8846(02)00827-X","volume":"32","author":"V Papadakis","year":"2002","unstructured":"Papadakis V, Tsimas S. Supplementary cementing materials in concrete: Part i: efficiency and design. Cem Concr Res. 2002;32(10):1525\u201332.","journal-title":"Cem Concr Res"},{"issue":"1","key":"9517_CR26","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.conbuildmat.2008.01.014","volume":"23","author":"BR Prasad","year":"2009","unstructured":"Prasad BR, Eskandari H, Reddy BV. Prediction of compressive strength of SCC and HPC with high volume fly ash using ANN. Constr Build Mater. 2009;23(1):117\u201328.","journal-title":"Constr Build Mater"},{"issue":"5","key":"9517_CR27","doi-asserted-by":"publisher","first-page":"709","DOI":"10.1016\/j.conbuildmat.2009.10.037","volume":"24","author":"J Sobhani","year":"2010","unstructured":"Sobhani J, Najimi M, Pourkhorshidi AR, Parhizkar T. Prediction of the compressive strength of no-slump concrete: a comparative study of regression, neural network and anfis models. Constr Build Mater. 2010;24(5):709\u201318.","journal-title":"Constr Build Mater"},{"issue":"Suppl 1","key":"9517_CR28","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1007\/s00366-020-01137-1","volume":"38","author":"G Zhang","year":"2022","unstructured":"Zhang G, Ali ZH, Aldlemy MS, Mussa MH, Salih SQ, Hameed MM, Al-Khafaji ZS, Yaseen ZM. Reinforced concrete deep beam shear strength capacity modelling using an integrative bio-inspired algorithm with an artificial intelligence model. Eng Comput. 2022;38(Suppl 1):15\u201328.","journal-title":"Eng Comput"},{"key":"9517_CR29","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.enconman.2019.01.005","volume":"183","author":"AA Alnaqi","year":"2019","unstructured":"Alnaqi AA, Moayedi H, Shahsavar A, Nguyen TK. Prediction of energetic performance of a building integrated photovoltaic\/thermal system thorough artificial neural network and hybrid particle swarm optimization models. Energy Convers Manage. 2019;183:137\u201348.","journal-title":"Energy Convers Manage"},{"key":"9517_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.conbuildmat.2020.120950","volume":"266","author":"H Nguyen","year":"2021","unstructured":"Nguyen H, Vu T, Vo TP, Thai H-T. Efficient machine learning models for prediction of concrete strengths. Constr Build Mater. 2021;266: 120950.","journal-title":"Constr Build Mater"},{"issue":"12","key":"9517_CR31","doi-asserted-by":"publisher","first-page":"1727","DOI":"10.1061\/(ASCE)MT.1943-5533.0000324","volume":"23","author":"P Zohrevand","year":"2011","unstructured":"Zohrevand P, Mirmiran A. Behavior of ultrahigh-performance concrete confined by fiber-reinforced polymers. J Mater Civ Eng. 2011;23(12):1727\u201334.","journal-title":"J Mater Civ Eng"},{"key":"9517_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.strusafe.2021.102098","volume":"91","author":"M Zhang","year":"2021","unstructured":"Zhang M, Akiyama M, Shintani M, Xin J, Frangopol DM. Probabilistic estimation of flexural loading capacity of existing rc structures based on observational corrosion-induced crack width distribution using machine learning. Struct Saf. 2021;91: 102098.","journal-title":"Struct Saf"},{"key":"9517_CR33","first-page":"02625","volume":"19","author":"P Guo","year":"2023","unstructured":"Guo P, Mahjoubi S, Liu K, Meng W, Bao Y. Self-updatable ai-assisted design of low-carbon cost-effective ultra-high-performance concrete (UHPC). Case Stud Const Mater. 2023;19:02625.","journal-title":"Case Stud Const Mater"},{"key":"9517_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.cemconcomp.2024.105723","volume":"153","author":"P Guo","year":"2024","unstructured":"Guo P, Meng W, Bao Y. Knowledge-guided data-driven design of ultra-high-performance geopolymer (UHPG). Cement Concrete Compos. 2024;153: 105723.","journal-title":"Cement Concrete Compos"},{"issue":"5","key":"9517_CR35","first-page":"635","volume":"24","author":"C Karina","year":"2017","unstructured":"Karina C, Chun P, Okubo K. Tensile strength prediction of corroded steel plates by using machine learning approach. Steel Compos Struct. 