A knowledge model-based BIM framework for automatic code-compliant quantity take-off

Líu, Hao; Cheng, Jack Chin Pang; Gan, Vincent J.L.; Zhou, Shanjing

doi:10.1016/j.autcon.2021.104024

Cited by 24 publications

(13 citation statements)

References 37 publications

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“…Therefore, BIM-based QTO still depends on manual or semi-automatic efforts to refine the extracted quantities and proceed with downstream analysis and subsequent processes [21]. These non-automated interventions include exchanging data and dealing with elements and activities that were not modelled but needed to be expressed in projects' complementary documentation of quantities and specs [22]. A highly detailed model would minimise these issues.…”

Section: Literature Reviewmentioning

confidence: 99%

“…It allowed the inferring of knowledge about different product features, enabling the representation of model information in a compatible format for QTO and cost estimate tasks. Liu et al [22] tackled conventional model limitations to provide fine-grained information for QTOspecific purposes based on the standard method of measurement (SMM) by proposing a knowledge model-based BIM framework for automatic code-compliant quantity takeoff. Their framework comprises the processing of modelled and non-modelled elements, combining the establishment of modelling requirements and inferring methods for structural elements.…”

Section: Literature Reviewmentioning

confidence: 99%

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Framework for (semi) automatised construction specification and quantity takeoff in the context of small and medium architectural design offices

Vieira

Campos²,

Granja

et al. 2022

Archit. Struct. Constr.

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BIM-based construction specification and quantity takeoff (QTO) processes have proven faster and more reliable than traditional methods. However, their execution and integration still rely on time-consuming and error-prone manual or semi-automatic interventions to deal with the lack of completeness, suitability and granularity of produced BIM models in design practice, mostly related to elements and activities not digitally represented in these models. In addition, besides the BIM authoring tool, they demand other proprietary platforms to obtain the expected outcomes, leading to interoperability issues and representing an obstacle to small and medium architectural design offices in terms of needed investment and customisations inherent to their business model. This work addresses the problem by proposing a BIM-based framework for automatised coordinated construction specification and quantity takeoff. It encompasses information modelling requirements, establishing a construction specification database and an add-in on the authoring tool that automatically produces joint construction specifications and quantity QTO documentation. The proposed system improves current BIM-based practices by mitigating the number of non-automatised intermediary steps, not requiring any other proprietary BIM platform rather than the authoring tool. Besides, it structures alternative ways of digitally representing elements and activities not graphically expressed in the model, allowing automated, more efficient, faster and cheaper production of coordinated reports of quantities and construction specifications. Two real projects validated the implementation of the framework, as well as the capabilities of the add-in, demonstrating its applicability in practical cases.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Framework for (semi) automatised construction specification and quantity takeoff in the context of small and medium architectural design offices

Vieira

Campos²,

Granja

et al. 2022

Archit. Struct. Constr.

View full text Add to dashboard Cite

show abstract

“…Meanwhile, data‐driven models from multiple participants can combine for ensemble learning [28]. Besides, in a multi‐agent system, local models can merge into a global optimal model through reinforcement learning [29] or knowledge graph [30–33]. Moreover, Wang et al.…”

Section: Related Workmentioning

confidence: 99%

“…Meanwhile, data-driven models from multiple participants can combine for ensemble learning [28]. Besides, in a multiagent system, local models can merge into a global optimal model through reinforcement learning [29] or knowledge graph [30][31][32][33]. Moreover, Wang et al [34] applied a blockchain in collaborative fault diagnosis, where the blockchain provided a decentralised platform and claimed the immutable ownership of data and knowledge of each participant.…”

Section: Collaborative Fault Diagnosismentioning

confidence: 99%

Privacy‐preserving gradient boosting tree: Vertical federated learning for collaborative bearing fault diagnosis

Xia

Zheng

et al. 2022

IET Collab Intel Manufact

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Data-driven fault diagnosis approaches have been widely adopted due to their persuasive performance. However, data are always insufficient to develop effective fault diagnosis models in real manufacturing scenarios. Despite numerous approaches that have been offered to mitigate the negative effects of insufficient data, the most challenging issue lies in how to break down the data silos to enlarge data volume while preserving data privacy. To address this issue, a vertical federated learning (FL) model, privacy-preserving boosting tree, has been developed for collaborative fault diagnosis of industrial practitioners while maintaining anonymity. Only the model information will be shared under the homomorphic encryption protocol, safeguarding data privacy while retaining high accuracy. Besides, an Autoencoder model is provided to encourage practitioners to contribute and then improve model performance. Two bearing fault case studies are conducted to demonstrate the superiority of the proposed approach by comparing it with typical scenarios. This present study's findings offer industrial practitioners insights into investigating the vertical FL in fault diagnosis.

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“…Therefore, the information contained in the BIM-based design model can be directly used for cost estimation [11,12], which greatly improves the feedback efficiency between cost and design [13]. By extracting geometric data and semantic properties of building components [14], BIM technology can automatically measure quantity from BIM model, resulting in time saving and more reliability compared with the traditional method [15,16].…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of Quantity Calculation Method Based on BIM Data

Chen

Jiang

et al. 2022

Sustainability

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Manual quantity takeoff using two-dimensional (2D) drawings and personal knowledge is error-prone and time-consuming. Theoretically, quantity can be automatically calculated from building information model more quickly and reliably by extracting geometric data and semantic attributes of building elements. Specific construction classification systems embedded in mainstream modeling software for building information modeling (BIM) make it difficult for countries adopting different systems to calculate quantity directly. This paper proposes a BIM-based quantity takeoff code mapping (BQTCM) method to solve the above issue, and develops a quantity takeoff code mapping plug-in (QTCMP) on a BIM modeling software based on the proposed BQTCM method to obtain an accurate bill of quantities directly and efficiently. Moreover, by conducting a statistical analysis and examining a case study, this paper verifies the accuracy and efficiency of quantity takeoff attained from the proposed BQTCM method and QTCMP.

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A knowledge model-based BIM framework for automatic code-compliant quantity take-off

Cited by 24 publications

References 37 publications

Framework for (semi) automatised construction specification and quantity takeoff in the context of small and medium architectural design offices

Framework for (semi) automatised construction specification and quantity takeoff in the context of small and medium architectural design offices

Privacy‐preserving gradient boosting tree: Vertical federated learning for collaborative bearing fault diagnosis

Design and Implementation of Quantity Calculation Method Based on BIM Data

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