{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T19:35:46Z","timestamp":1774035346587,"version":"3.50.1"},"reference-count":73,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Global Postdoc Fellowship Program of the Czech Technical University in Prague"},{"DOI":"10.13039\/501100001824","name":"Czech Science Foundation","doi-asserted-by":"publisher","award":["24-10892S"],"award-info":[{"award-number":["24-10892S"]}],"id":[{"id":"10.13039\/501100001824","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Buildings"],"abstract":"<jats:p>Enhancing interlayer bond strength remains a critical challenge in the extrusion-based 3D printing of cementitious materials. This study investigates the optimisation of interlayer bond strength in extrusion-based 3D-printed cementitious materials through a combined application of Response Surface Methodology (RSM) and Artificial Neural Networks (ANNs). Using a concise yet comprehensive dataset, RSM provided interpretable main effects, curvature, and interactions, while the ANN captured non-linearities beyond quadratic forms. Comparative analysis revealed that the RSM model achieved higher predictive accuracy (R2=0.95) compared to the ANN model (R2=0.87). Desirability-based optimisation confirmed the critical importance of minimising casting delays to mitigate interlayer weaknesses, with RSM suggesting a water-to-cement (W\/C) ratio of approximately 0.45 and a minimal time gap of less than 5 min, while ANN predicted slightly lower optimal W\/C values but with reduced reliability due to the limited dataset. Sensitivity analysis using partial dependence plots (PDPs) further highlighted that ordinary Portland cement (OPC) content and W\/C ratio are the dominant factors, contributing approximately 2.0 and 1.8 MPa respectively to the variation in predicted bond strength, followed by superplasticiser dosage and silica content. Variables such as water content, viscosity-modifying agent, and time gap exhibited moderate influence, while sand and fibre content had marginal effects within the tested ranges. These results demonstrate that RSM provides robust predictive performance and interpretable optimisation guidance, while ANN offers flexible non-linear modelling but requires larger datasets to achieve stable generalisation. Integrating both methods offers a complementary pathway to advance mix design and process control strategies in 3D concrete printing.<\/jats:p>","DOI":"10.3390\/buildings15203794","type":"journal-article","created":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T14:53:19Z","timestamp":1761058399000},"page":"3794","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Optimisation of Interlayer Bond Strength in 3D-Printed Concrete Using Response Surface Methodology and Artificial Neural Networks"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1944-730X","authenticated-orcid":false,"given":"Lenganji","family":"Simwanda","sequence":"first","affiliation":[{"name":"Klokner Institute, Czech Technical University in Prague, Solinova 7, 160 00 Prague, Czech Republic"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9596-2948","authenticated-orcid":false,"given":"Abayomi B.","family":"David","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Stellenbosch University, Stellenbosch 7602, South Africa"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6595-998X","authenticated-orcid":false,"given":"Gatheeshgar","family":"Perampalam","sequence":"additional","affiliation":[{"name":"School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough TS1 3BX, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5297-0071","authenticated-orcid":false,"given":"Oladimeji B.","family":"Olalusi","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Durban University of Technology, Pietermaritzburg 3209, South Africa"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9346-3204","authenticated-orcid":false,"given":"Miroslav","family":"Sykora","sequence":"additional","affiliation":[{"name":"Klokner Institute, Czech Technical University in Prague, Solinova 7, 160 00 Prague, Czech Republic"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"106746","DOI":"10.1016\/j.cemconres.2022.106746","article-title":"The realities of additively manufactured concrete structures in practice","volume":"156","author":"Bos","year":"2022","journal-title":"Cem. Concr. Res."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Li, Z., Hojati, M., Wu, Z., Piasente, J., Ashrafi, N., Duarte, J., and Radli\u0144ska, A. (2020). Fresh and hardened properties of extrusion-based 3D-printed cementitious materials: A review. Sustainability, 12.","DOI":"10.3390\/su12145628"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"198","DOI":"10.47481\/jscmt.1143239","article-title":"Bibliographic analysis on 3D printing in the building and construction industry: Printing systems, material properties, challenges, and future trends","volume":"7","author":"Shahzad","year":"2022","journal-title":"J. Sustain. Constr. Mater. