Multi-objective optimization of sustainable cement-zeolite improved sand based on life cycle assessment and artificial intelligence
Bibliographic record
Abstract
Background: Cement-zeolite improved sand can be used in diverse civil engineering applications. However, earlier research has not duly optimized its production process to attain best mechanical strength, lowest cost, and least environmental impact. This study proposes a multi-objective optimization approach using back-propagation neural network (BPNN) to predict the mechanical strength, along with an adaptive geometry estimation-based multi-objective evolutionary algorithm (AGE-MOEA) to identify the best parameters for cement-zeolite-improved sand, filling a long-lasting research gap. Methods: A collection of unconfined compression tests was used to evaluate cemented sand specimens treated with stabilizers including portland cement (at dosages of 2, 4, 6, 8, and 10%) and six dosages of natural zeolite as partial replacement for cement (0, 10, 30, 50, 70, and 90%) at different curing times of 7, 28, and 90 days. The study further conducts a detailed analysis of life cycle assessment (LCA) to show how partial zeolite replacement for cement impacts the environment. Through a tuning process, the BPNN model found the optimal architecture and accurately predicted the unconfined compressive strength of cement-zeolite improved sand systems. This allowed the AGE-MOEA to optimize zeolite and cement dosages, density, curing time, and environmental impact. Results: The results of this study reveal that the optimal range of zeolite was between 30-45%, which not only increased cemented sand strength, but also reduced the cost and environmental impact. It is also shown that increasing the zeolite replacement to 25-30% can increase the ultimate strength of cemented sand, yet exceeding this limit can cause the strength to decrease. Conclusions: Zeolite has the potential to serve as an alternative for cement in applications that involve cemented sand, while still achieving mechanical strength performance, which is comparable or even superior. From an LCA standpoint, using zeolite as partial cement replacement in soil improvement projects is a promising alternative.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".