Integrating Conversational AI and Augmented Reality with BIM for faster and collaborative on-site Construction Assemblage (Conversational-BIM)

将对话式 AI 和增强现实与 BIM 相集成,以实现更快、协作的现场施工装配(对话式 BIM)

基本信息

  • 批准号:
    EP/S031480/1
  • 负责人:
  • 金额:
    $ 155.39万
  • 依托单位:
  • 依托单位国家:
    英国
  • 项目类别:
    Research Grant
  • 财政年份:
    2019
  • 资助国家:
    英国
  • 起止时间:
    2019 至 无数据
  • 项目状态:
    已结题

项目摘要

The traditional approach to construction is notorious for poor productivity and inadequate contribution to economic development (ONS, 2017). With the aim of boosting productivity, the construction sector must transform its methods of construction and adopt effective digital technologies (TIP, 2017). The adoption of BIM has transformed the way buildings are designed and enhanced the implementation of building manufacturing technologies such as Design for Manufacturing and Assembly (DFMA). However, the adoption of BIM by onsite frontline workers for assembly of manufactured building components is non-existent. This results in loss of the productivity gain from using BIM for design and manufacturing phases of the process (BCI, 2016). Onsite frontline workers spend more time interfacing with BIM tools than they spend on completing the actual assembly tasks. Current BIM interfaces are not practicable for onsite operations because they are too slow, hazardous and distracting for onsite frontline workers (Construction News, 2017). On this basis, the research will introduce advanced Natural Language Processing (NLP) and Conversational Artificial-Intelligence for enabling onsite frontline workers to verbally communicate with BIM systems. Assembly operations are complex and are often complicated by the uniqueness of each project, the inconsistency of assembly methods, and the diversity and alterations of project team. During onsite assembly operations, onsite frontline workers are required to quickly understand the procedure of installing building components to minimise assembly errors and reduce the overall project duration. The time spent by frontline workers can be reduced by 50% with the introduction of hands-free assembly support BIM system that utilises verbal communication. In addition to boosting productivity, it will further enhance error-free assembly operation through step-by-by assembly guide for pre-manufactured/pre-assembled building components.The development of technologies to aid easy adoption of BIM for onsite assembly has great potential to revolutionise the current approach to construction. However, apart from the slow pace and hazardous nature of current BIM interfaces, other limitations include visual obstruction, distraction and the associated health and safety challenge for frontline workers. This project aims to utilise Augmented Reality (AR) for providing visual support to access BIM systems and installation guides without obstructing or distracting the view of onsite workers. This will provide accurate and just-in-time information for online frontline workers to gradually follow the installation guide of manufactured building components. For example, an onsite assembly worker can merely ask, "hey Conversational-BIM, guide me through toilet installation" and the system will facilitate the assembly procedures through AR-assisted verbal instructions, the AR device will overlay the exact illustration of the assembly steps on the actual components onsite. It is important to note that onsite coordination between resources is vital for boosting productivity and guaranteeing faster and safer assembly (ICE, 2018). This project will therefore exploit advanced AI, computer visions, and AR technologies to develop an end-to-end BIM solution to support onsite assembly operations. In addition to boosting the productivity of frontline assembly workers, this project seeks to eliminate the tedious process of coordinating onsite activities which often involve multiple workers and machinery. Accordingly, the AR-assisted Conversational-BIM system will ensure a coordinated approach for remote experts to guide frontline workers and monitor project progress and productivity.
传统的建筑方法因生产力低下和对经济发展的贡献不足而臭名昭著(ONS, 2017)。为了提高生产力,建筑部门必须改变其施工方法并采用有效的数字技术(TIP, 2017)。BIM的采用改变了建筑的设计方式,并加强了建筑制造技术的实施,如制造和装配设计(DFMA)。然而,现场一线工人采用BIM来组装制造的建筑部件是不存在的。这导致在流程的设计和制造阶段使用BIM所获得的生产力损失(BCI, 2016)。现场一线工人花在BIM工具上的时间比他们花在完成实际组装任务上的时间要多。目前的BIM接口并不适用于现场操作,因为它们太慢,危险,分散了现场一线工人的注意力(建筑新闻,2017)。在此基础上,该研究将引入先进的自然语言处理(NLP)和会话人工智能,使现场一线工作人员能够与BIM系统进行口头交流。装配操作是复杂的,并且往往由于每个项目的独特性、装配方法的不一致性以及项目团队的多样性和变化而变得复杂。在现场装配过程中,现场一线工人需要快速理解建筑部件的安装程序,以尽量减少装配错误,缩短整个项目的工期。通过引入使用口头沟通的免提装配支持BIM系统,一线工人花费的时间可以减少50%。除了提高生产力外,它还将通过预先制造/预先组装的建筑部件的分步组装指导,进一步提高无错误的组装操作。技术的发展有助于BIM在现场组装中的轻松采用,这有可能彻底改变当前的施工方法。然而,除了当前BIM界面的缓慢速度和危险性质外,其他限制还包括视觉障碍、分心以及对一线工人的相关健康和安全挑战。该项目旨在利用增强现实(AR)为访问BIM系统和安装指南提供视觉支持,而不会妨碍或分散现场工人的视线。这将为在线一线工人提供准确和及时的信息,以逐步按照制造的建筑构件的安装指南进行安装。例如,现场的装配工人只需问一句:“嘿,conversation - bim,指导我安装厕所”,系统就会通过AR辅助的口头指令来简化装配过程,AR设备将在现场的实际组件上覆盖准确的装配步骤说明。需要注意的是,资源之间的现场协调对于提高生产率和确保更快、更安全的组装至关重要(ICE, 2018)。因此,该项目将利用先进的人工智能、计算机视觉和增强现实技术开发端到端BIM解决方案,以支持现场组装操作。除了提高一线装配工人的生产率外,该项目还旨在消除协调现场活动的繁琐过程,这些活动通常涉及多名工人和机器。因此,ar辅助的对话bim系统将确保远程专家指导一线工作人员并监控项目进度和生产力的协调方法。

