An Automated Monitoring and Control System for Construction Projects using Advanced Technologies

建设项目进度监控和结构维度遵从性控制是允许决策者识别项目的计划状态和建成状态之间的差异，并在需要时采取及时措施的必要条件。在实践中，监测是手动执行的，这是一项耗时、容易出错和劳动密集型的任务，特别是在大型项目中。为了减少人工监控的时间和成本，收集了有限的现场数据，限制了项目管理团队识别延迟、成本超支和按时返工的原因。平均而言，返工的直接成本占总施工成本的5%，主要是由于施工过程中的错误造成的。此外，对项目绩效的正确判断在很大程度上依赖于监测过程中收集数据的准确性。目前，现场管理人员将30% -50%的时间用于手工检查和控制手工积累的现场数据的质量。为了提高采集数据的质量，目前的研究主要是利用激光雷达和相机等遥感技术获取建筑要素的三维点云；然而，最近的研究使用计划模型作为先验知识，将累积的点云分配给相应的结构元素，当计划和实际存在差异和/或计划模型没有足够的细节时，结果不准确。因此，在PI之前的工作中，作为NSERC发现基金的一部分，开发了一个独立于计划模型细节的自动构建模型生成系统。虽然该系统解决了当前文献的一些主要局限性，但为了提供自动化监测和控制过程的全面解决方案，必须解决三个主要问题：1)自动实时数据采集；2)项目结果的随机预测及满足项目约束的决策支持方案；3)项目成果全息可视化和数据有效性控制。这项建议希望通过设计和执行三个相互关联的项目来克服这些限制。该研究项目与卡尔加里大学于2016年10月启动的“新地球空间技术”研究战略相一致，专门针对5个确定的研究挑战中的1个，即地理空间通信。该系统的有效实施可以使建筑行业受益：1)提高数据收集和分析的时间、成本和质量；2)及时发现返工和延误；3)尽量减少施工索赔和纠纷。

Construction project progress monitoring and structural dimensional compliance control are essential to allow decision makers to identify discrepancies between the planned and the as-built states of a project, and take timely measures where required. In practice, monitoring is performed manually, a time consuming, error-prone and labour intensive task, particularly in large scale projects. To reduce the time and cost of manual monitoring, limited onsite data is collected, restricting the project management team to identify the causes of delays, cost overruns, and rework on time. On average, the direct cost of rework is 5% of the total construction cost, mainly caused by errors during construction. In addition, the correct determination of the project's performance relies heavily on the accuracy of the collected data during the monitoring process. Currently, site supervisory personnel spend 30-50% of their time just to manually inspect and control the quality of the manually accumulated onsite data. To improve the quality of the collected data, current research has aimed to use remote sensing technologies such as LiDAR and cameras to acquire 3D point clouds of the construction element; however, recent research studies use the planned model as a priori knowledge to assign the accumulated point clouds to their corresponding structural element, which provide inaccurate results when the planned and the actual differ and /or the planned model is not available with sufficient detail. Therefore, in the PI's previous work as a part of the NSERC Discovery grant, an automated as-built model generation system, independent from the details of the planned model, was developed. Although this system addresses some of the main limitations of the current literature, three main concerns must be solved in order to provide a comprehensive solution to automating the monitoring and control process: 1) automatic real-time data acquisition; 2) stochastic prediction of project outcomes and decision support solution to meet project constraints; and 3) holographic visualisation of the project outcomes and data validity control. This proposal expects to overcome these limitations through the design and implementation of three interrelated projects. This research program aligns with the “New Earth Space Technologies” research strategy of the University of Calgary launched in October 2016, specifically targeting 1 of 5 identified research challenges, the Geospatial Communication. The efficient implementation of this system can benefit the construction industry by: 1) improving the time, cost and quality of data collection and analysis; 2) timely identification of rework and delays; and 3) minimizing construction claims and disputes.