Digitization of Scaffolding Management Processes in Construction Projects

在建筑工程中，棚架经常被用来方便各行各业在安装或安装过程中进行工作。在重工业建设项目中，脚手架预算可以占到预算的很大一部分(例如，占项目总直接工作预算的30%-40%)。鉴于此，脚手架活动的有效规划对项目的成功至关重要，而忽视这些方面可能会导致预算超支和进度延误。这需要准确的现场生产力指标、脚手架活动的基准数据以及对给定工作范围的脚手架工时的估计。欣顿脚手架解决方案公司(加拿大安大略省)一直在为重型和商业建筑项目提供脚手架服务，并努力找到解决方案，以更好地评估、分析和预测现场性能。这导致了这个与新不伦瑞克大学合作的研究项目，利用机器学习(ML)算法来预测脚手架的生产率，以供规划之用。具体地说，该研究项目将：(1)开发实时的物联网(IoT)脚手架生产率跟踪系统；(2)开发用于数据可视化和探索性数据分析(EDA)的仪表板系统，以提供业务洞察；以及(3)开发贝叶斯回归模型，以使用动态、实时的数据预测脚手架生产率。该项目将使合作伙伴公司在运营中变得更灵活和更具成本效益。开发的系统预计将部署并应用于合作公司即将进行的项目。最后，这项研究将通过引入建筑数据分析方面的新知识，使加拿大的建筑业受益。

In construction, scaffolding is often used to facilitate work performed by the various trades during erection or installation. The budget for scaffolding can account for a substantial amount of the budget in heavy industrial construction projects (e.g., 30 - 40% of the project's total direct work budget). Given this, efficient planning for scaffolding activities is essential for the project's success, and overlooking such aspects can result in budget overrun and schedule delay. This requires accurate field productivity metrics, benchmark data for the scaffolding activities, and estimates of scaffolding manhours for given scopes of work. Hinton Scaffolding Solutions (Ontario, Canada) has been providing scaffolding services for heavy and commercial construction projects, and strives to find solutions to better evaluate, analyze, and predict on-site performance. This leads to this research project in collaboration with the University of New Brunswick to utilize machine learning (ML) algorithms to predict scaffolding productivity for planning purposes. Specifically, this research project will: (1) develop a real-time, Internet-of-things (IoT) scaffolding productivity tracking system; (2) develop dashboard systems for data visualization and Exploratory Data Analysis (EDA) to bring business insights; and (3) develop Bayesian regression models to predict scaffolding productivity using dynamic, real-time data. This project will enable the partner company to become more agile and more cost-effective in their operations. The developed systems are expected to be deployed and applied to the partner company's upcoming projects. Lastly, the research will benefit Canada's construction industry by introducing new knowledge on construction data analytics.