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Pemas, S., Sougioultzi, K., Kouroutzidou, C., Stefanidou, M., Konstantinidis, A., and Pechlivani, E. (2024). Enhancing clay-based 3D-printed mortars with polymeric mesh reinforcement techniques. Polymers, 16.","DOI":"10.3390\/polym16152182"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Shaw, R., Maurya, K., and Maity, D. (2021, January 23\u201324). 3D concrete printing: A road map for future of automated construction in India. Proceedings of the 36th National Convention of Civil Engineers & National Conference on Innovation, Mechanization and Modern Techniques in Civil Engineering, Ranchi, India.","DOI":"10.36375\/prepare_u.iei.a133"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Satish, S., and Umar, T. (2025). Bridging innovation and practice: Assessing the readiness for 3D printing in construction. Eng. Constr. Archit. Manag.","DOI":"10.1108\/ECAM-09-2024-1284"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"63","DOI":"10.54203\/jceu.2024.5","article-title":"3D printing in civil engineering: Pioneering affordable housing solutions","volume":"14","author":"Firoozi","year":"2024","journal-title":"J. Civ. Eng. Urban."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kantaros, A., Zacharia, P., Drosos, C., Papoutsidakis, M., Pallis, E., and Ganetsos, T. (2025). Smart infrastructure and additive manufacturing: Synergies, advantages, and limitations. Appl. Sci., 15.","DOI":"10.2139\/ssrn.5196883"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1080\/17452759.2018.1500420","article-title":"Time gap effect on bond strength of 3D-printed concrete","volume":"14","author":"Tay","year":"2018","journal-title":"Virtual Phys. Prototyp."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Lee, H., Kim, J., Moon, J., Kim, W., and Seo, E. (2019). Evaluation of the mechanical properties of a 3D-printed mortar. Materials, 12.","DOI":"10.3390\/ma12244104"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Lee, Y., Lee, S., Kim, J., Jeong, H., Han, S., and Kim, K. (2024). Interlayer bond strength of 3D printed concrete members with ultra high performance concrete (UHPC) mix. Buildings, 14.","DOI":"10.3390\/buildings14072060"},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"33","DOI":"10.21809\/rilemtechlett.2019.84","article-title":"Surface modification as a technique to improve inter-layer bonding strength in 3D-printed cementitious materials","volume":"4","author":"Putten","year":"2019","journal-title":"RILEM Tech. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"110840","DOI":"10.1016\/j.engfracmech.2025.110840","article-title":"Interfacial bond effects on the shear strength and damage in 3D-printed concrete structures: A combined experimental and numerical study","volume":"315","author":"Shahzad","year":"2025","journal-title":"Eng. Fract. Mech."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.proeng.2016.07.357","article-title":"3D printing of buildings and building components as the future of sustainable construction?","volume":"151","author":"Hager","year":"2022","journal-title":"Procedia Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2503682","DOI":"10.1002\/adfm.202503682","article-title":"Dynamic boronate ester chemistry facilitating 3D printing interlayer adhesion and modular 4D printing of polylactic acid","volume":"35","author":"Peng","year":"2025","journal-title":"Adv. Funct. Mater."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"112998","DOI":"10.1016\/j.jobe.2025.112998","article-title":"Optimization design and regression model analysis of mechanical properties of 3D printed concrete","volume":"108","author":"Qi","year":"2025","journal-title":"J. Build. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wang, C., Lian, J., Fang, Y., Fan, G., Yang, Y., Huang, W., and Shi, S. (2025). Rheological optimization of 3D-printed cementitious materials using response surface methodology. Materials, 18.","DOI":"10.3390\/ma18173933"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Jawad, H.H., Kordani, N., Bagheri, A., and Derazkola, H.A. (2025). Optimization of 3D printing parameters for enhanced mechanical strength: Effects of glass fiber reinforcement and fill ratio using RSM and ANOVA. J. Compos. Sci., 9.","DOI":"10.3390\/jcs9020063"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"e25973","DOI":"10.1016\/j.heliyon.2024.e25973","article-title":"RSM-based modelling for predicting and optimizing the rheological and mechanical properties of fibre-reinforced laterized self-compacting concrete","volume":"10","author":"Patil","year":"2024","journal-title":"Heliyon"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"105970","DOI":"10.1016\/j.rineng.2025.105970","article-title":"A neural network-based model for assessing 3D printable concrete performance in robotic fabrication","volume":"27","author":"Cui","year":"2025","journal-title":"Results Eng."