AI-powered 3D printed concrete technology: a new paradigm of intelligent construction integrating materials, equipment, and structures

Abstract

3D printed concrete (3DPC) technology is driving the construction industry toward automation and sustainable practices. However, its widespread adoption remains hindered by inherent material anisotropy, unpredictable process control, and complex structural design. To overcome these bottlenecks, artificial intelligence (AI) has emerged as a transformative solution. This paper provides a comprehensive review of AI in 3DPC across three core dimensions, highlighting a paradigm shift from an empirical, open-loop pipeline to a unified cyber-physical framework driven by bidirectional information feedback. At the material level, machine learning (ML) enables inverse design and multi-objective optimization of mix proportions. During the printing process, the integration of machine vision and adaptive control establishes a robust perception-decision-execution closed-loop system, ensuring deposition quality and geometric fidelity. At the structural level, generative design and topology optimization facilitate the creation of complex geometries. Meanwhile, AI models enable multi-scale performance evaluations, ranging from micro-defect identification to macro-scale load-bearing capacity assessment. Despite these achievements, bottlenecks such as data heterogeneity and physics-agnostic models persist. Future research is expected to focus on cross-layer coupling, physics-informed modeling, and digital twin interoperability. By continuously feeding process execution and structural evaluation data back into material formulation, this new paradigm is poised to transform 3DPC into a fully self-adaptive and autonomous construction ecosystem.

Keywords

3D printed concrete / Artificial intelligence / Machine learning / Computer vision / Mix design / Process control / Multi-scale performance prediction

References

[1] Abedi M, Waris MB, Alawi M, Jabri K. Transformative low-carbon 3D-printed infrastructure: Machine learning-driven self-sensing and self-heating limestone calcined clay cement (LC3) composites. Construction and Building Materials, 2025, 493 143123

[2] Alade IO, Zhang Y, Xu X. Modeling and prediction of lattice parameters of binary spinel compounds (AM2X4) using support vector regression with Bayesian optimization. New Journal of Chemistry, 2021, 45(34): 15255-15266

[3] Alizamir M, Kim S, Ikram RMA, Ahmed KO, Heddam S, Gholampour A. A reliable hybrid extreme learning machine-metaheuristic framework for enhanced strength prediction of 3D-printed fiber-reinforced concrete. Results in Engineering, 2025, 27 105715

[4] Alyami M, Khan M, Fawad M, Nawaz R, Hammad AWA, Najeh T, Gamil Y. Predictive modeling for compressive strength of 3D printed fiber-reinforced concrete using machine learning algorithms. Case Studies in Construction Materials, 2024, 20 e02728

[5] Alyami M, Khan M, Javed MF, Ali M, Alabduljabbar H, Najeh T, Gamil Y. Application of metaheuristic optimization algorithms in predicting the compressive strength of 3D-printed fiber-reinforced concrete. Developments in the Built Environment, 2024, 17 100307

[6] Angiulli G, Versaci M, Burrascano P, Laganá F. A data-driven Gaussian process regression model for concrete complex dielectric permittivity characterization. Sensors, 2025, 25(20 6350

[7] Arif M, Jan F, Rezzoug A, Afridi MA, Luqman M, Khan WA, Kujawa M, Alabduljabbar H, Khan M. Data-driven models for predicting compressive strength of 3D-printed fiber-reinforced concrete using interpretable machine learning algorithms. Case Studies in Construction Materials, 2024, 21 e03935

[8] Barhemat R, Mahjoubi S, Meng W, Bao Y. AI-assisted design, 3d printing, and evaluation of architecture polymer-concrete composites (APCC) With high specific flexural strength and high specific toughness. SSRN Electronic Journal, 2023

[9] Barhemat R, Mahjoubi S, Meng W, Bao Y. Automated design of architectured polymer-concrete composites with high specific flexural strength and toughness using sequential learning. Construction and Building Materials, 2024, 449 138311

[10] Buswell RA, Silva WRL, Bos FP, Schipper HR, Lowke D, Hack N, Kloft H, Mechtcherine V, Wangler T, Roussel N. A process classification framework for defining and describing Digital Fabrication with Concrete. Cement and Concrete Research, 2020, 134 106068

[11] Chang Z, Wan Z, Xu Y, Schlangen E, Šavija B. Convolutional neural network for predicting crack pattern and stress-crack width curve of air-void structure in 3D printed concrete. Engineering Fracture Mechanics, 2022, 271 108624

[12] Chou J, Tsai C, Pham A, Lu Y. Machine learning in concrete strength simulations: Multi-nation data analytics. Construction and Building Materials, 2014, 73: 771-780

