{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T18:36:16Z","timestamp":1774982176171,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,2,23]],"date-time":"2024-02-23T00:00:00Z","timestamp":1708646400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["2023A1515012136"],"award-info":[{"award-number":["2023A1515012136"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Materials"],"abstract":"<jats:p>To enhance the quality stability of 3D printing concrete, this study introduces a novel machine learning (ML) model based on a stacking strategy for the first time. The model aims to predict the interlayer bonding strength (IBS) of 3D printing concrete. The base models incorporate SVR, KNN, and GPR, and subsequently, these models are stacked to create a robust stacking model. Results from 10-fold cross-validation and statistical performance evaluations reveal that, compared to the base models, the stacking model exhibits superior performance in predicting the IBS of 3D printing concrete, with the R2 value increasing from 0.91 to 0.96. This underscores the efficacy of the developed stacking model in significantly improving prediction accuracy, thereby facilitating the advancement of scaled-up production in 3D printing concrete.<\/jats:p>","DOI":"10.3390\/ma17051033","type":"journal-article","created":{"date-parts":[[2024,2,23]],"date-time":"2024-02-23T10:47:30Z","timestamp":1708685250000},"page":"1033","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Advancing Construction 3D Printing with Predictive Interlayer Bonding Strength: A Stacking Model Paradigm"],"prefix":"10.3390","volume":"17","author":[{"given":"Dinglue","family":"Wu","sequence":"first","affiliation":[{"name":"Poly Changda Engineering Co., Ltd., Guangzhou 510620, China"}]},{"given":"Qiling","family":"Luo","sequence":"additional","affiliation":[{"name":"College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China"},{"name":"Guangdong Provincial Key Laboratory of Durability for Marine Civil Engineering, Shenzhen Key Laboratory for Low-Carbon Construction Material and Technology, Shenzhen 518060, China"},{"name":"Key Lab of Coastal Urban Resilient Infrastructure, MOE, Shenzhen 518060, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4760-0009","authenticated-orcid":false,"given":"Wujian","family":"Long","sequence":"additional","affiliation":[{"name":"College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China"},{"name":"Guangdong Provincial Key Laboratory of Durability for Marine Civil Engineering, Shenzhen Key Laboratory for Low-Carbon Construction Material and Technology, Shenzhen 518060, China"},{"name":"Key Lab of Coastal Urban Resilient Infrastructure, MOE, Shenzhen 518060, China"}]},{"given":"Shunxian","family":"Zhang","sequence":"additional","affiliation":[{"name":"Poly Changda Engineering Co., Ltd., Guangzhou 510620, China"}]},{"given":"Songyuan","family":"Geng","sequence":"additional","affiliation":[{"name":"College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China"},{"name":"Guangdong Provincial Key Laboratory of Durability for Marine Civil Engineering, Shenzhen Key Laboratory for Low-Carbon Construction Material and Technology, Shenzhen 518060, China"},{"name":"Key Lab of Coastal Urban Resilient Infrastructure, MOE, Shenzhen 518060, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1286","DOI":"10.1016\/j.promfg.2019.06.089","article-title":"An Overview on 3D Printing Technology: Technological, Materials, and Applications","volume":"35","author":"Shahrubudin","year":"2019","journal-title":"Procedia Manuf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.culher.2017.02.010","article-title":"3D Printing: State of the Art and Future Perspectives","volume":"26","author":"Balletti","year":"2017","journal-title":"J. Cult. Herit."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1007\/s43452-020-00038-w","article-title":"A Critical Review of 3D Printing in Construction: Benefits, Challenges, and Risks","volume":"20","author":"Romdhane","year":"2020","journal-title":"Arch. Civ. Mech. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"104693","DOI":"10.1016\/j.autcon.2022.104693","article-title":"3D Printing with Cementitious Materials: Challenges and Opportunities for the Construction Sector","volume":"146","year":"2023","journal-title":"Autom. Constr."