Abstract

The work reports the systematic approach to the study of artificial intelligence (AI) in addressing the complexity of inline inspection (ILI) data management to forecast the risk in natural gas pipelines. A recent conventional standard may not be sufficient to address the variation data of corrosion defects and inherent human subjectivity. Such methodology undermines the accuracy assessment confidence and is ineffective in reducing inspection costs. In this work, a combination of unsupervised and supervised machine learning and deep learning has profoundly accelerated the probability of failure (PoF) assessment and analysis. K-means clustering and Gaussian mixture models show direct relevance between the corrosion depth and corrosion rate, while the overlapping PoF value is scattered in three clusters. Logistic regression, support vector machine, k-nearest neighbors, and ensemble classifiers of AdaBoost, random forest, and gradient boosting are constructed using particular features, labels, and hyperparameters. The algorithm correctly predicted the score of PoF from 4790 instances and confirmed the 25% metal loss at a location of 13.399 m. The artificial neural network (ANN) is designed with various layers (input, hidden, and output) architecture. It is optimized using an activation function to predict that 74% of the pipeline's anomalies that classified at low-medium and medium-high risk. Furthermore, it provides a quick and precise prediction about the external defects at 13.1 m and requires the personnel to conduct wrapping composite. This work can be used as a standard guideline for risk assessment based on ILI and applies to industry and academia.

References

1.
Khan
,
F.
,
Yarveisy
,
R.
, and
Abbassi
,
R.
,
2021
, “
Risk-Based Pipeline Integrity Management: A Road Map for the Resilient Pipelines
,”
J. Pipeline Sci. Eng.
,
1
(
1
), pp.
74
87
.10.1016/j.jpse.2021.02.001
2.
Biezma
,
M. V.
,
Andrés
,
M. A.
,
Agudo
,
D.
, and
Briz
,
E.
,
2020
, “
Most Fatal Oil & Gas Pipeline Accidents Through History: A Lessons Learned Approach
,”
Eng. Fail. Anal.
,
110
, p.
104446
.10.1016/j.engfailanal.2020.104446
3.
Luo
,
J.
,
Luo
,
S.
,
Li
,
L.
,
Zhang
,
L.
,
Wu
,
G.
, and
Zhu
,
L.
,
2019
, “
Stress Corrosion Cracking Behavior of X90 Pipeline Steel and Its Weld Joint at Different Applied Potentials in Near-Neutral Solutions
,”
Nat. Gas Ind. B
,
6
(
2
), pp.
138
144
.10.1016/j.ngib.2018.08.002
4.
Restrepo
,
C. E.
,
Simonoff
,
J. S.
, and
Zimmerman
,
R.
,
2009
, “
Causes, Cost Consequences, and Risk Implications of Accidents in U.S. Hazardous Liquid Pipeline Infrastructure
,”
Int. J. Crit. Infrastruct. Prot.
,
2
(1-2), pp.
38
50
.10.1016/j.ijcip.2008.09.001
5.
Yoon
,
S.
,
Lee
,
D. H.
, and
Jung
,
H. J.
,
2019
, “
Seismic Fragility Analysis of a Buried Pipeline Structure Considering Uncertainty of Soil Parameters
,”
Int. J. Press. Vessels Pip.
,
175
, p.
103932
.10.1016/j.ijpvp.2019.103932
6.
Shafeek
,
H. I.
,
Gadelmawla
,
E. S.
,
Abdel-Shafy
,
A. A.
, and
Elewa
,
I. M.
,
2004
, “
Automatic Inspection of Gas Pipeline Welding Defects Using an Expert Vision System
,”
NDT & E Int.
,
37
(
4
), pp.
301
307
.10.1016/j.ndteint.2003.10.004
7.
Shi
,
L.
,
Wang
,
C.
, and
Zou
,
C.
,
2014
, “
Corrosion Failure Analysis of L485 Natural Gas Pipeline in CO2 Environment
,”
Eng. Fail. Anal.
,
36
, pp.
372
378
.10.1016/j.engfailanal.2013.11.009
8.
Lu
,
Y.
,
Liu
,
R.
,
Wang
,
K.
,
Tang
,
Y.
, and
Cao
,
Y.
,
2021
, “
A Study on the Fuzzy Evaluation System of Carbon Dioxide Flooding Technology
,”
Energy Sci. Eng.
