bluuebunny/crossref_metadata_2025_split huggingface dataset:
Some Practical Benefits of Detailed Forensic Analysis
Authors: Leis Brian , Wang Yong-Yi , Eshraghi Amin , Rapp Steve , Vervake Gary 
Published: December 2022

Abstract
Integrity threats can be difficult to manage in cases where the condition assessment leads to sometimes uncertain results, and/or the mechanisms affecting that condition are complex, and so are sometimes poorly characterized. Clarity in such cases derives from forensic analysis, as illustrated in this paper, which presents and discusses such work to: 1) better understand the scope and nature of the threat involved, 2) quantify a case-specific speed of crack growth, 3) establish the viability of the predictive tools being used, and 4) establish whether a once active threat had been effectively dealt with by the operational and maintenance changes that were made to manage it.
The results of two recent laboratory hydrotests are summarized concerning pups containing SCC colonies, as are the details of forensic analyses of the cracking exposed by that testing. The high-level goals were to: establish the form(s) of SCC active on the pipeline; quantify its average cracking speed (ACS) relative to published guidance; and determine whether the changes that had been made to manage that SCC had proven effective through evidence of dormant cracking.
Fractography and metallography done to identify traits characteristic of SCC clearly showed intergranular features and other key traits of high-pH cracking. As expected, the SCC colonies showed a wide range of crack sizes. The fractography also showed clear evidence of a prior hydrotest that along with other benchmarks over time led to a case-specific ACS, which was slower than published industry guidance. Comparison of predictions concerning failure pressure and other aspects made prior to the hydrotests established the viability of the predictive tools that had been used. Finally, indicators of dormancy such as heavily corroded intergranular features that continued down secondary cracking were used to establish that the SCC local to the dig sites considered had been mitigated.
Susceptibility of a Virtual Pipeline Network to Fatigue and Cracking Threats in Hydrogen Service
Authors: Burkinshaw Ollie , Sandana Daniel , Gallon Neil , Bhatia Arti 
Published: December 2022

Abstract
Over the coming decade many projects will be initiated to convert existing natural gas pipelines to hydrogen service, often with ambitious schedules. However, there is currently little experience globally in successfully converting pipelines to hydrogen service and operating them safely. In North America, the gas transmission infrastructure represents construction from the 1920s up to the present day. This infrastructure contains a wide spectrum of different pipeline materials with large variations in properties, and an array of different resident and time-dependent integrity threats.
Prior to embarking on changes to hydrogen service, it is imperative to understand and mitigate the effects associated with the change in risk profile, driven by the known effects of hydrogen gas on the toughness and fatigue resistance of steel pipelines. These effects are complicated by the significant variation and uncertainty in the extent to which different pipeline materials will be affected.
Many papers and industry projects have examined the effects of hydrogen on material properties. However, few have assessed the scale of the challenge posed to safe operation and integrity management involved with repurposing an entire infrastructure. This paper uses a novel approach to explore how the current natural gas transmission network might stand up to a hypothetical switch to 100% hydrogen.
Available data gathered through inspections of gas transmission pipelines in North America will be utilised to create a virtual pipeline network. This virtual system is built from a range of different pipeline sizes, attributes and material properties to most closely represent a ‘typical’ proportion of the gas transmission network in North America. An array of cracks and crack-like integrity threats with sizes and morphologies that reflect typical frequencies and severities observed from real projects will be introduced into this virtual network.
This virtual network is a construct that allows the impact of hydrogen conversion on integrity management to be explored in a way that is representative of what a gas transmission pipeline operator may expect to encounter across a range of assets. The impacts will be explored through different scenarios, representing different extents of reductions in toughness and increases in fatigue crack growth rates, based on available material test data. This approach will provide an indication of the number of features that may be currently stable in natural gas but that may fail in hydrogen service. This hypothetical exercise will draw insights into the practicalities of safe operation of pipelines being contemplated for hydrogen service and the scale of the task that would be necessary to navigate this transition.
Estimating Pipeline Probability of Failure Due to External Interference Damage Using Machine Learning Algorithms Trained on In-Line Inspection Data
Authors: White James , Taylor Katherine , Martin Jonny , Carrell Steven , Palmer-Jones Roland 
Published: December 2022

