Journal of Pipeline Systems Engineering and Practice

Due to the poor condition of the sewer and water networks in many communities across the United States, many rehabilitation projects are being undertaken to improve their condition. With limited availability of funds, early and accurate prediction of project costs is highly desirable. Accurate prediction of cost does not only assure allocation of adequate budgets for successful completion but also assists in proper utilization of available limited resources. This paper describes the development of cost estimating models for sewer and water network repair projects. To develop these models, data from a set of 54 projects were used. Data pertaining to these projects were first processed to identify the factors that highly impact the overall cost. These factors were then further processed using two approaches, namely, artificial neural networks and regression analysis, to develop the cost estimating models. A comparison of the accuracy of the predictions from two approaches indicated that the artificial neural network approach provided better accuracy.

This paper presents a method to analyze and evaluate buried culverts and cut-and-cover tunnels for seismic loading in addition to standard static loading from dead and live loads. Using CANDE-2007, a plane-strain finite-element program, the soil-structure problem is characterized by a cross-sectional slice through the structure and surrounding soil envelope. First, the static-design loads are applied with a series of incremental load steps. Next, the seismic loading condition is simulated by specifying quasistatic displacements at the peripheral boundaries of the soil envelope to produce a shear-racking distortion equivalent to the maximum free-field seismic shear strain from the design earthquake. The proposed method is based on two recently completed NCHRP projects and is presented here in detail. An initial linear-elastic study investigates two basic culvert shapes, circular and rectangular, wherein moment and thrust distributions from CANDE-2007 are shown to compare favorably with closed-form solutions. A second study investigates a nonlinear reinforced concrete box culvert installation under the combination of static and seismic loadings. Plots of moment, thrust, and shear distributions show how and where the seismic loading alters the response of static loading including the effect on safety factors for steel yielding, concrete crushing, and shear failure. It is concluded that the proposed seismic method is rational, easy to apply, and fulfills an engineering need heretofore unfulfilled. The procedure applies to any culvert shape, size, and material, and the safety of the culvert installation may be assessed either by working stress or load resistance factor design.

Manifolds are used extensively in pipeline engineering applications. They may be classified as combining-flow in which the flow rate increases along the length of the manifold, and dividing-flow in which the flow rate decreases along the manifold length. The present paper addresses dividing-flow manifolds with the emphasis on pressure-distribution systems. The seminal theoretical and experimental work of McNown on discrete tee takeoffs, applicable to pressure-distribution-system manifolds, is discussed and elaborated upon for flow across the run of the tee and into the tee branch. Pertinent theoretical and experimental work for discrete orifice takeoffs is summarized. The information thus presented facilitates discussion of certain concepts presently incorporated in pressure-distribution-system design. Certain erroneous concepts are identified. A perspective is provided and improvements in related aspects of manifold design are suggested. A separate paper, presented at the ASCE/EWRI World Environmental and Water Resources Congress 2010 Conference, builds on the present paper by specifically addressing wastewater pressure-distribution systems.

The process involved in the local scour below pipelines is so complex that makes it difficult to establish a general empirical model to provide an accurate estimation for scour. This technical note describes the use of genetic programming (GP) to estimate the pipeline scour depth. The data sets of laboratory measurements were collected from published literature and used to train the network or evolve the program. The developed network and evolved programs were validated by using the observations that were not involved in the training. The performance of GP was found to be more effective when compared with the results of regression equations and artificial neural networks modeling in predicting the scour depth around pipelines.