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Purpose Augmented reality (AR) has become one of the most promising technologies in construction since it can seamlessly connect the physical construction environment and virtual contents. In view of the recent research efforts, this study attempts to summarize the latest research achievements and inform future development of AR in construction. Design/methodology/approach The review was conducted in three steps. First, a keyword search was adopted, and 546 papers were found from Scopus and Web of Science. Second, each paper was screened based on the selection criteria, and a final set of 69 papers was obtained. Third, specific AR applications and the associated technical details were extracted from the 69 papers for further analysis. Findings The review shows that: (1) design assessment, process monitoring and maintenance management and operation were the most frequently cited AR applications in the design, construction, and operation stages, respectively; (2) information browser and tangible interaction were more frequently adopted than collaborative interaction and hybrid interaction; and (3) AR has been integrated with BIM, computer vision, and cloud computing for enhanced functions. Originality/value The contributions of this study to the body of knowledge are twofold. First, this study extends the understanding of AR applications in the construction setting. Second, this study identifies possible improvements in the design and development of AR systems in order to leverage their benefits to construction.
Building information modeling (BIM) of cultural heritages, i.e., historic building information modeling (HBIM), advances the monitoring, maintenance, restoration, and virtual exhibitions of historical buildings. However, due to the elaborate styles and the unavoidable erosion and renovation, the reconstruction of HBIM from the prevalent raw data, such as point clouds and images, is very challenging, especially parametrical and semantic modeling. Recent studies have noticed the potential of architectonic grammar for facilitating parametric and semantic reconstruction. In this paper, we investigate the manual modeling of cultural heritage with the architectonic grammar and propose a roadmap consisting of four levels of automation, i.e., ‘calibration,’ ‘selection,’ ‘combination,’ and ‘generation,’ of the architectonic grammar reconstruction. Further quality improvement and cost analysis of these four levels show that ‘calibration’ and ‘selection’ are the most suitable options currently for real-world applications. This study inspires the future application of architectonic grammar to facilitate the parametric and semantic HBIM reconstruction and explores the prospect of a new HBIM reconstruction schema.
Productivity and safety in the construction industry have long been hindered by the many uncertainties and lack of awareness in the semi-controlled site environment. The digital twinning of construction objects aims at offering digital replicas with real-time, trustable evidence for automated monitoring, human-centric decision-making, or fully automatic cyber-physical systems. This paper revisits the pose estimation methods for the digital twinning of various on-site construction objects, including construction components, equipment, and humans. From a machine learning perspective, all the pose estimation methods can be categorized into four classes, i.e., filtering, supervised, reinforcement, and unsupervised. The inputs, processes, output, and target objects of each class are introduced with demonstrative cases. Comparisons on the pros and the cons of the methods reveal the best choices for digital twinning under different objectives, such as a safer site and more productive construction, as well as constraints such as pose accuracy, computational time, and overall cost. The complexities of digital twinning different construction objects are compared to explain the distribution of existing cases in the literature. Opportunities and possible research directions in the new era of AI and blockchain are recommended at the end.
Those attempting to integrate building information modeling (BIM) and blockchain soon encounter the enormous challenge of information redundancy. Storage of duplicated building information in decentralized ledgers already creates redundancy, and this is exacerbated as the BIM model develops and is utilized. This paper presents a novel semantic differential transaction (SDT) approach to minimizing information redundancy in the nascent field of BIM and blockchain integration. Whereas the conventional thinking is to store an entire BIM model or its signature code in blockchain, SDT captures local model changes as SDT records and assembles them into a BIM change contract (BCC). In this way, the version history of a BIM project becomes a chain of timestamped BCCs, and stakeholders can promptly synchronize BIM changes in blockchain. We test our approach in two pilot cases. The results show that SDT captures, in near real time, sequential and simultaneous BIM changes at less than 0.02% of the Industry Foundation Classes file size. We also prove model restoration from the lightweight BCCs in a small-scale BIM project. In addressing the fundamental issue of information redundancy in BIM and blockchain integration, this research can help the industry advance beyond the rhetoric to develop operable blockchain BIM systems.