The construction industry is a vital sector of the global economy, but it
faces many productivity challenges in various processes, such as design,
planning, procurement, inspection, and maintenance. Generative artificial
intelligence (AI), which can create novel and realistic data or content, such
as text, image, video, or code, based on some input or prior knowledge, offers
innovative and disruptive solutions to address these challenges. However, there
is a gap in the literature on the current state, opportunities, and challenges
of generative AI in the construction industry. This study aims to fill this gap
by providing a state-of-the-art analysis of generative AI in construction, with
three objectives: (1) to review and categorize the existing and emerging
generative AI opportunities and challenges in the construction industry; (2) to
propose a framework for construction firms to build customized generative AI
solutions using their own data, comprising steps such as data collection,
dataset curation, training custom large language model (LLM), model evaluation,
and deployment; and (3) to demonstrate the framework via a case study of
developing a generative model for querying contract documents. The results show
that retrieval augmented generation (RAG) improves the baseline LLM by 5.2,
9.4, and 4.8% in terms of quality, relevance, and reproducibility. This study
provides academics and construction professionals with a comprehensive analysis
and practical framework to guide the adoption of generative AI techniques to
enhance productivity, quality, safety, and sustainability across the
construction industry.

施工行业的生成性人工智能技术的机会和挑战，以及基于自身数据构建定制生成性人工智能解决方案的框架的最新状态分析，为学术界和建筑专业人员提供综合分析和实用框架，以指导生成性人工智能技术在提高建筑行业的生产力、质量、安全性和可持续性方面的应用。

建筑行业中的生成人工智能：现状分析

Generative AI in the Construction Industry: A State-of-the-art Analysis

Federated Learning (FL) is a concept first introduced by Google in 2016, in
which multiple devices collaboratively learn a machine learning model without
sharing their private data under the supervision of a central server. This
offers ample opportunities in critical domains such as healthcare, finance etc,
where it is risky to share private user information to other organisations or
devices. While FL appears to be a promising Machine Learning (ML) technique to
keep the local data private, it is also vulnerable to attacks like other ML
models. Given the growing interest in the FL domain, this report discusses the
opportunities and challenges in federated learning.

介绍谷歌于 2016 年首次提出的联邦学习，多个设备在中央服务器的监督下协作学习机器学习模型，可保证本地数据的隐私性和安全性，同时存在攻击风险，就此探讨了联邦学习的机会和挑战所在。