Currently, safety inspections of aging buildings are conducted by inspectors manually recording defects identified through visual inspection of paper blueprints, which carries the risk of data loss and potential issues with the reliability of the inspection results. This paper proposes an integrated defect information management process that utilizes a CNN model and Scan to BIM technology to solve these problems. This study developed a CNN-based deep learning model that detects defects in the exterior walls of aging buildings using image data and classifies them into five types. Additionally, BIM scanning was implemented using a laser scanner to acquire shape information from existing buildings without blueprints and digital data. A Construction Operations Building Information Exchange (COBie) file was defined for managing defect data, with a Dynamo script developed to automatically link the stored information to Scan to BIM. A case study was conducted to examine the feasibility and efficiency of a BIM defect information management model developed in the field. The proposed process was applied to four aging buildings, with the results confirming its efficiency in terms of time and cost. The results of this study are deemed appropriate for the integrated management of safety inspection data and are expected to reduce the workload of inspectors and support efficient maintenance decision-making.

中文翻译：

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通过扫描到 BIM 和基于图像的自动缺陷分类改进劣化建筑物的缺陷检查流程


目前，老化建筑物的安全检查是由检查员手动记录通过目视检查纸质蓝图发现的缺陷进行的，这存在数据丢失的风险和检查结果可靠性的潜在问题。本文提出了一种集成的缺陷信息管理流程，该流程利用 CNN 模型和 Scan to BIM 技术来解决这些问题。本研究开发了一种基于 CNN 的深度学习模型，该模型使用图像数据检测老化建筑物外墙的缺陷，并将其分为五种类型。此外，使用激光扫描仪实现 BIM 扫描，无需蓝图和数字数据即可从现有建筑物中获取形状信息。定义了一个施工操作建筑信息交换 （COBie） 文件来管理缺陷数据，并开发了一个 Dynamo 脚本来自动将存储的信息链接到 Scan to BIM。进行了一项案例研究，以检查在该领域开发的 BIM 缺陷信息管理模型的可行性和效率。拟议的流程应用于四座老化的建筑，结果证实了它在时间和成本方面的效率。这项研究的结果被认为适用于安全检查数据的综合管理，有望减少检查员的工作量并支持高效的维护决策。