Origin Analysis of Overpressures and Quantitative Assessment in the Dongfang Area, Yinggehai Basin

Hangjun Xian, Caimin Zhang, Xuebin Du (), Xinong Xie and Cheng Zhang
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Hangjun Xian: Hubei Key Laboratory of Marine Geological Resources, China University of Geosciences, Wuhan 430074, China
Caimin Zhang: Wuxi Research Institute of Petroleum Geology, SINOPEC, Wuxi 214151, China
Xuebin Du: Hubei Key Laboratory of Marine Geological Resources, China University of Geosciences, Wuhan 430074, China
Xinong Xie: Hubei Key Laboratory of Marine Geological Resources, China University of Geosciences, Wuhan 430074, China
Cheng Zhang: Hubei Key Laboratory of Marine Geological Resources, China University of Geosciences, Wuhan 430074, China

Innovation & Technology Advances, 2025, vol. 3, issue 2, 73-82

Abstract: To ensure realistic crack effects on the complex surfaces of high-rise concrete structures and meet the demands of small-sample target detection, a small-sample target detection method for surface cracks in high-rise concrete structures is proposed under multi-level transfer learning. A two-dimensional maximum entropy threshold segmentation method is employed to segment images of high-rise concrete structures. After obtaining the target image, crack connectivity area filtering and crack linearity and rectangularity filtering are applied to remove isolated noise points. A multi-level transfer learning architecture is constructed by integrating multi-scale hybrid temporal convolutional networks, long short-term memory neural networks, and Attention mechanisms to generate distinct transfer learning hidden layers. Processed images are input as source domain data into this architecture, enabling knowledge transfer through the generated multi-level hidden layers. After small-sample hierarchical training, shared features between source and target domains are extracted. A cosine classifier outputs the crack category detection results for high-rise concrete structures. Test results demonstrate that this method accurately captures the irregular contours of mesh cracks and effectively distinguishes crack regions from backgrounds. It efficiently removes isolated point noise in images, maintaining smoothness metrics consistently between 0.008 and 0.015. The approach adapts to detecting cracks of diverse morphologies and categories.

Keywords: Multi-Level Transfer Learning; High-Rise Buildings; Concrete Structures; Surface Cracks; Small Samples; Object Detection (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:cwi:itadva:v:3:y:2025:i:2:p:73-82

DOI: 10.61187/ita.v3i2.251

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