,
9
(
2
), pp.
239
255
.10.1002/ese3.844
9.
Singh
,
M.
, and
Pokhrel
,
M.
,
2018
, “
A Fuzzy Logic-Possibilistic Methodology for Risk-Based Inspection (RBI) Planning of Oil and Gas Piping Subjected to Microbiologically Influenced Corrosion (MIC)
,”
Int. J. Press. Vessels Pip.
,
59
, pp.
45
54
.10.1016/j.ijpvp.2017.11.005
10.
Rachman
,
A.
,
Zhang
,
T.
, and
Ratnayake
,
R. M. C.
,
2021
, “
Applications of Machine Learning in Pipeline Integrity Management: A State-of-the-Art Review
,”
Int. J. Press. Vessels Pip.
,
193
, p.
104471
.10.1016/j.ijpvp.2021.104471
11.
Kabbabe Poleo
,
K.
,
Crowther
,
W. J.
, and
Barnes
,
M.
,
2021
, “
Estimating the Impact of Drone-Based Inspection on the Levelised Cost of Electricity for Offshore Wind Farms
,”
Results Eng.
,
9
, p.
100201
.10.1016/j.rineng.2021.100201
12.
Rachman
,
A.
, and
Ratnayake
,
R. M. C.
,
2019
, “
Machine Learning Approach for Risk-Based Inspection Screening Assessment
,”
Reliab. Eng. Syst. Saf.
,
185
, pp.
518
532
.10.1016/j.ress.2019.02.008
13.
Delgadillo
,
H. H
, Geelen, C., Kakes, R., Loendersloot, R., Yntema, D., Tinga, T., and Akkerman, R.,
2020
, “
Ultrasonic Inline Inspection of a Cement-Based Drinking Water Pipeline
,”
Eng. Struct.
,
210
, p.
110413
.10.1016/j.engstruct.2020.110413
14.
Hamed
,
Y.
,
Shafie
,
A.
,
Mustaffa
,
Z.
, and
Rusma
,
N.
,
2019
, “
Error-Reduction Approach for Corrosion Measurements of Pipeline Inline Inspection Tools
,”
Meas. Control
,
52
(1-2), pp.
28
36
.10.1177/0020294018813643
15.
Aditiyawarman
,
T.
,
Kaban
,
A. P. S.
, and
Soedarsono
,
J. W.
,
2022
, “
A Recent Review of Risk-Based Inspection Development to Support Service Excellence in the Oil and Gas Industry: An Artificial Intelligence Perspective
,”
ASCE-ASME J. Risk Uncertain. Eng. Syst. Part B Mech. Eng.
,
9
(
1
), p.
010801
.10.1115/1.4054558
16.
Schmitt
,
J.
,
Bönig
,
J.
,
Borggräfe
,
T.
,
Beitinger
,
G.
, and
Deuse
,
J.
,
2020
, “
Predictive Model-Based Quality Inspection Using Machine Learning and Edge Cloud Computing
,”
Adv. Eng. Inf.
,
45
, p.
101101
.10.1016/j.aei.2020.101101
17.
Bhaskaran
,
P. E.
,
Chennippan
,
M.
, and
Subramaniam
,
T.
,
2020
, “
Future Prediction & Estimation of Faults Occurrences in Oil Pipelines by Using Data Clustering With Time Series Forecasting
,”
J. Loss Prev. Process Ind.
,
66
, p.
104203
.10.1016/j.jlp.2020.104203
18.
Beer
,
M.
,
Ferson
,
S.
, and
Kreinovich
,
V.
,
2013
, “
Imprecise Probabilities in Engineering Analyses
,”
Mech. Syst. Signal Process.
,
37
(
1–2
), pp.
4
29
.10.1016/j.ymssp.2013.01.024
19.
Moura
,
R.
,
Beer
,
M.
,
Patelli
,
E.
,
Lewis
,
J.
, and
Knoll
,
F.
,
2017
, “
Learning From Accidents: Interactions Between Human Factors, Technology and Organizations as a Central Element to Validate Risk Studies
,”
Saf. Sci.
,
99
(Pt. B), pp.
196
214
.10.1016/j.ssci.2017.05.001
20.
Rachman
,
A.
