Reliability-Based Management of Water Distribution \nNetworks
Bibliographic record
Abstract
Reliability-Based Management of Water Distribution Networks \nAlaa Salman, Ph.D. \nConcordia University, 2011 \nCanada’s civil infrastructure systems have been in use for over 79 % of their expected service life. Municipalities in Canada have noted that 59% of their water systems needed repair and the condition of 43% of these systems is unacceptable. Therefore, a significant volume of rehabilitation projects are necessary to improve infrastructure performance. Reliability and criticality \nassessments (RCA) as well as the ability to determine the most suitable methods of rehabilitation are urgently needed in order to allocate the available budget efficiently. The research presented in this thesis aims at developing a priority index (PI) for intervention that considers the combination of RCA for water networks. Sound techniques are utilized to develop the PI such as reliability theory, simple multi-attribute rating technique (SMART), and Analytical Hierarchy Process (AHP). \nThe reliability assessment encompasses two levels: (1) segment and (2) subnetwork reliabilities. The priority index (PI) for intervention is crucial to schedule \nsegment rehabilitation. Simple Multi Attribute Rating Technique (SMART) is used to select the most suitable methods of rehabilitation for these components. \nSelection of a rehabilitation method is based on several factors: (1) technical feasibility, (2) whether the selection is contractually acceptable, (3) cost \niv effectiveness, (4) environmental impact, and (5) whether the rehabilitation method is a new technology or not. The output of rehabilitation selection model is the method of rehabilitation for components coupled with the associated costs and durations for rehabilitation activities for each sub-network. The final stage of this research is to schedule these rehabilitation activities. Scheduling of the rehabilitation activities related to water main networks depends mainly on available budget and planning time. Other factors, such as network reliability, criticality, location, contract size, and rehabilitation method(s), also \naffect the scheduling process. This research presents a method for optimizing the scheduling of rehabilitation \nwork for water distribution networks. The method utilizes unsupervised neural networks (UNNs) and Mixed Integer Non Linear Programming (MINLP) and performs the scheduling in two stages. In the first stage, UNNs are used to group \nwater mains according to their locations and rehabilitation methods. In the second stage, MINLP is used to determine the number of rehabilitation contract packages and to generate an optimized schedule based on these packages \nconsidering network reliability, criticality, contract size, and planning time. Data on water network are collected from the city of Hamilton, Ontario, Canada. Four \nsub-networks are selected randomly from the entire network to represent four types of land use; undeveloped, residential, park, and commercial/industrial. The \ndata is used as a test bed to validate and demonstrate the use of the developed research methodology. An automated tool (DSSWATER), based on the developed methodology, is developed to assist users and decision makers. The \ndeveloped models and tools are expected to be beneficial to municipal engineers and managers as well as to academics.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".