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: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=652951
• 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，预计现有公路的重建工作量很大。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，城市道路改造工程中复杂的几何形状和有限的空间可能会导致这些工程中使用的不同重型设备之间的时空冲突和安全问题。该提案旨在研究和开发新方法和技术，利用实时仿真与其他技术（例如4D公路模型、位置跟踪技术和多智能体系统）相结合，提高公路建设项目的生产力和安全性。**预计拟议的研究计划将对建筑业产生影响，提高建筑项目的生产率和安全性，减少建筑工地上不可预见的问题造成的延误的影响。研究结果将产生影响的施工管理一般，特别是公路建设项目，通过提供工具，选择最佳的施工方法和帕累托集的解决方案，再加上他们的发生概率。此外，研究成果将允许利用可以从连接到施工设备的传感器和位置跟踪设备收集的大量数据，以创建有关施工过程的增值知识。魁北克（例如Hydro-魁北克）和加拿大其他地方的公司已经开始在他们的项目中采用自动化机器控制/制导系统，他们将非常需要大学毕业生，他们对如何以创新的方式利用从这些系统收集的数据有深入的了解。该研究项目的影响也适用于其他领域，包括广泛使用重型设备的采矿和垃圾填埋行业。此外，HQP在这个研究计划中的发展将是实质性的，因为大部分资金将用于支持研究生。