2017;24(5):635\u201341.","journal-title":"Steel Compos Struct"},{"issue":"11","key":"9517_CR36","doi-asserted-by":"publisher","first-page":"3934","DOI":"10.3390\/ma15113934","volume":"15","author":"X-Y Huang","year":"2022","unstructured":"Huang X-Y, Wu K-Y, Wang S, Lu T, Lu Y-F, Deng W-C, Li H-M. Compressive strength prediction of rubber concrete based on artificial neural network model with hybrid particle swarm optimization algorithm. Materials. 2022;15(11):3934.","journal-title":"Materials"},{"key":"9517_CR37","doi-asserted-by":"publisher","first-page":"1025","DOI":"10.1016\/j.ijepes.2015.06.026","volume":"73","author":"N Kumarappan","year":"2015","unstructured":"Kumarappan N, Suresh K. Combined SA PSO method for transmission constrained maintenance scheduling using levelized risk method. Int J Elect Power Energy Syst. 2015;73:1025\u201334.","journal-title":"Int J Elect Power Energy Syst"},{"issue":"12","key":"9517_CR38","doi-asserted-by":"publisher","first-page":"7937","DOI":"10.1007\/s00500-021-05676-7","volume":"25","author":"M Cheng","year":"2021","unstructured":"Cheng M, Liu B. Application of an extended VES production function model based on improved pso algorithm. Soft Comput. 2021;25(12):7937\u201345.","journal-title":"Soft Comput"},{"key":"9517_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2021.122487","volume":"239","author":"J Chang","year":"2022","unstructured":"Chang J, Li Z, Huang Y, Yu X, Jiang R, Huang R, Yu X. Multi-objective optimization of a novel combined cooling, dehumidification and power system using improved m-PSO algorithm. Energy. 2022;239: 122487.","journal-title":"Energy"},{"key":"9517_CR40","doi-asserted-by":"publisher","first-page":"2","DOI":"10.25103\/jestr.142.09","volume":"14","author":"S Paudel","year":"2021","unstructured":"Paudel S, Bhusal B. Investigation of modelling approaches for non-linear analysis of reinforced concrete frames. J Eng Sci Technol Rev. 2021;14:2.","journal-title":"J Eng Sci Technol Rev"},{"key":"9517_CR41","unstructured":"Shi K. An empirical analysis of automl tools and techniques with automated feature engineering. Master\u2019s thesis, University of Windsor (Canada) 2022."},{"key":"9517_CR42","first-page":"4697","volume":"33","author":"AG Wilson","year":"2020","unstructured":"Wilson AG, Izmailov P. Bayesian deep learning and a probabilistic perspective of generalization. Adv Neural Inf Process Syst. 2020;33:4697\u2013708.","journal-title":"Adv Neural Inf Process Syst"},{"issue":"1","key":"9517_CR43","doi-asserted-by":"publisher","first-page":"9539","DOI":"10.1038\/s41598-022-12890-2","volume":"12","author":"V Rathakrishnan","year":"2022","unstructured":"Rathakrishnan V, Bt Beddu S, Ahmed AN. Predicting compressive strength of high-performance concrete with high volume ground granulated blast-furnace slag replacement using boosting machine learning algorithms. Sci Rep. 2022;12(1):9539.","journal-title":"Sci Rep"},{"key":"9517_CR44","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-45014-9_1","volume-title":"Ensemble methods in machine learning","author":"TG Dietterich","year":"2000","unstructured":"Dietterich TG. Ensemble methods in machine learning. Cham: Springer; 2000."},{"issue":"4","key":"9517_CR45","doi-asserted-by":"publisher","first-page":"794","DOI":"10.3390\/ma14040794","volume":"14","author":"A Ahmad","year":"2021","unstructured":"Ahmad A, Farooq F, Niewiadomski P, Ostrowski K, Akbar A, Aslam F, Alyousef R. Prediction of compressive strength of fly ash based concrete using individual and ensemble algorithm. Materials. 2021;14(4):794.","journal-title":"Materials"},{"issue":"2","key":"9517_CR46","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1016\/j.ijsbe.2016.09.003","volume":"5","author":"F Khademi","year":"2016","unstructured":"Khademi F, Jamal SM, Deshpande N, Londhe S. Predicting strength of recycled aggregate concrete using artificial neural network, adaptive neuro-fuzzy inference system and multiple linear regression. Int J Sustain Built Environ. 