Hybrid deep learning model for automating constraint modelling in advanced working packaging

Wu, Chengke; Wang, Xiangyu; Wu, Peng; Wang, Jun; Jiang, Rui; Chen, Mengcheng; Swapan, Mohammad Shahidul Hasan

doi:10.1016/j.autcon.2021.103733

Cited by 21 publications

(13 citation statements)

References 52 publications

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“…Current AI approaches (e.g., big data analytics and deep learning models) are generally superficial, because they are limited to statistically identifying some patterns from enormous data following independent identical distributions (namely, i.i.d). This limitation often affects AI approaches' performance when they are implemented in the engineering sector, because: (1) they are very datademanding, but collecting such big data is impractical in practical projects; and (2) raw data largely determine the model performance; in other words, it is difficult to transfer pre-trained models for different engineering problems, which are subject to data following different distributions (Wu, Wang, et al, 2021). Thus, in the future intelligent Engineering Brain, AI models shall capture and understand the underlying casual-effect mechanisms among project entities and events, which feature strong reasoning capacities and can adapt to different problem-solving and decision-making demands with a small amount of data (Schölkopf et al, 2021).…”

Section: Intelligent Recognition Reasoning and Decision-making Based ...mentioning

confidence: 99%

Engineering Brain: Metaverse for future engineering

Wang

Jun

et al. 2022

AI Civ. Eng.

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The past decade has witnessed a notable transformation in the Architecture, Engineering and Construction (AEC) industry, with efforts made both in the academia and industry to facilitate improvement of efficiency, safety and sustainability in civil projects. Such advances have greatly contributed to a higher level of automation in the lifecycle management of civil assets within a digitalised environment. To integrate all the achievements delivered so far and further step up their progress, this study proposes a novel theory, Engineering Brain, by effectively adopting the Metaverse concept in the field of civil engineering. Specifically, the evolution of the Metaverse and its key supporting technologies are first reviewed; then, the Engineering Brain theory is presented, including its theoretical background, key components and their inter-connections. Outlooks of this theory’s implementation within the AEC sector are offered, as a description of the Metaverse of future engineering. Through a comparison between the proposed Engineering Brain theory and the Metaverse, their relationships are illustrated; and how Engineering Brain may function as the Metaverse for future engineering is further explored. Providing an innovative insight into the future engineering sector, this study can potentially guide the entire industry towards its new era based on the Metaverse environment.

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Section: Intelligent Recognition Reasoning and Decision-making Based ...mentioning

confidence: 99%

Engineering Brain: Metaverse for future engineering

Wang

Jun

et al. 2022

AI Civ. Eng.

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“…Constraint modelling is an indispensable step in construction planning [33]. It identifies constraint entities, establishes interconnections among entities, and presents them as knowledge facts [34].…”

Section: Semantic Webmentioning

confidence: 99%

“…Although Constraint modelling provides an effective way to achieve a reliable schedule for higher productivity [2], it is always hard to model complex constraints in conventional information models because it involves rich semantics and spans multiple domains [34]. Hence, previous researchers adopted semantic web technologies to fill this gap by creating domain-specific representations of constraint-related information.…”

Section: Semantic Webmentioning

confidence: 99%

“…Thus, they proposed an ontology model named LinkOnt and linked it to real-time sensor data to dynamically deduce constraint violations. Despite the difference, ontology generally provides users with capable tools for representation, sharing, and reusing constraint-related knowledge in a machine-readable format [11,34,35]. Besides ontology, some studies [13,36] also point out that linked data is a helpful semantic web technology because it can support constraint checking by improving data interoperability, linking across domains, and generating logical proofs and inference.…”

Section: Semantic Webmentioning

confidence: 99%

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Exploring a Digital Twin Framework for lean management of constraints in construction: A literature review

Mao

González

Zou

2022

IOP Conf. Ser.: Earth Environ. Sci.