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"106078","DOI":"10.1016\/j.cemconres.2020.106078","article-title":"Influence of Process Parameters on the Interlayer Bond Strength of Concrete Elements Additive Manufactured by Shotcrete 3D Printing (SC3DP)","volume":"134","author":"Kloft","year":"2020","journal-title":"Cem. Concr. Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"132121","DOI":"10.1016\/j.conbuildmat.2023.132121","article-title":"Improving Interlayer Bond in 3D Printed Concrete through Induced Thermo-Hydrokinetics","volume":"393","author":"Munemo","year":"2023","journal-title":"Constr. Build. Mater."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.cemconres.2019.02.017","article-title":"Hardened Properties of 3D Printed Concrete: The Influence of Process Parameters on Interlayer Adhesion","volume":"119","author":"Wolfs","year":"2019","journal-title":"Cem. Concr. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"106559","DOI":"10.1016\/j.cemconres.2021.106559","article-title":"Modelling the Interlayer Bond Strength of 3D Printed Concrete with Surface Moisture","volume":"150","author":"Moelich","year":"2021","journal-title":"Cem. Concr. Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1126\/science.aaa8415","article-title":"Machine Learning: Trends, Perspectives, and Prospects","volume":"349","author":"Jordan","year":"2015","journal-title":"Science"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"100045","DOI":"10.1016\/j.dibe.2021.100045","article-title":"Machine Learning in Construction: From Shallow to Deep Learning","volume":"6","author":"Xu","year":"2021","journal-title":"Dev. Built Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4032","DOI":"10.1080\/15376494.2021.1917021","article-title":"Comparative Analysis of Different Machine Learning Algorithms to Predict Mechanical Properties of Concrete","volume":"29","author":"Koya","year":"2022","journal-title":"Mech. Adv. Mater. Struct."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.conbuildmat.2018.05.201","article-title":"A Modified Firefly Algorithm-Artificial Neural Network Expert System for Predicting Compressive and Tensile Strength of High-Performance Concrete","volume":"180","author":"Bui","year":"2018","journal-title":"Constr. Build. Mater."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"101326","DOI":"10.1016\/j.jobe.2020.101326","article-title":"Compressive Strength Prediction of Eco-Efficient GGBS-Based Geopolymer Concrete Using GEP Method","volume":"31","author":"Shahmansouri","year":"2020","journal-title":"J. Build. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1145\/212094.212114","article-title":"Overfitting and Undercomputing in Machine Learning","volume":"27","author":"Dietterich","year":"1995","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"113232","DOI":"10.1016\/j.eswa.2020.113232","article-title":"Ensemble Learning by Means of a Multi-Objective Optimization Design Approach for Dealing with Imbalanced Data Sets","volume":"147","year":"2020","journal-title":"Expert. Syst. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"135279","DOI":"10.1016\/j.jclepro.2022.135279","article-title":"Prediction of Compressive Strength of Rice Husk Ash Concrete Based on Stacking Ensemble Learning Model","volume":"382","author":"Li","year":"2023","journal-title":"J. Clean. Prod."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Izadgoshasb, H., Kandiri, A., Shakor, P., Laghi, V., and Gasparini, G. (2021). Predicting Compressive Strength of 3D Printed Mortar in Structural Members Using Machine Learning. Appl. Sci., 11.","DOI":"10.3390\/app112210826"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ali, A., Riaz, R.D., Malik, U.J., Abbas, S.B., Usman, M., Shah, M.U., Kim, I.-H., Hanif, A., and Faizan, M. (2023). Machine Learning-Based Predictive Model for Tensile and Flexural Strength of 3D-Printed Concrete. Materials, 16.","DOI":"10.3390\/ma16114149"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Schossler, R.T., Ullah, S., Alajlan, Z., and Yu, X. (2024, January 29). Improving Decision-Making in 3d Concrete Printing Through Shap-Guided Machine Learning: Predictive Models and Feature Importance for Yield Stress and Viscosity. Available online: https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=4536032.","DOI":"10.2139\/ssrn.4536032"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Marcucci, A., Gaggiotti, C., and Ferarra, L. A Prediction of the Printability of Concrete through Artificial Neural Networks (ANN). Mater. Today Proc., 2023. in press.","DOI":"10.1016\/j.matpr.2023.07.310"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2510","DOI":"10.1109\/TITS.2020.3035647","article-title":"Predicting Bus Passenger Flow and Prioritizing Influential Factors Using Multi-Source Data: Scaled Stacking Gradient Boosting Decision Trees","volume":"22","author":"Wu","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1023\/B:STCO.0000035301.49549.88","article-title":"A Tutorial on Support Vector Regression","volume":"14","author":"Smola","year":"2004","journal-title":"Stat. Comput."