2016;5(2):355\u201369.","journal-title":"Int J Sustain Built Environ"},{"key":"9517_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.conbuildmat.2020.119889","volume":"260","author":"WB Chaabene","year":"2020","unstructured":"Chaabene WB, Flah M, Nehdi ML. Machine learning prediction of mechanical properties of concrete: critical review. Constr Build Mater. 2020;260: 119889.","journal-title":"Constr Build Mater"},{"key":"9517_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2021.126032","volume":"292","author":"F Farooq","year":"2021","unstructured":"Farooq F, Ahmed W, Akbar A, Aslam F, Alyousef R. Predictive modeling for sustainable high-performance concrete from industrial wastes: a comparison and optimization of models using ensemble learners. J Clean Prod. 2021;292: 126032.","journal-title":"J Clean Prod"},{"key":"9517_CR49","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1007\/BF00116251","volume":"1","author":"JR Quinlan","year":"1986","unstructured":"Quinlan JR. Induction of decision trees. Machine Learn. 1986;1:81\u2013106.","journal-title":"Machine Learn"},{"key":"9517_CR50","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-84858-7","volume-title":"The elements of statistical learning: data mining, inference, and prediction","author":"T Hastie","year":"2009","unstructured":"Hastie T. The elements of statistical learning: data mining, inference, and prediction. Cham: Springer; 2009."},{"key":"9517_CR51","doi-asserted-by":"publisher","DOI":"10.1201\/9781315139470","volume-title":"Classification and Regression Trees","author":"L Breiman","year":"2017","unstructured":"Breiman L. Classification and Regression Trees. London: Routledge; 2017."},{"key":"9517_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/j.clet.2023.100661","volume":"15","author":"S Paudel","year":"2023","unstructured":"Paudel S, Pudasaini A, Shrestha RK, Kharel E. Compressive strength of concrete material using machine learning techniques. Cleaner Eng Technol. 2023;15: 100661.","journal-title":"Cleaner Eng Technol"},{"key":"9517_CR53","doi-asserted-by":"crossref","unstructured":"Chengsheng T, Huacheng L, Bing X. Adaboost typical algorithm and its application research. In: MATEC Web of Conferences. 2017;139:00222. EDP Sciences.","DOI":"10.1051\/matecconf\/201713900222"},{"key":"9517_CR54","unstructured":"Chen T, He T, Benesty M, Khotilovich V, Tang Y, Cho H, Chen K, Mitchell R, Cano I, Zhou T, et al. Xgboost: extreme gradient boosting. R package version. 0.4-2 2015;1(4):1\u20134."},{"key":"9517_CR55","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C. Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785\u2013794;2016.","DOI":"10.1145\/2939672.2939785"},{"key":"9517_CR56","doi-asserted-by":"publisher","DOI":"10.1016\/j.engstruct.2022.114768","volume":"269","author":"N-H Nguyen","year":"2022","unstructured":"Nguyen N-H, Tong KT, Lee S, Karamanli A, Vo TP. Prediction compressive strength of cement-based mortar containing metakaolin using explainable categorical gradient boosting model. Eng Struct. 2022;269: 114768.","journal-title":"Eng Struct"},{"issue":"1","key":"9517_CR57","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1007\/s42107-023-00804-0","volume":"25","author":"A Gogineni","year":"2024","unstructured":"Gogineni A, Panday IK, Kumar P, Paswan RK. Predicting compressive strength of concrete with fly ash and admixture using XGBOOST: a comparative study of machine learning algorithms. Asian J Civil Eng. 2024;25(1):685\u201398.","journal-title":"Asian J Civil Eng"},{"issue":"6","key":"9517_CR58","doi-asserted-by":"publisher","first-page":"3772","DOI":"10.1002\/suco.202100732","volume":"23","author":"B Han","year":"2022","unstructured":"Han B, Wu Y, Liu L. Prediction and uncertainty quantification of compressive strength of high-strength concrete using optimized machine learning algorithms. Struct Concr. 2022;23(6):3772\u201385.","journal-title":"Struct Concr"},{"issue":"3","key":"9517_CR59","doi-asserted-by":"publisher","first-page":"669","DOI":"10.1016\/j.ijforecast.2015.12.003","volume":"32","author":"S Kim","year":"2016","unstructured":"Kim S, Kim H. A new metric of absolute percentage error for intermittent demand forecasts. Int J Forecast. 