Journal of Pipeline Systems Engineering and Practice

Buried energy pipelines span for hundreds of thousands of kilometres, crossing zones of soil instability and may need to be designed to resist differential ground movements. As a part of a comprehensive experimental and numerical study to examine the flexural performance of steel and fibreglass pressure pipes when loaded by lateral earth movements, this paper presents the numerical work simulating glass-fiber-reinforced polymers (GFRP) pipes that are 102 mm (4 in.) in nominal diameter and 1,830 mm (6 ft) long, subjected to lateral soil loading. The study investigated the effect of pipe laminate structure for crossply ($+85/-6$), and angle-ply ($+55/-56$) laminates on soil resistance capacity, and the net pipe deflections at different burial depth-to-diameter (H/D) ratios of 3, 5, and 7. The development and verification of three-dimensional finite-element model against experimental data and a comparison against numerical data for steel control pipes of comparable dimensions and pressure rating is also presented and discussed. It was shown that the numerical model for the GFRP pipes can successfully capture the soil resistance capacity and pipe behavior in terms of load displacement and net bending deflection. Also, effect of material variation and fiber orientation were in agreement with observed behavior of test data.

In this paper, network resilience is quantified by generalization of various notions used in the mathematical theory of network resilience. This algorithm is then used to create a quantitative framework for the resilience characterization of high-pressure natural gas networks. The key concepts are the notions of a network with associated delivery function and delivery importance. No model is available in the literature to measure the performance of high-pressure natural gas networks. In this paper, a new index measuring delivery importance of a gas network is proposed to evaluate the resilience of the entire network. The methodology can be used to consider and quantify the effects of any type of natural disasters such as earthquake and fire, or artificial hazards such as deliberate attacks on the system that may lead to the disruption of the system. The principles and theories applied to the natural gas supply and distribution network in this study can also be applied to other infrastructures that are interconnected and operate as a network, such as the electrical power and water supply and distribution systems. Furthermore, the present paper shows the characteristics of production and demand of natural gas in Iran and provides a data-based estimation of the future growth in production and consumption that is used in assessment of the resilience at a specific time in future.

It is imperative that water supply networks (WSNs) continue to perform even after subjected to natural and anthropogenic hazards, and it is even more important in the case of earthquakes for fighting fires that usually follow. Past earthquakes have caused significant damage to WSNs rendering them dysfunctional and potentially threatening human survival. Although there has been a lot of research conducted in the past on the behavior of buried pipelines, there are few metrics and models that are readily usable for improving the performance of large WSNs prone to seismic hazards. This paper proposes an easy-to-use metric for quantifying resilience and an optimization framework for improving WSN resilience subjected to budgetary constraints. The proposed resilience metric is dependent on hazard intensity, estimated pipeline response, and network topology. The use of the proposed metric and the optimization framework are demonstrated on a large (1,874 pipelines and 1,474 demand nodes), real-world WSN in a coastal region prone to earthquakes. The resilience value of the existing WSN used for demonstration is found to increase by approximately 8% with a $10 million investment. The proposed metric and the optimization framework can be helpful in rehabilitation planning and capital improvement works by water utilities.

In this study, the multivariate adaptive regression splines (MARS) technique was applied to estimate scour depth around pipelines. To this purpose, 90 data sets related to effective dimensionless parameters on pipeline scouring phenomena were gathered from literature. A gamma test (GT) was used to define the most-effective parameters on scouring phenomena below pipelines. Performance of MARS model was compared with multilayer perceptron (MLP) neural network and empirical formulas. Results of the GT showed that $e/D$, ${\tau }^{*}$, and $y/D$ are the most important parameters for scour depth. Results of MARS model with coefficient of determination (0.91) and root-mean square error (0.05) indicated that this model has suitable performance for predicting scour depth under pipelines and results of this model are more accurate compared to empirical formulas. Comparing results of MARS model and MLP showed that accuracy of MARS model is slightly lower than that of the MLP.

This paper describes a record setting project in the horizontal directional drilling field involving twin pipes with a diameter of 711 mm and a length of 3,300 m. The Yangtze River is the longest river in China. The Jiangdu-Rudong gas pipeline, which is part of the Chinese gas pipeline trunk network, was constructed to transport gas from the north side to the south side of the River. The width of the river surface is 3,000 m, and the length of the crossing is 3,300 m. It was recommended by engineers that twin pipes with a diameter of 711 mm be used instead of the existing pipe with a diameter of 1,016 mm. The required depth of penetration for the crossing was determined by an analysis of 43 years of historical river scour data from 1966 to 2009 combined with a calculated result using a river scour depth formula. The recommended construction scheme included arranging two drilling rigs, laying magnetic-signal submarine cable, using the intersect method, and designing a new flat-barrel combination-type reamer. This major project was finished successfully on July 27, 2013.

The velocity at limit of deposition, namely, the velocity below which the sediments start to form stationary deposits on the pipe invert, plays a key role in designing an effective storm sewer system. In fact, an accurate evaluation of the aforesaid velocity permits to prevent the formation of sediment deposits in the pipe, and, consequently, a decreasing of the hydraulic capacity of the same pipe. Nowadays, different methods based on artificial intelligence are applied to analyze and solve problems of scientific and practical interest, among which the analysis of sediment transport mechanism in clean storm sewer networks. In this study, neuro-fuzzy based group method of data handling (NF-GMDH), an adaptive learning network, has been utilized to predict the velocity at limit of deposition in partially-filled circular storm sewer networks under noncohesive bed load sediment transport and clean pipe conditions. The NF-GMDH network has been developed using the particle swarm optimization (PSO). Four different dimensionless groups have been used as input parameters to evaluate the dimensionless velocity at limit of deposition (or, equivalently, the densimetric particle Froude number at limit of deposition) through the NF-GMDH-PSO model. A published experimental data sets were collected from the literature to train and test the adaptive learning network used in this study. The performances of training and testing stages for NFGMDH-PSO have been evaluated using specific statistical indexes. The results showed that NF-GMDH-PSO model is able to give more accurate predictions of the velocity at limit of deposition compared with those obtained using semiempirical equations.