{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T14:12:24Z","timestamp":1766067144608,"version":"3.48.0"},"reference-count":84,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T00:00:00Z","timestamp":1765497600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T00:00:00Z","timestamp":1765843200000},"content-version":"vor","delay-in-days":4,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-025-11068-w","type":"journal-article","created":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T08:58:19Z","timestamp":1765529899000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Passive determination of anisotropic compressive strength of 3D printed concrete using multiple neural networks enhanced with explainable machine learning (XML)"],"prefix":"10.1038","volume":"15","author":[{"given":"Imtiaz","family":"Iqbal","sequence":"first","affiliation":[]},{"given":"Tala","family":"Kasim","sequence":"additional","affiliation":[]},{"given":"Svetlana","family":"Besklubova","sequence":"additional","affiliation":[]},{"given":"Ali","family":"Mustafa","sequence":"additional","affiliation":[]},{"given":"Mujib","family":"Rahman","sequence":"additional","affiliation":[]},{"given":"Hisham","family":"Alabduljabbar","sequence":"additional","affiliation":[]},{"given":"Furqan","family":"Ahmad","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,12,12]]},"reference":[{"key":"11068_CR1","doi-asserted-by":"publisher","unstructured":"Ning, X., Liu, T., Wu, C. & Wang, C. 3D Printing in Construction: Current Status, Implementation Hindrances, and Development Agenda, Advances in Civil Engineering, vol. 2021, (2021). https:\/\/doi.org\/10.1155\/2021\/6665333","DOI":"10.1155\/2021\/6665333"},{"key":"11068_CR2","doi-asserted-by":"publisher","unstructured":"Othuman Mydin, M. A., Sani, N. M. & Phius, A. F. Investigation of industrialised building system performance in comparison to conventional construction method, MATEC Web of Conferences, vol. 10, pp. 1\u20136, (2014). https:\/\/doi.org\/10.1051\/matecconf\/20141004001","DOI":"10.1051\/matecconf\/20141004001"},{"key":"11068_CR3","doi-asserted-by":"publisher","unstructured":"Josa, I. & de la Fuente, A. Traditional and modern methods of construction: comparative study of the sustainability of single-family homes. Structural Concrete No June. 1\u201319. https:\/\/doi.org\/10.1002\/suco.202400802 (2024).","DOI":"10.1002\/suco.202400802"},{"key":"11068_CR4","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.compgeo.2014.08.010","volume":"63","author":"F Kang","year":"2015","unstructured":"Kang, F., Han, S., Salgado, R. & Li, J. System probabilistic stability analysis of soil slopes using Gaussian process regression with Latin hypercube sampling. Comput. Geotech. 63, 13\u201325. https:\/\/doi.org\/10.1016\/j.compgeo.2014.08.010 (2015).","journal-title":"Comput Geotech"},{"key":"11068_CR5","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/j.habitatint.2016.08.002","volume":"57","author":"C Mao","year":"2016","unstructured":"Mao, C. et al. Cost analysis for sustainable off-site construction based on a multiple-case study in China. Habitat Int. 57, 215\u2013222. https:\/\/doi.org\/10.1016\/j.habitatint.2016.08.002 (2016).","journal-title":"Habitat Int."},{"key":"11068_CR6","doi-asserted-by":"publisher","first-page":"106550","DOI":"10.1016\/j.jobe.2023.106550","volume":"72","author":"J Jayawardana","year":"2023","unstructured":"Jayawardana, J., Sandanayake, M., Jayasinghe, J. A. S. C., Kulatunga, A. K. & Zhang, G. A comparative life cycle assessment of prefabricated and traditional construction \u2013 A case of a developing country. J. Building Eng. 72, 106550. https:\/\/doi.org\/10.1016\/j.jobe.2023.106550 (2023). February.","journal-title":"J. Building Eng."},{"issue":"6","key":"11068_CR7","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1080\/09613210701467040","volume":"35","author":"CJ Kibert","year":"2007","unstructured":"Kibert, C. J. The next generation of sustainable construction. Building Res. Inform. 35 (6), 595\u2013601. https:\/\/doi.org\/10.1080\/09613210701467040 (2007).","journal-title":"Building Res. Inform."},{"key":"11068_CR8","doi-asserted-by":"publisher","first-page":"108526","DOI":"10.1016\/j.jcsr.2024.108526","volume":"215","author":"Y Liu","year":"2024","unstructured":"Liu, Y. et al. Variable fatigue loading effects on corrugated steel box girders with recycled concrete. J. Constr. Steel Res. 215, 108526. https:\/\/doi.org\/10.1016\/j.jcsr.2024.108526 (2024).","journal-title":"J. Constr. Steel Res."