Innovative optimization of seashell ash-based lightweight foamed concrete: Enhancing physicomechanical properties through ANN-GA hybrid approach
Bibliographic record
Abstract
Abstract This study presents a novel approach to sustainable construction by utilizing three types of seashell ashes, namely, oyster shell ash (OSA), scallop shell ash (SSA), and mussel shell ash (MSA), as partial replacements for cement in lightweight foamed concrete (LFC). This novel application of aquaculture waste as an additive enhances the creation of more sustainable and resilient construction materials for urban settings. The physicomechanical properties of LFC, such as compressive strength (CS), flexural strength (FS), split tensile strength (STS), water absorption (WA), and porosity ( P ), were assessed utilizing response surface methodology (RSM) and artificial neural network (ANN) with K -fold cross-validation. The research examines the influence of additive type (OSA, SSA, MSA), curing duration (7–28 days), and additive concentration (0–30%) on the characteristics of LFC. Analysis of variance indicated that curing time exerted the most substantial effect on CS, FS, and STS, but additive content had a more pronounced impact on WA and P . The findings indicated favorable enhancements in CS, FS, and STS with curing durations of 28 days and additive concentrations between 4 and 20%. Replacing cement with OSA, SSA, and MSA showed favorable benefits on LFC characteristics. The predictive effectiveness of the DNN-IGWO, ANN, RSM, and Support vector machine models was evaluated using several error metrics, including mean absolute deviation, mean absolute percentage error, root mean square error, and coefficient of determination ( R 2 ). The results showed that the hybrid DNN-IGWO model outperformed all other approaches, providing significantly higher accuracy across all attributes studied. Moreover, the incorporation of evolutionary algorithms utilizing DNN-IGWO models facilitated the discovery of optimal solutions for the multi-objective optimization of LFC properties. The optimization exposed intrinsic trade-offs between targets, such as CS vs WA and CS vs P , underscoring the necessity for meticulous equilibrium in the optimization process. This study constitutes a notable advancement in sustainable development goals in construction materials by improving concrete characteristics through the incorporation of seashell ash and sophisticated optimization methods.
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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 | 0.000 | 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".