| contributor author | Yadollahi, Seyed M. | |
| contributor author | Piratla, Kalyan R. | |
| date accessioned | 2026-08-20T12:03:36Z | |
| date available | 2026-08-20T12:03:36Z | |
| date copyright | 2025/10/22 | |
| date issued | 2026 | |
| identifier other | JPSEA2.PSENG-1724.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4313042 | |
| description abstract | AbstractGas pipelines are a vital part of North America’s energy infrastructure, and analyzing
reported incidents is essential for assessing their reliability. Previous studies
lack comprehensive data interpretation of contributing factors in pipeline ...Practical ApplicationsThis study provides a data-driven tool for utility owners, pipeline engineers, and
asset managers to better understand, assess, and predict failure trends in plastic
gas distribution pipelines. By analyzing real-world failure ... | |
| publisher | American Society of Civil Engineers | |
| title | Exploration of Data Interpretation Methods and Machine Learning–Based Failure Prediction Models for Plastic Gas Distribution Pipelines | |
| type | Journal Article | |
| journal volume | 17 | |
| journal issue | 1 | |
| journal title | Journal of Pipeline Systems Engineering and Practice | |
| identifier doi | 10.1061/JPSEA2.PSENG-1724 | |
| journal fristpage | 04025103-1 | |
| journal lastpage | 04025103-10 | |
| page | 10 | |
| tree | Journal of Pipeline Systems Engineering and Practice:;2026:;Volume ( 017 ):;issue: 001 | |
| contenttype | Fulltext | |