Effect of model architecture and input parameters to improve performance of artificial intelligence models for estimating concrete strength using SonReb
Bibliographic record
Abstract
The use of Artificial Intelligence (AI) with the non-intrusive SonReb method, which combines Ultrasonic Pulse Velocity (UPV) and Rebound Number (RN) to predict concrete compressive strength, has attracted increasing attention in recent years. This study introduces a novel approach to improve AI models for predicting concrete strength, making them more suitable for future practical applications. One of the key challenge in AI models is the number of input parameters; while more inputs often improve accuracy, they are typically impractical for most applications dealing with existing structures (e.g., requiring detailed concrete mix design information that is often unavailable). SonReb AI-based models which use only two input parameters (UPV and RN) have shown reasonable accuracy, but their general use is limited by adoption of different testing standards which precludes the development of large databases. This study aims to improve two-parameter SonReb-based AI models through the addition of a binary input variable that represents the type of the specimen geometry (cube or cylinder) and investigates the effect of model architecture by comparing three different AI algorithms: Artificial Neural Networks (ANN), Deep Neural Networks (DNN), and Adaptive Neuro-Fuzzy Inference Systems (ANFIS). Six AI models were developed using 514 data points from experimental tests and collected data, and an unbiased data splitting method was applied for training and testing. The results showed that including specimen geometry improved model accuracy across all AI algorithms. The results of this study show that regardless of AI architecture, the proposed novel approach not only improves the accuracy of models, but also enables the use of larger databases containing both cubic and cylindrical specimens. • A new AI model to predict concrete compressive strength using the SonReb method. • A new approach enables models based on both cylindrical and cubic standards. • Comparison of machine learning (ANN and ANFIS) and deep learning (DNN) algorithms.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".