[13] Cui W, Ji D, Shen L, Su S, Shi X, Liu J, Bai Z, Sun Y, Gong J, Tao Y. A neural network-based model for assessing 3D printable concrete performance in robotic fabrication. Results in Engineering, 2025, 27 105970

[14] Darmanin, R. N., & Bugeja, M. K. (2017). A review on multi-robot systems categorised by application domain. 2017 25th Mediterranean Conference on Control and Automation (MED), Valletta, Malta, 2017, pp. 701–706. https://doi.org/10.1109/MED.2017.7984200

[15] Dixit S, Stefańska A. Bio-logic, a review on the biomimetic application in architectural and structural design. Ain Shams Engineering Journal, 2023, 141 101822

[16] Dörfler K, Dielemans G, Leutenegger S, Jenny SE, Pankert J, Sustarevas J, Lachmayer L, Raatz A, Lowke D. Advancing construction in existing contexts: Prospects and barriers of 3d printing with mobile robots for building maintenance and repair. Cement and Concrete Research, 2024, 186 107656

[17] Flatt RJ, Wangler T. On sustainability and digital fabrication with concrete. Cement and Concrete Research, 2022, 158 106837

[18] García R, Dokladalova E, Dokládal P, Caron J-F, Mesnil R, Margerit P, Charrier M. Inline monitoring of 3D concrete printing using computer vision. Additive Manufacturing, 2022, 60 103175

[19] Geng S, Cheng B, Long W, Luo Q, Dong B, Xing F. Co-driven physics and machine learning for intelligent control in high-precision 3D concrete printing. Automation in Construction, 2025, 176 106294

[20] Geng S, Luo Q, Cheng B, Li L, Wen D, Long W. Intelligent multi-objective optimization of 3D printing low-carbon concrete for multi-scenario requirements. Journal of Cleaner Production, 2024, 445 141361

[21] Geng S, Luo Q, Liu K, Li Y, Hou Y, Long W. Research status and prospect of machine learning in construction 3D printing. Case Studies in Construction Materials, 2023, 18 e01952

[22] Geng S, Mei L, Cheng B, Luo Q, Xiong C, Long W. Revolutionizing 3D concrete printing: Leveraging RF model for precise printability and rheological prediction. Journal of Building Engineering, 2024, 88 109127

[23] Ghasemi A, Naser MZ. Tailoring 3D printed concrete through explainable artificial intelligence. Structures, 2023, 56 104850

[24] Gosselin C, Duballet R, Roux P, Gaudillière N, Dirrenberger J, Morel P. Large-scale 3D printing of ultra-high performance concrete–—a new processing route for architects and builders. Materials and Design, 2016, 100: 102-109

[25] Huang S, Xu W, Anton A, Dillenburger B. Self-supporting lamellae: Shape variation methods for the 3D concrete printing of large overhang structures. Additive Manufacturing, 2024, 91 104329

[26] Iqbal M, Nazar S, Yang J, Mahmoud HA. Integrated machine learning and response surface methodology for comprehensive rheological characterization of low-carbon binders. Case Studies in Construction Materials, 2025, 22 e04475

[27] Jiang Q, Liu Q, Wu S, Zheng H, Sun W. Modification effect of nanosilica and polypropylene fiber for extrusion-based 3D printing concrete: Printability and mechanical anisotropy. Additive Manufacturing, 2022, 56 102944

[28] Jin B, Xu X. Machine learning coffee price predictions. Journal of Uncertain Systems, 2024, 1704): 2450023

[29] Jin B, Xu X. Regional steel price index predictions for the southwest Chinese market through machine learning. Ironmaking and Steelmaking, 2024

[30] Jin B, Xu X. Chinese energy security index price forecasting through the neural network. Innovation and Emerging Technologies, 2025, 12: 2550036

[31] Jin B, Xu X. High-frequency CSI300 spot and futures price predictions via the neural network. Journal of Uncertain Systems, 2025

[32] Jin B, Xu X. Late and early indica rice's price forecasts through neural networks. International Journal of Big Data Mining for Global Warming, 2025

[33] Jin B, Xu X. Predictions of residential property price indices for China via machine learning models. Quality and Quantity, 2025, 59(2): 1481-1513

[34] Jin B, Xu X. A study of contemporaneous residential real estate price causation across major jiangsu province cities: methodology using vector error-correction models and directed acyclic graphs. Economics Open, 2025, 01: 2550008

[35] Jin B, Xu X. Contemporaneous causal analysis of housing prices across guangdong’s major cities: employing vector error-correction modeling and directed acyclic graphs. Journal of Uncertain Systems, 2026