Abstract
External interference damage is one of the main causes of pipeline failure reported in publicly available industry statistics from agencies such as the Canada Energy Regulator (CER) and the United States Pipeline and Hazardous Materials Safety Administration (PHMSA). Thus, failures due to external interference are often the most significant contributors to pipeline probability of failure in risk assessments and can play a significant role in operator decisions regarding risk-control expenditures, for example when it comes to the installation of additional impact protection, pipeline diversion or pressure restrictions.
The probability of failure due to external interference damage can be estimated by combining the probability that damage occurs (i.e. that the pipeline is hit), the probability that the impact is sufficient to cause instant failure and the probability of degradation to failure, given that damage has occurred. Degradation to failure is assessed using industry standard engineering models (such as the limit state functions given in Annex O of CSA Z662-19 [1]). However, the key challenge is predicting where, when, and with what energy the external interference damage may happen.
The prediction of a “hit rate,” or impact frequency, can often be subjective or based on statistics, which may not always be applicable or accurate for use on the pipeline under assessment. Top-of-line (TOL) deformation damage (dents) reported by in-line inspection (ILI) are a clear indicator of past external interference, which could have been introduced by third parties, contractors or the operator themselves. ILI data from ROSEN’s Integrity Data Warehouse (IDW) — which at the time of writing contains results from over 18,000 inspections — has been used to train machine learning models to estimate the frequency of external interference damage (per km-year). The distribution of dent sizes combined with pipe parameters is used to estimate a distribution of dent force.
The following may all influence the likelihood and energy of external interference damage and may be considered as predictor variables in a machine learning model:
• Local population density
• Land use
• Excavator types (typical bucket dimensions)
• Frequency of crossings (road, rail, other services)
• Pipeline burial depth
• Additional impact protection
• Pipeline markers and warning tape
• Patrol and surveillance frequency
• Operational control activities
• Pipeline material properties
This paper presents an approach to estimate the probability of failure due to external interface damage that use more accurate and justifiable impact frequency statistics, which are generated using worldwide ILI data and additional influencing factors based on pipeline exposure, resistance and mitigations.
A Risk-Based Design Approach for Uncased Pipe Under Roads and Railways
Authors: Nathoo Hafeez , Nessim Maher , Stephens Mark 
Published: December 2022

Abstract
A risk-based pressure design approach has been developed for uncased pipe under roads and railways. Similar to the approach currently used in Canadian Standard Association’s Standard Z662, the approach uses a set of hoop stress factors to calculate the minimum wall thickness from the pipe’s pressure, diameter, and specified minimum yield strength. The hoop stress factors were calibrated to meet specified reliability targets considering the risk factors specific to roads and railways, which include elevated probabilities of mechanical damage due to higher construction activity rates, safety impact on road users, and potential costs of traffic interruption in case of a pipeline failure. The hoop stress factors are defined as a function of the safety class, which is determined according to the approach described in a companion IPC paper.
This paper describes the development approach and provides a comparison between its results and the designs obtained from the current CSA Z662 approach. An analysis confirming adequacy of the resulting wall thicknesses to withstand normal traffic loads is also presented. The approach is being proposed as an alternative to the hoop stress factors currently used in CSA Z662.
Full-Scale Coating Abrasion Test to Supplement Laboratory Testing
Authors: Futch David B. , D'Ambrosio David 
Published: December 2022

Abstract
Laboratory coating qualifications often require determining the ability of the coating to resist abrasion and gouging. These tests help inform a pipeline operator on the coating’s ability in extreme conditions, often those that may be required during a horizontal directional drill (HDD) when specifying an abrasion resistant overcoat (ARO) would be considered. However, these laboratory bench-top tests subject the coating to subscale loading that typically is not of the magnitude that can be experienced during pipeline construction.
An HDD is a trenchless pipeline installation method where a pipeline is pulled through a pilot hole and is likely to increase in popularly over the coming decades as current pipeline right of ways become more congested and urban sprawl increases. As HDDs increase, their construction will be pushed to the extreme: longer distances, larger/heavier pipe, and through more challenging formations. Drilling through more challenging formations, such as granite, will subject additional abrasion and gouging to the pipe coating. Therefore, there remains a need to supplement traditional laboratory bench-top testing with a full-scale alternative that more closely represents the abrasion and gouging risk experienced during an HDD.
This paper provides an overview of common subscale abrasion testing as well as the newly developed, innovative full-scale test. This full-scale test was designed to subject a pipe sample and ARO coating to varying obstructions pneumatically engaged against the outside pipe/coating surface. Obstructions utilized during this test development included: (1) a granite block selected to represent a natural formation, (2) a round tip selected to represent wear, and (3) carbide-tipped machining inserts selected to simulate aggressive gouging. Applying larger loads pneumatically to each obstruction simulates higher loading during an HDD — either due to a longer pull, heavier pipe, or variations in drilling mud/ballast. Finally, this paper will review industry accepted HDD load calculations providing a selection chart to determine the thickness of an applied ARO sacrificial overwrap.
Effectiveness of Subsurface Drainage for Mitigation of Landslides Affecting Pipelines
Authors: Rashidi Mohammad , Ebrahimi Ali , Mosaiebian Arash 
Published: December 2022

