{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:21:27Z","timestamp":1777706487231,"version":"3.51.4"},"reference-count":31,"publisher":"SAGE Publications","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2022,9,22]]},"abstract":"<jats:p>The use of recycled glass in the concrete mix instead of natural coarse aggregates and supplemental cementitious material has several advantages, including the conservation of natural resources, the reduction of CO2 emissions, and cost savings. However, due to their qualities, the mechanical properties of concrete containing Ground Glass Particles (GGP) differ from those of natural aggregates concrete. As a result, assessing the compressive strength (CS) of concrete with GGP is crucial. Therefore, this paper proposes the hybrid Machine Learning (ML) model including the Gradient Boosting (GB) and Bayesian optimization (BO) algorithms for predicting the compressive strength of concrete containing GGP. The hybrid ML model is developed and validated based on the training dataset (70% of the data) and the test dataset (30% of the remaining data), respectively. The performance of hybrid ML model is evaluated by three criteria, such as the Pearson correlation coefficient (R), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The K-Fold Cross-Validation technique is also used to verify the reliability of the hybrid ML model). The best performance of the hybrid ML model is determined with the R\u200a=\u200a0.9843, RMSE\u200a=\u200a1.7256 (MPa), and MAE\u200a=\u200a1.3154 (MPa) for training dataset and R\u200a=\u200a0.9784, RMSE\u200a=\u200a2.4338 (MPa) and MAE\u200a=\u200a1.9618 (MPa) for testing dataset. Based on the best hybrid ML model, the sensitivity analysis including SHapley Additive exPlanation (SHAP) and Partial Dependence Plots (PDP) 2D are investigated to obtain an in-depth examination of each individual input variable on the predicted compressive strength of concrete contaning GGP. The sensitivity analysis shows that four factors, such as curing age, surface area, TiO2, and temperature have the most effect on the compressive strength of concrete containing GGP.<\/jats:p>","DOI":"10.3233\/jifs-213298","type":"journal-article","created":{"date-parts":[[2022,6,10]],"date-time":"2022-06-10T11:17:13Z","timestamp":1654859833000},"page":"5913-5927","source":"Crossref","is-referenced-by-count":2,"title":["Using hybrid machine learning model including gradient boosting and Bayesian optimization for predicting compressive strength of concrete containing ground glass particles"],"prefix":"10.1177","volume":"43","author":[{"given":"Van Quan","family":"Tran","sequence":"first","affiliation":[{"name":"University of Transport Technology, Hanoi, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linh Quy","family":"Nguyen","sequence":"additional","affiliation":[{"name":"University of Transport Technology, Hanoi, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-213298_ref1","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.resconrec.2012.10.006","article-title":"Management and recycling of waste glass in concrete products: Current situations in Hong Kong","volume":"70","author":"Ling","year":"2013","journal-title":"Resour. Conserv. Recycl."},{"key":"10.3233\/JIFS-213298_ref2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-74294-4_5"},{"key":"10.3233\/JIFS-213298_ref3","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1016\/j.conbuildmat.2014.08.092","article-title":"Recycled waste glass as fine aggregate replacement in cementitious materials based on Portland cement","volume":"72","author":"Rashad","year":"2014","journal-title":"Constr. Build. Mater."},{"issue":"7","key":"10.3233\/JIFS-213298_ref4","doi-asserted-by":"publisher","first-page":"1309","DOI":"10.1016\/j.conbuildmat.2009.12.030","article-title":"Use of fine glass as ASR inhibitor in glass aggregate mortars","volume":"24","author":"Idir","year":"2010","journal-title":"Constr. Build. Mater"},{"issue":"5","key":"10.3233\/JIFS-213298_ref5","doi-asserted-by":"publisher","first-page":"713","DOI":"10.1016\/j.conbuildmat.2007.01.019","article-title":"Properties of concrete contains mixed colour waste recycled glass as sand and cement replacement","volume":"22","author":"Taha","year":"2008","journal-title":"Constr. Build. Mater."