},{"issue":"2","key":"11068_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s43452-020-00038-w","volume":"20","author":"S El-Sayegh","year":"2020","unstructured":"El-Sayegh, S., Romdhane, L. & Manjikian, S. A critical review of 3D printing in construction: benefits, challenges, and risks. Archives Civil Mech. Eng. 20 (2), 1\u201325. https:\/\/doi.org\/10.1007\/s43452-020-00038-w (2020).","journal-title":"Archives Civil Mech. Eng."},{"key":"11068_CR10","doi-asserted-by":"publisher","unstructured":"Ghasemi, A. & Naser, M. Z. Tailoring 3D printed concrete through explainable artificial intelligence. Structures 56 https:\/\/doi.org\/10.1016\/j.istruc.2023.07.040 (2023).","DOI":"10.1016\/j.istruc.2023.07.040"},{"key":"11068_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.matpr.2023.03.191","author":"MN Uddin","year":"2023","unstructured":"Uddin, M. N., Mahamoudou, F., Deng, B. Y., Elobaid Musa, M. M. & Tim Sob, L. W. Prediction of rheological parameters of 3D printed polypropylene fiber-reinforced concrete (3DP-PPRC) by machine learning. Mater Today Proc No Xxxx. https:\/\/doi.org\/10.1016\/j.matpr.2023.03.191 (2023).","journal-title":"Mater Today Proc No Xxxx"},{"key":"11068_CR12","doi-asserted-by":"publisher","unstructured":"Ambily, P. S., Rajendran, N. & Kaliyavaradhan, S. K. Mix design, optimization and performance evaluation of extrusion-based 3D printable concrete, Proceedings of Institution of Civil Engineers: Construction Materials, pp. 1\u201319, (2023). https:\/\/doi.org\/10.1680\/jcoma.23.00077","DOI":"10.1680\/jcoma.23.00077"},{"key":"11068_CR13","doi-asserted-by":"publisher","unstructured":"Aramburu, A., Calderon-Uriszar-Aldaca, I. & Puente, I. 3D printing effect on the compressive strength of concrete structures. Constr. Build. Mater. 354 https:\/\/doi.org\/10.1016\/j.conbuildmat.2022.129108 (2022).","DOI":"10.1016\/j.conbuildmat.2022.129108"},{"key":"11068_CR14","doi-asserted-by":"publisher","unstructured":"Aramburu, A., Calderon-Uriszar-Aldaca, I. & Puente, I. Bonding strength of steel rebars perpendicular to the hardened 3D-printed concrete layers. Constr. Build. Mater. 340 https:\/\/doi.org\/10.1016\/j.conbuildmat.2022.127827 (2022).","DOI":"10.1016\/j.conbuildmat.2022.127827"},{"key":"11068_CR15","doi-asserted-by":"publisher","first-page":"137417","DOI":"10.1016\/j.conbuildmat.2024.137417","volume":"440","author":"K Zhang","year":"2024","unstructured":"Zhang, K., Lin, W., Zhang, Q., Wang, D. & Luo, S. Evaluation of anisotropy and statistical parameters of compressive strength for 3D printed concrete. Constr. Build. Mater. 440, 137417. https:\/\/doi.org\/10.1016\/j.conbuildmat.2024.137417 (2024).","journal-title":"Constr. Build. Mater."},{"key":"11068_CR16","doi-asserted-by":"publisher","unstructured":"Singh, A., Liu, Q., Xiao, J. & Lyu, Q. Mechanical and macrostructural properties of 3D printed concrete dosed with steel fibers under different loading direction. Constr. Build. Mater. 323 https:\/\/doi.org\/10.1016\/j.conbuildmat.2022.126616 (2022).","DOI":"10.1016\/j.conbuildmat.2022.126616"},{"key":"11068_CR17","doi-asserted-by":"publisher","unstructured":"Xiao, J., Han, N., Zhang, L. & Zou, S. Mechanical and microstructural evolution of 3D printed concrete with polyethylene fiber and recycled sand at elevated temperatures. Constr. Build. Mater. 293 https:\/\/doi.org\/10.1016\/j.conbuildmat.2021.123524 (2021).","DOI":"10.1016\/j.conbuildmat.2021.123524"},{"key":"11068_CR18","doi-asserted-by":"publisher","unstructured":"Sun, X., Zhou, J., Wang, Q., Shi, J. & Wang, H. PVA fibre reinforced high-strength cementitious composite for 3D printing: mechanical properties and durability. Addit. Manuf. 49 https:\/\/doi.org\/10.1016\/j.addma.2021.102500 (2022).","DOI":"10.1016\/j.addma.2021.102500"},{"key":"11068_CR19","doi-asserted-by":"publisher","unstructured":"Pham, L., Tran, P. & Sanjayan, J. Steel fibres reinforced 3D printed concrete: influence of fibre sizes on mechanical performance. Constr. Build. Mater. 