Abstract
Landslides can pose a threat to the integrity of new and existing pipelines if they are not mitigated. Improving subsurface drainage of groundwater is one of the most widely used stabilization strategies for mitigating landslides affecting pipelines because subsurface drainage requires minimal design and costs and can improve the overall stability. The appropriate design and implementation of this approach could lower the groundwater table within the landslide as a primary factor triggering landslide movement by reducing the driving force and increasing the shear strength or resisting force within the landslide mass. Thus, the subsurface drains are conventionally employed in the mitigation of most landslides that may threaten pipelines either as a single strategy or in conjunction with other measures.
This paper presents how effective subsurface drainage systems are for improving slope stabilization in various site conditions. Included in the discussion is the predesign investigation considerations. The results of a series of two-dimensional limit equilibrium and seepage analyses are presented to evaluate the effectiveness of subsurface drainage systems. Site conditions explored in this paper include the location of the right-of-way compared to the boundary of landslide, geometry of landslide, and groundwater level. A model that uses genetic expression programming as a computational intelligence technique is introduced that predicts the effectiveness of subsurface drainage systems.
Geospatial Database Development: Supporting Geohazard Risk Assessments Through Real-Time Data and Geospatial Analytics
Authors: Johnson Clayton , Schmidt Stephen , Taylor Justin , deLaChapelle John 
Published: December 2022

Abstract
TC Energy owns and operates over 32,000 miles of natural gas pipelines within 38 states in the United States, a sizable portion of which crosses through terrain highly susceptible to geologic hazards. To better support geohazard risk management, TC Energy has implemented a customized web-based geohazard platform (GeoForce) to identify, inventory, and track geohazards across their U.S. pipeline system. The platform was built within the Environmental Systems Research Institute (ESRI) ArcGIS Enterprise environment and leverages a diverse amount of ESRI product offerings. The platform is hosted on ArcGIS Portal and includes multiple custom apps and dashboards which allow users to efficiently view, summarize, add, and update geohazard data. Another key component of the system is a connection to ArcGIS GeoEvent Server. GeoEvent Server allows for the delivery of near real-time geohazard threat notifications through emails and dashboards (i.e., seismic events, flooding, precipitation). The notifications also provide detailed information for the sections of the system affected by the event, and in the case of a seismic event, a suggested course of action in alignment with TC Energy procedures. ArcGIS Image Server was leveraged to host over 4 terabytes of LiDAR imagery which can be used in concert with the other geohazard datasets present in the database. Custom geospatial scripts were also developed to create a near real-time link between the GeoForce master database and TC Energy’s master data mart, asset information, regulatory, and other organizational data. These scripts flag and report where spatial and/or attribute data have changed or may no longer be valid and therefore require follow-up action to support risk management (e.g., when pipelines are abandoned, HCAs are updated, or pipe properties are updated). The scripts also store the centerline attribute changes in a table for further review to identify potential trends. The GeoForce database is also built as a launching platform for proactive analytics, and eventually predictive analytics for critical precipitation thresholds for landslide risk management, landslide susceptibility mapping, system-wide risk scoring, seismic events and ILI bending strain coincident with geohazards. Details related to the threat alerts that are issued by the geospatial system are stored and visualized in a PowerBI integration. With the incorporation of ArcGIS Notebook Server, algorithms will be developed that will review the historical threat repository that is being generated and issue threat alerts based on probability of a hazardous event occurring in proximity of the pipeline system.
Morphological Change Detection at Pipeline Crossings Using Remote Sensing: A Proof-of-Concept Study
Authors: Schueder Rudy , Engelbrecht Jeanine 
Published: December 2022

