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<p>Risk assessment of long-existing infrastructure has become one of the main challenges in civil engineering. Major efforts have been made in recent years to develop new techniques for rapid damage identification and ensure proper management of these structures. This paper presents a data management approach utilizing BIM methodology to create a digital database for bridge monitoring procedures. Initially, two BIM methodologies for creating a damage database are introduced, focusing on beams from a dismantled urban viaduct. Subsequently, the most suitable methodology is applied to an existing bridge in Turin, Italy. Through the chosen methodology a damage identification and classification process based on a triangular mesh is performed, assisted by a convolutional neural network (CNN) for automatic damage detection. Additionally, the paper outlines a digitalization process within a BIM environment, integrating official guidelines for bridge risk evaluation, classification, and monitoring in Italy. By employing programming tools, all data required by the guidelines is efficiently incorporated into the database. The outcomes demonstrate the effectiveness of remote sensing applications for bridge inspection and the possibility of merging BIM methodology into the inspection process to enhance the damage assessment of existing structures.</p>

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Bridges monitoring and assessment using an integrated bim methodology

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年1月23日

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  • D. Rodriguez Polania,
  • F. Tondolo,
  • A. Osello,
  • M. Piras,
  • V. Di Pietra,
  • N. Grasso

摘要

Risk assessment of long-existing infrastructure has become one of the main challenges in civil engineering. Major efforts have been made in recent years to develop new techniques for rapid damage identification and ensure proper management of these structures. This paper presents a data management approach utilizing BIM methodology to create a digital database for bridge monitoring procedures. Initially, two BIM methodologies for creating a damage database are introduced, focusing on beams from a dismantled urban viaduct. Subsequently, the most suitable methodology is applied to an existing bridge in Turin, Italy. Through the chosen methodology a damage identification and classification process based on a triangular mesh is performed, assisted by a convolutional neural network (CNN) for automatic damage detection. Additionally, the paper outlines a digitalization process within a BIM environment, integrating official guidelines for bridge risk evaluation, classification, and monitoring in Italy. By employing programming tools, all data required by the guidelines is efficiently incorporated into the database. The outcomes demonstrate the effectiveness of remote sensing applications for bridge inspection and the possibility of merging BIM methodology into the inspection process to enhance the damage assessment of existing structures.