, and
Chandima
,
R. R. M.
,
2018
, “
Artificial Neural Network Model for Risk-Based Inspection Screening Assessment of Oil and Gas Production System
,”
The 28th International Ocean and Polar Engineering Conference
, Sapporo, Japan, Paper No. ISOPE-I-18-288.https://onepetro.org/ISOPEIOPEC/proceedings-abstract/ISOPE18/All-ISOPE18/ISOPE-I-18-288/20562
21.
Gu
,
J.
,
Zhang
,
H.
,
Chen
,
L.
, and
Lian
,
S.
,
2019
, “
The Application of the Big Data Algorithm for Pipeline Lifetime Analysis
,” Chinese Automation Congress (
CAC
), Hangzhou, China, Nov. 22–24, pp.
824
829
.10.1109/CAC48633.2019.8996228
22.
Gao
,
Z.
,
Lu
,
G.
,
Liu
,
M.
, and
Cui
,
M.
,
2008
, “
A Novel Risk Assessment System for Port State Control Inspection
,”
IEEE International Conference on Intelligence and Security Informatics
, Taipei, June 17–20, pp.
242
244
.10.1109/ISI.2008.4565068
23.
Spinner
,
T.
,
Schlegel
,
U.
,
Schäfer
,
H.
, and
El-Assady
,
M.
,
2020
, “
Explainer: A Visual Analytics Framework for Interactive and Explainable Machine Learning
,”
IEEE Trans. Vis. Comput. Graph
,
26
(
1
), pp.
1064
1074
.10.1109/TVCG.2019.2934629
24.
Fan
,
W.
,
Chen
,
Y.
,
Li
,
J.
,
Sun
,
Y.
,
Feng
,
J.
,
Hassanin
,
H.
, and
Sareh
,
P.
,
2021
, “
Machine Learning Applied to the Design and Inspection of Reinforced Concrete Bridges: Resilient Methods and Emerging Applications
,”
Structures
,
33
, pp.
3954
3963
.10.1016/j.istruc.2021.06.110
25.
Zhang
,
T.
,
Bai
,
H.
, and
Sun
,
S.
,
2021
, “
A Self-Adaptive Deep Learning Algorithm for Intelligent Natural Gas Pipeline Control
,”
Energy Rep.
,
7
, pp.
3488
3496
.10.1016/j.egyr.2021.06.011
26.
Chang
,
M. K.
,
Chang
,
R. R.
,
Shu
,
C. M.
, and
Lin
,
K. N.
,
2005
, “
Application of Risk Based Inspection in Refinery and Processing Piping
,”
J. Loss Prev. Process Ind.
, 18(4–6), pp.
397
402
.10.1016/j.jlp.2005.06.036
27.
Shishesaz
,
M. R.
,
Nazarnezhad Bajestani
,
M.
,
Hashemi
,
S. J.
, and
Shekari
,
E.
,
2013
, “
Comparison of API 510 Pressure Vessels Inspection Planning With API 581 Risk-Based Inspection Planning Approaches
,”
Int. J. Press. Vessels Pip.
, 111–112, pp.
202
208
.10.1016/j.ijpvp.2013.07.007
28.
Mazumder
,
R. K.
,
Salman
,
A. M.
, and
Li
,
Y.
,
2021
, “
Failure Risk Analysis of Pipelines Using Data-Driven Machine Learning Algorithms
,”
Struct. Saf.
,
89
, p.
102047
.10.1016/j.strusafe.2020.102047
29.
Mangalathu
,
S.
, and
Jeon
,
J.-S.
,
2019
, “
Machine Learning–Based Failure Mode Recognition of Circular Reinforced Concrete Bridge Columns: Comparative Study
,”
J. Struct. Eng.
,
145
(
10
), pp.
1
13
.10.1061/(ASCE)ST.1943-541X.0002402
30.
Lu
,
L.
, and
Liang
,
L.
,
2021
, “
Numerical Investigation of Corroded Middle-High Strength Pipeline Subjected to Combined Internal Pressure and Axial Compressive Loading
,”
Energy Sci. Eng.
,
9
, pp.
798
811
.10.1002/ese3.830
31.
Ikpe
,
E.
,
Kumar
,
J.
, and
Jergeas
,
G.
,
2014
, “
Comparison of Alberta Industrial and Pipeline Projects and U.S. Projects Performance
,”
Am. J. Ind. Bus. Manag.
,
4
, pp.
474
481
.10.4236/ajibm.2014.49053
32.