250 https:\/\/doi.org\/10.1016\/j.conbuildmat.2020.118785 (2020).","DOI":"10.1016\/j.conbuildmat.2020.118785"},{"key":"11068_CR20","doi-asserted-by":"publisher","unstructured":"Zhu, B. et al. Development of 3D printable engineered cementitious composites with ultra-high tensile ductility for digital construction. Mater. Des. 181 https:\/\/doi.org\/10.1016\/j.matdes.2019.108088 (2019).","DOI":"10.1016\/j.matdes.2019.108088"},{"key":"11068_CR21","doi-asserted-by":"publisher","unstructured":"Aminpour, N. & Memari, A. Analysis of anisotropic behavior in 3D concrete printing for mechanical property evaluation, Journal of Building Engineering, vol. 99, no. October p. 111652, 2025, (2024). https:\/\/doi.org\/10.1016\/j.jobe.2024.111652","DOI":"10.1016\/j.jobe.2024.111652"},{"key":"11068_CR22","doi-asserted-by":"publisher","unstructured":"Liu, C. et al. Analysis of the mechanical performance and damage mechanism for 3D printed concrete based on pore structure. Constr. Build. Mater. 314 https:\/\/doi.org\/10.1016\/j.conbuildmat.2021.125572 (2022).","DOI":"10.1016\/j.conbuildmat.2021.125572"},{"key":"11068_CR23","doi-asserted-by":"publisher","unstructured":"Wang, Y., Qiu, L., Hu, Y., Chen, S. & Liu, Y. Influential factors on mechanical properties and microscopic characteristics of underwater 3D printing concrete. J. Building Eng. 77 https:\/\/doi.org\/10.1016\/j.jobe.2023.107571 (2023).","DOI":"10.1016\/j.jobe.2023.107571"},{"issue":"1","key":"11068_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-024-69271-0","volume":"14","author":"W Bin Inqiad","year":"2024","unstructured":"Bin Inqiad, W. et al. Soft computing models for prediction of bentonite plastic concrete strength. Sci. Rep. 14 (1), 1\u201324. https:\/\/doi.org\/10.1038\/s41598-024-69271-0 (2024).","journal-title":"Sci. Rep."},{"key":"11068_CR25","doi-asserted-by":"publisher","first-page":"105727","DOI":"10.1016\/j.autcon.2024.105727","volume":"167","author":"W Chen","year":"2024","unstructured":"Chen, D. et al. Enhancement of underwater dam crack images using multi-feature fusion. Autom. Constr. 167, 105727. https:\/\/doi.org\/10.1016\/j.autcon.2024.105727 (2024).","journal-title":"Autom. Constr."},{"issue":"5","key":"11068_CR26","doi-asserted-by":"publisher","first-page":"1187","DOI":"10.1134\/S2075113324700783","volume":"15","author":"VA Poluektova","year":"2024","unstructured":"Poluektova, V. A. & Poluektov, M. A. Artificial intelligence in materials science and modern concrete technologies: analysis of possibilities and prospects. Inorg. Mater. Appl. Res. 15 (5), 1187\u20131198. https:\/\/doi.org\/10.1134\/S2075113324700783 (2024).","journal-title":"Inorg. Mater. Appl. Res."},{"issue":"8","key":"11068_CR27","doi-asserted-by":"publisher","first-page":"5865","DOI":"10.1007\/s42107-024-01151-4","volume":"25","author":"A Satyanarayana","year":"2024","unstructured":"Satyanarayana, A., Dushyanth, V. B. R., Riyan, K. A., Geetha, L. & Kumar, R. Assessing the seismic sensitivity of Bridge structures by developing fragility curves with ANN and LSTM integration. Asian J. Civil Eng. 25 (8), 5865\u20135888. https:\/\/doi.org\/10.1007\/s42107-024-01151-4 (2024).","journal-title":"Asian J. Civil Eng."},{"key":"11068_CR28","doi-asserted-by":"publisher","unstructured":"Kumar, R., Rai, B. & Samui, P. Prediction of mechanical properties of high-performance concrete and ultrahigh-performance concrete using soft computing techniques: A critical review, Structural Concrete, no. February pp. 1309\u20131337, 2024, (2024). https:\/\/doi.org\/10.1002\/suco.202400188","DOI":"10.1002\/suco.202400188"},{"key":"11068_CR29","doi-asserted-by":"publisher","first-page":"109222","DOI":"10.1016\/j.mtcomm.2024.109222","volume":"39","author":"WB Inqiad","year":"2024","unstructured":"Inqiad, W. B. et al. Comparison of boosting and genetic programming techniques for prediction of tensile strain capacity of engineered cementitious composites (ECC). Mater. Today Commun. 39, 109222. https:\/\/doi.org\/10.1016\/j.mtcomm.2024.109222 (2024). March.","journal-title":"Mater. Today Commun."