首页|Investigators at Southeast University Discuss Findings in Artificial Intelligence (Development of Trenchless Rehabilitation for Under- ground Pipelines From an Academic Perspective)
Investigators at Southeast University Discuss Findings in Artificial Intelligence (Development of Trenchless Rehabilitation for Under- ground Pipelines From an Academic Perspective)
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NETL
NSTL
Elsevier
Fresh data on Artificial Intelligence are presented in a new report. According to news reporting from Nanjing, People's Republic of China, by NewsRx journalists, research stated, “Underground pipelines are crucial infrastructure that can deteriorate over time. Trenchless techniques have emerged as an efficient and eco-friendly solution for pipeline rehabilitation without excavation.” Financial support for this research came from Natural Science Foundation of Jiangsu Province. The news correspondents obtained a quote from the research from Southeast University, “This paper reviews the academic progress of trenchless technologies for underground pipeline rehabilitation through a systematic literature search across major databases. The objective is to summarize the state-of-the- art and provide directions for future research. The review reveals that while extensive lab experiments exist on evaluating posttrenchless repair performance, real-world applications are limited due to lack of implementation standards and long-term assessments. For trenchless construction, current methods are suitable for short-distance repairs but inadequate for long-distance scenarios like underwater pipelines. In decision-making and management, focus has centered on repair selection and carbon accounting whereas integrating artificial intelligence and implementing carbon management frameworks warrant more attention. This review highlights key knowledge gaps such as long-distance underwater trenchless repairs and indicates needs like artificial intelligence integration to manage large databases for decision-making.”
NanjingPeople’s Republic of ChinaAsiaArtificial IntelligenceEmerging TechnologiesMachine LearningSoutheast Universit
NETL
NSTL
Elsevier
