Journal of Pipeline Systems Engineering and Practice

The shortest route from home to work may not be the best alternative, and a longer route may be fastest or safest. Hence, the best route reflective of one factor may not be the ultimate best alternative. This paper discusses a geographic information system (GIS)–based application to analyze and rank a number of potential pipeline routes based on accumulated weights of individual segments composing the route. The goal of this application was to automate the process of defining more than one potential route. Designers and stakeholders determine project-specific criteria and assign weighting factors to each. Additionally, individual scores are assigned to each segment in the so-called decision matrix which is a project-specific spreadsheet. The weights and costs are extracted from the decision matrix and imported as attributes for each segment in the GIS pipe network layer based on the unique segment ID. A network analyst routine was programmatically used to identify a number of potential pipeline routes that were then stored in an output feature class and eventually exported to Microsoft Excel, where a summary chart was generated to summarize the findings. The application was used on a test case in the western United States. The application was developed using Visual Basic for Application and ArcObjects and was intended for use in ESRI’s ArcGIS Desktop 9.3.

Automatic burst and leak detection in water distribution systems plays an important role in water saving and management. This research develops a novel burst detection method of using adaptive Kalman filtering on hydraulic measurements of flow and pressure at district meter area (DMA) level. Adaptive Kalman filtering is used to model normal water usage (or alternatively water pressure), so the residual of the filter (e.g., the difference between the predicted flow and the measured flow) represents the amount of abnormal water usage relating to the bursts (or newly occurred leaks) in the downstream network. The results from a series of engineered tests which simulated flushing show that the size of the bursts and leaks strongly correlates with the residual of the filter. Finally, the method was applied to data from several real DMAs in the north of England, and the results show that the detected bursts correspond well to known historical operational information such as customer complaints’ records and work management (repair) data. The results suggest that flow measurement data are more sensitive to a burst or leak than the pressure measurement data.

A water utility company has many actions and decisions to take in order to run operations efficiently and effectively. One of them is to decide which types of pipe(s) to install as they do not all perform the same. Because these pipes may remain installed underground for more than 100 years, the decision to install is substantially binding. The pipe types considered here are ductile iron (DI), cast iron, PVC, and concrete cylinder (CC). The approach was to study cost and quality control measures and then apply data envelopment analysis in linear programming to determine the relative inefficiency of the specific pipe type in comparison to the rest of the pipe system. Data were taken from the main-break database and records and reports published by the Honolulu Board of Water Supply. Quality control measures evaluated included availability and reliability at the system and individual levels. It was determined that CC pipes were more inefficient than the others. Multiple scenarios of output measures were evaluated, such as combinations of system and individual levels, availability, and reliability. In all such evaluations, it was conclusively seen that CC pipes were relatively inefficient. Consequently, it is recommended the utility plan to replace CC pipes with corresponding DI and PVC piping.

The processes involved in the local scour at culverts are so complex and that makes it difficult to establish a general empirical model to provide accurate estimation for scour. This paper describes the use of adaptive neurofuzzy inference system (ANFIS) to estimate the scour depth at culvert outlets. The data sets of laboratory measurements were compiled from published literature and used to train the ANFIS network. The developed network was validated by using the observations that were not involved in training. The performance of ANFIS was found to be more effective $\left(R^2=0.94\right)$ when compared with the results of regression equations and artificial neural networks modeling in predicting the scour depth at culvert outlets $\left(R^2=0.78\right)$ . Further work is required to collect field data of scour at culvert outlets to train the genetic programming approach and validate its usefulness.