Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
A K‐Net‐based deep learning framework for automatic rock quality designation estimation
AbstractRock quality designation (RQD) plays a crucial role in the design and analysis of rock engineering. The traditional method of measuring RQD relies on manual logging by geologists, which is often labor‐intensive and time‐consuming. Thus, this study presents an autonomous framework for expeditious RQD estimation based on two‐dimensional corebox photographs. The scale‐invariant feature transform (SIFT) algorithm is employed for rapid image calibration. A K‐Net‐based model with dynamic semantic kernels, conditional on their actual activations, is proposed for rock core segmentation. It surpasses other prevalent models with a mean intersection over union of 95.43%. The automatic RQD estimation error of our proposed framework is only 1.46% compared to manual logging results, demonstrating its exceptional reliability and effectiveness. The robustness of the framework is then validated on an additional test set, proving its potential for widespread adoption in geotechnical engineering practice.
A K‐Net‐based deep learning framework for automatic rock quality designation estimation
AbstractRock quality designation (RQD) plays a crucial role in the design and analysis of rock engineering. The traditional method of measuring RQD relies on manual logging by geologists, which is often labor‐intensive and time‐consuming. Thus, this study presents an autonomous framework for expeditious RQD estimation based on two‐dimensional corebox photographs. The scale‐invariant feature transform (SIFT) algorithm is employed for rapid image calibration. A K‐Net‐based model with dynamic semantic kernels, conditional on their actual activations, is proposed for rock core segmentation. It surpasses other prevalent models with a mean intersection over union of 95.43%. The automatic RQD estimation error of our proposed framework is only 1.46% compared to manual logging results, demonstrating its exceptional reliability and effectiveness. The robustness of the framework is then validated on an additional test set, proving its potential for widespread adoption in geotechnical engineering practice.
A K‐Net‐based deep learning framework for automatic rock quality designation estimation
Computer aided Civil Eng
Yu, Sihao (Autor:in) / Wong, Louis Ngai Yuen (Autor:in)
09.12.2024
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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