},{"key":"ref_22","first-page":"e03935","article-title":"Data-driven models for predicting compressive strength of 3D-printed fiber-reinforced concrete using interpretable machine learning algorithms","volume":"21","author":"Arif","year":"2024","journal-title":"Case Stud. Constr. Mater."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"13027","DOI":"10.1007\/s00521-021-05999-4","article-title":"Neural network predictions of the simulated rheological response of cement paste in the FlowCyl","volume":"33","author":"Sheiati","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_24","first-page":"e01605","article-title":"Hybrid machine learning and multi-objective optimization for intelligent design of green and low-carbon concrete","volume":"45","author":"Peng","year":"2025","journal-title":"Sustain. Mater. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"104875","DOI":"10.1016\/j.rineng.2025.104875","article-title":"Optimizing mechanical properties of recycled aggregate concrete with graphene oxide and steel fibers: A predictive approach using ANN and RSM","volume":"26","author":"Azhagarsamy","year":"2025","journal-title":"Results Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"04024034","DOI":"10.1061\/JCCOF2.CCENG-4453","article-title":"Modeling of Steel-Reinforced Grout Composite System-to-Concrete Bond Capacity Using Artificial Neural Networks","volume":"28","author":"Ombres","year":"2024","journal-title":"J. Compos. Constr."},{"key":"ref_27","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_28","doi-asserted-by":"crossref","first-page":"131851","DOI":"10.1016\/j.conbuildmat.2023.131851","article-title":"AI-assisted optimisation of green concrete mixes incorporating recycled concrete aggregates","volume":"391","author":"Zandifaez","year":"2023","journal-title":"Constr. Build. Mater."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1038\/s44296-025-00058-8","article-title":"Artificial intelligence in the design, optimization, and performance prediction of concrete materials: A comprehensive review","volume":"3","author":"Luo","year":"2025","journal-title":"npj Mater. Sustain."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"107396","DOI":"10.1016\/j.jobe.2023.107396","article-title":"Multi-objective optimization of concrete mix design based on machine learning","volume":"76","author":"Zheng","year":"2023","journal-title":"J. Build. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Getachew, E.M., Taffese, W.Z., Espinosa-Leal, L., and Yehualaw, M.D. (2025). Machine learning applications in sustainable construction materials: A scientometrics review of global trends, themes, and future directions. Sustainability, 17.","DOI":"10.3390\/su17188453"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"108486","DOI":"10.1016\/j.jobe.2024.108486","article-title":"Optimization and prediction of mechanical properties of composite concrete with crumb rubber using RSM and hybrid DNN\u2013HHO algorithm","volume":"84","author":"Anjali","year":"2024","journal-title":"J. Build. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"108147","DOI":"10.1016\/j.istruc.2024.108147","article-title":"Interlayer bond strength prediction of 3D printable concrete using artificial neural network: Experimental and modeling study","volume":"71","author":"Mousavi","year":"2025","journal-title":"Structures"},{"key":"ref_34","first-page":"1","article-title":"A practical guide from Ordinary Least Squares to causal machine learning","volume":"1","author":"Bittmann","year":"2025","journal-title":"Int. Food Agribus. Manag. Rev."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1080\/00224065.2017.11917988","article-title":"Response Surface Methodology: Process and Product Optimization Using Designed Experiments 4th edition","volume":"49","author":"Jensen","year":"2017","journal-title":"J. Qual. Technol."},{"key":"ref_36","unstructured":"Fard, S., and Zainuddin, Z. (2013, January 7\u20138). The universal approximation capabilities of 2pi-periodic approximate identity neural networks. Proceedings of the 2013 International Symposium on Computational and Business Intelligence, Guangzhou, China."},{"key":"ref_37","first-page":"7","article-title":"Universal approximation property of weighted approximate identity neural networks","volume":"259","author":"Fard","year":"2015","journal-title":"Proc. