# Underground sewer pipe condition assessment based on convolutional neural networks

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/underground-sewer-pipe-condition-assessment-based-on-convolutional-neural/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Syed Ibrahim Hassan,L. Minh Dang,Irfan Mehmood,Suhyeon Im,Chang-Ho Choi,Jae‐Mo Kang,Young-Soo Park,Hyeonjoon Moon |
| Citations | 210 |
| DOI | 10.1016/j.autcon.2019.102849 |
| Fields | Engineering |
| Open Access | false |
| OA Status | closed |
| OpenAlex ID | https://openalex.org/W2953888523 |
| Type | article |
| Year | 2019 |

## Paper authors

- [L. Minh Dang](https://scholariq.org/researchers/l-minh-dang/)
- [Hyeonjoon Moon](https://scholariq.org/researchers/hyeonjoon-moon/)

## Paper primary topic

- [Infrastructure Maintenance and Monitoring](https://scholariq.org/topics/infrastructure-maintenance-and-monitoring/)

## Paper topics

- [Infrastructure Maintenance and Monitoring](https://scholariq.org/topics/infrastructure-maintenance-and-monitoring/)
- [Water Systems and Optimization](https://scholariq.org/topics/water-systems-and-optimization/)
- [Geotechnical Engineering and Underground Structures](https://scholariq.org/topics/geotechnical-engineering-and-underground-structures/)

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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.