Li
,
Z.
,
Liang
,
Y.
,
Ni
,
W.
,
Liao
,
Q.
,
Xu
,
N.
,
Li
,
L.
,
Zheng
,
J.
, and
Zhang
,
H.
,
2022
, “
Pipesharing: Economic-Environmental Benefits From Transporting Biofuels Through Multiproduct Pipelines
,”
Appl. Energy
,
311
, pp.
1
20
.10.1016/j.apenergy.2022.118684
33.
Kaban
,
A. P. S.
,
Ridhova
,
A.
,
Priyotomo
,
G.
,
Elya
,
B.
,
Maksum
,
A.
,
Sadeli
,
Y.
,
Sutopo
,
S.
,
Aditiyawarman
,
T.
,
Riastuti
,
R.
, and
Soedarsono
,
J. W.
,
2021
, “
Development of White Tea Extract as Green Corrosion Inhibitor in Mild Steel Under 1 M Hydrochloric Acid Solution
,”
Eastern-Eur. J. Enterp. Technol.
,
2
(
6 (110
), pp.
6
20
.10.15587/1729-4061.2021.224435
34.
Arlan
,
A. S.
,
Subekti
,
N.
,
Soedarsono
,
J. W.
, and
Rustandi
,
A.
,
2018
, “
Corrosion Inhibition by a Caesalpinia Sappan l Modified Imidazoline for Carbon Steel Api 5l Grade x60 in Hcl 1m Environment
,”
Mater. Sci. Forum
,
929
, pp.
158
170
.10.4028/www.scientific.net/MSF.929.158
35.
Soedarsono
,
J. W.
,
Shihab
,
M. N.
,
Azmi
,
M. F.
, and
Maksum
,
A.
,
2018
, “
Study of Curcuma Xanthorrhiza Extract as Green Inhibitor for API 5 L X42 Steel in 1M HCl Solution
,”
IOP Conf. Ser.: Earth Environ. Sci.
, 105, epub.10.1088/1755-1315/105/1/012060
36.
Paul Setiawan Kaban
,
A.
,
Mayangsari
,
W.
,
Anwar
,
M. S.
,
Maksum
,
A.
,
Riastuti
,
R.
,
Aditiyawarman
,
T.
, and
Soedarsono
,
J. W.
,
2022
, “
Experimental and Modelling Waste Rice Husk Ash as a Novel Green Corrosion Inhibitor Under Acidic Environment
,”
Mater. Today Proc.
, 62, pp. 4225–4234.10.1016/j.matpr.2022.04.738
37.
Wasef
,
M.
, and
Rafla
,
N.
,
2021
, “
Hardware Implementation of Multi-Rate Input SoftMax Activation Function
,” IEEE International Midwest Symposium on Circuits and Systems (
MWSCAS
), Lansing, MI, Aug. 9–11, pp.
783
786
.10.1109/MWSCAS47672.2021.9531761
38.
Mhaskar
,
H. N.
, and
Poggio
,
T.
,
2016
, “
Deep Vs. shallow Networks: An Approximation Theory Perspective
,”
Anal. Appl.
,
14
(
06
), pp.
829
848
.10.1142/S0219530516400042
39.
El-Abbasy
,
M. S.
,
Senouci
,
A.
,
Zayed
,
T.
,
Parvizsedghy
,
L.
, and
Mirahadi
,
F.
,
2016
, “
Unpiggable Oil and Gas Pipeline Condition Forecasting Models
,”
J. Perform. Constr. Facil.
,
30
(
1
), pp.
1
19
.10.1061/(ASCE)CF.1943-5509.0000716
40.
Mangalathu
,
S.
,
Hwang
,
S. H.
, and
Jeon
,
J. S.
,
2020
, “
Failure Mode and Effects Analysis of RC Members Based on Machine-Learning-Based SHapley Additive Explanations (SHAP) Approach
,”
Eng. Struct
,
219
, p.
110927
.10.1016/j.engstruct.2020.110927
41.
Zhao
,
Y.
,
Zhu
,
W.
,
Wei
,
P.
,
Fang
,
P.
,
Zhang
,
X.
,
Yan
,
N.
,
Liu
,
W.
,
Zhao
,
H.
, and
Wu
,
Q.
,
2022
, “
Classification of Zambian Grasslands Using Random Forest Feature Importance Selection During the Optimal Phenological Period
,”
Ecol. Indic.
