An Integrated Framework for Modeling Construction Competencies and Performance Using Artificial Intelligence and Fuzzy Simulation

建筑业是加拿大最大的产业之一，也是该国经济的支柱。施工组织在不断变化的环境中运作，其特点是复杂，相互关联的事件和过程涉及广泛的参与者和互动。能力被定义为一个组织的技能、知识、技术、资源和实践，使其能够执行其工作和履行其职能，可以评估和用于区分上级和一般绩效。建模施工能力及其对绩效的影响对于预测绩效、识别需要改进的能力以及制定绩效增强策略至关重要。与建模能力相关的最重要的挑战之一是，它们是使用多个标准进行评估的，包括客观和主观的衡量标准，后者最好使用语言术语进行评估。语言术语可以使用模糊逻辑（人工智能的一个组成部分）有效地建模;人工智能已被确定为加拿大政府创新战略的优先事项。模糊逻辑是一种模拟人类推理和使用语言术语进行计算的技术。它使研究人员能够在缺乏精确的数字数据的情况下得出明确的结论，并表示由于主观性造成的不确定性。该研究计划的总体目标是开发一个首创的多层次建模框架，使研究人员和从业人员能够在多个评估级别（个人，活动，项目和组织）建模施工能力及其相互关系，并将其与每个级别的绩效联系起来。该框架将用于提高施工组织的绩效。第二个目标是开发新的模糊混合技术来模拟动态的，包含随机和主观不确定性的建设方面。这些混合方法将以人工智能技术（模糊逻辑和机器学习）与模拟技术（基于代理的建模和系统动力学）相结合为基础。通过为组织提供多层次的能力知识，本研究的成果将带来建设组织实践，领导和文化的转型变革。该框架将为建筑组织提供技术优势，使他们能够识别和提高对绩效影响最大的能力。因此，这项研究将有助于加拿大建筑业的创新，盈利能力和竞争力的整体水平，在国内和国际上，为加拿大经济做出重大贡献。

Construction is one of Canada's largest industries, and it is a mainstay of the country's economy. Construction organizations function in a constantly changing environment, characterized by complex, interconnected events and processes involving a wide range of participants and interactions. The competenciesdefined as the skills, knowledge, technologies, resources, and practices of an organizationthat enable it to execute its work and fulfil its functions can be assessed and used to distinguish superior from average performance. Modeling construction competencies and their effects on performance is essential for forecasting performance, identifying those competencies that require improvement, and developing performance enhancement strategies. One of the most significant challenges associated with modeling competencies is that they are assessed using multiple criteria and consist of both objective and subjective measures, the latter of which are best evaluated using linguistic terms. Linguistic terms can be effectively modeled using fuzzy logic, a component of artificial intelligence; artificial intelligence has been identified as a priority in the Canadian government's innovation strategy. Fuzzy logic is a technique for modeling human reasoning and for computing with linguistic terms. It enables researchers to draw definite conclusions in the absence of precise numerical data and to represent uncertainty due to subjectivity. The overall goal of this research program is to develop a first-of-its-kind multilevel modeling framework to enable researchers and practitioners to model construction competencies and their interrelationships at multiple levels of assessment (individual, activity, project, and organizational) and relate them to performance at each level. This framework will be used to improve the performance of construction organizations. A secondary goal is to develop new fuzzy hybrid techniques to model aspects of construction that are dynamic and contain both random and subjective uncertainty. These hybrid methods will be based on the integration of artificial intelligence techniques (fuzzy logic and machine learning) with simulation techniques (agent-based modeling and system dynamics). By providing organizations with knowledge on competencies at multiple levels, the outcomes of this research will bring about transformational changes in construction organizational practices, leadership, and culture. The framework will provide a technological advantage to construction organizations by enabling them to identify and improve the competencies that have the most significant impact on performance. Thus this research will contribute to the overall level of innovation, profitability, and competitiveness of the Canadian construction industry, both nationally and internationally, making a significant contribution to the Canadian economy.