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Keywords: machine learning
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Proceedings Papers

Proc. ASME. IPC2022, Volume 1: Pipeline Safety Management Systems; Project Management, Design, Construction, and Environmental Issues; Strain-Based Design and Assessment; Risk and Reliability; Emerging Fuels and Greenhouse Gas Emissions, V001T07A008, September 26–30, 2022
Publisher: American Society of Mechanical Engineers
Paper No: IPC2022-87066
... it to a single city area. This work has been extended to cover the entire province of Saskatchewan. The model relies on logistic regression and a machine learning algorithm to associate the historical failure rate with the asset type, age, pipe material, diameter, pressure, and an array of geographical-dependent...
Proceedings Papers

Proc. ASME. IPC2022, Volume 1: Pipeline Safety Management Systems; Project Management, Design, Construction, and Environmental Issues; Strain-Based Design and Assessment; Risk and Reliability; Emerging Fuels and Greenhouse Gas Emissions, V001T07A009, September 26–30, 2022
Publisher: American Society of Mechanical Engineers
Paper No: IPC2022-87093
... 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...
Proceedings Papers

Proc. ASME. IPC2022, Volume 1: Pipeline Safety Management Systems; Project Management, Design, Construction, and Environmental Issues; Strain-Based Design and Assessment; Risk and Reliability; Emerging Fuels and Greenhouse Gas Emissions, V001T07A019, September 26–30, 2022
Publisher: American Society of Mechanical Engineers
Paper No: IPC2022-87211
... detailed information (e.g., dent profiles) for those potentially severe dents. An innovative approach based on machine learning predictions stemming from a representative dictionary of finite element analysis (FEA) generated prototypes was developed. The proposed approach predicts multiple severity-based...
Proceedings Papers

Proc. ASME. IPC2022, Volume 1: Pipeline Safety Management Systems; Project Management, Design, Construction, and Environmental Issues; Strain-Based Design and Assessment; Risk and Reliability; Emerging Fuels and Greenhouse Gas Emissions, V001T07A032, September 26–30, 2022
Publisher: American Society of Mechanical Engineers
Paper No: IPC2022-87347
... to estimate grade. As part of this work, a supervised classification machine learning (ML) model was developed to predict pipe grade using NDE chemical composition measurements as inputs. While using the ML-based model provides substantial improvement over yield strength (YS) in predicting pipe grade...
Proceedings Papers

Proc. ASME. IPC2022, Volume 2: Pipeline and Facilities Integrity, V002T03A004, September 26–30, 2022
Publisher: American Society of Mechanical Engineers
Paper No: IPC2022-87236
... on ILI historical results (i.e., create the “training” dataset); (2) leverage classification trees to identify statistically relevant data observations of key variables; (3) apply machine learning techniques to develop probabilistic and/or causal models that predict target outcomes from the combinations...
Proceedings Papers

Proc. ASME. IPC2022, Volume 1: Pipeline Safety Management Systems; Project Management, Design, Construction, and Environmental Issues; Strain-Based Design and Assessment; Risk and Reliability; Emerging Fuels and Greenhouse Gas Emissions, V001T07A005, September 26–30, 2022
Publisher: American Society of Mechanical Engineers
Paper No: IPC2022-86906
... Abstract This paper discusses a model developed and applied to evaluate the probability of Stress Corrosion Cracking (SCC) failure in a large gas pipeline system spanning approximately 8,500 miles. A machine learning algorithm (neural network) was applied to the system, which has experienced...
Proceedings Papers

Proc. ASME. IPC2022, Volume 2: Pipeline and Facilities Integrity, V002T03A059, September 26–30, 2022
Publisher: American Society of Mechanical Engineers
Paper No: IPC2022-87207
... 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...
Proceedings Papers

Proc. ASME. IPC2022, Volume 3: Operations, Monitoring, and Maintenance; Materials and Joining, V003T04A013, September 26–30, 2022
Publisher: American Society of Mechanical Engineers
Paper No: IPC2022-87155
... of the algorithms in operational software systems to support pipeline geohazard management programs. earth observation machine learning computer vision morphological change detection synthetic aperture radar multispectral imaging geohazard management Proceedings of the 2022 14th International...
Proceedings Papers

Proc. ASME. IPC2020, Volume 1: Pipeline and Facilities Integrity, V001T03A068, September 28–30, 2020
Publisher: American Society of Mechanical Engineers
Paper No: IPC2020-9331
... be that the estimated depth of a feature is 36%wt in an interval of [30%, 48%] of wall thickness with 80% confidence. This is believed to greatly reduce the level of uncertainty when it comes to failure pressure estimation or other type of pipeline risk assessment. The advancement in Machine Learning today, deep...
Proceedings Papers

Proc. ASME. IPC2020, Volume 1: Pipeline and Facilities Integrity, V001T03A078, September 28–30, 2020
Publisher: American Society of Mechanical Engineers
Paper No: IPC2020-9624
... to maximize the amount of information gained from costly field work. This approach — which relies on supervised machine learning — leads to a marked improvement in the classification of crack-like indications from EMAT, and allows future investigations to be prioritized according to the likelihood of finding...