Cost Estimating Methods for RC Structures by Quantity Takeoff and Quantity Prediction in the Design Development Stage

Cho, Jaeho; Jin, Chun

doi:10.3130/jaabe.14.65

Cited by 9 publications

(4 citation statements)

References 16 publications

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“…Aram et al [1] proposed a knowledge-based framework to calculate the quantities of precast concrete, with a domain knowledge base to infer and provide information for QTO and cost estimation tasks. Cho and Chun [28] used BIM object information to compute the quantities of concrete and formwork, and used Decision Tree Model and Case-based Reasoning to predict rebar quantities. Rajabi et al [29] used a set of QS logic to estimate the quantities of the Mechanical, Electrical and plumbing (MEP) trades.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Líu

Cheng

Gan

et al. 2022

Automation in Construction

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Section: Literature Reviewmentioning

confidence: 99%

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

Líu

Cheng

Gan

et al. 2022

Automation in Construction

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“…Lim et al (2016) proposed an automatic algorithm to calculate quantities of rebar from 3D structural models. Cho and Chun (2015) developed a method that integrates quantity takeoff logics and data mining techniques to estimate quantities of reinforced concrete structures based on BIM models. Rajabi et al (2015) created a system to estimate quantities of lighting and heating appliances from BIM models that do not contain those appliances.…”

Section: Bim-based Quantity Takeoffmentioning

confidence: 99%

Development of BIM-based quantity takeoff for light-gauge steel wall framing systems

Khosakitchalert¹,

Yabuki²,

Fukuda³

2020

ITcon

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Quantity takeoff based on building information modeling (BIM) is more reliable, accurate, and rapid than the traditional quantity takeoff approach. However, the quality of BIM models affects the quality of BIM-based quantity takeoff. Our research focuses on drywalls, which consist of wall framings and wall panels. If BIM models from the design phases do not contain wall framing models, contractors or sub-contractors cannot perform quantity takeoff for purchasing materials. Developing wall framing models under a tight schedule in the construction phase is time-consuming, cost-intensive, and error-prone. The increased geometries in a BIM model also slow down the software performance. Therefore, in this research, an automatic method is proposed for calculating quantities of wall framings from drywalls in a BIM model. Building elements that overlap with the drywalls are subtracted from the drywall surfaces before calculation. The quantities of wall framings are then embedded into the properties of drywall in the BIM model and hence they can be extracted directly from the BIM model. A prototype system is developed and the proposed method is validated in an actual construction project. The results of the case study showed that the prototype system took 282 s to deliver accurate quantities of wall framings with deviations of 0.11 to 0.30% when compared to a baseline, and the file size of the BIM model after applying the proposed method was increased very slightly from 47.0 MB to 47.1 MB. This research contributes to developing an approach for quantity takeoff of wall framings that are not present in a BIM model. Accurate quantities of wall framings can be obtained while the time and cost of developing wall framings for quantity takeoff can be saved. The proposed method does not increase the geometries in the BIM model; therefore, the file size of the model does not increase greatly, which stabilizes the software performance.

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“…Kim et al [11] compared the performance of cost models between case-based reasoning (CBR) and artificial neural network (ANN) methods. Artificial intelligence (AI) approaches are being employed as an effective strategy [12][13][14][15][16][17][18][19][20][21].…”

Section: Cost Model Evaluation Factorsmentioning

confidence: 99%

Cost Estimation Model Using Modified Parameters for Construction Projects

Ahn

Lee

et al. 2019

Advances in Civil Engineering

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Construction projects require huge amounts of capital and have many risk factors due to the unique industry characteristics. For a project to be successful, accurate cost estimation during the design phase is very important. Thus, this research aims to develop a cost estimation model where a modification method integrates influential factors with significant parameters. This study identified a modified parameter-making process, which integrates many influential factors into a small number of significant parameters. The proposed model estimates the cost using quantity-based modified parameters multiplied by their price. A case study was conducted with 24-residence building project, and the estimation accuracy of the suggested method and a CBR model were compared. The proposed model achieved higher overall cost-estimation accuracy and stability. A large number of influence factors can be modified as simple representatives and overcome the limitations of a conventional cost estimation model. The paper originality relates to providing a modified parameter-making process to enhance reliability of a cost estimation. In addition, the suggested cost model can actively respond to the iterative requirements of recalculation of the cost.

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Cost Estimating Methods for RC Structures by Quantity Takeoff and Quantity Prediction in the Design Development Stage

Cited by 9 publications

References 16 publications

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

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

Development of BIM-based quantity takeoff for light-gauge steel wall framing systems

Cost Estimation Model Using Modified Parameters for Construction Projects

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