[36] Jin W, Caron J, Plamondon CM. Minimizing the carbon footprint of 3D printing concrete: Leveraging parametric LCA and neural networks through multiobjective optimization. Cement and Concrete Composites, 2025, 157 105853

[37] Kazemian A, Khoshnevis B. Real-time extrusion quality monitoring techniques for construction 3D printing. Construction and Building Materials, 2021, 303 124520

[38] Khan MAH, Ahmed A, Ali T, Qureshi MZ, Islam S, Ahmed H, Ajwad A, Khan MA. Comprehensive review of 3D printed concrete, life cycle assessment, AI and ML models: Materials, engineered properties and techniques for additive manufacturing. Sustainable Materials and Technologies, 2025, 43 e01164

[39] Li S, Nguyen-Xuan H, Tran P. Digital design and parametric study of 3D concrete printing on non-planar surfaces. Automation in Construction, 2023, 145 104624

[40] Lin T, Chang C, Yang B, Hung C, Wen K. AI-powered shotcrete robot for enhancing structural integrity using ultra-high performance concrete and visual recognition. Automation in Construction, 2023, 155 105038

[41] Lin W, Wang L, Li Z, Bai G, Wang Q, Qu Y. Multi-scale fabrication and challenges in 3D printing of special -shaped concrete structures. Journal of Building Engineering, 2025, 111 113134

[42] Liu J, Alexander J, Li Y. Gaussian process regression-based model error diagnosis and quantification using experimental data of prestressed concrete beams in shear. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part a: Civil Engineering, 2025, 11(1): 04024095

[43] Liu S, Liu T, Alqurashi M, Abdou Elabbasy AA, Alanazi N, Shakor P. Advancing 3D-printed fiber-reinforced concrete for sustainable construction: A comparative optimization based study of hybrid machine intelligence models for predicting mechanical strength and CO₂ emissions. Case Studies in Construction Materials, 2025, 23 e05259

[44] Lori AR, Mehrali M. Filament geometry control of printable geopolymer using experimental and data driven approaches. Construction and Building Materials, 2025, 461 139853

[45] Luo Z, Li D, Wan J, Wang S, Wang G, Cheng M, Li T. Multi-agent collaboration mechanisms based on distributed online meta-learning for mass personalization. Journal of Industrial Information Integration, 2025, 46 100852

[46] Malik UJ, Riaz RD, Rehman SU, Usman M, Riaz RE, Hamza R. Advancing mix design prediction in 3D printed concrete: Predicting anisotropic compressive strength and slump flow. Case Studies in Construction Materials, 2024, 21 e03510

[47] Ma Xinrui, Wang Xianlin, Chen Shizhi. Trustworthy machine learning-enhanced 3D concrete printing: Predicting bond strength and designing reinforcement embedment length. Automation in Construction, 2024, 168: 105754

[48] Mechtcherine V, Bos FP, Perrot A, da Silva WRL, Nerella VN, Fataei S, Wolfs RJM, Sonebi M, Roussel N. Extrusion-based additive manufacturing with cement-based materials–—Production steps, processes, and their underlying physics: A review. Cement and Concrete Research, 2020, 132 106037

[49] Mirwais M, Adeel M, Rahmani AW, Rahmani AN. AI-driven generative design for next-generation 3D concrete printing in architecture: State of the art. European Journal of Applied Science, Engineering and Technology, 2025, 3(2): 225-232

[50] Mütevelli İG, Aldemir A. Machine-learning networks to predict the ultimate axial load and displacement capacity of 3D printed concrete walls with different section geometries. Structures, 2024, 66 106879

[51] Nair SAO, Sant G, Neithalath N. Mathematical morphology-based point cloud analysis techniques for geometry assessment of 3D printed concrete elements. Additive Manufacturing, 2022, 49 102499

[52] Nazar S, Yang J, Faisal Javed M, Khan K, Li L, Liu Q. An evolutionary machine learning-based model to estimate the rheological parameters of fresh concrete. Structures, 2023, 48: 1670-1683

[53] Okonkwo C, Awolusi I. Environmental sensing in autonomous construction robots: Applicable technologies and systems. Automation in Construction, 2025, 172 106075

[54] Raissi M, Perdikaris P, Karniadakis GE. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 2019, 378: 686-707

[55] Rehman AU, Kim I, Kim J. Towards full automation in 3D concrete printing construction: Development of an automated and inline sensor-printer integrated instrument for in-situ assessment of structural build-up and quality of concrete. Developments in the Built Environment, 2024, 17 100344