,
135
, p.
108529
.10.1016/j.ecolind.2021.108529
42.
Colome
,
A.
, and
Torras
,
C.
,
2018
, “
Dimensionality Reduction in Learning Gaussian Mixture Models of Movement Primitives for Contextualized Action Selection and Adaptation
,”
IEEE Robot. Autom. Lett.
,
3
(
4
), pp.
3922
3929
.10.1109/LRA.2018.2857921
43.
Maldonado
,
S.
, and
Weber
,
R.
,
2009
, “
A Wrapper Method for Feature Selection Using Support Vector Machines
,”
Inf. Sci.
,
179
, pp.
2208
2217
.10.1016/j.ins.2009.02.014
44.
Lv, Z., Wang, L., Guan, Z., Wu, J., Du, X., Zhao, H., and Guizani, M.
,
2019
, “
An Optimizing and Differentially Private Clustering Algorithm for Mixed Data in SDN-Based Smart Grid
,”
IEEE Access
,
7
, pp.
45773
45782
.10.1109/ACCESS.2019.2909048
45.
Patel
,
E.
, and
Kushwaha
,
D. S.
,
2020
, “
Clustering Cloud Workloads: K-Means Vs Gaussian Mixture Model
,”
Procedia Comput. Sci.
,
171
, pp.
158
167
.10.1016/j.procs.2020.04.017
46.
Yang
,
Y.
,
Liao
,
Q.
,
Wang
,
J.
, and
Wang
,
Y.
,
2022
, “
Application of Multi-Objective Particle Swarm Optimization Based on Short-Term Memory and K-Means Clustering in Multi-Modal Multi-Objective Optimization
,”
Eng. Appl. Artif. Intell
,
112
(
0952–1976
), p.
104866
.10.1016/j.engappai.2022.104866
47.
Breiman
,
L.
,
1996
, “
Bagging Predictors
,”
Mach. Learn.
,
24
(
2
), pp.
123
140
.
48.
Breiman
,
L.
,
1996
, “
Bagging Predictors
,”
Mach. Learn.
, 24, pp.
123
140
.10.1007/BF00058655
49.
Allison
,
P. D.
,
2012
,
Logistic Regression Using SAS: Theory and Application
, Wiley, Hoboken, NJ.
50.
Ksia¸żek
,
W.
,
Gandor
,
M.
, and
Pławiak
,
P.
,
2021
, “
Comparison of Various Approaches to Combine Logistic Regression With Genetic Algorithms in Survival Prediction of Hepatocellular Carcinoma
,”
Comput. Biol. Med.
,
134
, p.
104431
.10.1016/j.compbiomed.2021.104431
51.
Murayama
,
K.
, and
Kawano
,
S.
,
2021
, “
Sparse Bayesian Learning With Weakly Informative Hyperprior and Extended Predictive Information Criterion
,”
IEEE Trans. Neural Networks Learn. Syst
, pp. 1–13.10.1109/TNNLS.2021.3131357
52.
Konstantinov
,
A. V.
, and
Utkin
,
L. V.
,
2021
, “
Interpretable Machine Learning With an Ensemble of Gradient Boosting Machines
,”
Knowl.-Based Syst.
,
222
, p.
106993
.10.1016/j.knosys.2021.106993
53.
Elith
,
J.
,
Leathwick
,
J. R.
,
Hastie
,
T.
, and
Leathwick
,
J. R.
,
2008
, “
Elith, Leathwick & Hastie a Working Guide to Boosted Regression Trees
,”
J. Anim. Ecol.
,
77
, pp.
802
813
.10.1111/j.1365-2656.2008.01390.x
54.
Ben Seghier
,
M. E. A.
,
Höche
,
D.
, and
Zheludkevich
,
M.
,
2022
, “
Prediction of the Internal Corrosion Rate for Oil and Gas Pipeline: Implementation of Ensemble Learning Techniques
,”
J. Nat. Gas Sci. Eng.
,
99
, p.
104425
.10.1016/j.jngse.2022.104425
55.
Sharafati
,
A.
,
Asadollah
,
S. B. H. S.
, and
Hosseinzadeh
,
M.
,
2020
, “
The Potential of New Ensemble Machine Learning Models for Effluent Quality Parameters Prediction and Related Uncertainty
,”
Process Saf. Environ. Prot.
