Innovative optimization of seashell ash-based lightweight foamed concrete: Enhancing physicomechanical properties through ANN-GA hybrid approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 |
| 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 it