[56] Rehman AU, Kim JH. 3D concrete printing: A systematic review of rheology, mix designs, mechanical, microstructural, and durability characteristics. Materials, 2021

[57] Rehman SU, Riaz RD, Usman M, Kim I. Augmented data-driven approach towards 3D printed concrete mix prediction. Applied Sciences, 2024

[58] Reiter L, Wangler T, Roussel N, Flatt RJ. The role of early age structural build-up in digital fabrication with concrete. Cement and Concrete Research, 2018, 112: 86-95

[59] Salaimanimagudam MP, Jayaprakash J. Synergistic potential of topology optimization and lattice structures in concrete 3D printed beams. Structures, 2025, 76 108892

[60] Schossler RT, Ullah S, Alajlan Z, Yu X. Data-driven analysis in 3D concrete printing: Predicting and optimizing construction mixtures. AI in Civil Engineering, 2025, 4(1): 1

[61] Soori M, Jough FKG, Dastres R, Arezoo B. Additive manufacturing modification by artificial intelligence, machine learning, and deep learning: A review. Additive Manufacturing Frontiers, 2025, 4(2 200198

[62] Sun J, Aslani F, Mann D, Huang B, Peng J. Mechanical and piezoresistive behaviour of 3D printed self-sensing one-way concrete slab. Structures, 2025, 78 109160

[63] Tay YWD, Panda B, Paul SC, Noor Mohamed NA, Tan MJ, Leong KF. 3D printing trends in building and construction industry: A review. Virtual and Physical Prototyping, 2017, 123): 261-276

[64] Uddin MN, Mahamoudou F, Deng B-Y, Elobaid Musa MM, Tim Sob LW. Prediction of rheological parameters of 3D printed polypropylene fiber-reinforced concrete (3DP-PPRC) by machine learning. Materials Today: Proceedings, 2023

[65] Versteege J, Wolfs RJM, Salet TAM. Data-driven additive manufacturing with concrete: Enhancing in-line sensory data with domain knowledge Part I: Geometry. Automation in Construction, 2025, 172 106020

[66] Versteege J, Wolfs RJM, Salet TAM. Data-driven additive manufacturing with concrete: Enhancing in-line sensory data with domain knowledge, Part II: Moisture and heat. Automation in Construction, 2025, 177 106327

[67] Wang L, Zhang Y, Wang Z, Chen J, Yang L, Xia J, Zhang Y, Zhang J, Zhu W, Zhang H, Chen Y, Li X, Yu Z, Fan D, Yang Q, Kong Y. Additive manufacturing in construction using unmanned aerial vehicle: Design, implementation, and material properties. Journal of Building Engineering, 2024, 98 111363

[68] Wang X, Liu X, Xu Y, Cao J, Zhang H, Zhang H. A general adaptive layer height continuous path planning algorithm for concrete 3D printing of complex porous structures based on multi-objective optimization and reinforcement learning. Structures, 2025, 80 109926

[69] Wang X, Zuo T, Xu Y, Liu X, Zhang H, Wang Q, Zhang H. Reinforcement learning-based continuous path planning and automated concrete 3D printing of complex hollow components. Automation in Construction, 2025, 177 106290

[70] Xiao J, Ji G, Zhang Y, Ma G, Mechtcherine V, Pan J, Wang L, Ding T, Duan Z, Du S. Large-scale 3D printing concrete technology: Current status and future opportunities. Cement and Concrete Composites, 2021, 122 104115

[71] Xu W, Huang S, Han D, Zhang Z, Gao Y, Feng P, Zhang D. Toward automated construction: The design-to-printing workflow for a robotic in-situ 3D printed house. Case Studies in Construction Materials, 2022, 17 e01442

[72] Xu X. Short-run price forecast performance of individual and composite models for 496 corn cash markets. Journal of Applied Statistics, 2017, 44(14): 2593-2620

[73] Xu X. Corn cash price forecasting. American Journal of Agricultural Economics, 2020, 102(4): 1297-1320

[74] Xu X, Zhang Y. Individual time series and composite forecasting of the Chinese stock index. Machine Learning with Applications, 2021, 5 100035

[75] Xu X, Zhang Y. Machine learning the concrete compressive strength from mixture proportions. ASME Open Journal of Engineering, 2022

[76] Xu X, Zhang Y. An integrated vector error correction and directed acyclic graph method for investigating contemporaneous causalities. Decision Analytics Journal, 2023, 7 100229

[77] Yang W, Wang L, Ma G, Feng P. An integrated method of topological optimization and path design for 3D concrete printing. Engineering Structures, 2023, 291 116435