Abstract
A proof-of-concept research and development study was undertaken to determine to what degree changes in river morphology could be detected from satellite imagery in support of pipeline geohazard monitoring programs. Algorithms for the detection of the wetted extent and river centerline were developed in the Python programming language and applied to synthetic aperture radar and multispectral imaging data. Additional algorithms were developed to highlight meaningful changes in wetted extent and river centerline occurring between subsequent data acquisitions. The results showed that it was possible to detect morphological change using satellite imagery, but that the process was effective for rivers with a higher signal to noise ratio represented by the ratio of river width to image pixel size. For smaller rivers (20–50 m), the change in the centerline location was a more useful metric of morphological change. The change detection algorithms were successful at highlighting possibly important changes between subsequent data acquisitions but were limited by the relatively short catalogue of available images to compare changes against. Future work involves the improvement and deployment of the algorithms in operational software systems to support pipeline geohazard management programs.
Integrity Assessment of Linepipes for Transporting High Pressure Hydrogen Based on ASME B31.12
Authors: Ishikawa Nobuyuki , Sakimoto Takahiro , Shimamura Junji , Wang Jiawei , Wang Yong-Yi 
Published: December 2022

Abstract
Current hydrogen pipeline code ASME B31.12 requires that pipe materials shall be qualified for adequate resistance to fracture in hydrogen gas based on Article KD-10 of ASME BPVC, Sec. VIII, Division 3. In order to assess the integrity of a hypothetical hydrogen pipeline, fracture toughness and fatigue crack growth tests under gaseous hydrogen at up to 21MPa were first conducted using a recent Grade X65 linepipe with fine grained bainitic microstructure. Fatigue crack growth in the pressurized linepipe with semi-elliptical surface flaw was calculated by the procedures described in the Article KD-10 using the da/dN data obtained from the X65 linepipe and the fatigue crack growth equation specified in ASME B31.12. Pressure cycles were applied to the pipe with a surface flaw to investigate the effects of pressure range and design factor. The critical crack size was analyzed using the failure assessment diagram (FAD) concept which is also specified in Article KD-10. Significant fatigue crack growth was not observed under the lower design factor such as fD = 0.5 with small pressure range, while fatigue crack growth was drastically accelerated under the higher design factor and large pressure fluctuation. Integrity assessment by FAD analysis for longitudinal semi-elliptical crack and girth weld flaw clarified how the toughness value affects the critical condition.
Enhancing Results of Ultra-High-Resolution Axial Magnetic Flux Leakage (MFL-A Ultra) Inspection Data Utilizing Finite-Element Modeling (FEM) Simulations
Authors: Schneider Edgar , Spille Johannes , Fleming Jeff , Saad Ziad , Siggers Kevin 
Published: December 2022

Abstract
The standard evaluation methods for MFL-A inspections are based on simple calibration approaches that lead to stable results as long as the corrosion tends to be isolated and shallow. This paper presents a method suitable for complex and deep corrosion that utilizes FEM simulations on ultra-high resolution MFL-A data.
It follows two main aspects: The first aspect is to learn from dig verifications by using laser scans of corrosion anomalies as a finite-element model to simulate the corresponding magnetic flux to confirm the magnetic flux measurement of the ILI tool. This way the simulation model is validated.
The other aspect is to apply the achieved insights as a new depth sizing concept: the complete corrosion geometry as indicated by the ILI measurement is put into the validated FEM model to simulate the magnetic flux that should correspond to the one measured by the ILI tool.
In this new sizing method the influence of complex corrosion is considered, as all surrounding features are part of the model. Several models are calculated in order to minimize the impact of ambiguity of depth results. This increases the accuracy and, ultimately, stabilize pipeline operators’ integrity assessments.
In the big picture, novel approaches to MFL-A data interpretation, such as FEM, empower operators to make more informed decisions specific to their asset integrity management programs while reducing the costs, uncertainty and conservatism associated with these decisions.
Development of a Near-Neutral pH Stress Corrosion Cracking Growth Model for Pipelines Using Machine Learning Algorithms
Authors: Sun Haotian , Zhou Wenxing , Kang Jidong 
Published: December 2022

Abstract
Near-neutral pH stress corrosion cracking (NNpHSCC) is one of the leading causes of failure for buried pipelines. Characterizing the NNpHSCC growth rate accurately remains a challenging task for the pipeline industry. In this study, an NNpHSCC growth model for buried pipelines is developed based on experimental data obtained from full-scale tests conducted at the CanmetMATERIALS of Natural Resources Canada of pipe specimens that are in contact with near-neutral pH environment and subjected to cyclic internal pressures. Four machine learning algorithms, namely the random forest (RF), extremely randomized trees (ET), gradient boosting (GB) and extreme gradient boosting (XGB), are employed to estimate the crack growth rates da/dN from input variables characterizing the pipe geometry, internal pressure and environmental condition. The machine learning models are trained through hyperparameter tuning and k-fold cross validation to improve the model robustness. Model performances are validated and compared using an independent test dataset. This study provides an initial step in using machine learning tools to develop robust NNpHSCC growth models suitable for practical applications.