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

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)
批准号:
EP/S031480/1
负责人:
Lukumon Oyedele
金额:
$155.39万
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
项目状态:
已结题
起止时间:
2019 至 --

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中文摘要
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英文摘要
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.
期刊论文(10)
专著(0)
科研奖励(0)
会议论文
DOI: 10.1016/j.jobe.2019.100850
发表时间: 2019-11-01
期刊: JOURNAL OF BUILDING ENGINEERING
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: Results in Materials
影响因子: --
作者: [Bello S]
通讯作者: Bello S
DOI: 10.1016/j.mlwa.2021.100204
发表时间: 2022-03-15
期刊: MACHINE LEARNING WITH APPLICATIONS
影响因子: --
作者: [Barrera-Animas, Ari Yair, Oyedele, Lukumon O., Akanbi, Lukman Adewale]
通讯作者: Akanbi, Lukman Adewale
DOI: 10.1016/j.aei.2019.101013
发表时间: 2020-01-01
期刊: ADVANCED ENGINEERING INFORMATICS
影响因子: 8.8
作者: [Bilal, Muhammad, Oyedele, Lukumon O.]
通讯作者: Oyedele, Lukumon O.
6
    Deconstruction and Recovery Information Modelling (DRIM): A Tool for identifying and reclaiming valuable materials at end-of-life of Buildings
    • 批准号:
      EP/N509012/1
    • 项目类别:
      Research Grant
    • 资助金额:
      $27.9万
    • 财政年份:
      2016
    • 负责人:
      Lukumon Oyedele
    • 依托单位:
    海外基金