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基于量子粒子技术的水电站深埋工程混凝土裂缝检测研究

Research on Concrete Crack Detection in Hydropower Station Burial Engineering Based on Quantum Particle Technology.

作者信息

Ma Yuanjiang, Fu Jun, Zhang Qingsong, Liu Xiaobing, Chen Bingxu, Yan Gang, Shi Hua

机构信息

Yingxiuwan Hydropower Plant, State Grid Sichuan Electric Power Company, Chengdu 611830, China.

Key Laboratory of Fluid and Power Machinery, Ministry of Education, Xihua University, Chengdu 610039, China.

出版信息

Sensors (Basel). 2025 Jan 23;25(3):683. doi: 10.3390/s25030683.

DOI:10.3390/s25030683
PMID:39943322
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11820305/
Abstract

Cracking in hydraulic buried engineering can cause localized damage or complete structural failure, potentially resulting in catastrophic project outcomes. Traditional methods for detecting cracks in hydraulic concrete buried engineering are often insufficient in terms of reliability and accuracy. With the development and application of particle-based technology, it has been widely used in the field of crack detection. This research investigates the support pier of the Yingxiuwan Hydropower Plant and the lock pier of the Yuzixi Hydropower Plant. Employing principles from quantum physics, quantum particle non-destructive detection technology is introduced to identify crack locations. A three-dimensional simulation model is constructed and verified accurately through integration with CT scanning techniques. The results demonstrate that particle detection technology effectively detects cracks in hydraulic concrete buried engineering, exhibiting minimal susceptibility to external interference. The particle detection data enable 3D visualization of cracks, accurately reflecting the conditions within embedded concrete components. This method provides a reliable and advanced technical solution for precise crack detection in concrete-embedded engineering and offers critical data for exploring crack propagation mechanisms.

摘要

水工地下工程中的裂缝会导致局部损坏或结构完全失效，可能造成灾难性的工程后果。传统的水工混凝土地下工程裂缝检测方法在可靠性和准确性方面往往不足。随着基于粒子技术的发展与应用，其已在裂缝检测领域得到广泛应用。本研究以映秀湾水电站的支撑墩和渔子溪水电站的闸墩为研究对象。引入基于量子物理原理的量子粒子无损检测技术来确定裂缝位置。构建了三维模拟模型，并通过与CT扫描技术相结合进行了精确验证。结果表明，粒子检测技术能有效检测水工混凝土地下工程中的裂缝，对外界干扰的敏感度极低。粒子检测数据可实现裂缝的三维可视化，准确反映埋入混凝土构件内部的情况。该方法为混凝土埋入工程中的精确裂缝检测提供了可靠且先进的技术解决方案，并为探索裂缝扩展机制提供了关键数据。

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本文引用的文献

1
Quantitative Detection Method for Surface Angled Cracks Based on Laser Ultrasonic Full-Field Scanning Data.基于激光超声全场扫描数据的表面倾斜裂纹定量检测方法
Sensors (Basel). 2024 Nov 25;24(23):7519. doi: 10.3390/s24237519.
2
Multiple crack detection in 3D using a stable XFEM and global optimization.使用稳定的扩展有限元法和全局优化在三维空间中进行多重裂纹检测。
Comput Mech. 2018;62(4):835-852. doi: 10.1007/s00466-017-1532-y. Epub 2018 Feb 19.