},{"issue":"4","key":"10.3233\/JIFS-213298_ref6","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1016\/j.cemconres.2007.12.001","article-title":"Influence of a fine glass powder on cement hydration: Comparison to fly ash and modeling the degree of hydration","volume":"38","author":"Schwarz","year":"2008","journal-title":"Cem. Concr. Res."},{"issue":"1","key":"10.3233\/JIFS-213298_ref7","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/S0008-8846(99)00213-6","article-title":"Studies on concrete containing ground waste glass","volume":"30","author":"Shao","year":"2000","journal-title":"Cem. Concr. Res."},{"key":"10.3233\/JIFS-213298_ref8","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.cemconcomp.2014.10.004","article-title":"Influence of different particle sizes on reactivity of finely ground glass as supplementary cementitious material (SCM)","volume":"56","author":"Mirzahosseini","year":"2015","journal-title":"Cem. Concr. Compos."},{"issue":"6","key":"10.3233\/JIFS-213298_ref9","doi-asserted-by":"publisher","first-page":"04014190","DOI":"10.1061\/(ASCE)MT.1943-5533.0001151","article-title":"Effect of Combined Glass Particles on Hydration in Cementitious Systems","volume":"27","author":"Mirzahosseini","year":"2015","journal-title":"J. Mater. Civ. Eng."},{"key":"10.3233\/JIFS-213298_ref10","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.cemconres.2014.01.015","article-title":"Effect of curing temperature and glass type on the pozzolanic reactivity of glass powder","volume":"58","author":"Mirzahosseini","year":"2014","journal-title":"Cem. Concr. Res."},{"issue":"9","key":"10.3233\/JIFS-213298_ref11","doi-asserted-by":"publisher","first-page":"1391","DOI":"10.1016\/S0008-8846(02)00787-1","article-title":"Investigations on the compressive strength of silica fume concrete using statistical methods","volume":"32","author":"Bhanja","year":"2002","journal-title":"Cem. Concr. Res."},{"key":"10.3233\/JIFS-213298_ref12","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1016\/j.advengsoft.2013.09.004","article-title":"Prediction of concrete compressive strength: Research on hybrid models genetic based algorithms and ANFIS","volume":"67","author":"Yuan","year":"2014","journal-title":"Adv. Eng. Softw."},{"key":"10.3233\/JIFS-213298_ref13","doi-asserted-by":"publisher","first-page":"554","DOI":"10.1016\/j.conbuildmat.2013.08.078","article-title":"Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength","volume":"49","author":"Chou","year":"2013","journal-title":"Constr. Build. Mater."},{"key":"10.3233\/JIFS-213298_ref14","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1016\/j.conbuildmat.2019.02.117","article-title":"Prediction of permeability and unconfined compressive strength of pervious concrete using evolved support vector regression","volume":"207","author":"Sun","year":"2019","journal-title":"Constr. Build. Mater."},{"key":"10.3233\/JIFS-213298_ref15","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.enggeo.2019.02.021","article-title":"Determination of Young\u2019s modulus of jet grouted coalcretes using an intelligent model","volume":"252","author":"Sun","year":"2019","journal-title":"Eng. Geol."},{"key":"10.3233\/JIFS-213298_ref16","doi-asserted-by":"publisher","first-page":"e6656084","DOI":"10.1155\/2021\/6656084","article-title":"Compressive Strength Prediction of Stabilized Dredged Sediments Using Artificial Neural Network","volume":"2021","author":"Tran","year":"2021","journal-title":"Adv. Civ. Eng."