},{"key":"11068_CR30","doi-asserted-by":"publisher","unstructured":"Liu, Z. et al. Modelling and parameter optimization for filament deformation in 3D cementitious material printing using support vector machine, Compos B Eng, vol. 193, no. June p. 108018, 2020, (2019). https:\/\/doi.org\/10.1016\/j.compositesb.2020.108018","DOI":"10.1016\/j.compositesb.2020.108018"},{"key":"11068_CR31","doi-asserted-by":"publisher","first-page":"106850","DOI":"10.1016\/j.istruc.2024.106850","volume":"66","author":"R Kumar","year":"2024","unstructured":"Kumar, R., Kumar, S., Rai, B. & Samui, P. Development of hybrid gradient boosting models for predicting the compressive strength of high-volume fly Ash self-compacting concrete with silica fume. Structures 66, 106850. https:\/\/doi.org\/10.1016\/j.istruc.2024.106850 (2024).","journal-title":"Structures"},{"issue":"2","key":"11068_CR32","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1080\/17452759.2020.1713580","volume":"15","author":"W Lao","year":"2020","unstructured":"Lao, W., Li, M., Wong, T. N., Tan, M. J. & Tjahjowidodo, T. Improving surface finish quality in extrusion-based 3D concrete printing using machine learning-based extrudate geometry control. Virtual Phys. Prototyp. 15 (2), 178\u2013193. https:\/\/doi.org\/10.1080\/17452759.2020.1713580 (2020).","journal-title":"Virtual Phys. Prototyp."},{"key":"11068_CR33","doi-asserted-by":"publisher","first-page":"e03510","DOI":"10.1016\/j.cscm.2024.e03510","volume":"21","author":"UJ Malik","year":"2024","unstructured":"Malik, U. J. et al. Advancing mix design prediction in 3D printed concrete: predicting anisotropic compressive strength and slump flow. Case Stud. Constr. Mater. 21, e03510. https:\/\/doi.org\/10.1016\/j.cscm.2024.e03510 (2024).","journal-title":"Case Stud. Constr. Mater."},{"key":"11068_CR34","doi-asserted-by":"publisher","unstructured":"Ali, A. et al. Machine Learning-Based predictive model for tensile and flexural strength of 3D-Printed concrete. Materials 16 (11). https:\/\/doi.org\/10.3390\/ma16114149 (2023).","DOI":"10.3390\/ma16114149"},{"issue":"1","key":"11068_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-025-88923-3","volume":"15","author":"S Sathvik","year":"2025","unstructured":"Sathvik, S. et al. Analyzing the influence of manufactured sand and fly Ash on concrete strength through experimental and machine learning methods. Sci. Rep. 15 (1), 1\u201323. https:\/\/doi.org\/10.1038\/s41598-025-88923-3 (2025).","journal-title":"Sci. Rep."},{"key":"11068_CR36","doi-asserted-by":"publisher","unstructured":"Rehman, S. U., Riaz, R. D., Usman, M. & Kim, I. H. Augmented Data-Driven approach towards 3D printed concrete mix prediction. Appl. Sci. (Switzerland). 14 (16). https:\/\/doi.org\/10.3390\/app14167231 (2024).","DOI":"10.3390\/app14167231"},{"key":"11068_CR37","doi-asserted-by":"publisher","unstructured":"Ma, X. R., Wang, X. L. & Chen, S. Z. Trustworthy machine learning-enhanced 3D concrete printing: Predicting bond strength and designing reinforcement embedment length, Autom Constr, vol. 168, no. PA, p. 105754, (2024). https:\/\/doi.org\/10.1016\/j.autcon.2024.105754","DOI":"10.1016\/j.autcon.2024.105754"},{"key":"11068_CR38","doi-asserted-by":"publisher","unstructured":"Chang, Z., Wan, Z., Xu, Y., Schlangen, E. & \u0160avija, B. Convolutional neural network for predicting crack pattern and stress-crack width curve of air-void structure in 3D printed concrete. Eng. Fract. Mech. 271 https:\/\/doi.org\/10.1016\/j.engfracmech.2022.108624 (2022).","DOI":"10.1016\/j.engfracmech.2022.108624"},{"key":"11068_CR39","doi-asserted-by":"publisher","unstructured":"M\u00fctevelli \u00d6zkan, \u0130. G. & Aldemir, A. Machine-learning networks to predict the ultimate axial load and displacement capacity of 3D printed concrete walls with different section geometries. Structures 66, no. https:\/\/doi.org\/10.1016\/j.istruc.2024.106879 (July, 2024).","DOI":"10.1016\/j.istruc.2024.106879"},{"key":"11068_CR40","doi-asserted-by":"publisher","unstructured":"Zhu, Z. et al. Fracture damage and energy evolution of fissured coal subject to triaxial unloading condition. Int. J. Coal Sci. Technol. 12 (1). https:\/\/doi.org\/10.1007\/S40789-025-00778-1 (Dec. 2025).","DOI":"10.1007\/S40789-025-00778-1"},{"key":"11068_CR41","doi-asserted-by":"publisher","first-page":"136470","DOI":"10.1016\/j.conbuildmat.2024.136470","volume":"431","author":"M Van Tran","year":"2024","unstructured":"Van Tran, M., Ly, D. K., Nguyen, T. & Tran, N. Robust prediction of workability properties for 3D printing with steel slag aggregate using bayesian regularization and evolution algorithm. Constr. Build. Mater. 431, 136470. https:\/\/doi.org\/10.1016\/j.conbuildmat.2024.136470 (2024).","journal-title":"Constr. Build. Mater."