Bibliographic record
Abstract
Infrastructure is a critical element in the countries’ growth and development. Poor management of these systems would lead to their failure and in turn to disastrous situations. According to the United States Environmental Protection Agency (EPA) fifth report on drinking water infrastructure, the investments in the drinking water utilities need a total amount of $384.2 billion for the next 15 years, i.e. until December 2030. Also, according to the 2013 American’s Infrastructure Report Card, the Drinking Water System (DWS) is graded as “D”, implying a status between poor and fair, with an increasing failure probability. Similarly, as stated in the last 2016 Canadian Infrastructure Report Card, the water system received a ranking of “Good”, representing an ‘adequate for now’ status. However, about 29 percent of pipelines condition is rated between fair and very poor, signifying that an urgent repair is needed with total replacement cost of $ 60 billion. Meanwhile, due to budget deficits, municipalities find it is a challenge to prioritize which asset to repaire or rehabilitate. Thus, a lot of research is done to predict the probability of failure. Yet, most of this research is limited to the consequence of failure and the criticality of water pipelines. \nThe main objective of this study is to develop a priority index induced by a combination of the criticality and performance of water distribution network. In this research, criticality factors that affect the water distribution networks are identified. Criticality is divided into three main aspects: (i) Economic, (ii) Environmental/Operational and (iii) Social factors. Each of these key elements is divided into subfactors with different attributes to describe the actual status of the proposed area. Paprika and Swing techniques are used to determine the weights of subfactors. The effect values are obtained from experts from North America, Europe and Qatar through questionnaires and meetings. After all the required data are collected, the data are analyzed and incorporated into the criticality model to determine the criticality index for each pipeline in the desired location. A sensitivity analysis is conducted to define the factors with the highest and the lowest impact on the criticality index. It is determined that the “Road type” sub-factor has the highest influence on the criticality index, based on Qatar’s data analysis. Meanwhile, the “Pipeline diameter” sub-factor has the greatest impact on the criticality index, based on North America and Europe data analysis. \nThe developed criticality index is utilized with the performance index to develop the priority index, which is illustrated on the emerged priority scale and matrix for a better evaluation of the current asset status. It is concluded that “Ville Marrie” sector is found to have the highest priority index in Montreal city, equals to 4.42. While,“Bizard Island” has the lowest priority index value in the city, equals to 3.69. The developed model will guide municipalities and governments to generate a capital plan and allocate the available budget to the most critical parts of their networks. These results are also used as a reference to highlight the key areas in each sector of the designed city that need an urgent repair. This will decrease the risks, defects and health hazards of the water networks while maintaining the safety and durability of the water distribution networks in a cost-effective manner.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".