Pipe Condition Assessment Research Articles

Condition assessment to estimate failure rates in buried metallic pipelines

Pipe condition assessment provides useful information to assist rehabilitation and replacement decisions. Due to high cost or difficult pipe access, condition assessment must be limited to specific locations. To address this limitation, this paper presents a methodology in which values from discrete sampling positions are used to quantify systematic and random variation of pipe condition, and the statistical basis of applying the information to determine the failure probability in the network. The methodology includes development of a sampling program on a network zoned according to the soil type, using extreme value statistics to describe the distribution of data from limited samples within each zone and where appropriate extrapolation of the distributions to larger areas of the network. Structural reliability methods, specifically Level II first-order-second moment reliability analysis, are then applied with distributions of pipe conditions as input in order to estimate pipe failure probability and failure rate predictions. A practical example of a mild steel pipeline subjected to external corrosion is used to illustrate the value of this technique in practice. An overview of commercially available and emerging techniques suitable for condition assessment of pipes is presented. This includes direct assessment by electromagnetic, ultrasonic and acoustic techniques and indirect assessment through measurement of soil electrical and chemical properties.

Innovative method for assessment of underground sewer pipe condition

The accurate evaluation of current underground infrastructure must be done before any crucial decisions regarding lifecycle analysis, rehabilitation and replacement intervals and appropriate remedial methods can be made. Unfortunately, traditional technologies and management approaches have been limited by the use of insufficient data in the evaluation of the structural integrity of an aged infrastructure. This paper describes an innovative method for assessment of underground pipe, utilizing Ground Penetrating Radar (GPR) and Digital Scanning and Evaluation Technology (DSET) to collect accurate information about the condition of a buried pipeline. Prediction modeling, using logistic regression, to measure the extent of deterioration of the infrastructure system, is described. A case study applying this innovative technology to sections of large diameter PVC lined concrete pipe in the City of Phoenix, Arizona, is presented.

Pipe inspection using a laser-based transducer and automated analysis techniques

This paper presents a new sensing methodology for the automated inspection of pipes. Standard inspection systems, as they are for example used in waste pipes and drains, are based on closed-circuit television cameras which are mounted on remotely controlled platforms and connected to remote video recording facilities. Two of the main disadvantages of such camera-based inspection systems are: 1) the poor quality of the acquired images due to difficult lighting conditions and 2) the susceptibility to error during the offline video assessment conducted by human operators. The objective of this research is to overcome these disadvantages and to create an intelligent sensing approach for improved and automated pipe-condition assessment. This approach makes use of a low-cost lighting profiler and a camera which acquires images of the light projections on the pipe wall. A novel method for extracting and analyzing intensity variations in the acquired images is introduced. The image data analysis is based on differential processing leading to highly-noise tolerant algorithms, particularly well suited for the detection of small faults in harsh environments. With the subsequent application of artificial neural networks, the system is capable of recognizing defective areas with a high success rate. Experiments in a range of waste pipes with different diameters and material properties have been conducted and test results are presented.

Condition assessment of underground sewer pipes using a modified digital image processing paradigm

The National Science Foundation (NSF) has estimated the total US investment in civil infrastructure at $US 20 trillion. The investments in sewer pipeline collection systems represent a major component of this figure. Recent studies have shown that to upgrade or restore municipal infrastructure in North America to an acceptable level the cost will be in excess of $US 100 billion. Many of these systems are eroding due to aging, excessive demand, misuse, poor construction, mismanagement, and neglect. Visual inspection based on Closed Circuit Television (CCTV) surveys is used widely in North America to assess the condition of underground sewer pipes. The human eye is extremely effective at recognition and classification, but it is not suitable for assessing pipe defects in thousand of miles of pipeline due to fatigue, subjectivity, and cost. Automatic recognition of various pipe defects is of considerable interest since it has the potential to overcome problems of fatigue, subjectivity, and ambiguity, leading to economic benefits. In this paper, we present a system for the application of computer vision techniques to the automatic assessment of the structural condition of underground sewer pipes that overcomes the inherent limitations of existing digitizing paradigms. An image enhancement method that eliminates the non-uniform background illumination while ensuring that the enhanced image will be faithful to the source image and enables quantification of distress features is presented. Algorithms for analysis of joint and lateral recognition are also presented and should allow engineers to be able to gain the goal of a fully automated underground sewer defect detection system based on digital image processing of SSET surveys according to the principles demonstrated in this paper.