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1959","DOI":"10.1162\/089976602760128081","article-title":"Training V-Support Vector Regression: Theory and Algorithms","volume":"14","author":"Chang","year":"2002","journal-title":"Neural Comput."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1016\/j.asoc.2016.12.048","article-title":"Automatic Plaque Segmentation Based on Hybrid Fuzzy Clustering and k Nearest Neighborhood Using Virtual Histology Intravascular Ultrasound Images","volume":"53","author":"Rezaei","year":"2017","journal-title":"Appl. Soft Comput."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.asoc.2014.09.052","article-title":"Single Imputation with Multilayer Perceptron and Multiple Imputation Combining Multilayer Perceptron and K-Nearest Neighbours for Monotone Patterns","volume":"29","year":"2015","journal-title":"Appl. Soft Comput."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.cageo.2019.02.009","article-title":"Design and Implementation of a Parallel Geographically Weighted K-Nearest Neighbor Classifier","volume":"127","author":"Pu","year":"2019","journal-title":"Comput. Geosci."},{"key":"ref_27","first-page":"133","article-title":"Introduction to Gaussian Processes","volume":"168","author":"MacKay","year":"1998","journal-title":"NATO ASI Ser. F Comput. Syst. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1342","DOI":"10.1109\/34.735807","article-title":"Bayesian Classification with Gaussian Processes","volume":"20","author":"Williams","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"6240","DOI":"10.1002\/int.22549","article-title":"Meta-Learning and the New Challenges of Machine Learning","volume":"36","author":"Monteiro","year":"2021","journal-title":"Int. J. Intell. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Doan, T., and Kalita, J. (2015, January 14\u201317). Selecting Machine Learning Algorithms Using Regression Models. Proceedings of the 2015 IEEE International Conference on Data Mining Workshop (ICDMW), Atlantic City, NJ, USA.","DOI":"10.1109\/ICDMW.2015.43"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"140","DOI":"10.38094\/jastt1457","article-title":"A Review on Linear Regression Comprehensive in Machine Learning","volume":"1","author":"Maulud","year":"2020","journal-title":"J. Appl. Sci. Technol. Trends"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"104934","DOI":"10.1016\/j.envint.2019.104934","article-title":"A Comparison of Linear Regression, Regularization, and Machine Learning Algorithms to Develop Europe-Wide Spatial Models of Fine Particles and Nitrogen Dioxide","volume":"130","author":"Chen","year":"2019","journal-title":"Environ. Int."},{"key":"ref_33","first-page":"100768","article-title":"Performance Improvement of Empirical Models for Estimation of Global Solar Radiation in India: A k-Fold Cross-Validation Approach","volume":"40","author":"Saud","year":"2020","journal-title":"Sustain. Energy Technol. Assess."},{"key":"ref_34","unstructured":"Kohavi, R. (1995, January 20\u201325). A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. Proceedings of the International. Joint Conference on Artificial Intelligence, IJCAI, Montreal, QC, Canada."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/s11222-009-9153-8","article-title":"Estimation of Prediction Error by Using K-Fold Cross-Validation","volume":"21","author":"Fushiki","year":"2011","journal-title":"Stat. Comput."},{"key":"ref_36","first-page":"443","article-title":"Daily Sediment Yield Modeling with Artificial Neural Network Using 10-Fold Cross Validation Method: A Small Agricultural Watershed, Kapgari, India","volume":"4","author":"Singh","year":"2011","journal-title":"Int. J. Earth Sci. Eng."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Pham, B.T., Qi, C., Ho, L.S., Nguyen-Thoi, T., Al-Ansari, N., Nguyen, M.D., Nguyen, H.D., Ly, H.