A Framework for Efficient Condition Assessment of the Building Infrastructure
Bibliographic record
Abstract
Currently, in North America, a large percentage of infrastructure assets, including education and healthcare buildings, are deteriorating rapidly due to age and over capacity. The budget constraints under which municipalities and public agencies operate also make the sustainability of these buildings a serious challenge. This is particularly so when capital renewal programs are downsized to save money, thus hindering the proper inspection of buildings and the allocation of renewal funds. In addition, building inspections and condition assessments are generally resource intensive, subjective, time-consuming, and costly. To support capital renewal decisions that pertain to buildings, this research introduces a comprehensive condition assessment framework that overcomes the drawbacks of the existing processes. A prototype of the framework utilizing hand-held devices has been developed and tested on the capital renewal program of the Toronto District School Board (TDSB). \nThe framework is innovative on three main fronts: (1) it utilizes available reactive-maintenance records to predict the condition of components and to prioritize inspection tasks among limited available resources; (2) it employs a unique visual guidance system that is based on extensive surveys and field data collection to support uniform condition assessment of building components; and (3) it introduces a location-based inspection process with a standardized building hierarchy. The research contributes to restructuring the inspection and condition assessment processes, providing a better understanding of the interactions among building components, integrating capital renewal and maintenance data, and developing a practical condition assessment framework that is economical, less-subjective, and suitable for use by individuals with less experience. The framework also incorporates permanent documentation of the condition of the asset along its life cycle, and aids in scheduling inspections so as to maintain low-cost condition tracking. Ultimately, the proposed system will provide timely and sufficient information to facilitate accurate repair decisions for maintaining the building infrastructure. \nThe framework is of benefit to both researchers and practitioners. Its formulation is innovative and helps building owners automate most inspection tasks, quantify the impact of alternative funding scenarios, and reduce the cost of asset management. In addition, because asset management is a less-developed multi-billion dollar business, the research is expected to establish leading technology and know-how that will help Canadian companies gain a competitive global advantage. At the municipality level, the proposed prototype is expected to assist managers in arriving at decisions that will ensure the cost-effective operation of buildings and uninterrupted service to the public.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".