,
140
, pp.
68
78
.10.1016/j.psep.2020.04.045
56.
Mantas
,
C. J.
,
Castellano
,
J. G.
,
Moral-García
,
S.
, and
Abellán
,
J.
,
2019
, “
A Comparison of Random Forest Based Algorithms: Random Credal Random Forest Versus Oblique Random Forest
,”
Soft Comput.
,
23
(
21
), pp.
10739
10754
.10.1007/s00500-018-3628-5
57.
Breiman
,
L.
,
2001
, “
Random Forests
,”
Mach. Learn.
,
45
, pp.
5
32
.10.1023/A:1010933404324
58.
Li
,
H.
,
Zhang
,
Z.
, and
Liu
,
Z.
,
2017
, “
Application of Artificial Neural Networks for Catalysis: A Review
,”
Catalysts
,
7
(
10
), p.
306
.10.3390/catal7100306
59.
He
,
X.
,
Green
,
S.
,
McFarland
,
J.
, and
Adams
,
G.
,
2014
, “Enhancement of Internal Corrosion Threat Guidelines for Nominally Dry Natural Gas Pipelines,”
CORROSION
2014, San Antonio, TX.https://onepetro.org/NACECORR/proceedingsabstract/CORR14/All-CORR14/NACE-2014-3887/122929
60.
Onuoha
,
C.
,
McDonnell
,
S.
, and
Pozniak
,
E.
,
2018
, “Predicted and Actual Dig Outcome of Dry Gas Internal Corrosion Direct Assessment of Unpiggable Pipelines,”
CORROSION
2018, Phoenix, AZ.https://onepetro.org/NACECORR/proceedingsabstract/CORR18/All-CORR18/NACE-2018-11300/126063
61.
Halimu
,
C.
,
Kasem
,
A.
, and
Newaz
,
S. H. S.
,
2019
, “
Empirical Comparison of Area Under ROC Curve (AUC) and Mathew Correlation Coefficient (MCC) for Evaluating Machine Learning Algorithms on Imbalanced Datasets for Binary Classification
,”
Third International Conference on Machine Learning and Soft Computing
, pp.
1
6
.10.1145/3310986.3311023
62.
Schempp
,
P.
,
Köhler
,
S.
,
Preuss
,
K.
, and
Tröger
,
M.
,
2019
, “
New Approach for Sulphidation Prediction in Crude Oil Refineries
,” Proceedings of the European Corrosion Congress, Eurocorr 2019, European Corrosion Congress, Seville, Spain, Sept. 9–13, Paper No.
233938
.https://www.researchgate.net/publication/335326265_New_approach_for_sulphidation_prediction_in_crude_oil_refineries
63.
Phan
,
H. C.
, and
Duong
,
H. T.
,
2021
, “
Predicting Burst Pressure of Defected Pipeline With Principal Component Analysis and Adaptive Neuro Fuzzy Inference System
,”
Int. J. Press. Vessels Pip.
,
189
, p.
104274
.10.1016/j.ijpvp.2020.104274
64.
Khandekar
,
R.
,
Kortsarz
,
G.
, and
Mirrokni
,
V.
,
2012
, “
Advantage of Overlapping Clusters for Minimizing Conductance
,”
LATIN 2012: Theoretical Informatics
. LATIN 2012. Lecture Notes in Computer Science, Fernández-Baca, D. (ed.), Vol. 7256. Springer, Berlin, Heidelberg.10.1007/978-3-642-29344-3_42
65.
Bouzenad
,
A. E.
,
El Mountassir
,
M.
,
Yaacoubi
,
S.
,
Dahmene
,
F.
,
Koabaz
,
M.
,
Buchheit
,
L.
, and
Ke
,
W.
,
2019
, “
A Semi-Supervised Based k-Means Algorithm for Optimal Guided Waves Structural Health Monitoring: A Case Study
,”
Inventions
,
4
(
1
), p.
17
.10.3390/inventions4010017
66.
Yan
,
L.
,
Diao
,
Y.
,
Lang
,
Z.
, and
Gao
,
K.
,
2020
, “
Corrosion Rate Prediction and Influencing Factors Evaluation of Low-Alloy Steels in Marine Atmosphere Using Machine Learning Approach
,”
Sci. Technol. Adv. Mater
10.1080/14686996.2020.1746196.
