Journal of Pipeline Systems Engineering and Practice

In recent years, wireless sensor network systems have increasingly been used to monitor infrastructure health. Advances in electronics and sensing systems have enabled the development of various pressure-sensing methods for pipe-pressure monitoring. This article presents laboratory-based test results as part of the development and validation of a pipeline pressure-monitoring method based on force sensitive resistors (FSR). Additionally, to validate the data, the proposed pressure-sensing method is compared with a commercially available direct-pressure sensor. Analysis of the data shows a significant correlation (correlation factor = 0.9928) between the commercial sensor and the proposed sensor. These results showed that the proposed method has an acceptable accuracy and reliability even though it is not ultimately intended for absolute-pressure measurements, but for monitoring relative pressure changes in pipes.

This study presents the production and full-scale testing of specific synthetic fiber-reinforced concrete pipes in a dry cast environment. Production and testing were conducted to identify the structural behavior of concrete pipes with synthetic fibers for the first time in the United States. Three production sites were selected to utilize different pipe manufacturing equipment. Each plant’s mix design was kept to be the same as those used in the production of the conventional reinforced concrete pipes (RCP), except for replacing the steel cage with synthetic fibers and adjusting the amount of water in the composite mix of concrete and fibers. Synthetic fiber-reinforced concrete pipes (SY-FRCP) were tested based on industry standard specifications for strength requirements for pipe diameters ranging from 375 mm to 600 mm (15 in. to 36 in.). Full-scale structural strength and stiffness tests were conducted based on industry standard test methods, three-edge bearing tests, for obtaining the load deformation characteristics of SY-FRCP. Control test pipes were also produced and tested for comparison purposes by using the conventional industry standard specifications for reinforced concrete pipes with steel cage. The load deformation plots along with the crack pattern formation for both SY-FRCP and RCP were compared. The results of this study indicated the BASF MasterFiber MAC Matric synthetic fibers could be used as an alternative reinforcement of concrete pipes.

Accurate prediction of water pipeline structural and functional deterioration plays an essential role in the utility asset management process and capital investment planning. A key to implementing an asset management strategy is a comprehensive understanding of asset condition and performance and how asset condition and performance change over time. The primary objective of this paper is to develop a fuzzy inference performance index for metallic drinking water pipelines. Furthermore, the developed fuzzy inference performance index is applied to the Western Virginia Water Authority (WVWA). Evaluation of the performance index is accomplished through testing of artificial, field, and lab data. Overall, the fuzzy inference performance model was successfully developed to aid in the evaluation of current drinking water infrastructure systems. The fuzzy inference performance model incorporated with an infrastructure management plan will aid in the prioritization of future inspection, repair, rehabilitation, and replacement of water mains.

Productivity prediction and cost estimation of horizontal directional drilling (HDD) as a trenchless technology technique involves a large number of objective and subjective factors, which should be carefully identified and studied. To consider the effect of these factors on productivity prediction, the research presented in this paper assists in developing a productivity model for HDD operations. Potential factors impacting productivity are identified and studied based upon the literature and HDD experts across North America and abroad. A neurofuzzy (NF) approach is employed to develop the HDD productivity prediction model operating in clay, rock, and sandy soils. The merits of this approach involve decreasing uncertainties in results, addressing nonlinear relationships, and dealing well with imprecise and linguistic data. The NF model is tested using actual project data, which showed robust results with average validity percentages of 94.7, 82.3, and 86.7% for clay, rock, and sandy soils, respectively. The model is also used to produce productivity curves (production rate versus influencing factors) for each soil type. An automated user-friendly productivity prediction tool (HDD-PP) is developed to predict HDD productivity based on the NF model. This analysis has proved helpful for contractors, consultants, and HDD professionals in predicting execution time and estimating cost of HDD projects during the preconstruction phase.

The accurate determination of choke performance in oil pipelines is considered a main challenge for petroleum engineers. This study introduces a new correlation based on genetic programming (GP) to describe choke performance in both critical and subcritical flow conditions in pipeline flow regimes. The GP model is developed using field data collected from various Iranian oil well pipelines. Statistical error parameters are used to compare the accuracy of results obtained from the new correlation with other empirical models. The GP model predicted the choke performance for critical flow condition with relatively lower error (normalized mean square error = 0.006) and higher accuracy ($R=0.993\%$). In addition, the results of the model in a subcritical flow regime showed that the GP model could be applied effectively and yield high accuracy for choke performance pipeline forecasting.

In the present study, the group method of data handling (GMDH) network is applied to predict scour depth below pipelines exposed to waves. The GMDH network is trained using a back-propagation (BP) algorithm. The pipeline scour is modeled as a function of three-dimensionless parameters, including the Keulegan-Carpenter number, the ratio of the initial gap to pipe diameter, and the Shields parameter. The performances of the GMDH network are compared with the adaptive neuro-fuzzy inference system (ANFIS) model, the model tree (MT), and empirical equation. The results indicated that the GMDH network produced a more accurate prediction of scour depth compared with other models.