Critical Performance Assessment of Precast Concrete Quality in High-Rise Buildings Compliance with National Standards
Bibliographic record
Abstract
This study critically assesses Precast Concrete (PC) quality compliance in Indonesian high-rise buildings by comparing material strength, geometric tolerances, and seismic connection performance against SNI requirements. Using five years of official inspection data (2020–2025) from 120 certified lots (BSN/PUPR), the analysis identifies recurring non-compliance, including a 15.0% compressive strength defect rate, 8.5% AOQ, and 17.1% reinforcement cover deficiencies that may compromise structural durability. Wet joints exhibit better seismic resilience (μ = 4.5) than dry joints (μ = 3.8), though dry joints show pinching effects that reduce energy dissipation.Key root causes include moisture instability, admixture dosing errors, and precision gaps, revealing a persistent mismatch between documented procedures and actual statistical quality control. The study recommends implementing automated QC systems and strengthening SNI certification through more rigorous statistical approaches. Future work should integrate machine-learning predictive models using real-time factory data to enhance defect prevention.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".