Digital twin-enabled built environment sensing and monitoring through semantic enrichment of BIM with SensorML

Wang, Tao; Gan, Vincent J.L.; Hu, Difeng; Líu, Hao

doi:10.1016/j.autcon.2022.104625

Cited by 47 publications

(6 citation statements)

References 34 publications

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“…In [24], so called-Data Acquisition Plans (DAPs), which are dataflows organized according to a direct acyclic graph, are proposed to integrate different information according to domain ontologies on sensors' observations. Furthermore, recent works have proposed data annotation and enrichment solutions for specific domains, like smart agriculture [25], smart buildings [26], or smart grids [27].…”

Section: B Data Semantic Enrichmentmentioning

confidence: 99%

Data Enrichment Toolchain: A Data Linking and Enrichment Platform for Heterogeneous Data

Sánchez,

Lanza,

Santana

et al. 2023

IEEE Access

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Proliferation of data sources associated to Internet of Things (IoT) deployment as well as those bound to Open Data Portals (e.g. European Data Portal, Municipalities Open Data Portals, etc.) and Social Media platforms is creating an abundance of information that is called to bring benefits for both the private and public sectors, through the development of added-value services, increasing administrations' transparency and availability or fostering efficiency of public services. However, pieces of information without a context are significantly less valuable. Raw data lacks semantics and it is highly heterogeneous from one data-source to another. This poses a challenge to make it useful. To turn all this data into valuable information it is necessary to enable its combination so that meaningful context can be created. Moreover, it is fundamental to define the mechanisms enabling the adoption and orchestration of advanced (typically AIenabled) data processing techniques to be applied over the harmonized datasets and data-streams. This paper presents the Data Enrichment Toolchain (DET) that provides the necessary harmonization and enrichment to datasets and data-streams coming from heterogeneous sources. The value of the enriched data lies on the one hand in the transfer of the data into a semantically grounded knowledge graph and, on the other hand, in the creation of new data through linking, aggregating and reasoning on the data. In both cases, the benefit of employing linked-data modelling and semantics comes from the extension of the metadata that is associated to every piece of information. Furthermore, the experimental evaluation of the DET implementation that we have carried out is also presented in the paper.

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Section: B Data Semantic Enrichmentmentioning

confidence: 99%

Data Enrichment Toolchain: A Data Linking and Enrichment Platform for Heterogeneous Data

Sánchez,

Lanza,

Santana

et al. 2023

IEEE Access

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“…Another work using semantic technology to extend the IFC standard is that of Wang et al [136], which concerns the field of facilities management by means of sensors and Internet of Things systems. Other researchers, such as Rampini and Re Cecconi in 2023, proposed the use of synthetic images, alongside real ones, generated from 3D BIM models; this proposal aimed to improve the performance of training object detection models in facilities management [137].…”

Section: Semantic Technologymentioning

confidence: 99%

Systematic Literature Review of Open Infrastructure BIM

Salzano,

Intignano,

Mottola

et al. 2023

Buildings

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Representation and modeling using the building information modeling (BIM) methodology of civil works have become the subject of increasing attention in recent years, thanks to the potential offered by Open Infrastructure BIM (I-BIM). However, the complexity of infrastructure works, i.e., the variety of construction and technological systems, makes Open I-BIM very complex and challenging. The lack of systemic knowledge on the subject is another challenging factor. The aim of the following research work is to provide a synoptic overview of the existing scientific research, accompanied by the most recent studies in the field of computer modeling, its applications, and the main opportunities that Open I-BIM offers to the infrastructure sector. After a thorough review of 198 scientific articles published between 2013 and 2023, this study systematically presents a holistic review and critical reflection on the current status of the use of Open BIM in the infrastructure sector, with a focus on the development of the tools and methods used. The outcome of this work constitutes a systematic review of the literature with a bibliometric analysis on Open I-BIM, which is able to provide a knowledge base for identifying research trends, common problems, and the potential of developed methods.

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“…However, its application goes beyond these domains. In the architectural, engineering, and construction (AEC) sector, 3D reconstruction serves as an invaluable tool in capturing as‐built geometric information, effectively facilitating digital twinning for systematic asset maintenance where timely and accurate information is crucial for sustaining structural integrity and ensuring longevity (T. Wang & Gan, 2023; T. Wang et al., 2022). Moreover, up‐to‐date 3D models are integral in monitoring construction progress, ensuring adherence to design plans, and identifying any discrepancies in real time (Zhu et al., 2022).…”

Section: Introductionmentioning

confidence: 99%

Multi‐view stereo for weakly textured indoor 3D reconstruction

Wang,

Gan

2024

Computer aided Civil Eng

Self Cite

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A 3D reconstruction enables an effective geometric representation to support various applications. Recently, learning‐based multi‐view stereo (MVS) algorithms have emerged, replacing conventional hand‐crafted features with convolutional neural network‐encoded deep representation to reduce feature matching ambiguity, leading to a more complete scene recovery from imagery data. However, the state‐of‐the‐art architectures are not designed for an indoor environment with abundant weakly textured or textureless objects. This paper proposes AttentionSPP‐PatchmatchNet, a deep learning‐based MVS algorithm designed for indoor 3D reconstruction. The algorithm integrates multi‐scale feature sampling to produce global‐context‐aware feature maps and recalibrates the weight of essential features to tackle challenges posed by indoor environments. A new dataset designed exclusively for indoor environments is presented to verify the performance of the proposed network. Experimental results show that AttentionSPP‐PatchmatchNet outperforms state‐of‐the‐art algorithms with relative 132.87% and 163.55% improvements at the 10 and 2 mm threshold, respectively, making it suitable for accurate and complete indoor 3D reconstruction.

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Digital twin-enabled built environment sensing and monitoring through semantic enrichment of BIM with SensorML

Cited by 47 publications

References 34 publications

Data Enrichment Toolchain: A Data Linking and Enrichment Platform for Heterogeneous Data

Data Enrichment Toolchain: A Data Linking and Enrichment Platform for Heterogeneous Data

Systematic Literature Review of Open Infrastructure BIM

Multi‐view stereo for weakly textured indoor 3D reconstruction

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