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Underground pipelines are an essential part of the transportation infrastructure. The structural deterioration of pipelines crossing railways and their subsequent failures can entail critical consequences for society and industry, resulting in direct and indirect costs for all the stakeholders involved. Therefore, continuous and accurate condition assessment is critical for the effective management and maintenance of pipeline networks within the transportation infrastructure. The aim of this study has been to identify failure modes and consequences related to pipelines crossing railway corridors. Expert opinions have been collected through interviews and two sets of questionnaires have been distributed to the 291 municipalities in Sweden, with 137 responses in total. The failure analysis has revealed that pipe deformation has the highest impact, followed by pipe rupture at locations where pipelines cross railway infrastructure. For underground pipelines under railway infrastructure, ageing and the external load were awarded a higher ranking than other potential causes of pipeline failure. [ABSTRACT FROM AUTHOR]

This paper proposes a quick and accurate method for reconstructing defect profiles in pipelines from magnetic flux leakage (MFL) signals employing a modified harmony search (MHS) algorithm. To improve the quality of the initial harmony memory, a multiple selection opposition-based learning strategy is presented in the MHS algorithm. Moreover, the pitch adjusting rate is set to increase linearly, and the distance bandwidth (bw) is designed to be dynamically adjusted to shorten the calculation time. A specially designed modified sum-squared error with weights is proposed and employed as the fitness function to improve the fitting degree between the reference and predicted signals in the defect response segment. The weights are determined by the characteristics of reference signals. We demonstrate the adequacy of the performance of the proposed model using both simulated and real MFL signals. We also present comparative results of the proposed model and other methods. The results prove that the proposed method can achieve higher efficiency while maintaining excellent accuracy. [ABSTRACT FROM AUTHOR]

In this work, a novel stochastic model framework for predicting the external corrosion growth in buried pipeline structures has been developed, and a reliability-based temporal and spatial maintenance strategy is presented. The spatial correlation of soil properties is modelled via hidden Markov random field. The temporal correlation of the corrosion rate is characterised by the geometric Brownian bridge process. A Bayesian inferential framework is employed to estimate the model parameters of the corrosion growth model using in-line inspection data. The proposed corrosion growth model was validated with actual inspection data. In the reliability analysis, the impact of device detectability is considered and hence the estimated failure probability is more realistic. The proposed maintenance strategy is directly based on the time-specific and location-specific failure probability. The application of the proposed model and maintenance strategy is illustrated through a real-life pipeline system. The results indicate that the proposed maintenance strategy is an adaptive and dynamic scheme that is able to improve the efficiency of inspections. [ABSTRACT FROM PUBLISHER]

The safety of underground pipelines is the primary focus of water and wastewater industry. Due to low visibility and lack of proper information regarding the condition of underground pipes, assessment and maintenance are frequently neglected until a disastrous failure occurs. The reduction of pipe thickness due to corrosion undermines the pipe resistance capacity which in turn reduces the factor of safety of the whole distribution system. Providing an acceptable level of service and overcoming practical difficulties, the concerned industry has to plan how to operate, maintain and renew (repair or replace) the system under the budget constraints. This paper is concerned with estimating reliability of non-pressure flexible underground pipes subjected to externally applied loading and material corrosion during the whole service life. The reliability with respect to time due to corrosion induced deflection, buckling, wall thrust, bending stress is estimated. Then the study is extended to determine intervention year for maintenance and to identify the most appropriate renewal solution by minimizing the risk of failure and whole life cycle cost using Genetic Algorithm (GA). An example is presented to validate the proposed method with a view to prevent unexpected failure of flexible pipes at the minimal cost by prioritizing maintenance based on failure severity and system reliability. [ABSTRACT FROM AUTHOR]

The article focuses on the experiences of the Italian Welding Institute of using the automatic guided-wave control systems for pipe inspection. It says that such inspection method of pipes is considered rapid, cheap and efficient survey technique. The Institute is known to be the first to utilise such technique in the country. It highlights the complexity of analysing the data provided by guided wave techniques and the need of expert personnel in order to avoid confusing the ultrasound signals.

