Modeling Spatial and Functional Interdependencies of Civil Infrastructure Networks
Bibliographic record
Abstract
Asset management targets the sustainability of civil infrastructure throughout combining engineering and economic principles to meet customers' needs and avoid likely catastrophic failures. In the past decade, researchers commonly focused on developing techniques for understanding and controlling the performance of isolated infrastructure networks by using various simulations and statistical and optimization techniques. However, the developed models overlooked the spatial and functional interdependencies between various civil infrastructure. For instance, consider failure in a water main, the structural and functional capacity of the spatially interdependent road may likely be compromised thus affecting other surrounding roads' functionality. This raises the call for developing integrated asset management tools for identifying interdependent assets and capturing to which extent one asset failure can affect neighboring assets' performance. This paper provides a framework for capturing spatially and functionally interdependent assets that consists of two models: 1) a spatial interdependency model and 2) a functional interdependency model. The spatial interdependency model utilizes ArcGIS geoprocessing tools in determining geographically interdependent assets. The spatial interdependency model encapsulates the interdependent assets in a set of new layers and a new generated database containing characteristics of such interdependencies. However, the functional interdependency model employs graph theory principles in determining an asset's degree of connectivity with its neighboring assets. The functional model will aid in recognizing the likely influence of an asset failure on its neighboring assets' performance using two proposed parameters: 1) neighborhood centrality and 2) significant point variance. A case study using City of London water and road network will be used to demonstrate the potential for applying the proposed framework.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".