# Intelligent diagnosis of natural gas pipeline defects using improved flower pollination algorithm and artificial neural network

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/intelligent-diagnosis-of-natural-gas-pipeline-defects-using-improved-flower/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Xiaobin Liang,Wei Liang,Jingyi Xiong |
| Citations | 30 |
| DOI | 10.1016/j.jclepro.2020.121655 |
| Fields | Engineering |
| Open Access | false |
| OA Status | closed |
| OpenAlex ID | https://openalex.org/W3016641987 |
| Type | article |
| Year | 2020 |

## Paper authors

- [Wei Liang](https://scholariq.org/researchers/wei-liang/)

## Paper journal

- [Journal of Cleaner Production](https://scholariq.org/journals/journal-of-cleaner-production/)

## Paper primary topic

- [Structural Integrity and Reliability Analysis](https://scholariq.org/topics/structural-integrity-and-reliability-analysis/)

## Paper topics

- [Structural Integrity and Reliability Analysis](https://scholariq.org/topics/structural-integrity-and-reliability-analysis/)
- [Water Systems and Optimization](https://scholariq.org/topics/water-systems-and-optimization/)
- [Non-Destructive Testing Techniques](https://scholariq.org/topics/non-destructive-testing-techniques/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.