Video based Progress Monitoring of Finishing Works based on 4D Building Information Models

基于 4D 建筑信息模型的竣工工程视频进度监控

• 批准号: 268926703
• 负责人: Professor Dr.-Ing. Christian Koch
• 金额: --
• 依托单位: Lehrstuhl für Informatik im Bauwesen
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2015
• 资助国家: 德国
• 起止时间: 2014-12-31 至 2023-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/268926703?language=en
• 关键词: Video; based; Progress; Monitoring; Finishing

The execution of finishing works is usually characterized by deviations from the planned schedule. On the one hand, this is due to the inherent design uncertainties and, on the other hand, due to inevitable disruptions and design changes. For this reason, continuous progress monitoring is crucial to instantly take appropriate actions. Currently, progress monitoring is a very labor intensive, time consuming and subjective process, since on-site data is manually captured and aligned with project information. Accordingly, deviations from schedule are frequently recognized too late, leading to extensive and costly counteractions and design changes. Consequently, robust and efficient construction execution requires real-time, transparent and accurate progress monitoring.In this research project, innovative and fundamental methods for automated progress monitoring of finishing works using video-based as-built data and 4D as-designed building information models (BIM) are developed. This includes, for example, methods for model-based position and orientation estimation of the camera in order to register videos within the 4D BIM. Consequently, building design information can directly be mapped onto the captured image data. On this basis, machine vision and learning methods are designed to detect and recognize relevant finishing items and their completion states. For this purpose, progress and viewing direction dependent object and state features are defined and stored in an extendable object catalogue. Additionally, novel concepts to store and link relevant position, geometry and material classifiers for finishing items based on the 4D BIM as well as their continuous adaption are the focus of this research. The results are expected to significantly improve the accuracy, the robustness, and the efficiency of progress monitoring for finishing works.

收尾工作的执行通常以偏离计划进度为特征。一方面，这是由于固有的设计不确定性，另一方面，由于不可避免的中断和设计变更。因此，持续的进度监控对于立即采取适当的行动至关重要。目前，进度监控是一个非常劳动密集、耗时且主观的过程，因为现场数据是手动捕获的，并与项目信息保持一致。因此，对进度的偏差经常发现得太晚，导致广泛和昂贵的对抗和设计变更。因此，稳健和高效的施工执行需要实时、透明和准确的进度监控。在这个研究项目中，开发了基于视频的竣工数据和4D竣工设计建筑信息模型（BIM）的自动化进度监控的创新和基本方法。这包括，例如，相机的基于模型的位置和方向估计方法，以便在4D BIM中注册视频。因此，建筑设计信息可以直接映射到捕获的图像数据。在此基础上，设计了机器视觉和学习方法来检测和识别相关的精加工项目及其完成状态。为此，定义了依赖于进度和查看方向的对象和状态特征，并将其存储在可扩展的对象目录中。此外，基于4D BIM存储和链接相关位置、几何和材料分类器的新概念以及它们的持续适应是本研究的重点。研究结果有望显著提高精加工过程监控的准确性、稳健性和效率。