},{"key":"11068_CR42","doi-asserted-by":"publisher","unstructured":"Nazar, S. et al. An evolutionary machine learning-based model to estimate the rheological parameters of fresh concrete, Structures, vol. 48, pp. 1670\u20131683, (2023). https:\/\/doi.org\/10.1016\/j.istruc.2023.01.019","DOI":"10.1016\/j.istruc.2023.01.019"},{"key":"11068_CR43","doi-asserted-by":"publisher","unstructured":"Kumar, R. et al. Estimation of the compressive strength of ultrahigh performance concrete using machine learning models, Intelligent Systems with Applications, vol. 25, no. December p. 200471, 2025, (2024). https:\/\/doi.org\/10.1016\/j.iswa.2024.200471","DOI":"10.1016\/j.iswa.2024.200471"},{"key":"11068_CR44","doi-asserted-by":"publisher","unstructured":"Taffese, W. Z. & Espinosa-Leal, L. Multitarget regression models for predicting compressive strength and chloride resistance of concrete. J. Building Eng. 72 https:\/\/doi.org\/10.1016\/j.jobe.2023.106523 (Aug. 2023).","DOI":"10.1016\/j.jobe.2023.106523"},{"key":"11068_CR45","doi-asserted-by":"publisher","unstructured":"Ukwaththa, J., Herath, S. & Meddage, D. P. P. A review of machine learning (ML) and explainable artificial intelligence (XAI) methods in additive manufacturing (3D Printing). Mater. Today Commun. 41, 110294. https:\/\/doi.org\/10.1016\/J.MTCOMM.2024.110294 (Dec. 2024).","DOI":"10.1016\/J.MTCOMM.2024.110294"},{"key":"11068_CR46","doi-asserted-by":"publisher","first-page":"770","DOI":"10.1016\/j.conbuildmat.2019.01.008","volume":"202","author":"G Ma","year":"2019","unstructured":"Ma, G., Li, Z., Wang, L., Wang, F. & Sanjayan, J. Mechanical anisotropy of aligned fiber reinforced composite for extrusion-based 3D printing. Constr. Build. Mater. 202, 770\u2013783. https:\/\/doi.org\/10.1016\/j.conbuildmat.2019.01.008 (2019).","journal-title":"Constr. Build. Mater."},{"key":"11068_CR47","doi-asserted-by":"publisher","first-page":"112808","DOI":"10.1016\/j.compstruct.2020.112808","volume":"254","author":"T Ding","year":"2020","unstructured":"Ding, T., Xiao, J., Zou, S. & Zhou, X. Anisotropic behavior in bending of 3D printed concrete reinforced with fibers. Compos. Struct. 254, 112808. https:\/\/doi.org\/10.1016\/j.compstruct.2020.112808 (2020). July.","journal-title":"Compos. Struct."},{"key":"11068_CR48","doi-asserted-by":"publisher","unstructured":"Van Der Putten, J., Vijayan, R. A., De Schutter, G. & Van Tittelboom, K. Development of 3d printable cementitious composites with the incorporation of polypropylene fibers. Materials 14 (16). https:\/\/doi.org\/10.3390\/ma14164474 (2021).","DOI":"10.3390\/ma14164474"},{"key":"11068_CR49","doi-asserted-by":"publisher","unstructured":"Yu, J. & Leung, C. K. Y. Impact of 3D Printing Direction on Mechanical Performance of strain-hardening Cementitious Composite (SHCC)vol. 19 (Springer International Publishing, 2019). https:\/\/doi.org\/10.1007\/978-3-319-99519-9_24","DOI":"10.1007\/978-3-319-99519-9_24"},{"key":"11068_CR50","doi-asserted-by":"publisher","first-page":"106384","DOI":"10.1016\/j.cemconres.2021.106384","volume":"143","author":"AR Arunothayan","year":"2021","unstructured":"Arunothayan, A. R. et al. Fiber orientation effects on ultra-high performance concrete formed by 3D printing. Cem. Concr Res. 143, 106384. https:\/\/doi.org\/10.1016\/j.cemconres.2021.106384 (2021). no. February.","journal-title":"Cem. Concr Res."},{"key":"11068_CR51","doi-asserted-by":"publisher","unstructured":"Ye, J., Cui, C., Yu, J., Yu, K. & Xiao, J. Fresh and anisotropic-mechanical properties of 3D printable ultra-high ductile concrete with crumb rubber. Compos. B Eng. 211 https:\/\/doi.org\/10.1016\/j.compositesb.2021.108639 (2021).","DOI":"10.1016\/j.compositesb.2021.108639"},{"key":"11068_CR52","doi-asserted-by":"publisher","first-page":"118785","DOI":"10.1016\/j.conbuildmat.2020.118785","volume":"250","author":"L Pham","year":"2020","unstructured":"Pham, L., Tran, P. & Sanjayan, J. Steel fibres reinforced 3D printed concrete: influence of fibre sizes on mechanical performance. Constr. Build. Mater. 250, 118785. https:\/\/doi.org\/10.1016\/j.conbuildmat.2020.118785 (2020).","journal-title":"Constr. Build. Mater."},{"key":"11068_CR53","doi-asserted-by":"publisher","first-page":"106388","DOI":"10.1016\/j.cemconres.2021.106388","volume":"143","author":"K Yu","year":"2021","unstructured":"Yu, K., McGee, W., Ng, T. Y., Zhu, H. & Li, V. C. 3D-printable engineered cementitious composites (3DP-ECC): fresh and hardened properties. Cem. Concr Res. 143, 106388. https:\/\/doi.org\/10.1016\/j.cemconres.2021.106388 (2021).","journal-title":"Cem. Concr Res."