Using image analysis to quantify defects and prioritize repairs in built-up roofs
Bibliographic record
Abstract
Purpose This paper aims to use convolutional neural networks (CNNs) to provide an objective approach to classify deteriorated building assets according to the type and extent of damage. This research supports automated inspection of buildings and focuses on roofing elements as one of the most critical and externally distressed elements in buildings. Design/methodology/approach In this paper, 5,000+ images of deteriorated roofs from several buildings were collected to design a CNN system that automatically identifies and sizes roofing defects. Experimenting with different CNN formulations, the best accuracy is achieved using two-stage CNNs. The first-stage CNN classifies images into defect/no defect, while the second stage classifies the defected images according to the damage type. Based on the image classification, optimization is used to prioritize roof repairs by maximizing the return from limited rehabilitation funds. Findings The developed CNNs reached 95% and 97% accuracy for the first and second phases, respectively, which is higher than achieved in previous literature efforts. Using the proposed model to automate inspection and condition assessment activities proved to be faster than conventional methods. Repair/replace strategy for a case study of 21 campus buildings based on their condition and budgetary constraints was suggested. Research limitations/implications Future research includes testing different data acquisition technologies (e.g. infrared imaging), performing severity-based classification and integrating with BIM for defect localization. Originality/value This study provides an objective approach to automate asset condition assessment and improve funding decisions using a combination of image analysis and optimization techniques. The proposed approach is applicable toward other asset types and components.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".