FW-HTF-RM: Improving Construction Work Performance through Human-Centered Augmented Reality

美国的建筑业占国内生产总值近15%，占总劳动力的6%以上。建筑业负责更新美国的基础设施，以满足对清洁水，交通和生活空间的日常需求。最近，建筑业经历了严重的劳动力短缺，这促使人们需要向工匠提供最佳信息，以有效、安全地建设基础设施，并促进未来工人的社会和经济福祉。 越来越多的文献已经证明了增强现实（AR）和人工智能（AI）在改变工作场所方面的令人兴奋的潜力，但大多数现有的研究都集中在办公室，工厂和医疗工作者身上。该项目旨在探索这些技术是否以及如何具体改善施工中的工作绩效。在目前的建筑业实践中，设计信息是通过二维平面图和书面说明书提供给建筑工人的。新技术开始使设计信息能够以多维模型表示，这些模型模拟施工过程并有助于可视化。随着AR和AI技术的成熟，人们认为它们可以通过增强对信息的访问来促进更有效的建设。然而，目前还不清楚如何用AR呈现创新信息，以最好地支持未来的建设工作。此外，由于人工智能被认为是创建项目信息的工具，我们必须了解来自人类或人工智能的设计的感知来源如何影响用户对信息的信任和对后续决策的自信。在这个项目中，研究人员将测试两个主要假设：（1）AR中提供的设计信息的详细程度如何以及在多大程度上影响工作绩效的维度;以及（2）设计信息的感知来源如何以及在多大程度上影响对信息的信任和决策的自信。次要假设将测试设计的细节水平和感知起源如何影响空间推理;规划的方法和时间安排;以及对错误和安全隐患的预期。这些假设将通过实验进行测试，其中设计信息的详细程度被操纵，错误，安全隐患和不确定性被故意嵌入测试任务中。实验将涉及一个实际建筑项目的比例模型。200名手工艺工人和200名本科生将参加研究实验，以代表研究条件下的专家和新手技能水平。除了定量分析外，每次实验后，研究参与者将进行访谈，以解释结果。这项研究将有可能建立新的知识，什么和如何设计信息可以最好地提供给未来的工艺工人使用新兴技术，使高效，安全和高质量的建设。这个奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。

The construction industry in the United States accounts for nearly 15% of Gross Domestic Product and over 6% of the total workforce. The construction industry is responsible for renewing US infrastructure to meet daily needs for clean water, transportation, and living space. Recently, construction has experienced significant workforce shortages that have precipitated the need to deliver craft workers with optimal information to construct infrastructure efficiently, safely, and in a manner that promotes future workers' social and economic well-being. A growing body of literature has demonstrated the exciting potential of augmented reality (AR) and artificial intelligence (AI) to transform workplaces, but most existing studies focus on office, factory, and medical workers. This project aims to explore if and how these technologies specifically improve work performance in construction. In current construction industry practice, design information is provided to construction workers through two-dimensional plans and written specifications. New technology is beginning to enable design information to be represented in multi-dimensional models that simulate the construction process and aid with visualization. As AR and AI technologies mature, it is posited that they can promote more effective construction via enhanced access to information. However, it is unclear how innovative information should be presented with AR to best support future construction work. Additionally, as AI is considered for the creation of project information, we must understand how the perceived origin of a design from either humans or AI impacts the user's trust of the information and self-confidence in subsequent decisions.In this project, researchers will test two main hypotheses: (1) How and to what extent the level of detail of design information delivered in AR impacts dimensions of work performance and (2) How and to what extent the perceived origin of design information impacts trust in the information and self-confidence in decisions. Secondary hypotheses will test how level of detail and perceived origin of design impact spatial reasoning; methods and timing of planning; and anticipation of errors and safety hazards. These hypotheses will be tested via an experiment where the level of detail of design information is manipulated and errors, safety hazards, and uncertainty are purposely embedded in the test task. The experiment will involve a scale model of an actual construction project. Two hundred craft workers and two hundred undergraduate students will participate in the research experiment to represent expert and novice skill levels in the study conditions. In addition to quantitative analyses, interviews will be conducted with the study participants after each experimental trial to explain the results. This research will have the potential to build new knowledge on what and how design information can be best delivered to future craft workers using emerging technologies to enable efficient, safe, and high-quality construction.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.