The development and application of artificial intelligence tools to improve construction risk management

在过去的二十年里，建筑业越来越被视为更广泛的制造业的一部分。最近，越来越多地使用基于数字的技术来帮助制造业，例如，开发人工智能(AI)技术以提高制造业生产率，部分是通过减少未来思维和制造过程中的风险和不确定性。该研究项目将利用“传统”制造业迄今的人工智能活动，并着手为国民经济基础设施部门开发和应用一套技术和工具。这将不仅仅是对现有方法的重新利用--基础设施具有与传统制造非常不同的特点，了解这种非常不同和复杂的背景将是开发具有持久价值的实用工具的关键，重点是利用以前的经验来降低投标和承担资本基础设施项目的风险。基础设施部门是世界经济的重要贡献者，每年在与建筑相关的产品和服务上的支出约为10万亿美元。然而，该部门的生产率增长和发展估计为1%，低于2.8%的世界经济总量，也远远低于制造业等可比行业的3.6%。这可以归因于几个因素，包括风险文化、项目规模和狭窄的利润率。基础设施项目经常会发现，信息可以以非结构化格式呈现，例如与现有的图纸、勘测和技术数据一起呈现。这些信息往往充满了不确定性和主观性，特别是当涉及到记录保存可能不令人满意的旧的现有网站时。这些问题可能会使人们难以识别风险的大小和可能性，并使其合理化。因此，需要时间和资源来提取准确和相关的信息，这些信息可能会更好地用于企业的其他更具生产力的价值创造领域。本研究项目的目的是开发能够支持基础设施公司风险管理的工具。这将主要侧重于学习识别风险，但也支持分析和管理与公司活动有关的风险，即资本项目和资产管理服务。这些工具将需要与公司现有的程序相结合，以确保有效地利用这些工具来支持团队交付业务活动。这些工具应有助于提高风险识别和分析的效率和准确性，以使团队能够更适当地将资源重新分配到风险较高的领域。

Over the last two decades the construction sector has increasingly been seen as a part of the wider set of manufacturing industries. More recently, there has been an increasing use of digitally based technologies to help manufacturing, including for example the development of Artificial Intelligence (AI) techniques to improve manufacturing productivity, partly by reducing risk and uncertainty in both future thinking and manufacturing processes. This research project will harness AI activity to date in 'traditional' manufacturing industries and proceed to develop and apply a suite of techniques and tools for the sector of National Economic Infrastructure. This will not merely be a repurposing of existing methods - Infrastructure has very different characteristics from traditional manufacturing and understanding this very different and complex context will be key to developing practical tools of lasting value, with a strong focus of using previous experience to reduce risk in both bidding and undertaking capital infrastructure projects. The infrastructure sector is a significant contributor to the world economy, with an annual spend of approximately $10 trillion on construction-related products and services. However, productivity growth and development within the sector, estimated at 1%, is below the total world economy, at 2.8%, and far below comparable sectors, such as manufacturing, estimated at 3.6%. This can be attributed to several factors, including risk culture, project size and tight profit margins.Infrastructure projects will often find that information can be presented in unstructured formats, such as with existing drawings, surveys and technical data. This information can often be filled with uncertainty and subjectivity, particularly when related to old existing sites where record keeping may be unsatisfactory. These issues can make it difficult to identify and rationalise the magnitude and probability of risks. Therefore, time and resource are required to extract accurate and relevant information that may be better used in other, more productive value generating areas of the business.The aim of this research project is to develop tools that can support the management of infrastructure firms' risks. This will primarily focus on learning to identify risks, but also to support the analysis and management of risk associated with a company's activities, i.e. capital projects and asset management services. The tools will need to integrate with the company's existing procedures to ensure effective utilisation to support teams in the delivery of business activities. The tools should help to increase efficiency and accuracy of risk identification and analysis to allow teams to redistribute resource to areas of greater risk more appropriately.