67.
Ossai
,
C. I.
,
2019
, “
A Data-Driven Machine Learning Approach for Corrosion Risk Assessment—A Comparative Study
,”
Big Data Cogn. Comput.
,
3
(
2
), p.
28
.10.3390/bdcc3020028
68.
Matthews
,
C.
,
2009
, “
An Introduction to API 570
,”
A Quick Guide to API 570 Certified Pipework Inspector Syllabus
, 1st ed., Woodhead Publishing, Sawston, UK.
69.
Son
,
S.
, and
Oh
,
K.-Y.
,
2022
, “
Integrated Framework for Estimating Remaining Useful Lifetime Through a Deep Neural Network
,”
Appl. Soft Comput.
,
122
, p.
108879
.10.1016/j.asoc.2022.108879
70.
Yan
,
T.
,
Lei
,
Y.
,
Li
,
N.
,
Wang
,
B.
, and
Wang
,
W.
,
2021
, “
Degradation Modeling and Remaining Useful Life Prediction for Dependent Competing Failure Processes
,”
Reliab. Eng. Syst. Saf.
,
212
, p.
107638
.10.1016/j.ress.2021.107638
71.
Vu
,
H. L.
,
Ng
,
K. T. W.
,
Richter
,
A.
, and
An
,
C.
,
2022
, “
Analysis of Input Set Characteristics and Variances on k-Fold Cross Validation for a Recurrent Neural Network Model on Waste Disposal Rate Estimation
,”
J. Environ. Manage
,
311
, p.
114869
.10.1016/j.jenvman.2022.114869
72.
Jassim
,
M. S.
,
Coskuner
,
G.
, and
Zontul
,
M.
,
2022
, “
Comparative Performance Analysis of Support Vector Regression and Artificial Neural Network for Prediction of Municipal Solid Waste Generation
,”
Waste Manag. Res.
,
40
, pp.
195
204
.10.1177/0734242X211008526
73.
Katsamenis
,
I.
,
Protopapadakis
,
E.
,
Doulamis
,
A.
,
Doulamis
,
N.
, and
Voulodimos
,
A.
,
2020
, “
Pixel-Level Corrosion Detection on Metal Constructions by Fusion of Deep Learning Semantic and Contour Segmentation
,”
Advances in Visual Computing. ISVC 2020. Lecture Notes in Computer Science
, Vol. 12509, Springer, Cham, Switzerland.10.1007/978-3-030-64556-4_13
74.
Hooda
,
N.
,
Bawa
,
S.
, and
Rana
,
P. S.
,
2020
, “
Optimizing Fraudulent Firm Prediction Using Ensemble Machine Learning: A Case Study of an External Audit
,”
Appl. Artif. Intell.
,
34
(
1
), pp.
20
30
.10.1080/08839514.2019.1680182
75.
Edelmann
,
D.
,
Móri
,
T. F.
, and
Székely
,
G. J.
,
2021
, “
On Relationships Between the Pearson and the Distance Correlation Coefficients
,”
Stat. Probab. Lett.
,
169
, pp.
1
6
.10.1016/j.spl.2020.108960
76.
Zhao
,
M
, Huang, T., Liu, C., Chen, M., Ji, S., Christopher, D. M., Li, X.,
2021
, “
Leak Localization Using Distributed Sensors and Machine Learning for Hydrogen Releases From a Fuel Cell Vehicle in a Parking Garage
,”
Int. J. Hydrogen Energy
,
46
, pp.
1420
1433
.10.1016/j.ijhydene.2020.09.218
77.
Perumal
,
K. E.
,
2014
, “
Corrosion Risk Analysis, Risk Based Inspection and a Case Study Concerning a Condensate Pipeline
,”
Procedia Eng.
,
86
, pp.
597
605
.10.1016/j.proeng.2014.11.085
78.
Zardasti
,
L.
,
Yahaya
,
N.
,
Noor
,
N. M.
, and
Valipour
,
A.
,
2020
, “
Quantifying Reputation Loss of Pipeline Operator From Various Stakeholders' Perspectives – Part 1: Prioritization
,”
J. Loss Prev. Process Ind.
,
63
, p.
104034
.10.1016/j.jlp.2019.104034
79.
Sarah
,
S.
,
Gourisaria
,
M. K.
,
Khare
,
S.
, and
Das
,
H.