-B., Van Le, H., and Prakash, I. (2020). A Novel Hybrid Soft Computing Model Using Random Forest and Particle Swarm Optimization for Estimation of Undrained Shear Strength of Soil. Sustainability, 12.","DOI":"10.3390\/su12062218"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"100312","DOI":"10.1016\/j.cscee.2023.100312","article-title":"Utilizing Time Series Data from 1961 to 2019 Recorded around the World and Machine Learning to Create a Global Temperature Change Prediction Model","volume":"7","author":"Malakouti","year":"2023","journal-title":"Case Stud. Chem. Environ. Eng."},{"key":"ref_39","first-page":"113","article-title":"Development of Prediction Model of Steel Fiber-Reinforced Concrete Compressive Strength Using Random Forest Algorithm Combined with Hyperparameter Tuning and k-Fold Cross-Validation","volume":"5","author":"Imran","year":"2021","journal-title":"East. -Eur. J. Enterp. Technol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"103571","DOI":"10.1016\/j.cemconcomp.2020.103571","article-title":"Interlayer Bonding Improvement of 3D Printed Concrete with Polymer Modified Mortar: Experiments and Molecular Dynamics Studies","volume":"110","author":"Wang","year":"2020","journal-title":"Cem. Concr. Compos."},{"key":"ref_41","first-page":"102327","article-title":"Correlation of Interlayer Properties and Rheological Behaviors of 3DPC with Various Printing Time Intervals","volume":"47","author":"Xu","year":"2021","journal-title":"Addit. Manuf."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"125809","DOI":"10.1016\/j.conbuildmat.2021.125809","article-title":"The Relationship between the Rheological Behavior and Interlayer Bonding Properties of 3D Printing Cementitious Materials with the Addition of Attapulgite","volume":"316","author":"Yao","year":"2022","journal-title":"Constr. Build. Mater."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"109619","DOI":"10.1016\/j.compositesb.2022.109619","article-title":"Effect of Sulphoaluminate Cement on Fresh and Hardened Properties of 3D Printing Foamed Concrete","volume":"232","author":"Liu","year":"2022","journal-title":"Compos. B Eng."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.autcon.2017.08.019","article-title":"Effects of Interlocking on Interlayer Adhesion and Strength of Structures in 3D Printing of Concrete","volume":"83","author":"Zareiyan","year":"2017","journal-title":"Autom. Constr."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"107684","DOI":"10.1016\/j.matdes.2019.107684","article-title":"Method of Enhancing Interlayer Bond Strength in Construction Scale 3D Printing with Mortar by Effective Bond Area Amplification","volume":"169","author":"Marchment","year":"2019","journal-title":"Mater. Des."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Pan, T., Jiang, Y., He, H., Wang, Y., and Yin, K. (2021). Effect of Structural Build-up on Interlayer Bond Strength of 3D Printed Cement Mortars. Materials, 14.","DOI":"10.3390\/ma14020236"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"120094","DOI":"10.1016\/j.conbuildmat.2020.120094","article-title":"Effect of Printing Parameters on Interlayer Bond Strength of 3D Printed Limestone-Calcined Clay-Based Cementitious Materials: An Experimental and Numerical Study","volume":"262","author":"Chen","year":"2020","journal-title":"Constr. Build. Mater."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"118305","DOI":"10.1016\/j.conbuildmat.2020.118305","article-title":"A Novel Additive Mortar Leveraging Internal Curing for Enhancing Interlayer Bonding of Cementitious Composite for 3D Printing","volume":"244","author":"Ma","year":"2020","journal-title":"Constr. Build. Mater."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"106386","DOI":"10.1016\/j.cemconres.2021.106386","article-title":"Investigation of Interlayer Adhesion of 3D Printable Cementitious Material from the Aspect of Printing Process","volume":"143","author":"Weng","year":"2021","journal-title":"Cem. Concr. Res."}],"container-title":["Materials"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1996-1944\/17\/5\/1033\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:03:58Z","timestamp":1760105038000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1996-1944\/17\/5\/1033"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,23]]},"references-count":49,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["ma17051033"],"URL":"https:\/\/doi.org\/10.3390\/ma17051033","relation":{},"ISSN":["1996-1944"],"issn-type":[{"value":"1996-1944","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,23]]}}}