},{"issue":"3","key":"10.3233\/JIFS-213298_ref17","doi-asserted-by":"publisher","first-page":"e0265747","DOI":"10.1371\/journal.pone.0265747","article-title":"Developing random forest hybridization models for estimating the axial bearing capacity of pile","volume":"17","author":"Pham","year":"2022","journal-title":"PLOS ONE"},{"key":"10.3233\/JIFS-213298_ref18","doi-asserted-by":"publisher","first-page":"127103","DOI":"10.1016\/j.conbuildmat.2022.127103","article-title":"Machine learning approach for investigating chloride diffusion coefficient of concrete containing supplementary cementitious materials","volume":"328","author":"Quan Tran","year":"2022","journal-title":"Constr. Build. Mater."},{"key":"10.3233\/JIFS-213298_ref19","doi-asserted-by":"publisher","first-page":"126578","DOI":"10.1016\/j.conbuildmat.2022.126578","article-title":"Evaluating compressive strength of concrete made with recycled concrete aggregates using machine learning approach","volume":"323","author":"Quan Tran","year":"2022","journal-title":"Constr. Build. Mater."},{"key":"10.3233\/JIFS-213298_ref20","doi-asserted-by":"publisher","first-page":"104086","DOI":"10.1016\/j.jobe.2022.104086","article-title":"Using machine learning techniques for predicting autogenous shrinkage of concrete incorporating superabsorbent polymers and supplementary cementitious materials","volume":"49","author":"Hilloulin","year":"2022","journal-title":"J. Build. Eng."},{"issue":"3","key":"10.3233\/JIFS-213298_ref21","doi-asserted-by":"publisher","first-page":"876","DOI":"10.1108\/EC-08-2018-0348","article-title":"New machine learning prediction models for compressive strength of concrete modified with glass cullet","volume":"36","author":"Mirzahosseini","year":"2019","journal-title":"Eng. Comput."},{"issue":"3","key":"10.3233\/JIFS-213298_ref22","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"issue":"1","key":"10.3233\/JIFS-213298_ref23","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"10.3233\/JIFS-213298_ref24","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.2307\/2699986","article-title":"Greedy Function Approximation: A Gradient Boosting Machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"10.3233\/JIFS-213298_ref26","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"issue":"6","key":"10.3233\/JIFS-213298_ref28","doi-asserted-by":"publisher","first-page":"04014190","DOI":"10.1061\/(ASCE)MT.1943-5533.0001151","article-title":"Effect of Combined Glass Particles on Hydration in Cementitious Systems","volume":"27","author":"Mirzahosseini","year":"2015","journal-title":"J. Mater. Civ. Eng."},{"issue":"4","key":"10.3233\/JIFS-213298_ref29","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1016\/S0167-9473(01)00065-2","article-title":"Stochastic gradient boosting","volume":"38","author":"Friedman","year":"2002","journal-title":"Comput. Stat. Data Anal."},{"issue":"1","key":"10.3233\/JIFS-213298_ref30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/BF00120661","article-title":"Bayesian methods in global optimization","volume":"1","author":"Betr\u00f2","year":"1991","journal-title":"J. Glob. Optim."},{"issue":"1","key":"10.3233\/JIFS-213298_ref31","doi-asserted-by":"publisher","first-page":"26","DOI":"10.11989\/JEST.1674-862X.80904120","article-title":"Hyperparameter Optimization for Machine Learning Models Based on Bayesian Optimization","volume":"17","author":"Wu","year":"2019","journal-title":"J. Electron. Sci. Technol."},{"issue":"5","key":"10.3233\/JIFS-213298_ref32","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"issue":"3","key":"10.3233\/JIFS-213298_ref33","doi-asserted-by":"publisher","first-page":"679","DOI":"10.22044\/jme.2021.11222.2104","article-title":"A State of the art Catboost-Based T-Distributed Stochastic Neighbor Embedding Technique to Predict Back-break at Dewan Cement Limestone Quarry","volume":"12","author":"Kamran","year":"2021","journal-title":"J. Min. Environ."}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-213298","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:46:58Z","timestamp":1777456018000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-213298"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,22]]},"references-count":31,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.3233\/jifs-213298","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,22]]}}}