,
2022
, “
Heart Disease Prediction Using Core Machine Learning Techniques—A Comparative Study
,” S. Tiwari, M. C. Trivedi, M. L. Kolhe, K. Mishra, B. K. Singh, eds.,
Advances in Data and Information Sciences. Lecture Notes in Networks and Systems
, Vol. 318, Springer, Singapore.10.1007/978-981-16-5689-7_22
80.
Robles-Velasco
,
A.
,
Cortés
,
P.
,
Muñuzuri
,
J.
, and
Onieva
,
L.
,
2020
, “
Prediction of Pipe Failures in Water Supply Networks Using Logistic Regression and Support Vector Classification
,”
Reliab. Eng. Syst. Saf.
,
196
, p.
106754
.10.1016/j.ress.2019.106754
81.
Chaturvedi
,
D. K.
,
Sinha
,
A. P.
, and
Malik
,
O. P.
,
2015
, “
Short Term Load Forecast Using Fuzzy Logic and Wavelet Transform Integrated Generalized Neural Network
,”
Int. J. Electr. Power Energy Syst.
, 67, pp.
230
237
.10.1016/j.ijepes.2014.11.027
82.
Jain
,
S.
, and
Saha
,
A.
,
2021
, “
Improving Performance With Hybrid Feature Selection and Ensemble Machine Learning Techniques for Code Smell Detection
,”
Sci. Comput. Prog.
,
212
, p.
102713
.10.1016/j.scico.2021.102713
83.
Shahraki
,
A.
,
Abbasi
,
M.
, and
Haugen
,
Ø.
,
2020
, “
Boosting Algorithms for Network Intrusion Detection: A Comparative Evaluation of Real AdaBoost, Gentle AdaBoost and Modest AdaBoost
,”
Eng. Appl. Artif. Intell.
,
94
, p.
103770
.10.1016/j.engappai.2020.103770
84.
Saikia
,
P.
,
Baruah
,
R. D.
,
Singh
,
S. K.
, and
Chaudhuri
,
P. K.
,
2020
, “
Artificial Neural Networks in the Domain of Reservoir Characterization: A Review From Shallow to Deep Models
,”
Comput. Geosci.
,
135
, p.
104357
.10.1016/j.cageo.2019.104357
85.
Goyal
,
M.
,
Goyal
,
R.
,
Venkatappa Reddy
,
P.
, and
Lall
,
B.
,
2020
, “
Activation Functions
,”
Studies in Computational Intelligence
, Springer, Warsaw, Poland.
86.
Chu
,
J.
,
Liu
,
X.
,
Zhang
,
Z.
,
Zhang
,
Y.
, and
He
,
M.
,
2021
, “
A Novel Method Overcomeing Overfitting of Artificial Neural Network for Accurate Prediction: Application on Thermophysical Property of Natural Gas
,”
Case Stud. Therm. Eng.
,
28
, p.
101406
.10.1016/j.csite.2021.101406
87.
Al-Moubaraki
,
A. H.
, and
Obot
,
I. B.
,
2021
, “
Top of the Line Corrosion: Causes, Mechanisms, and Mitigation Using Corrosion Inhibitors
,”
Arab. J. Chem.
,
14
(
5
), p.
103116
.10.1016/j.arabjc.2021.103116
88.
NACE,
2010
, “Pipeline External Corrosion Direct Assessment Methodology,”
NACE
, Houston, TX, Standard No. SP 0502–2010.
89.
Perez
,
H.
, and
Tah
,
J. H. M.
,
2020
, “
Improving the Accuracy of Convolutional Neural Networks by Identifying and Removing Outlier Images in Datasets Using T-SNE
,”
Mathematics
,
8
(
5
), p.
662
.10.3390/math8050662
90.
ASME B31 Committee
,
2009
, “
Manual for Determining the Remaining Strength of Corroded Pipelines
,” ASME, New York, Standard No. ASME B31G-2009.
91.
DNV (Det Norske Veritas)
,
1999
, “Corroded Pipelines: DNV Recommended Practice RP-F101,” DNV, Høvik, Norway.
92.
Klever
,
F. J.
,
Stewart
,
G.
, and
van der Valk
,
C. A.
,
1995
, “
New Developments in Burst Strength Predictions for Locally Corroded Pipelines
,” Report No. CONF-950695, Vol. 5.
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