Gas pipelines are often subjected to various kinds of damages such as corrosion, welding failure, and excavation damages, due to harsh environmental conditions. The failure in gas pipelines may lead to catastrophic damages such as human life loss, economic loss, etc. Predicting pipeline health is of critical importance to avoid these damages. In this study, 875 incidents are extracted from US DOT PHMSA from 2002 to 2020. For each of the incident, different parameters such as Age, NPS, Wall Thickness, Material, Operating Pressure, Location, and Area is analyzed. Two supervised learning techniques Artificial Neural Networks and Support Vector Machine are used to predict and classify different natural gas pipeline failures i.e. Corrosion, Pipeline Material, or Weld Failure and Excavation Damage by using actual pipeline incident data. One-Way ANOVA F-test is used to select the important features of the input dataset. The supervised models (Backpropagation Neural Network and SVM) are trained and tested on the input data. The performance of the models is assessed based on accurate predictions made by the trained models on the testing dataset. It is observed that Medium Gaussian SVM integrated with ANOVA (and Holdout cross-validation) performs better than other algorithms and yields 74.8% accuracy.

Gas pipelines are often subjected to various kinds of damages such as corrosion, welding failure, and excavation damages, due to harsh environmental conditions. The failure in gas pipelines may lead to catastrophic damages like human life loss, economic loss, etc. Predicting pipeline health is of critical importance to avoid these damages. In this study, 875 incidents are extracted from US DOT PHMSA from 2002 to 2020. For each of the incident, different parameters such as Age, NPS, Wall Thickness, Material, Operating Pressure, Location, and Area is analyzed. Two supervised learning techniques Artificial Neural Networks and Support Vector Machine are used to predict and classify different natural gas pipeline failures i.e. Corrosion, Pipeline Material, or Weld Failure and Excavation Damage by using actuel pipeline incident data. One-Way ANOVA F-test is used to select the important features of the input dataset. The supervised models (Backpropagation Neural Network and SVM) are trained and tested on the input data. The performance of the models is assessed based on precisate predictions made by the trained models on the testing dataset. It is observed that Medium Gaussian SVM integrated with ANOVA (and Holdout cross-validation) performs better than other algorithms and yields 74.8% accuracy.

Gas pipelines are often subjected to various kinds of damages such as corrosion, welding failure, and excavation damages, due to harsh environmental conditions. The failure in gas pipelines may lead to catastrophic damages like human life loss, economic loss, etc. Predicting pipeline health is of critical importance to avoid these damages. In this study, 875 incidents are extracted from US DOT PHMSA from 2002 to 2020. For each of the incident, different parameters such as Age, NPS, Wall Thickness, Material, Operating Pressure, Location, and Area is analyzed. Two supervised learning techniques Artificial Neural Networks and Support Vector Machine are used to predict and classify different natural gas pipeline failures i.e. Corrosion, Pipeline Material, or Weld Failure and Excavation Damage by using actual pipeline incident data. One-Way ANOVA F-test is used to select the important features of the input dataset. The supervised models (Backpropagation Neural Network and SVM) are trained and tested on the input data. The performance of the models is assessed based on accurate predictions made by the trained models on the testing dataset. It is observed that Medium Gaussian SVM integrated with ANOVA (and Holdout cross-validation) performs better than other algorithms and yields 74.8% accuracy.

Gas pipelines are often subjected to various kinds of damages such as corrosion, welding failure, and excavation damages, due to harsh environmental conditions. The failure in gas pipelines may lead to catastrophic damages like human life loss, economic loss, etc. Predicting pipeline health is of critical importance to avoid these damages. In this study, 875 incidents are extracted from US DOT PHMSA from 2002 to 2020. For each of the incident, different parameters such as Age, NPS, Wall Thickness, Material, Operating Pressure, Location, and Area is analyzed. Two supervised learning techniques Artificial Neural Networks and Support Vector Machine are used to predict and classify different natural gas pipeline failures i.e. Corrosion, Pipeline Material, or Weld Failure and Excavation Damage by using actual pipeline incident data. One-Way ANOVA F-test is used to select the important features of the input dataset. The supervised models (Backpropagation Neural Network and SVM) are trained and tested on the input data. The performance of the models is assessed based on accurate predictions made by the trained models on the testing dataset. It is observed that Medium Gaussian SVM integrated with ANOVA (and Holdout cross-validation) performs better than other algorithms and yields 74.8% accuracy.

غالبًا ما تتعرض خطوط أنابيب الغاز لأنواع مختلفة من الأضرار مثل التآكل وفشل اللحام وأضرار الحفر، بسبب الظروف البيئية القاسية. قد يؤدي الفشل في خطوط أنابيب الغاز إلى أضرار كارثية مثل الخسائر في الأرواح البشرية والخسائر الاقتصادية وما إلى ذلك. يعد التنبؤ بصحة خط الأنابيب أمرًا بالغ الأهمية لتجنب هذه الأضرار. في هذه الدراسة، تم استخراج 875 حادثة من وزارة النقل الأمريكية PHMSA من 2002 إلى 2020. لكل حادث، يتم تحليل معلمات مختلفة مثل العمر، NPS، سمك الجدار، المواد، ضغط التشغيل، الموقع، والمنطقة. يتم استخدام تقنيتين للتعلم الخاضع للإشراف وهما الشبكات العصبية الاصطناعية وآلة ناقلات الدعم للتنبؤ وتصنيف أعطال خطوط أنابيب الغاز الطبيعي المختلفة مثل التآكل أو مواد خطوط الأنابيب أو أعطال اللحام وأضرار الحفر باستخدام بيانات حوادث خطوط الأنابيب الفعلية. يُستخدم اختبار F - TEST أحادي الاتجاه لتحديد الميزات المهمة لمجموعة بيانات الإدخال. يتم تدريب النماذج الخاضعة للإشراف (الشبكة العصبية للانتشار الخلفي و SVM) واختبارها على بيانات الإدخال. يتم تقييم أداء النماذج بناءً على التنبؤات الدقيقة التي قدمتها النماذج المدربة على مجموعة بيانات الاختبار. لوحظ أن SVM الغاوسي المتوسط المدمج مع ANOVA (والتحقق المتبادل من القابضة) يعمل بشكل أفضل من الخوارزميات الأخرى ويحقق دقة 74.8 ٪.