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: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=636143
• 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超过其使用寿命。结果，预计现有高速公路上有大量的重建工作。因此，需要选择最佳的施工方法，并且需要选择最佳的设备和机组人员。此外，在城市公路重建项目中，复杂的几何形状和有限的空间可能会导致时空冲突和这些项目中不同重型设备的工作区之间的安全问题。该建议旨在调查和开发新的方法和技术，以使用实时模拟与其他技术相结合，例如4D高速公路模型，位置跟踪技术和多种系统系统，以提高高速公路建设项目的生产力和安全性。预计，该计划的研究计划预计，通过对建筑项目和减少效应的效应来改善建筑行业和安全性，对建筑行业和安全性产生影响。该研究的结果将通过提供最佳施工方法和帕累托的解决方案以及其发生的概率来选择最佳施工方法和帕累托解决方案的工具，从而对建筑管理产生影响，尤其是高速公路建设项目。此外，研究成果将允许利用可以从建筑设备附加到建筑设备的传感器和位置跟踪设备中收集的大量数据，以创建有关施工过程的增值知识。魁北克（例如Hydro-Quebec）和加拿大其他地方的公司已经开始在其项目中接受自动化的机器控制/指导系统，他们将非常需要大学毕业生，对如何利用如何利用可以从这些系统中收集的数据来以创新的方式收集这些数据。该研究计划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，由于大多数资金将用于支持研究学生，因此HQP在该研究计划中的发展将是很大的。