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1009","DOI":"10.1016\/j.renene.2015.09.023","article-title":"Development of a dynamic artificial neural network model of an absorption chiller and its experimental validation","volume":"86","author":"Lazrak","year":"2016","journal-title":"Renew. Energy"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"100211","DOI":"10.1016\/j.clema.2023.100211","article-title":"Optimized artificial neural network model for accurate prediction of compressive strength of normal and high strength concrete","volume":"10","author":"Khan","year":"2023","journal-title":"Clean. Mater."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M. (2019, January 4\u20138). Optuna: A Next-generation Hyperparameter Optimization Framework. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA. KDD \u201919.","DOI":"10.1145\/3292500.3330701"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Wu, D., Wang, J., Tong, M., Chen, K., and Zhang, Z. (2023). Performance optimization of FA-GGBS geopolymer based on response surface methodology. Polymers, 15.","DOI":"10.3390\/polym15081881"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"8274733","DOI":"10.1155\/2022\/8274733","article-title":"Experimental research on mix ratio of construction waste cemented filling material based on response surface methodology","volume":"2022","author":"Chen","year":"2022","journal-title":"Adv. Mater. Sci. Eng."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Cui, H., Peng, H., Yang, W., Yang, H., Zhang, C., and Zheng, D. (2024). Effect of thermal cycles and curing age on bonding strength of cement mortar using manufactured sand. Buildings, 14.","DOI":"10.3390\/buildings14030783"},{"key":"ref_44","first-page":"87","article-title":"A general overview of response surface methodology","volume":"5","author":"Khuri","year":"2017","journal-title":"Biom. Biostat. Int. J."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1778","DOI":"10.1111\/1365-2478.13484","article-title":"Automating hyperparameter optimization in geophysics with optuna: A comparative study","volume":"72","author":"Almarzooq","year":"2024","journal-title":"Geophys. Prospect."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Mairpady, A., Mourad, A., and Mozumder, M. (2021). Statistical and machine learning-driven optimization of mechanical properties in designing durable HDPE nanobiocomposites. Polymers, 13.","DOI":"10.3390\/polym13183100"},{"key":"ref_47","first-page":"E80","article-title":"Optimization of mechanical properties of PP\/EPDM\/clay nanocomposite fabricated by friction stir processing with response surface methodology and neural networks","volume":"38","author":"Nakhaei","year":"2016","journal-title":"Polym. Compos."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1016\/j.renene.2014.11.049","article-title":"Performance evaluation of artificial neural network coupled with genetic algorithm and response surface methodology in modeling and optimization of biodiesel production process parameters from shea tree (Vitellaria paradoxa) nut butter","volume":"76","author":"Betiku","year":"2015","journal-title":"Renew. Energy"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"015583","DOI":"10.1088\/2631-8695\/adc0ed","article-title":"Analysis and prediction of the mechanical properties of cold rolled Al-Li preforms using statistical and artificial neural network models","volume":"7","author":"Lingam","year":"2025","journal-title":"Eng. Res. Express"},{"key":"ref_50","first-page":"8267639","article-title":"Prediction of the mechanical properties of fibre-reinforced quarry dust concrete using response surface and artificial neural network techniques","volume":"2023","author":"Sridhar","year":"2023","journal-title":"Adv. Civ. Eng."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Reza, A., and Chen, L. (2021). Optimization and modeling of ammonia nitrogen removal from high strength synthetic wastewater using vacuum thermal stripping. Processes, 9.","DOI":"10.3390\/pr9112059"},{"key":"ref_52","first-page":"23","article-title":"Kajian literatur multi layer perceptron seberapa baik performa algoritma ini","volume":"1","author":"Pardede","year":"2022","journal-title":"J. ICT Appl. Syst."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"02010","DOI":"10.1051\/itmconf\/20235202010","article-title":"Stirling engine optimization using artificial neural networks algorithm","volume":"52","author":"Taki","year":"2023","journal-title":"ITM Web Conf."},{"key":"ref_54","unstructured":"Myers, R.H., Montgomery, D.C., and Anderson-Cook, C.M. (2016). Response Surface Methodology: Process and Product Optimisation Using Designed Experiments, Wiley. [4th ed.]."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"812","DOI":"10.1016\/j.conbuildmat.2012.11.109","article-title":"Application of artificial neural networks to predict the bond strength of FRP-to-concrete joints","volume":"40","author":"Mashrei","year":"2013","journal-title":"Constr. Build. Mater."},{"key":"ref_56","unstructured":"Haykin, S. (2009). Neural Networks and Learning Machines, Pearson. [3rd ed.]."