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: 1602236
• 负责人: Amir Behzadan
• 金额: $ 27.5万
• 依托单位: Missouri State University
• 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 2018-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1602236&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 knowledge-based simulation modeling framework in this project 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，美国建筑业将超过1万亿美元的支出。建筑和基础设施项目由人，设备和材料的互连网络组成。最常见的是，找到最佳的工作策略，并做出及时的运营决策，从而最大程度地提高生产力，同时最大程度地减少项目完成成本和时间并不是微不足道的。与制造和工业系统不同，建筑项目涉及动态（不断发展的）布局，复杂的资源相互作用，工作流和流程中的不确定性以及不可预见的条件，这可能会导致计划和不需要的延误。数字显示，只有30％的建筑项目按时完成并在预算之内完成。因此，建筑项目中运营级决策的准确性和及时性至关重要。该奖项支持基本研究，以通过将过程级别数据无缝整合到决策中来降低不确定性来提高建筑决策的准确性。这将通过建立理论基础并通过启用仿真模型的实时互动来实现当前的施工模拟建模的当前状态来实现这一目标，并通过反馈循环通过反馈循环来传达模拟输出，以引导事件中的事件中的事件。真正的项目。因此，这项研究的结果将使美国经济和社会受益，因为它会导致更好的决策，从而减少浪费，返工，成本，时间和确保安全。该项目的跨学科性质将有助于扩大代表性不足和多样化的学生团体参与整合研究和教学活动，并对工程学的积极影响。基于知识的模拟建模框架该项目可以使过程级别自动学习并适应不断变化和不断发展的建筑系统。作为此类仿真模型输入的过程级知识是从无处不在的感官数据中获得的，这些感官数据描述了现场过程的关系，相互作用和不确定性属性，并能够生成和维护更准确的仿真模型。这样一来，一些科学障碍尚未克服，以实现该框架的全部认证和应用。研究团队将设计和测试方法，这些方法从数据挖掘，机器学习，预测和控制中得出，以填补捕获和采矿复杂数据以及设备和人类船员互动中的元数据中现有的知识差距。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，模型，分析和投射施工系统的不确定性，因此有助于调整工作现场的资源分配和运营方案。