},{"key":"11068_CR54","doi-asserted-by":"publisher","unstructured":"Pham, L., Lin, X., Gravina, R. J. & Tran, P. Influence of pva and pp fibres at different volume fractions on mechanical properties of 3d printed concrete, vol. 101, no. January. Springer Singapore, (2021). https:\/\/doi.org\/10.1007\/978-981-15-8079-6_185","DOI":"10.1007\/978-981-15-8079-6_185"},{"key":"11068_CR55","doi-asserted-by":"publisher","first-page":"107309","DOI":"10.1016\/j.jobe.2023.107309","volume":"76","author":"C Wang","year":"2023","unstructured":"Wang, C., Chen, B., Vo, T. L. & Rezania, M. Mechanical anisotropy, rheology and carbon footprint of 3D printable concrete: A review. J. Building Eng. 76, 107309. https:\/\/doi.org\/10.1016\/j.jobe.2023.107309 (2023).","journal-title":"J. Building Eng."},{"issue":"4","key":"11068_CR56","doi-asserted-by":"publisher","first-page":"1328","DOI":"10.1109\/TKDE.2019.2946162","volume":"33","author":"Y Roh","year":"2021","unstructured":"Roh, Y., Heo, G. & Whang, S. E. A survey on data collection for machine learning: A big data-AI integration perspective. IEEE Trans. Knowl. Data Eng. 33 (4), 1328\u20131347. https:\/\/doi.org\/10.1109\/TKDE.2019.2946162 (2021).","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"11068_CR57","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.advengsoft.2015.05.007","volume":"88","author":"AH Gandomi","year":"2015","unstructured":"Gandomi, A. H. & Roke, D. A. Assessment of artificial neural network and genetic programming as predictive tools. Adv. Eng. Softw. 88, 63\u201372. https:\/\/doi.org\/10.1016\/j.advengsoft.2015.05.007 (2015).","journal-title":"Adv. Eng. Softw."},{"key":"11068_CR58","doi-asserted-by":"publisher","unstructured":"Wang, Y. et al. Organic petrology and geochemistry of the upper Ordovician-Lower silurian Renheqiao formation shales of the Baoshan block, Western yunnan, China. Int. J. Coal Sci. Technol. 12 (1), 49. https:\/\/doi.org\/10.1007\/S40789-025-00795-0 (Dec. 2025).","DOI":"10.1007\/S40789-025-00795-0"},{"issue":"7","key":"11068_CR59","doi-asserted-by":"publisher","first-page":"2085","DOI":"10.1007\/s00521-015-1997-6","volume":"31","author":"M Sarveghadi","year":"2019","unstructured":"Sarveghadi, M., Gandomi, A. H., Bolandi, H. & Alavi, A. H. Development of prediction models for shear strength of SFRCB using a machine learning approach. Neural Comput. Appl. 31 (7), 2085\u20132094. https:\/\/doi.org\/10.1007\/s00521-015-1997-6 (2019).","journal-title":"Neural Comput. Appl."},{"key":"11068_CR60","first-page":"321","volume":"2","author":"D Lowe","year":"1988","unstructured":"Lowe, D. & D. S. B. and Multivariable functional interpolation and adaptive networks. Complex. Syst. 2, 321\u2013355 (1988).","journal-title":"Complex. Syst."},{"key":"11068_CR61","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/S0925-2312(98)00027-7","volume":"20","author":"G Bugmann","year":"1998","unstructured":"Bugmann, G. Normalized Gaussian radial basis function networks. Neurocomputing 20, 1\u20133. https:\/\/doi.org\/10.1016\/S0925-2312(98)00027-7 (1998).","journal-title":"Neurocomputing"},{"issue":"2","key":"11068_CR62","doi-asserted-by":"publisher","first-page":"906","DOI":"10.31202\/ecjse.679113","volume":"7","author":"MA \u00c7akiro\u011flu","year":"2020","unstructured":"\u00c7akiro\u011flu, M. A. & S\u00fczen, A. A. Assessment and application of deep learning algorithms in civil engineering. El-Cezeri J. Sci. Eng. 7 (2), 906\u2013922. https:\/\/doi.org\/10.31202\/ecjse.679113 (2020).","journal-title":"El-Cezeri J. Sci. Eng."},{"key":"11068_CR63","doi-asserted-by":"publisher","unstructured":"Celik, G. & Ozdemir, M. Determination of concrete compressive strength from surface images with the integration of CNN and SVR methods. Meas. (Lond). 238, no. https:\/\/doi.org\/10.1016\/j.measurement.2024.115331 (April, 2024).","DOI":"10.1016\/j.measurement.2024.115331"},{"issue":"4","key":"11068_CR64","doi-asserted-by":"publisher","first-page":"2913","DOI":"10.1007\/s11277-022-10079-4","volume":"128","author":"J Naskath","year":"2023","unstructured":"Naskath, J., Sivakamasundari, G. & Begum, A. A. S. A study on different deep learning algorithms used in deep neural nets: MLP SOM and DBN. Wirel. Pers. Commun. 128 (4), 2913\u20132936. https:\/\/doi.org\/10.1007\/s11277-022-10079-4 (2023).","journal-title":"Wirel. Pers. Commun."