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A reliable leak detection approach is necessary for reducing water loss caused by leakages. Our research reviews the existing acoustic leak detection approaches, including devices for collecting sound, signal processing methods, and ways to detect the presence of leakages. Firstly, a scientometric analysis of relevant bibliographic data from the Scopus database in the last thirty years is carried out. Secondly, based on the results of the scientometric review, the technologies of acoustic leak detection have been systematically reviewed. Acoustic leak detection approaches are classified into non-real time and real-time methods and are thoroughly discussed. Thirdly, acoustic signals collection through noise loggers, wireless sensor networks, accelerometers, hydrophones, or fiber optic sensors is elaborated. Fourthly, signal features extraction using different methods, including time-domain analysis, frequency domain analysis, time-frequency analysis, correlation analysis, and other pre-processing methods are explained. Fifthly, the determination of the presence of leaks using various detection methods is detailed. This paper provides a useful reference for academia and industry for the further development of advanced acoustic leak detection methods. • Acoustic signals are collected by noise loggers, wireless sensor networks, and so on. • Features are extracted by different analysis and signal processing techniques. • Leaks detected by a threshold or classification model. • Unresolved challenges: avoiding false alarms, detecting weak leaks, and so on [ABSTRACT FROM AUTHOR]

A large portion of natural gas is transferred through transmission pipelines. For these facilities, in the United States, external corrosion has led to more than 1700 failures in the past decades, causing a property damage of approximately $189 M. Such numbers highlight the importance of maintaining such facilities in safe conditions to postpone corrosion failure. Given such criticality, addressing pipeline availability is of high importance from economic consequences perspective for a national economy. However, most of the developed methods merely rely on considering costs, reliability or condition levels as maintenance decision criteria and ignore the importance of continuity of operation and pipeline availability. In this research, a maintenance planning framework is proposed for external corrosion of gas transmission pipelines through an availability-centered reliability-based maintenance planning procedure. This framework is based on the pipeline reliability profile obtained from a Monte Carlo simulation. This simulation is carried on a previously developed failure prediction model for gas transmission pipelines buried in the Great Plains region of the U.S. and considers failure uncertainties. In addition, a discrete event simulation (DES) approach is proposed to assess an availability-cost indicator of different maintenance scheduling alternatives for the case study of a 24-inch gas pipeline. This research reveals that a combination of wrap and replacement maintenance actions at the service life of 30.1 and 40.5 years respectively, is the most effective maintenance alternative in terms of improvement of availability per unit cost for the presented case study. This framework can help pipeline professionals in maintaining such facilities by considering their criticality using an availability-based maintenance scheduling approach. Such method can complement the conventional cost-based industry practices. • If added to inspections, maintenance planning models help avoid excessive inspections. • As a critical asset, considering gas pipeline availability helps reduce shutdowns. • As decision criteria, availability change per cost provides a balance between the two. • Combination of composite wrap with replacement is identified as the most effective. [ABSTRACT FROM AUTHOR]

In order to achieve an efficient and a successful operation and maintenance plan for assets, management personnel should have detailed information on the condition of the assets to make informed strategic decisions and properly plan expenditure of capital investments. Condition assessment models for sewage pipelines can be considered as a helpful tool to achieve such objective and from which a decision regarding the required and appropriate intervention can be made. This paper presents a review for the different physical, Artificial Intelligence and statistical models that have been developed to assess the condition of sewage pipelines over a period from 1998 through 2019. The description of different techniques used in building the condition assessment models, and the data required to construct these models are presented. In addition, the major disadvantages and limitations of using these techniques in developing the models have also been discussed. The conducted literature review indicates that various condition assessment models were capable of precisely forecasting the future condition of sewer pipelines. Most of the developed assessment models have been validated with various identified techniques to ensure the adequacy of the predictions. The main problem in model development arises from data availability and liability as several factors were identified by researchers to impact the deterioration of sewer pipelines. In order to overcome this problem, municipalities must utilize the new emerging technologies to facilitate gathering the required dataset in a complete and precise manner. Also, certain techniques such as evidential reasoning or Bayesian Belief Network can be used due to their capabilities in dealing with missing data. Furthermore, the influence of the factors on the pipe condition were identified by some researchers. Although there were discrepancies in the findings, but the majority concluded that both age and material factors have high influence and pipe slope has low influence. • This paper reviews the models for assessing the condition of sewage pipeline. • Sewer pipelines deteriorate by physical, operational and environmental factors. • For assessment, physical, statistical and artificial intelligence models are used. • A combination of assessment models and regular inspection is essential. [ABSTRACT FROM AUTHOR]

• Hybrid SVR techniques are developed to predict the maximum depth of the pitting corrosion for oil and gas pipelines. • GA, PSO and FFA meta-heuristic optimization algorithms are used to determine optimum SVR hyper-parameters. • A comparative study between the developed hybrid models and empirical ones are presented. • The study demonstrates superior performances of the proposed hybrid models in terms of robustness and accuracy. Avoiding failures of corroded steel structures are critical in offshore oil and gas operations. An accurate prediction of maximum depth of pitting corrosion in oil and gas pipelines has significance importance, not only to prevent potential accidents in future but also to reduce the economic charges to both industry and owners. In the present paper, efficient hybrid intelligent model based on the feasibility of Support Vector Regression (SVR) has been developed to predict the maximum depth of pitting corrosion in oil and gas pipelines, whereas the performance of well-known meta-heuristic optimization techniques, such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Firefly Algorithm (FFA), are considered to select optimal SVR hyper-parameters. These nature-inspired algorithms are capable of presenting precise optimal predictions and therefore, hybrid models are developed to integrate SVR with GA, PSO, and FFA techniques. The performances of the proposed models are compared with the traditional SVR model where its hyper-parameters are attained through trial and error process on the one hand and empirical models on the other. The developed models have been applied to a large database of maximum pitting corrosion depth. Computational results indicate that hybrid SVR models are efficient tools, which are capable of conducting a more precise prediction of maximum pitting corrosion depth. Moreover, the results revealed that the SVR-FFA model outperformed all other models considered in this study. The developed SVR-FFA model could be adopted to support pipeline operators in the maintenance decision-making process of oil and gas facilities. [ABSTRACT FROM AUTHOR]