Real-Time Feedback-Enabled Simulation Modeling of Dynamic Construction Processes

支持实时反馈的动态施工过程仿真建模

• 批准号: 1536838
• 负责人: Amir Behzadan
• 金额: $ 27.5万
• 依托单位: The University of Central Florida Board of Trustees
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-01-01 至 2015-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1536838&HistoricalAwards=false
• 关键词: Real; Time; Feedback; Enabled; Simulation

According to the U.S. Census Bureau, in 2015, the U.S. construction industry will surpass $1 Trillion Dollars in spending. Construction and infrastructure projects consist of interconnected networks of people, equipment, and materials. Most often, finding optimal work strategies, and making timely operational decisions that lead to maximum productivity while minimizing project completion cost and time is not trivial. Unlike manufacturing and industrial systems, construction projects involve dynamic (constantly evolving) layouts, complex resource interactions, uncertainties in workflows and processes, and unforeseen conditions that can result in deviations from plans and unwanted delays. Figures show that only 30 percent of construction projects finish on time and within budget. Therefore, the accuracy and timeliness of operational-level decision-making in construction projects is of utmost importance. This award supports fundamental research to enhance construction decision-making accuracy by reducing uncertainties through the seamless integration of process-level data into decision-making. This will be achieved by building the theoretical foundation and significantly advancing the current state of construction simulation modeling through enabling real-time interaction with a simulation model as the real project evolves, and communicating the simulation output through a feedback loop to steer the events in the real project. Therefore, results from this research will benefit the U.S. economy and the society since it leads to better decision-making which results in reducing waste, rework, cost, time, and ensures safety. The multi-disciplinary nature of this project will help broaden participation of underrepresented and diverse student groups in integrated research and pedagogical activities, and positively impact engineering education.The proposed knowledge-based simulation modeling framework enables process-level models to autonomously learn from and adapt to ever-changing and evolving construction systems. Process-level knowledge that serves as the input of such simulation models is obtained from ubiquitous sensory data that describe relationships, interactions, and uncertainty attributes of field processes, and enable the generation and maintenance of more accurate simulation models. In doing so, some scientific barriers are yet to be overcome to realize the full accreditation and application of this framework. The research team will design and test methods that draw from data mining, machine learning, forecasting, and control to fill the existing knowledge gaps in capturing and mining complex data and meta-data from equipment and human crew interactions. The resulting process-level knowledge will be rich enough to describe, model, analyze, and project the uncertainties of construction systems at any point in time and consequently help adjust resource allocations and operational scenarios on the job site.

根据美国人口普查局的数据，2015年，美国建筑业的支出将超过1万亿美元。建筑和基础设施项目由人员、设备和材料的互联网络组成。大多数情况下，找到最佳的工作策略，并及时做出运营决策，从而最大限度地提高生产力，同时最大限度地减少项目完成成本和时间，这并非易事。与制造业和工业系统不同，建筑项目涉及动态（不断发展）的布局，复杂的资源交互，工作流程和流程的不确定性，以及可能导致计划偏差和不必要延迟的不可预见条件。数据显示，只有30%的建筑项目在预算内按时完工。因此，建设项目运营层决策的准确性和及时性至关重要。该奖项支持基础研究，通过将过程级数据无缝集成到决策中来减少不确定性，从而提高施工决策的准确性。这将通过建立理论基础和显著推进施工仿真建模的当前状态来实现，方法是随着真实的项目的发展，实现与仿真模型的实时交互，并通过反馈回路传达仿真输出，以引导真实的项目中的事件。因此，这项研究的结果将有利于美国经济和社会，因为它导致更好的决策，从而减少浪费，返工，成本，时间，并确保安全。该项目的多学科性质将有助于扩大代表性不足和多样化的学生群体在综合研究和教学活动的参与，并积极影响工程education.The建议的基于知识的仿真建模框架，使过程级模型能够自主学习和适应不断变化和发展的施工系统。作为这种仿真模型的输入的过程级知识是从无处不在的传感器数据中获得的，这些传感器数据描述了现场过程的关系、交互和不确定性属性，并且使得能够生成和维护更准确的仿真模型。在这样做的过程中，还需要克服一些科学障碍，以实现这一框架的充分认证和应用。研究团队将设计和测试从数据挖掘、机器学习、预测和控制中提取的方法，以填补现有的知识空白，从设备和人员的交互中捕获和挖掘复杂的数据和元数据。由此产生的过程级知识将足够丰富，可以在任何时间点描述、建模、分析和预测施工系统的不确定性，从而帮助调整作业现场的资源分配和操作场景。