},{"key":"ref_57","unstructured":"Montgomery, D.C., Runger, G.C., and Hubele, N.F. (2012). Engineering Statistics, Wiley. [5th ed.]."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Altharan, Y., Shamsudin, S., Lajis, M., Al-Alimi, S., Yusuf, N., Alduais, N., and Zhou, W. (2024). Optimizing strength of directly recycled aluminum chip-based parts through a hybrid rsm-ga-ann approach in sustainable hot forging. PLoS ONE, 19.","DOI":"10.1371\/journal.pone.0300504"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"17319","DOI":"10.1007\/s00521-023-08587-w","article-title":"MOCOVIDOA: A novel multi-objective coronavirus disease optimization algorithm for solving multi-objective optimization problems","volume":"35","author":"Khalid","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"14973","DOI":"10.1007\/s00521-023-08432-0","article-title":"Multi-objective chaos game optimization","volume":"35","author":"Khodadadi","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_61","first-page":"2280","article-title":"Explaining hyperparameter optimization via partial dependence plots","volume":"34","author":"Moosbauer","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1680\/jgeen.17.00011","article-title":"Strength assessment of chemically stabilised soft soils","volume":"172","author":"Correia","year":"2019","journal-title":"Proc. Inst. Civ. Eng.-Geotech. Eng."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Galindo-Rosales, F.J., Campo-Dea\u00f1o, L., Afonso, A.M., Alves, M.A., and Pinho, F.T. (2020). Yield Stress in Injection Grouts for Strengthening of Stone Masonry Walls. Proceedings of the Iberian Meeting on Rheology (IBEREO 2019), Springer.","DOI":"10.1007\/978-3-030-27701-7"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Zhou, J., Ban, C., Zhou, H., Ren, J., and Liu, Z. (2023). Experimental study on the shear strength and failure mechanism of cemented soil\u2013concrete interface. Materials, 16.","DOI":"10.3390\/ma16124222"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"291","DOI":"10.4012\/dmj.2022-164","article-title":"Influence of the nanostructural characteristics of inorganic fillers on the physical properties of resin cements","volume":"42","author":"Mizobuchi","year":"2023","journal-title":"Dent. Mater. J."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Ebonghas, I.P., and Odokonyero, L.C. (2025). Integrated experimental and microstructural analysis of basalt fiber-reinforced cemented soils: Multivariable strength prediction and residual behavior assessment. Preprints.","DOI":"10.21203\/rs.3.rs-6789638\/v1"},{"key":"ref_67","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_68","doi-asserted-by":"crossref","unstructured":"Yuan, Q., Gao, C., Huang, T., Zuo, S., Yao, H., Zhang, K., and Liu, J. (2022). Factors influencing the properties of extrusion-based 3D-printed alkali-activated fly ash-slag mortar. Materials, 15.","DOI":"10.3390\/ma15051969"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Raphael, B., Senthilnathan, S., Patel, A., and Bhat, S. (2023). A review of concrete 3D printed structural members. Front. Built Environ., 8.","DOI":"10.3389\/fbuil.2022.1034020"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"23010006","DOI":"10.1002\/adma.202310006","article-title":"Progress and opportunities for machine learning in materials and processes of additive manufacturing","volume":"36","author":"Ng","year":"2024","journal-title":"Adv. Mater."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"6665333","DOI":"10.1155\/2021\/6665333","article-title":"3D printing in construction: Current status, implementation hindrances, and development agenda","volume":"2021","author":"Ning","year":"2021","journal-title":"Adv. Civ. Eng."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"5431","DOI":"10.1007\/s00170-025-15457-3","article-title":"Multi-objective optimisation of fused filament fabrication of acrylonitrile butadiene styrene for enhancing mechanical performance and build time","volume":"137","author":"Nguyen","year":"2025","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"830","DOI":"10.54097\/bz5w2p29","article-title":"Navigating the path to responsible AI: Interpretable models and ethical implications","volume":"85","author":"Zhu","year":"2024","journal-title":"Highlights Sci. Eng. Technol."}],"container-title":["Buildings"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-5309\/15\/20\/3794\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T15:10:57Z","timestamp":1761059457000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-5309\/15\/20\/3794"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"references-count":73,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["buildings15203794"],"URL":"https:\/\/doi.org\/10.3390\/buildings15203794","relation":{},"ISSN":["2075-5309"],"issn-type":[{"value":"2075-5309","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}