3D Printing of Conventional and Geopolymer Concretes: Advancements, Challenges, Future Directions, and Cost Analysis
Bibliographic record
Abstract
Three-dimensional printing (3DP) concrete is revolutionizing the construction industry by significantly speeding up the building process, reducing labor costs, and minimizing material waste. This technology allows for the creation of complex and innovative architectural designs that are difficult to achieve with traditional methods. Additionally, it promotes sustainability by using eco-friendly materials and reducing the carbon footprint of construction projects. Therefore, 3DP concrete (3DP) can be a suitable solution for implementing special and complex design structures, and mass customization with the lowest cost, especially in labor cost reduction and the amount of waste materials. Considering the importance of using 3DP concrete and its increasing development, identifying all the positive and negative aspects of this technology will help researchers cover disadvantages by identifying the weak points and contribute to the development of this science. Therefore, in this paper, a comprehensive analysis of the primary 3DP procedures and their development in construction technology is performed. Additionally, two types of 3DP concrete technologies are studied in this paper ordinary portland cement-based concrete (OPCBC) and geopolymer-based concrete (GBC). In addition, the growth of material manufacturing and the pros and cons of using various cementitious materials in 3DP concrete are explored. The study also covers different 3DP concrete manufacturing methods and examines key properties such as environmental impact, compressive, tensile, and flexural strengths, density, porosity, elastic modulus, heat resistance, and creep. A comparison of the properties and benefits of OPCBC and GBC 3DP concrete is provided, followed by a cost analysis to assess the advantages of 3D-printed concrete construction. The results reveal that the 3DP process and material selection significantly impact pore development. Techniques that use layered materials, such as contour carving and fused deposition modeling (FDM), often exhibit a high prevalence of pores, which diminishes the mechanical properties of the concrete. These findings offer crucial insights for scholars and decision-makers by illuminating the latest advancements and gaps in the field. This deeper understanding helps refine research priorities, informs policy-making, and can direct resources more effectively. By grasping the current state of knowledge, stakeholders are better equipped to address challenges, capitalize on emerging opportunities, and drive innovation in the relevant domain.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".