Browsing by Author "Zhang, Zichao"

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    Achieving on-site trustworthy AI implementation in the construction industry: a framework across the AI lifecycle
    (MDPI AG, 2024-12-25) Yang, Lichao; Allen, Gavin; Zhang, Zichao; Zhao, Yifan
    In recent years, the application of artificial intelligence (AI) technology in the construction industry has rapidly emerged, particularly in areas such as site monitoring and project management. This technology has demonstrated its great potential in enhancing safety and productivity in construction. However, concerns regarding the technical maturity and reliability, safety, and privacy implications have led to a lack of trust in AI among stakeholders and end users in the construction industry, which slows the intelligent transformation of the industry, particularly for on-site AI implementation. This paper reviews frameworks for AI system design across various sectors and government regulations and requirements for achieving trustworthy and responsible AI. The principles for the AI system design are then determined. Furthermore, a lifecycle design framework specifically tailored for AI systems deployed in the construction industry is proposed. This framework addresses six key phases, including planning, data collection, algorithm development, deployment, maintenance, and archiving, and clarifies the design principles and development priorities needed for each phase to enhance AI system trustworthiness and acceptance. This framework provides design guidance for the implementation of AI in the construction industry, particularly for on-site applications, aiming to facilitate the intelligent transformation of the construction industry.
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    Keypoints-based heterogeneous graph convolutional networks for construction
    (Elsevier, 2023-09-22) Wang, Shuozhi; Yang, Lichao; Zhang, Zichao; Zhao, Yifan
    Artificial intelligence algorithms employed for classifying excavator-related activities predominantly rely on sensors embedded within individual machinery or computer vision (CV) techniques encompassing a large scene. The existing CV-based methods are often difficult to tackle an image including multiple excavators and other cooperating machinery. This study presents a novel framework tailored to the classification of excavator activities, accounting for both the excavator itself and the dumpers collaborating with the excavator during operations. Distinct from most existing related studies, this method centres on the transformed heterogeneous graph data constructed using the keypoints of all cooperating machinery extracted from an image. The resulting model leverages the relationships between the mechanical components of an excavator in varying activation states and the associations between the excavator and the collaborating machinery. The framework commences with a novel definition of keypoints representing different machinery relevant to the targetted activities. A customised Machinery Keypoint R-CNN method is then developed to extract these keypoints, forming the basis of graph notes. By considering the type, attribute and edge of nodes, a Heterogeneous Graph Convolutional Network is finally utilised for activity recognition. The results suggest that the proposed framework can effectively predict earthwork activities (with an accuracy of up to 97.5%) when the image encompasses multiple excavators and cooperating machinery. This solution holds promising potential for the automated measurement and management of earthwork productivity within the construction industry. Code and data are available at: https://github.com/gillesflash/Keypoints-Based-Heterogeneous-Graph-Convolutional-Networks.git.
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    A review of digital twin technologies for enhanced sustainability in the construction industry
    (MDPI, 2024-04-16) Zhang, Zichao; Wei, Zhuangkun; Court, Samuel; Yang, Lichao; Wang, Shuozhi; Thirunavukarasu, Arjun; Zhao, Yifan
    Carbon emissions present a pressing challenge to the traditional construction industry, urging a fundamental shift towards more sustainable practices and materials. Recent advances in sensors, data fusion techniques, and artificial intelligence have enabled integrated digital technologies (e.g., digital twins) as a promising trend to achieve emission reduction and net-zero. While digital twins in the construction sector have shown rapid growth in recent years, most applications focus on the improvement of productivity, safety and management. There is a lack of critical review and discussion of state-of-the-art digital twins to improve sustainability in this sector, particularly in reducing carbon emissions. This paper reviews the existing research where digital twins have been directly used to enhance sustainability throughout the entire life cycle of a building (including design, construction, operation and maintenance, renovation, and demolition). Additionally, we introduce a conceptual framework for this industry, which involves the elements of the entire digital twin implementation process, and discuss the challenges faced during deployment, along with potential research opportunities. A proof-of-concept example is also presented to demonstrate the validity of the proposed conceptual framework and potential of digital twins for enhanced sustainability. This study aims to inspire more forward-thinking research and innovation to fully exploit digital twin technologies and transform the traditional construction industry into a more sustainable sector.