},{"key":"11068_CR65","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1017\/S0962492900002919","volume":"8","author":"A Pinkus","year":"1999","unstructured":"Pinkus, A. Approximation theory of the MLP model in neural networks. Acta Numerica. 8, 143\u2013195. https:\/\/doi.org\/10.1017\/S0962492900002919 (1999).","journal-title":"Acta Numerica"},{"issue":"00","key":"11068_CR66","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/23080477.2024.2418007","volume":"00","author":"C Dong","year":"2024","unstructured":"Dong, C. Compressive strength prediction of high-performance concrete using MLP model. Smart Sci. 00 (00), 1\u201330. https:\/\/doi.org\/10.1080\/23080477.2024.2418007 (2024).","journal-title":"Smart Sci."},{"key":"11068_CR67","doi-asserted-by":"publisher","unstructured":"Wong, T. T. & Yeh, P. Y. Reliable Accuracy Estimates from k-Fold Cross Validation, IEEE Trans Knowl Data Eng, vol. 32, no. 8, pp. 1586\u20131594, (2020). https:\/\/doi.org\/10.1109\/TKDE.2019.2912815","DOI":"10.1109\/TKDE.2019.2912815"},{"key":"11068_CR68","doi-asserted-by":"publisher","unstructured":"Wang, X., Banthia, N. & Yoo, D. Y. Reinforcement bond performance in 3D concrete printing: explainable ensemble learning augmented by deep generative adversarial networks. Autom. Constr. 158 https:\/\/doi.org\/10.1016\/j.autcon.2023.105164 (2024).","DOI":"10.1016\/j.autcon.2023.105164"},{"key":"11068_CR69","doi-asserted-by":"publisher","unstructured":"Kashem, A. et al. Hybrid data-driven approaches to predicting the compressive strength of ultra-high-performance concrete using SHAP and PDP analyses, Case Studies in Construction Materials, vol. 20, no. February, p. e02991, (2024). https:\/\/doi.org\/10.1016\/j.cscm.2024.e02991","DOI":"10.1016\/j.cscm.2024.e02991"},{"key":"11068_CR70","doi-asserted-by":"publisher","first-page":"e01059","DOI":"10.1016\/j.cscm.2022.e01059","volume":"16","author":"IU Ekanayake","year":"2022","unstructured":"Ekanayake, I. U., Meddage, D. P. P. & Rathnayake, U. A novel approach to explain the black-box nature of machine learning in compressive strength predictions of concrete using Shapley additive explanations (SHAP). Case Stud. Constr. Mater. 16, e01059. https:\/\/doi.org\/10.1016\/j.cscm.2022.e01059 (2022).","journal-title":"Case Stud. Constr. Mater."},{"key":"11068_CR71","doi-asserted-by":"publisher","unstructured":"Zhang, Z., Xu, C., Cheng, G. & Von Lau, E. Towards carbon neutrality: A comprehensive study on the utilization and resource recovery of coal-based solid wastes. Int. J. Coal Sci. Technol. 12 (1). https:\/\/doi.org\/10.1007\/S40789-025-00775-4 (Dec. 2025).","DOI":"10.1007\/S40789-025-00775-4"},{"key":"11068_CR72","doi-asserted-by":"publisher","unstructured":"Chen, H. et al. Experimental study on damage law of coal seam under hydraulic fracturing and blast load. Int. J. Coal Sci. Technol. 12 (1). https:\/\/doi.org\/10.1007\/S40789-025-00781-6 (Dec. 2025).","DOI":"10.1007\/S40789-025-00781-6"},{"key":"11068_CR73","doi-asserted-by":"publisher","unstructured":"Zhou, C. et al. The role of fracture in dynamic tensile responses of fractured rock mass: insight from a particle-based model. Int. J. Coal Sci. Technol. 12 (1). https:\/\/doi.org\/10.1007\/S40789-025-00777-2 (Dec. 2025).","DOI":"10.1007\/S40789-025-00777-2"},{"key":"11068_CR74","doi-asserted-by":"publisher","first-page":"468","DOI":"10.1016\/j.conbuildmat.2018.03.232","volume":"172","author":"JG Sanjayan","year":"2018","unstructured":"Sanjayan, J. G., Nematollahi, B., Xia, M. & Marchment, T. Effect of surface moisture on inter-layer strength of 3D printed concrete. Constr. Build. Mater. 172, 468\u2013475. https:\/\/doi.org\/10.1016\/j.conbuildmat.2018.03.232 (2018).","journal-title":"Constr. Build. Mater."},{"key":"11068_CR75","doi-asserted-by":"publisher","unstructured":"Yan, J., Ma, D., Gao, X., Li, Q. & Hou, W. Fault zone mechanical response under co-exploitation of mine and geothermal energy: the combined effect of pore pressure and mining-induced stress. Int. J. Coal Sci. Technol. 12 (1). https:\/\/doi.org\/10.1007\/S40789-025-00786-1 (Dec. 2025).","DOI":"10.1007\/S40789-025-00786-1"},{"key":"11068_CR76","doi-asserted-by":"publisher","unstructured":"Kaya, E. et al. Effect of hydroxypropyl Methylcellulose and aggregate volume on fresh and hardened properties of 3D printable concrete. Constr. Build. Mater. 