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Record W7116065543 · doi:10.82417/9mtz-sm19

Design of eco-efficient printable mortar mixes incorporating eggshell residues

2025· other· en· W7116065543 on OpenAlexaff

Bibliographic record

VenueEspace ÉTS (ETS) · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsEggshellMortarEggshell membraneCementDurabilityCarbon footprintCalcium carbonateConstruction industry

Abstract

fetched live from OpenAlex

The construction industry faces a crucial challenge in its transition towards sustainability, particularly in the decarbonization of cement, a material responsible for a significant share of global CO2 emissions. Concrete, the most widely used material in the world after water, relies heavily on cement, generating approximately 0.9 ton of CO2 emissions per ton of cement, especially in developing countries. This substantial environmental impact has prompted research aimed at reducing the carbon footprint of cement-based materials by integrating waste-derived alternatives into construction processes.Among these alternatives, waste eggshells have emerged as a promising substitute for cement in mortar formulations. Eggshells are primarily composed of calcium carbonate, a renewable resource with properties comparable to limestone, traditionally used in cement production. Incorporating eggshell powder into mortar reduces waste and contributes to the circular economy by minimizing natural resource extraction and industrial by-product disposal. Concurrently, additive manufacturing, also referred to as 3D printing, introduces an innovative and sustainable approach to construction, enabling the development of resource-efficient processes adaptable to various material compositions,The objective of this research study is to develop an eco-efficient mortar mix by replacing cement with eggshell powder and evaluating its effect on key properties of printable mortars. Specifically, the study investigates the printability, mechanical performance, and long-term durability of mortar formulations containing eggshell powder. Experimental tests using a 15% substitution of cement with eggshell powder revealed that this affects both pumpability and buildability, reducing flowability and structural stability during the printing process. Hence, to counteract these challenges, higher dosages of chemical additives were required to achieve the necessary flowability and pumpability. However, at 28 days, compressive and flexural strength decreased by only 8% and 3% respectively, remaining within acceptable limits for 3D printing.This research aligns with several United Nations Sustainable Development Goals, in particular Goal 9 (Industry, innovation, and infrastructure), Goal 11 (Sustainable Cities and Communities), and Goal 12 (Responsible Consumption and Production). By promoting waste-derived materials and reducing the environmental impact of construction processes, this study contributes to more sustainable and resilient construction practices.The findings underscore the potential of eggshell-based mortars to support the development of 3D-printable construction materials with reduced cement content. This research highlights the importance of waste-derived materials promoting sustainable construction practices and provides practical insights into the formulation of eco-efficient printable mortars. Furthermore, these results serve as a foundation for future studies on scaling up eggshell-based printable concrete and advancing sustainable construction technologies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.075
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.004

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.

Opus teacher head0.018
GPT teacher head0.264
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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