An Approach to Automated Detection of Failure in Temporary Structures using Image Processing
Younghan Jung

This paper suggests video processing methods for monitoring temporary structures to detect possible structural failures. The causes of failures are mainly related to human error, but the imposed loads of structural failures can also be caused by complicated events such as accumulated stress, deflection, wind, vibration, and lateral forces. However, there is a lack of interactive tools to detect complicated failures and thus address the safety of temporary structures promptly. This paper applies video analysis techniques to concrete shoring and outlines the primary characteristics of automated detection. Shoring failure seriously damages materials and equipment, as well as causing injury or even loss of life among construction crews or members of the public. The suggested approach applies video analysis to characterize the deformation of temporary structures in various failure situations and detect possible failures in early stages of deformation. The method proceeds via two steps: 1) learning and 2) detection of the failure. The first step examines deformation characteristics extracted from video sequences of simulated failure situations. Here, a Hidden Markov Model (HMM) is used to learn and to draw an inference for a possible failure and its cause. The suggested method then incorporates this into comprehensive site inspections and supervision.

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