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Construction constraints are conditions or circumstances that impede the standard progress of production tasks in construction projects towards achieving specific production objectives. Construction constraints are highly dynamic and span different domains. Failing to manage constraints on time could result in out of sequence work and suboptimal productivity. As the construction sector embraces digital technologies, an emerging concept, Digital Twin, has been recognised as a potential solution to solve the endemic problems in construction process management. Digital Twin as a virtual model for cyber-physical bi-directional data-driven management and control of the physical counterpart has emerged over the past decade in the manufacturing industry. However, the implementation of Digital Twin in the construction sector is still in its infancy. Hence, there is an opportunity to adopt Digital Twin to enhance the current implementation of constraint management. This paper presents a literature review of constraint management-related studies, aiming to determine the potential constituents of the Digital Twin framework from a dual technological/lean-based perspective. A total of 55 papers published so far were selected using Scopus. The result shows the potential constituents can include: (1) Information technologies, (2) Swarm Intelligence and Genetic Algorithm, (3) Semantic Web, and (4) Lean-based methods. Our study provides valuable insight concerning which technologies could be used to support the development of DT in the construction sector. These findings can serve as a guideline for future researchers in the construction industry to develop their own DT.

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“…Jallan and Ashuri (2020) has created a state-of-the-art learning algorithm named FastText to identify risk and safety patterns and classify the text into appropriate risk types. Another study has focused on text analysis to improve safety through effectively managing the construction constraints by developing a bidirectional long short-term memory and conditional random field (Bi-LSTM-CRF) model and knowledge representation learning (KRL) model (C. Wu et al , 2021). Other studies have used state-of-the-art deep learning architectures for Natural Language Processing (NLP), Convolutional Neural Networks (CNN) and Hierarchical Attention Networks (HAN) to automatically classify accidents narratives and learn injury precursors from construction accident reports (Baker et al , 2020; Zhong et al , 2020).…”

Section: Health and Safety Using Deep Learningmentioning

confidence: 99%

The application of “deep learning” in construction site management: scientometric, thematic and critical analysis

Elghaish

Matarneh

Alhusban

2021

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Purpose The digital construction transformation requires using emerging digital technology such as deep learning to automate implementing tasks. Therefore, this paper aims to evaluate the current state of using deep learning in the construction management tasks to enable researchers to determine the capabilities of current solutions, as well as finding research gaps to carry out more research to bridge revealed knowledge and practice gaps. Design/methodology/approach The scientometric analysis is conducted for 181 articles to assess the density of publications in different topics of deep learning-based construction management applications. After that, a thematic and gap analysis are conducted to analyze contributions and limitations of key published articles in each area of application. Findings The scientometric analysis indicates that there are four main applications of deep learning in construction management, namely, automating progress monitoring, automating safety warning for workers, managing construction equipment, integrating Internet of things with deep learning to automatically collect data from the site. The thematic and gap analysis refers to many successful cases of using deep learning in automating site management tasks; however, more validations are recommended to test developed solutions, as well as additional research is required to consider practitioners and workers perspectives to implement existing applications in their daily tasks. Practical implications This paper enables researchers to directly find the research gaps in the existing solutions and develop more workable applications to bridge revealed gaps. Accordingly, this will be reflected on speeding the digital construction transformation, which is a strategy over the world. Originality/value To the best of the authors’ knowledge, this paper is the first of its kind to adopt a structured technique to assess deep learning-based construction site management applications to enable researcher/practitioners to either adopting these applications in their projects or conducting further research to extend existing solutions and bridging revealed knowledge gaps.

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Hybrid deep learning model for automating constraint modelling in advanced working packaging

Cited by 21 publications

References 52 publications

Engineering Brain: Metaverse for future engineering

Engineering Brain: Metaverse for future engineering

Exploring a Digital Twin Framework for lean management of constraints in construction: A literature review

The application of “deep learning” in construction site management: scientometric, thematic and critical analysis

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