项目成果

期刊论文数量(10)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Investigating profitability performance of construction projects using big data: A project analytics approach
  • DOI:
    10.1016/j.jobe.2019.100850
  • 发表时间:
    2019-11-01
  • 期刊:
  • 影响因子:
    6.4
  • 作者:
    Bilal, Muhammad;Oyedele, Lukumon O.;Delgado, Juan Manuel Davila
  • 通讯作者:
    Delgado, Juan Manuel Davila
A deep learning approach to concrete water-cement ratio prediction
混凝土水灰比预测的深度学习方法
  • DOI:
    10.1016/j.rinma.2022.100300
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Bello S
  • 通讯作者:
    Bello S
Rainfall prediction: A comparative analysis of modern machine learning algorithms for time-series forecasting
  • DOI:
    10.1016/j.mlwa.2021.100204
  • 发表时间:
    2022-03-15
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Barrera-Animas, Ari Yair;Oyedele, Lukumon O.;Akanbi, Lukman Adewale
  • 通讯作者:
    Akanbi, Lukman Adewale
Guidelines for applied machine learning in construction industry-A case of profit margins estimation
  • DOI:
    10.1016/j.aei.2019.101013
  • 发表时间:
    2020-01-01
  • 期刊:
  • 影响因子:
    8.8
  • 作者:
    Bilal, Muhammad;Oyedele, Lukumon O.
  • 通讯作者:
    Oyedele, Lukumon O.
Deep learning with small datasets: using autoencoders to address limited datasets in construction management
  • DOI:
    10.1016/j.asoc.2021.107836
  • 发表时间:
    2021-11
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Juan Manuel Davila Delgado;Lukumon O. Oyedele
  • 通讯作者:
    Juan Manuel Davila Delgado;Lukumon O. Oyedele
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Lukumon Oyedele其他文献

Lukumon Oyedele的其他文献

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{{ truncateString('Lukumon Oyedele', 18)}}的其他基金

Deconstruction and Recovery Information Modelling (DRIM): A Tool for identifying and reclaiming valuable materials at end-of-life of Buildings
解构和恢复信息模型 (DRIM):用于在建筑物报废时识别和回收有价值材料的工具
  • 批准号:
    EP/N509012/1
  • 财政年份:
    2016
  • 资助金额:
    $ 155.39万
  • 项目类别:
    Research Grant

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