Aug 2026· Materials· Vol 19· 0 citations· 119 references
Medicine
Abstract
3D Concrete Printing (3DCP) has emerged as a transformative construction technology, offering enhanced design flexibility, reduced material waste, improved construction efficiency, and safer working environments. Despite these advantages, the absence of standardised design provisions for structural 3DCP elements remains a major obstacle to its widespread adoption. This study develops statistical and machine learning models to predict the design strengths of hardened 3D-printed concrete. A comprehensive database of experimental results published between 2012 and 2025 was compiled and analysed, encompassing the compressive, flexural, and shear strengths of hardened 3DCP materials. Regression-based statistical modelling was employed to derive design strength equations and assess their consistency with conventional concrete design approaches. In parallel, several machine learning algorithms were developed using the same dataset to benchmark predictive performance and evaluate the robustness of the proposed statistical models. The statistical models achieved good predictive accuracy for compressive and flexural strengths, with coefficients of determination (R2) of 0.83 and 0.67, respectively. By contrast, the shear strength model exhibited greater variability (R2 = 0.39), reflecting the limited availability and considerable scatter of published experimental data. Benchmarking against the optimised machine learning model demonstrated excellent agreement with the proposed statistical equations, yielding coefficients of determination of 0.93 and 0.94 for compressive and flexural strengths, respectively. The proposed modelling framework provides preliminary design strength predictions for hardened 3D-printed concrete, contributing to the development of robust structural design guidelines and facilitating the wider adoption of 3DCP in construction practice.
A comprehensive data-driven framework integrating ensemble machine learning models with systematic hyperparameter sensitivity analysis and explainable artificial intelligence techniques is proposed, demonstrating that the XGB model significantly outperforms the other approaches, achieving superior accuracy and robust generalization.
Qaim Shah, Waheed Ali Khoso, Fawad Iqbal et al.· Discover Artificial Intellig...· 0 citations
A novel, data-driven framework for predicting and optimizing the mechanical performance of 3D-printed polylactic acid (PLA) composites reinforced with date pit (DP) particles under controlled annealing conditions is presented, enabling simultaneous property prediction and design optimization from a minimal experimental dataset.
An interpretable and uncertainty-aware machine-learning framework for estimating the shear capacity of FRCM-strengthened beams enables accurate, transparent, and uncertainty-aware assessment of shear capacity in FRCM-strengthened concrete beams.
Xiangsheng Liu, G. Figueredo, G. Gordon et al.· Journal of composites for co...· 0 citations
This article presents how ML approaches may speed up material discovery, reduce trial-and-error, and enable individualized design solutions for 3D-printed polymers across a variety of contexts.
N. Senthilkumar, S. Gopalakrishnan, S. Gopinath et al.· Interactions· 0 citations
Results suggest that, within the present five-fold cross-validation setting and limited-sample dataset, RBF kernel ridge regression captures the nonlinear relationships more effectively than conventional linear models; however, broader generalization should be verified using larger datasets and additional validation.
Yuchen Lin· International Conference on...· 0 citations
Flexural strength (FS) governs the structural performance of 3D-printed concrete (3DPC) under bending loads yet remains difficult to predict owing to the coupled influence of binder composition, supplementary cementitious materials, water-to-binder ratio, and fiber reinforcement geometry on interlayer fracture behavior. Much of this compositional diversity stems from supplementary cementitious materials, industrial by-products whose reuse as partial cement replacement lowers the embodied carbon of printable mixes. A machine learning framework was trained on 209 FS records covering OPC- and SAC-based systems (FS: 3.65–45.0 MPa; W/B: 0.15–0.65). Six composite features were constructed from physical principles, two of them specific to bending: Fiber_Pullout_Index encoding post-crack pullout energy and Binder_Efficiency capturing cement quality per unit water content at the fiber–matrix interface; Lasso regularization with the one-standard-error rule reduced the 19-variable space to 14 active predictors. Twenty regression algorithms spanning eight families were benchmarked under 30 independent partitions; the Friedman test rejected equal performance (χ2=337.02, p=4.88×10−60) and all 19 pairwise Wilcoxon comparisons against CatBoost were Holm-significant. CatBoost ranked first (mean rank of 18.17/20; 30-run R2=0.9302±0.0722; seed-42 partition: R2=0.9557; RMSE = 1.761 MPa; MAPE = 11.20%). n(W/B) emerged as the primary driver across SHAP, ALE and LIME, with a monotonic ALE profile spanning 8.99 MPa and no inflection over the full printable window; Binder_Efficiency ranked second (PDP range: 5.21 MPa), isolating cement grade and paste dilution as independent strength levers. Cross-conformal prediction provided finite-sample coverage guarantees without distributional assumptions (empirical coverage: 95.24%; conformity quantile: 3.83 MPa); bootstrap analysis put the epistemic component at a mean predictive SD of 0.946 MPa, a quarter of that quantile. External validation yielded R2=0.769 (Pearson R=0.923, RMSE = 1.91 MPa), with 19 of 20 predictions (95.0%) within Bland–Altman 95% limits of agreement, confirming transfer to a source study withheld from model training. A graphical user interface packaging the 14-feature CatBoost pipeline supports mix design queries without programming.
Fengping Qin, Yang Chen, Mengdi Hou et al.· Buildings· 0 citations