Automated multiclass structural damage detection and quantification using augmented reality
Bibliographic record
Abstract
Civil infrastructure worldwide is ageing and enduring increasingly adverse weather conditions. Traditional structural health monitoring (SHM) involves the expensive and time-consuming installation of contact sensors. For example, inspectors use costly large-scale equipment to reach a certain area of the structure and at different heights to inspect it, which can pose a risk to the inspector's safety. Moreover, the inspectors rely only on the batch data acquired during the inspection period, which are analyzed by engineers at a later time due to the limited availability of a real-time visualization approach for structural inspection within the traditional mode of SHM. To address these timely challenges, an Augmented Reality (AR)-based automated multiclass damage identification and quantification methodology is proposed in this paper. The interactive visualization framework of AR is integrated with the autonomous decision-making of Artificial Intelligence (AI) in a unified fashion to incorporate human-sensor interaction. The proposed system uses an AI model that is trained and optimized using the YOLOv5 architecture to detect and classify four different types of anomalies/damages (i.e., cracks, spalls, pittings, and joints). The AI model is then updated to quantify the length, area, and perimeter of any damage using segmentation to further assess its severity. Once the model is developed, the model is embedded with the AR device and tested through its interactive environment for SHM of various structures. The paper concludes that the proposed approach successfully classifies four types of damage with an accuracy of more than 90% for up to 2 m, and it also quantifies the length, area, and perimeter with less than 2% of error.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".