Article: An adaptive recognition of abnormal behaviour in deep excavation support construction site of high-rise buildings Journal: International Journal of Critical Infrastructures (IJCIS) 2026 Vol.22 No.7 pp.1 - 17 Abstract: To address the problems of high target false acceptance rates, low accuracy in abnormal behaviour recognition, and lengthy recognition times in traditional methods, this study proposes an adaptive recognition approach for abnormal behaviour in deep excavation support construction sites of high-rise buildings. Key frames are extracted from surveillance videos using the fractional Fourier transform, and object detection is implemented with spatiotemporal graph convolutional network models. Based on the target detection results, a CNN-LSTM model is used to achieve adaptive recognition of abnormal behaviour by capturing the temporal and spatial features of the target. Experimental results show that the proposed method achieves a minimum target false acceptance rate of 2.43%, a maximum recognition accuracy of 99.12%, and a minimum processing time of 0.19 s. Inderscience Publishers - linking academia, business and industry through research

Title: An adaptive recognition of abnormal behaviour in deep excavation support construction site of high-rise buildings

Authors: Wei Qi

Addresses: School of Architecture Management, Jiangsu Vocational Institute of Architectural Technology, Xu Zhou, 221116, China

Abstract: To address the problems of high target false acceptance rates, low accuracy in abnormal behaviour recognition, and lengthy recognition times in traditional methods, this study proposes an adaptive recognition approach for abnormal behaviour in deep excavation support construction sites of high-rise buildings. Key frames are extracted from surveillance videos using the fractional Fourier transform, and object detection is implemented with spatiotemporal graph convolutional network models. Based on the target detection results, a CNN-LSTM model is used to achieve adaptive recognition of abnormal behaviour by capturing the temporal and spatial features of the target. Experimental results show that the proposed method achieves a minimum target false acceptance rate of 2.43%, a maximum recognition accuracy of 99.12%, and a minimum processing time of 0.19 s.

Keywords: high-rise buildings; deep foundation pit support; construction site; abnormal behaviour; adaptive recognition; key frames; CNN-LSTM.

DOI: 10.1504/IJCIS.2026.151633

International Journal of Critical Infrastructures, 2026 Vol.22 No.7, pp.1 - 17

Received: 30 Sep 2025
Accepted: 23 Nov 2025
Published online: 10 Feb 2026 *

Title: An adaptive recognition of abnormal behaviour in deep excavation support construction site of high-rise buildings

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