ArticleMaterials (Basel, Switzerland)2023
Machine Learning-Based Predictive Model for Tensile and Flexural Strength of 3D-Printed Concrete.
Article in Materials (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Machine learning prediction of compressive strength in 3d printed fiber reinforced concrete using support vector regression and artificial neural networks with shapley additive explanations.Scientific reports · 2026Article
- The Influence of Pore Characteristics on the Mechanical Properties of 3D-Printed Concrete Based on the Phase-Field Method.Materials (Basel, Switzerland) · 2026Article
- Predictive modeling and experimental validation of mechanical-microstructural relationships in 3D-printed Onyx-fibre composites.Scientific reports · 2026Article
- Article
- Passive determination of anisotropic compressive strength of 3D printed concrete using multiple neural networks enhanced with explainable machine learning (XML).Scientific reports · 2025Article
- Advancing Construction 3D Printing with Predictive Interlayer Bonding Strength: A Stacking Model Paradigm.Materials (Basel, Switzerland) · 2024Article
- Material Extrusion Filament Width and Height Prediction via Design of Experiment and Machine Learning.Micromachines · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The additive manufacturing of concrete, also known as 3D-printed concrete, is produced layer by layer using a 3D printer. The three-dimensional printing of concrete offers several benefits compared to conventional concrete construction, such as reduced labor costs and wastage of materials. It can also be used to build complex structures with high precision and accuracy. However, optimizing the mix design of 3D-printed concrete is challenging, involving numerous factors and extensive hit-and-trail experimentation. This study addresses this issue by developing predictive models, such as the Gaussian Process Regression model, Decision Tree Regression model, Support Vector Machine model, and XGBoost Regression models. The input parameters were water (Kg/m
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.