TRAMS-Construct - Trustworthy, Responsible AI and ML for construction using aggregated and Secure site data.

该项目将评估一个平台的可行性，该平台对现场施工的摄像机、传感器和元数据馈送进行整理和预处理。它将支持中小企业使用值得信赖和负责任的人工智能(AI)和机器学习(ML)，为建筑承包商管理大型工地开发解决方案，重点放在优先使用案例(例如，提高生产率、智能合同和纠纷解决)。可行性研究将评估：*现场数据整理的要求。*端到端的整理，存储和处理/使用要求/障碍。*现场特定的施工管理数据需要优先用例解决方案。*数据预处理机会/要求。*部门和用例特定的挑战和好处，面临值得信赖和负责任的AI和ML。*支持收入和商业模式。可行性研究将由英国中小企业**Glideology**(现场数据捕获和结构化专家，重点关注建筑)领导，由英国数字中小企业支持，他们拥有人工智能和ML方面的专业知识，并在流程管理和沉浸式环境中具有信任和责任问题的经验。建筑业RTO BRE将支持用例开发、可行性研究报告、利益相关者参与和联盟建设。领先的建筑承包商将提供建筑用例环境。

The project will assess the feasibility of a platform that collates and pre-processes camera, sensor and meta data feeds from on-site construction. It will support SMEs using trustworthy and responsible Artificial Intelligence (AI) and Machine Learning (ML) to develop solutions for construction contractors managing large sites, focusing on priority use cases (e.g. productivity improvement, smart contracts and dispute resolution).The feasibility study will assess:* Requirements for on-site data collation.* End-to-end collation, storage and processing/use requirements/barriers.* Site-specific construction management data needs for priority use case solutions.* Data pre-processing opportunities/requirements.* Sector and use case specific challenges and benefits facing trustworthy and responsible AI and ML.* Supporting revenue and business models.The feasibility study will be led by UK SME **Glideology** (experts in on-site data capture and structuring with a focus on construction) supported by UK digital SMEs with expertise in AI and ML and experience of trust and responsibility issues in the context of process management and immersive environments.Construction sector RTO BRE will support use case development, feasibility study reporting, stakeholder engagement and consortium building. Leading construction contractors will provide the construction use case context.