456, no. https:\/\/doi.org\/10.1016\/j.conbuildmat.2024.139253 (November, 2024).","DOI":"10.1016\/j.conbuildmat.2024.139253"},{"key":"11068_CR77","doi-asserted-by":"publisher","first-page":"132119","DOI":"10.1016\/j.conbuildmat.2023.132119","volume":"394","author":"MA Dehghani Najvani","year":"2023","unstructured":"Dehghani Najvani, M. A., Heras Murcia, D., Soliman, E. & Reda Taha, M. M. Early-age strength and failure characteristics of 3D printable polymer concrete. Constr. Build. Mater. 394, 132119. https:\/\/doi.org\/10.1016\/j.conbuildmat.2023.132119 (2023).","journal-title":"Constr. Build. Mater."},{"key":"11068_CR78","doi-asserted-by":"publisher","first-page":"105048","DOI":"10.1016\/j.cemconcomp.2023.105048","volume":"139","author":"Y Wu","year":"2023","unstructured":"Wu, Y. et al. 3D printed concrete with recycled sand: pore structures and triaxial compression properties. Cem. Concr Compos. 139, 105048. https:\/\/doi.org\/10.1016\/j.cemconcomp.2023.105048 (2023).","journal-title":"Cem. Concr Compos."},{"key":"11068_CR79","doi-asserted-by":"publisher","unstructured":"Luo, Y., Huang, J., Wu, W., Si, X. & Zhu, C. Saturation effect on storage-dissipation properties and failure characteristics of red sandstone: energy mechanism of water in preventing rockburst. Int. J. Coal Sci. Technol. 12 (1). https:\/\/doi.org\/10.1007\/S40789-025-00770-9 (Dec. 2025).","DOI":"10.1007\/S40789-025-00770-9"},{"key":"11068_CR80","doi-asserted-by":"publisher","unstructured":"Wang, Q. et al. Separation and structural analysis of soot from typical entrained flow coal gasification fine slag. Int. J. Coal Sci. Technol. 12 (1). https:\/\/doi.org\/10.1007\/S40789-025-00772-7 (Dec. 2025).","DOI":"10.1007\/S40789-025-00772-7"},{"key":"11068_CR81","doi-asserted-by":"publisher","unstructured":"Aminpour, N. & Memari, A. Analysis of anisotropic behavior in 3D concrete printing for mechanical property evaluation, Journal of Building Engineering, vol. 99, no. November p. 111652, 2025, (2024). https:\/\/doi.org\/10.1016\/j.jobe.2024.111652","DOI":"10.1016\/j.jobe.2024.111652"},{"key":"11068_CR82","doi-asserted-by":"publisher","first-page":"103820","DOI":"10.1016\/j.cemconcomp.2020.103820","volume":"114","author":"X Guo","year":"2020","unstructured":"Guo, X., Yang, J. & Xiong, G. Influence of supplementary cementitious materials on rheological properties of 3D printed fly Ash based geopolymer. Cem. Concr Compos. 114, 103820. https:\/\/doi.org\/10.1016\/j.cemconcomp.2020.103820 (2020). September.","journal-title":"Cem. Concr Compos."},{"key":"11068_CR83","doi-asserted-by":"publisher","first-page":"103895","DOI":"10.1016\/j.jobe.2021.103895","volume":"48","author":"JF Dong","year":"2022","unstructured":"Dong, J. F., Wang, Q. Y.,  Guan, Z. W. & Chai, H. K. High-temperature behaviour of basalt fibre reinforced concrete made with recycled aggregates from earthquake waste. J. Build. Eng. 48, 103895. https:\/\/doi.org\/10.1016\/j.jobe.2021.103895 (2022).","journal-title":"J. Build. Eng."},{"key":"11068_CR84","doi-asserted-by":"publisher","unstructured":"Yan, F., Wang, E., Liu, X., Qi, C. & Jia, W. Experimental study on strain localization and slow deformation evolution in small-scale specimens. Int. J. Coal Sci. Technol. 12 (1). https:\/\/doi.org\/10.1007\/S40789-025-00771-8 (Dec. 2025).","DOI":"10.1007\/S40789-025-00771-8"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-11068-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-11068-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-11068-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T14:03:29Z","timestamp":1766066609000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-11068-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,12]]},"references-count":84,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["11068"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-11068-w","relation":{},"ISSN":["2045-2322"],"issn-type":[{"type":"electronic","value":"2045-2322"}],"subject":[],"published":{"date-parts":[[2025,12,12]]},"assertion":[{"value":"30 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 December 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":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"During the preparation of this work the authors used ChatGpt for the language enhancement. After using this tool\/service, the authors reviewed and edited the content as needed and takes full responsibility for the content of the publication.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"43885"}}