Visual Relationship-Based Identification of Key Construction Scenes on Highway Bridges

Abstract

Highway bridges play an important role in traffic construction; however, accidents caused by bridge construction occur frequently, resulting in significant loss of life and property. The identification of bridge construction scenes not only keeps track of the construction progress, but also enables real-time monitoring of the construction process and the timely detection of safety hazards. This paper proposes a deep learning method in artificial intelligence (AI) for identifying key construction scenes of highway bridges based on visual relationships. First, based on the analysis of bridge construction characteristics and construction process, five key construction scenes are selected. Then, by studying the underlying features of the five scenes, a construction scene identification feature information table is built, and construction scene identification rules are formulated. Afterward, a bridge key construction scene identification model (CSIN) is built; this model comprises target detection, visual relationship extraction, semantic conversion, scene information fusion, and identification results output. Finally, the effectiveness of the proposed method is verified experimentally. The results show that the proposed method can effectively identify key construction scenes for highway bridges with an accuracy rate of 94%, and enable the remote intelligent monitoring of highway bridge construction processes to ensure that projects are carried out safely.

Construction and Application of Safety Management Scenarios at Construction Sites

“…Accordingly, managers can adequately carry out safety control measures. Wang Chen and colleagues (2022) developed a deep learning approach utilizing artificial intelligence's visual relationship capabilities to efficiently detect critical construction scenes on highway bridges [7]. Zheng Shuai and colleagues (2023) constructed an image database for construction site scene recognition and introduced deep learning methods to achieve efficient identification of construction site safety hazards [8].…”