[78] Yao X, Lyu X, Sun J, Wang B, Wang Y, Yang M, Wei Y, Elchalakani M, Li D, Wang X. AI-based performance prediction for 3D-printed concrete considering anisotropy and steam curing condition. Construction and Building Materials, 2023, 375 130898

[79] Yuan PF, Zhan Q, Wu H, Beh HS, Zhang L. Real-time toolpath planning and extrusion control (RTPEC) method for variable-width 3D concrete printing. Journal of Building Engineering, 2022, 46 103716

[80] Zhang H, Hao L, Zhang S, Xiao J, Poon CS. Advanced measurement techniques for plastic shrinkage and cracking in 3D-printed concrete utilising distributed optical fiber sensor. Additive Manufacturing, 2023, 74 103722

[81] Zhang H, Tan Y, Hao L, Zhang S, Xiao J, Poon CS. Intelligent real-time quality control for 3D-printed concrete with near-nozzle secondary mixing. Automation in Construction, 2024, 160 105325

[82] Zhang K, Chermprayong P, Xiao F, Tzoumanikas D, Dams B, Kay S, Kocer BB, Burns A, Orr L, Alhinai T, Choi C, Darekar DD, Li W, Hirschmann S, Soana V, Ngah SA, Grillot C, Sareh S, Choubey A, Kovac M. Aerial additive manufacturing with multiple autonomous robots. Nature, 2022, 609(7928): 709-717

[83] Zhang T, Wang D, Lu Y. A Navier–Stokes-informed neural network for simulating the flow behavior of flowable cement paste in 3D concrete printing. Buildings, 2025, 15(2): 275

[84] Zhang X, Li M, Lim JH, Weng Y, Tay YWD, Pham H, Pham Q-C. Large-scale 3D printing by a team of mobile robots. Automation in Construction, 2018, 95: 98-106

[85] Zhang Y, Xu X. Solubility predictions through LSBoost for supercritical carbon dioxide in ionic liquids. New Journal of Chemistry, 2020, 44: 20544-20567

[86] Zhang Y, Xu X. Predicting multiple properties of pervious concrete through the Gaussian process regression. Advances in Civil Engineering Materials, 2021, 10(1): 56-73

[87] Zhang Y, Xu X. Solid particle erosion rate predictions through LSBoost. Powder Technology, 2021, 388: 517-525

[88] Zhang Y, Xu X. Disordered MgB2 superconductor critical temperature modeling through regression trees. Physica c: Superconductivity and Its Applications, 2022, 597: 1354062

[89] Zhang Y, Xu X. Modulus of elasticity predictions through LSBoost for concrete of normal and high strength. Materials Chemistry and Physics, 2022, 283 126007

[90] Zhao H, Jassmi HAI, Liu X, Wang Y, Chen Z, Wang J, Lei Z, Wang X, Sun J. Artificial intelligence based microcracks research in 3D printing concrete. Construction and Building Materials, 2024, 457 139049

[91] Zhao H, Sun J, Wang X, Wang Y, Su Y, Wang J, Wang L. Real-time and high-accuracy defect monitoring for 3D concrete printing using transformer networks. Automation in Construction, 2025, 170 105925

[92] Zhao H, Wang X, Chen Z, Liu X, Wang Y, Wang J, Sun J. Microcrack investigations of 3D printing concrete using multiple transformer networks. Automation in Construction, 2025, 172 106017

[93] Zhao H, Wang X, Sun J, Wu F, Liu X, Chen Z, Wang Y. Automated analysis system for micro-defects in 3D printed concrete. Automation in Construction, 2025, 173 106105

[94] Zhi Y, Chai H, Teng T, Akbarzadeh M. Automated toolpath design of 3D concrete printing structural components. Additive Manufacturing, 2025, 100 104662

[95] Zhong B, Wu H, Li H, Sepasgozar S, Luo H, He L. A scientometric analysis and critical review of construction related ontology research. Automation in Construction, 2019, 101: 17-31

[96] Zhu R, Egbe KI, Salehi H, Shi Z, Jiao P. Eco-friendly 3D printed concrete with fine aggregate replacements: Fabrication, characterization and machine learning prediction. Construction and Building Materials, 2024, 413 134905

[97] Zou Z, Peng B, Xie L, Song S. Enhanced Gaussian process model for predicting compressive strength of ultra-high-performance concrete (UHPC). Materials, 2024, 17(24): 6140

Funding

National Natural Science Foundation of China(No. 52178198)

Natural Science Foundation of Tianjin Municipality(No. 23JCZDJC00360)

RIGHTS & PERMISSIONS

The Author(s)