Multi-criteria spatial analysis for location selection of multi-purpose utility tunnels
Bibliographic record
Abstract
Multi-purpose utility tunnels (MUTs) integrate all underground utilities in one accessible tunnel. MUTs reduce the need for excavations and their associated costs, as well as the resulting traffic congestion. Several MUTs have been implemented in different parts of the world. Their locations have either been politically influenced or selected to preserve heritage sites or to meet the conditions of a newly developed city. Nevertheless, selecting the location in an existing city under street segments is affected by several criteria that have different spatial characteristics. Combining these characteristics and managing the trade-offs that exist between them determine the ranking of alternative MUT locations. The use of subjective and objective weights in the decision-making process will offer different perspectives from the decision-maker's perspective and the data itself, respectively. This paper aims to analyze spatial data as an input in the multi-criteria decision-making (MCDM) process of the MUT location selection. The objectives are: (1) defining the criteria that influence the MUT location selection, (2) defining the required GIS datasets for quantifying the criteria as scores for each candidate street segment, (3) analyzing the impacts of the dependencies between the criteria by comparing the ranking results of two MCDM methods (i.e., Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP)) combined with the Technique for Order Preference by the Similarity to Ideal Solution (TOPSIS), (4) analyzing the difference between using subjective weights or objective weights, and (5) developing a prototype system to integrate the MCDM methods in a GIS platform. A vector-based spatial analysis is conducted to identify the suitable locations for MUT construction based on 12 criteria representing physical condition information or affecting social costs. Two subjective MCDM methods (i.e., AHP and ANP) are used to generate each criterion's weights, and the ranking of alternatives is determined using TOPSIS. Another set of weights representing the objective weights are calculated for each criterion using the Shannon Entropy method. These weights are combined with TOPSIS to obtain an objective ranking of the alternatives. Based on the results from the different combinations (AHP + TOPSIS, ANP + TOPSIS, and ENTROPY + TOPSIS), the top alternative is always the same.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".