Abstract: Buried stormwater pipe networks play a key role in surface drainage systems for urban areas of Australia. The pipe networks are designed to convey water from rainfall and surface runoff only and do not transport sewage. The deterioration of stormwater pipes is commonly graded into structural and serviceability condition using CCTV inspection data in order to recognize two different deterioration processes and consequences. This study investigated the application of neural networks modelling (NNM) in predicting serviceability deterioration that is associated with reductions of pipe diameter until a complete blockage. The outcomes of the NNM are predictive serviceability condition for individual pipes, which is essential for planning proactive maintenance programs, and ranking of pipe factors that potentially contribute to the serviceability deterioration. In this study the Bayesian weight estimation using Markov Chain Monte Carlo simulation was used for calibrating the NNM on a case study in order to account for the uncertainty often encountered in NNM''s calibration using conventional back-propagation weight estimation. The performance and the ranked factors obtained from the NNM were also compared against a classical model using multiple discrimination analysis (MDA). The results showed that the predictive performance of the NNM using Bayesian weight estimation is better than that of the NNM using conventional backpropagation and MDA model. Furthermore, among nine input factors, ‘pipe age’ and ‘location’ appeared insignificant whilst ‘pipe size’, ‘slope’, ‘the number of trees’ and ‘climatic condition’ were found consistently important over both models for serviceability deterioration process. The remaining three factors namely, ‘structure’, ‘soil’ and ‘buried depth’ might be redundant factors. A better and more consistent data collection regime may help to improve the predictive performance of the NNM and identify the significant factors. [Copyright &y& Elsevier]

Recent advances in electronics, transducers, ultrasonic and computing technologies, have led to the development of inspection systems for underground facilities such as water lines, sewer pipes, oil and gas pipelines. Recent inspection technologies have been developed that require no human entry into underground structures; they are now fully automated, from data acquisition to data analysis, and eventually to condition assessment, which can be used during the manufacturing as well as maintenance stage. This paper describes the development of an automated data interpretation system for pipeline, which can be used during the manufacturing stage to maintain the highest standard of quality control and it can also be extended to the maintenance stage. The proposed system is highly desirable and useful where a large number of similar samples are to be investigated which can be applied to investigate various defects in metals as well as composites. The interpretation system obtains Ultrasonic C-scan data obtained through an ultrasonic water immersion or air scan system. The proposed system utilizes Artificial Neural Networks (ANN), and Genetic Algorithm to recognize various types of defects in a given specimen. Image processing and Wavelets techniques are used to determine the details of the damage geometry. An Expert System for composite repair mechanism is also being developed using the adaptive neuro-fuzzy inference system (ANFIS), to perform damage condition assessment as well as material degradation evaluation. MATLAB is used in developing a real time automated prototype system. [ABSTRACT FROM AUTHOR]

Repairing and replacing of worn-out underground pipes, such as sewer pipes, water-supply pipes, gas pipes, and communication cables by excavating not only cause traffic congestion but also produce large amount of waste. Also, the operation requires heavy equipments and longer operating time and high cost.In this study, the repairing–reinforcing process of underground pipes with glass fiber fabric polymer composites using resin transfer molding (RTM) which overcomes the problems of present trenchless technologies has been developed. The developed process requires shorter operation time and lower cost with smaller and simpler operating equipments than conventional trenchless technologies. For the faultless operation, a simple method to apply pressure and vacuum to the reinforcement was developed. The resin wetting and void removal during RTM process for very large and long-composite buried pipes were experimentally investigated, and the efficient void removal method was suggested. Cure status and resin filling were monitored with a commercial dielectrometry cure monitoring system, LACOMCURE.From the investigation, it has been found that the developed repair technology with appropriate process parameters and on-line cure monitoring has many advantages over conventional methods. [Copyright &y& Elsevier]

• Bayesian network model for buried gas pipeline failure analysis is presented. • The pipeline failure caused by corrosion and external interference is analyzed. • Failure frequency and leakage size are analyzed based on pipeline characteristics. • The pipeline in the city of Hefei is used to state the practicability of the model. • Critical pipeline parameters can be identified using this model. The unintentional release of urban buried gas pipeline may cause crucial consequences to the economy, society and environment. Corrosion and external interference are primary causes of pipeline failure incidents. Due to the complexity and unpredictability of outside influence on the buried gas pipeline, this paper presents an approach to analyze pipeline failure frequency and leakage size caused by corrosion and external interference based on pipeline characteristics. Bayesian network method is used to construct a knowledge model. Pipeline characteristics statistics and failure data are collected to build the relationships among variables in the model and verify the applicability of the model. Results show that the proposed model can estimate buried gas pipeline failure frequency and leakage size caused by corrosion and external interference. It is also capable of highlighting the critical parameters to pipeline failure. Practical application of the model is demonstrated on the underground gas pipeline in the City of H, China. Results indicate that proposed model can explicitly quantify uncertainties and then put forward practical measures for buried gas pipeline parameter design, laying plan and operating maintenance. [ABSTRACT FROM AUTHOR]