Three-dimensional (3D) concrete printing (3DCP) is an emerging digital construction technology that enables geometrically complex structures with reduced labour, material waste, and formwork. However, the sustainability of 3DCP remains constrained by its heavy reliance on Portland cement, a major source of global CO₂ emissions. This study systematically examines metakaolin (MK) and biochar (BC) as sustainable additives for 3DCP, focusing on their independent effects on mechanical performance, printability, dimensional stability, and environmental impact. A comprehensive literature review (2015 to June 2025) identified 254 publications, of which 21 met the inclusion criteria for quantitative meta-analysis, contributing a total of 95 datasets for compressive and flexural strength. Pooled effect sizes were calculated using a random-effects model, supported by risk-of-bias and heterogeneity analyses. The results indicate statistically significant improvements in mechanical properties, with an overall pooled ratio of means (ROM) of 1.12 (95% CI: 1.06–1.20; I² = 48.9%), representing the overall mechanical performance effect across all datasets, while ROM for compressive and flexural strength was calculated separately in the main analysis. Meta-regression revealed that BC increased compressive and flexural strengths by 7% and 9%, respectively, while MK achieved greater enhancements of 21% and 13.4%. Optimum performance was observed at 15–20% MK for compressive strength and 10–15% for flexural strength, whereas BC performed best at 3–5% and 2–5%, respectively. BC contributed to CO₂ reductions of up to 43% through clinker substitution and biogenic carbon sequestration. These findings demonstrate that MK and BC are complementary eco-efficient modifiers capable of enhancing both structural and environmental performance in 3DCP. Future research should address long-term durability, standardisation of printing parameters, and cradle-to-grave life cycle assessments to strengthen practical implementation.

3D printed concrete (3DPC) is an advanced additive manufacturing technique which aids in creating complex structures with precision and efficiency. This innovative technology offers substantial benefits, including minimised material waste, enhanced project completion speed, and the capability to fabricate complex and distinctive designs. While significant progress has been made, existing review studies primarily address specific domains such as materials, technical and non-technical challenges, rheological parameters, and modelling. However, limited research has explored quantitative data on leading countries and institutions, collaboration networks, profiles of influential authors, and key journals. Furthermore, there is a lack of comprehensive qualitative insights into the managerial, sustainability and environmental, and economic aspects of 3DPC, as well as advancements in material compatibility, mix design methods, and applications of emerging technologies. This study addresses these gaps by conducting a bibliometric and systematic review of 3DPC research from 2015 to 2024, with data sourced from Web of Science and Scopus. The bibliometric analysis revealed a steady growth in 3DPC studies, with annual publications increasing by over 800% between 2018 and 2024, and major contributions from China (28%), Australia (10%), and the USA (7%). Tongji University emerged as the leading institution, accounting for 8% of the total publications. The systematic review discusses the key advancements in technology and structural development while uniquely focusing on the managerial aspects of 3DPC. By integrating bibliometric and qualitative insights, this review provides a comprehensive understanding of the current state and future potential of 3DPC, offering valuable guidance for researchers and practitioners aiming to advance this transformative technology.

The reliable prediction of reinforcement bond strength in three-dimensional concrete printing (3DCP) remains a critical challenge due to material anisotropy, geometric variability, and complex interfacial mechanisms governing stress transfer. Conventional experimental approaches are resource intensive, while existing predictive studies remain limited in optimisation rigour, interpretability, and mechanical validation. To address these limitations, this study presents an interpretable machine learning framework using Extreme Gradient Boosting (XGB) algorithm optimised using Bayesian Tree Parzen Estimator and Taguchi optimisation techniques. A comprehensive database comprising 607 experimental pull-out test results retrieved from published literature was employed for model training and validation. K-fold cross-validation confirmed stable generalisation performance with TPE-XGB achieving a testing MAE of 1.46 MPa and RMSE of 1.7 MPa, marginally outperforming the TO-XGB model. Also, mechanical validation on unseen data configurations using finite element modelling demonstrated superior accuracy of trained XGB models compared to nonlinear finite element simulations. The finite element analysis reproduced rib induced stress transfer mechanisms with peak concrete stresses reaching 38.58 MPa at pull-out, supporting mechanical consistency. Moreover, interpretable machine learning analyses identified reinforcement diameter and embedment length as the primary governing parameters. The integrated framework provides a computationally efficient and mechanically consistent tool for bond strength prediction in reinforced 3DCP to support practical implementation.