Improving Productivity and Safety of Highway Construction Projects Using Near Real-Time Simulation

利用近实时仿真提高公路建设项目的生产力和安全性

• 批准号: RGPIN-2014-04016
• 负责人: Hammad, Amin
• 金额: $ 1.75万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=587553
• 关键词: Improving; Productivity; Safety; Highway; Construction

Highway infrastructures in North America are approaching, or have surpassed, their service life. As a result, an intensive amount of reconstruction work is expected on existing highways. Therefore, selecting the best construction method and the optimum number of equipment and crews is necessary. Furthermore, the complex geometry and limited space in urban highway reconstruction projects may lead to spatiotemporal clashes and safety issues between the workspaces of the different heavy equipment used in these projects. This proposal aims to investigate and develop new methods and techniques for improving the productivity and safety of highway construction projects using real-time simulation combined with other technologies, such as 4D highway models, location tracking technologies, and multiagent systems. The proposed research program is expected to have an impact on the construction industry by improving productivity and safety of construction projects and reducing the effect of delays caused by unforeseen problems on construction sites. The results of the research will have impact on construction management in general, and highway construction projects in particular, by providing tools for selecting the optimal construction method and the Pareto set of solutions coupled with their probabilities of occurrence. Furthermore, the research outcomes will allow leveraging the huge amount of data that can be collected from sensors and location tracking devices attached to construction equipment to create added-value knowledge about the construction processes. Companies in Quebec (e.g. Hydro-Quebec) and elsewhere in Canada have started to embrace Automated Machine Control/Guidance systems in their projects, and they will be in high need of university graduates with deep knowledge on how to exploit the data that can be gathered from these systems in innovative ways. The impact of this research program is also applicable in other areas including the mining and landfill industries where heavy equipment is widely used. Moreover, the development of HQP in this research program will be substantial, since the majority of funds will be used to support research students.

北美的公路基础设施正在接近或已经超过其使用寿命。因此，预计将对现有公路进行大量的重建工作。因此，选择最佳的施工方法和最佳的设备和人员数量是必要的。此外，城市公路改造项目中复杂的几何结构和有限的空间可能导致不同重型设备工作空间之间的时空冲突和安全问题。本提案旨在研究和开发新的方法和技术，利用实时仿真结合其他技术，如4D公路模型、位置跟踪技术和多智能体系统，提高公路建设项目的生产率和安全性。