2016;32(3):669\u201379. https:\/\/doi.org\/10.1016\/j.ijforecast.2015.12.003.","journal-title":"Int J Forecast"},{"issue":"1","key":"9517_CR60","doi-asserted-by":"publisher","first-page":"04024105","DOI":"10.1061\/JSDCCC.SCENG-1520","volume":"30","author":"R Palanivelu","year":"2025","unstructured":"Palanivelu R, Panchanatham B. Study on the mechanical properties of retrofitted concrete damaged under fire and rapid cooling. J Struct Des Const Pract. 2025;30(1):04024105.","journal-title":"J Struct Des Const Pract"},{"key":"9517_CR61","unstructured":"10262 I. Concrete mix proportioning\u2013guidelines. Bureau of Indian Standards, New Dehli, India 2019."},{"key":"9517_CR62","unstructured":"Indian\u00a0Standard I. 516 (1959) method of tests for strength of concrete. New Delhi 2002."},{"key":"9517_CR63","doi-asserted-by":"publisher","DOI":"10.1016\/j.conbuildmat.2021.125021","volume":"308","author":"H Song","year":"2021","unstructured":"Song H, Ahmad A, Farooq F, Ostrowski KA, Ma\u015blak M, Czarnecki S, Aslam F. Predicting the compressive strength of concrete with fly ash admixture using machine learning algorithms. Constr Build Mater. 2021;308: 125021.","journal-title":"Constr Build Mater"},{"issue":"1","key":"9517_CR64","doi-asserted-by":"publisher","first-page":"361","DOI":"10.3390\/app12010361","volume":"12","author":"Y Song","year":"2021","unstructured":"Song Y, Zhao J, Ostrowski KA, Javed MF, Ahmad A, Khan MI, Aslam F, Kinasz R. Prediction of compressive strength of fly-ash-based concrete using ensemble and non-ensemble supervised machine-learning approaches. Appl Sci. 2021;12(1):361.","journal-title":"Appl Sci"},{"key":"9517_CR65","volume":"11","author":"D Chakraborty","year":"2021","unstructured":"Chakraborty D, Awolusi I, Gutierrez L. An explainable machine learning model to predict and elucidate the compressive behavior of high-performance concrete. Res Eng. 2021;11: 100245.","journal-title":"Res Eng"},{"issue":"4","key":"9517_CR66","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1504\/IJMIC.2013.053535","volume":"18","author":"A Garg","year":"2013","unstructured":"Garg A, Tai K. Comparison of statistical and machine learning methods in modelling of data with multicollinearity. Int J Model Ident Control. 2013;18(4):295\u2013312.","journal-title":"Int J Model Ident Control"},{"issue":"1","key":"9517_CR67","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.epsr.2003.12.017","volume":"71","author":"TAA Victoire","year":"2004","unstructured":"Victoire TAA, Jeyakumar AE. Hybrid PSO-SQP for economic dispatch with valve-point effect. Elect Power Syst Res. 2004;71(1):51\u20139.","journal-title":"Elect Power Syst Res"},{"issue":"1","key":"9517_CR68","doi-asserted-by":"publisher","first-page":"5586737","DOI":"10.1155\/2022\/5586737","volume":"2022","author":"MM Hameed","year":"2022","unstructured":"Hameed MM, Abed MA, Al-Ansari N, Alomar MK. Predicting compressive strength of concrete containing industrial waste materials: novel and hybrid machine learning model. Adv Civil Eng. 2022;2022(1):5586737.","journal-title":"Adv Civil Eng"}],"container-title":["Discover Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10791-025-09517-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10791-025-09517-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10791-025-09517-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,29]],"date-time":"2025-03-29T03:31:34Z","timestamp":1743219094000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10791-025-09517-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,24]]},"references-count":68,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["9517"],"URL":"https:\/\/doi.org\/10.1007\/s10791-025-09517-1","relation":{},"ISSN":["2948-2992"],"issn-type":[{"value":"2948-2992","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,24]]},"assertion":[{"value":"22 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 March 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no Competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Comepting interests"}}],"article-number":"17"}}