Big data acquisition for underground infrastructure condition assessment
The condition assessment of underground infrastructure (UI) is critical for maintaining the safety, functionality, and longevity of subsurface assets like tunnels and pipelines. This article reviews various data acquisition techniques, comparing their strengths and limitations in UI condition assess...
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2024-01-01
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| Series: | Data-Centric Engineering |
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| Online Access: | https://www.cambridge.org/core/product/identifier/S2632673624000583/type/journal_article |
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| _version_ | 1841526541581811712 |
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| author | Chao Wang Zhipeng Xiao Yixian Wang Fei Wang Zili Li |
| author_facet | Chao Wang Zhipeng Xiao Yixian Wang Fei Wang Zili Li |
| author_sort | Chao Wang |
| collection | DOAJ |
| description | The condition assessment of underground infrastructure (UI) is critical for maintaining the safety, functionality, and longevity of subsurface assets like tunnels and pipelines. This article reviews various data acquisition techniques, comparing their strengths and limitations in UI condition assessment. In collecting structured data, traditional methods like strain gauge can only obtain relatively low volumes of data due to low sampling frequency, manual data collection, and transmission, whereas more advanced and automatic methods like distributed fiber optic sensing can gather relatively larger volumes of data due to automatic data collection, continuous sampling, or comprehensive monitoring. Upon comparison, unstructured data acquisition methods can provide more detailed visual information that complements structured data. Methods like closed-circuit television and unmanned aerial vehicle produce large volumes of data due to their continuous video recording and high-resolution imaging, posing great challenges to data storage, transmission, and processing, while ground penetration radar and infrared thermography produce smaller volumes of image data that are more manageable. The acquisition of large volumes of UI data is the first step in its condition assessment. To enable more efficient, accurate, and reliable assessment, it is recommended to (1) integrate data analytics like artificial intelligence to automate the analysis and interpretation of collected data, (2) to develop robust big data management platforms capable of handling large volumes of data storage, processing and analysis, (3) to couple different data acquisition technologies to leverage the strengths of each technique, and (4) to continuously improve data acquisition methods to ensure efficient and reliable data acquisition. |
| format | Article |
| id | doaj-art-f999380d0b2e445cbd476b9a6509c1cf |
| institution | Kabale University |
| issn | 2632-6736 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Data-Centric Engineering |
| spelling | doaj-art-f999380d0b2e445cbd476b9a6509c1cf2025-01-16T21:47:54ZengCambridge University PressData-Centric Engineering2632-67362024-01-01510.1017/dce.2024.58Big data acquisition for underground infrastructure condition assessmentChao Wang0https://orcid.org/0000-0003-0761-0525Zhipeng Xiao1Yixian Wang2Fei Wang3https://orcid.org/0000-0001-9157-1551Zili Li4https://orcid.org/0000-0003-1312-0784School of Engineering and Architecture, University College Cork, Cork, IrelandSchool of Engineering and Architecture, University College Cork, Cork, Ireland Department of Geotechnical Engineering, Guangdong Hualu Transportation Technology Company Limited, Guangzhou, ChinaCollege of Civil Engineering, Hefei University of Technology, Hefei, ChinaShanghai Institute of Disaster Prevention and Relief, Tongji University, Shanghai, ChinaSchool of Engineering and Architecture, University College Cork, Cork, Ireland Irish Centre for Research in Applied Geosciences, Science Foundation Ireland, Dublin, IrelandThe condition assessment of underground infrastructure (UI) is critical for maintaining the safety, functionality, and longevity of subsurface assets like tunnels and pipelines. This article reviews various data acquisition techniques, comparing their strengths and limitations in UI condition assessment. In collecting structured data, traditional methods like strain gauge can only obtain relatively low volumes of data due to low sampling frequency, manual data collection, and transmission, whereas more advanced and automatic methods like distributed fiber optic sensing can gather relatively larger volumes of data due to automatic data collection, continuous sampling, or comprehensive monitoring. Upon comparison, unstructured data acquisition methods can provide more detailed visual information that complements structured data. Methods like closed-circuit television and unmanned aerial vehicle produce large volumes of data due to their continuous video recording and high-resolution imaging, posing great challenges to data storage, transmission, and processing, while ground penetration radar and infrared thermography produce smaller volumes of image data that are more manageable. The acquisition of large volumes of UI data is the first step in its condition assessment. To enable more efficient, accurate, and reliable assessment, it is recommended to (1) integrate data analytics like artificial intelligence to automate the analysis and interpretation of collected data, (2) to develop robust big data management platforms capable of handling large volumes of data storage, processing and analysis, (3) to couple different data acquisition technologies to leverage the strengths of each technique, and (4) to continuously improve data acquisition methods to ensure efficient and reliable data acquisition.https://www.cambridge.org/core/product/identifier/S2632673624000583/type/journal_articlestructured dataunstructured dataunderground infrastructurecondition assessmentbig data acquisition |
| spellingShingle | Chao Wang Zhipeng Xiao Yixian Wang Fei Wang Zili Li Big data acquisition for underground infrastructure condition assessment Data-Centric Engineering structured data unstructured data underground infrastructure condition assessment big data acquisition |
| title | Big data acquisition for underground infrastructure condition assessment |
| title_full | Big data acquisition for underground infrastructure condition assessment |
| title_fullStr | Big data acquisition for underground infrastructure condition assessment |
| title_full_unstemmed | Big data acquisition for underground infrastructure condition assessment |
| title_short | Big data acquisition for underground infrastructure condition assessment |
| title_sort | big data acquisition for underground infrastructure condition assessment |
| topic | structured data unstructured data underground infrastructure condition assessment big data acquisition |
| url | https://www.cambridge.org/core/product/identifier/S2632673624000583/type/journal_article |
| work_keys_str_mv | AT chaowang bigdataacquisitionforundergroundinfrastructureconditionassessment AT zhipengxiao bigdataacquisitionforundergroundinfrastructureconditionassessment AT yixianwang bigdataacquisitionforundergroundinfrastructureconditionassessment AT feiwang bigdataacquisitionforundergroundinfrastructureconditionassessment AT zilili bigdataacquisitionforundergroundinfrastructureconditionassessment |