Design and development of BIM models to support operations and maintenance

Heaton, James T.; Parlikad, Ajith Kumar; Schooling, Jennifer

doi:10.1016/j.compind.2019.08.001

Cited by 132 publications

(74 citation statements)

References 16 publications

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“…In the operation and maintenance phase, digital twins can be used for equipment asset management [40], safety and prevention management, building space management, and building environment management. Shim [40,41] e program combines a 3D information model-based maintenance information management system with a digital detection system that uses image processing to enhance the decision-making process in terms of the bridge maintenance process reliability.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Highway Tunnel Pavement Performance Based on Digital Twin and Multiple Time Series Stacking

Zhang

et al. 2020

Advances in Civil Engineering

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The existing pavement performance prediction methods are limited to single-factor predictions, which often face the challenges of high cost, low efficiency, and poor accuracy. It is difficult to simultaneously solve the temporal, spatial, and exogenous dependencies between pavement performance data and maintenance, the service life of highways, the environment, and other factors. Digital twin technology based on the building information modeling (BIM) model, combined with machine learning, puts forward a new perspective and method for the accurate and timely prediction of pavement performance. In this paper, we propose a highway tunnel pavement performance prediction approach based on a digital twin and multiple time series stacking (MTSS). This paper (1) establishes an MTSS prediction model with heterogeneous stacking of eXtreme gradient boosting (XGBoost), the artificial neural network (ANN), random forest (RF), ridge regression, and support vector regression (SVR) component learners after exploratory data analysis (EDA); (2) proposes a method based on multiple time series feature extraction to accurately predict the pavement performance change trend, using the highway segment as the minimum computing unit and considering multiple factors; (3) uses grid search with the k-fold cross validation method to optimize hyperparameters to ensure the robustness, stability, and generalization ability of the prediction model; and (4) constructs a digital twin for pavement performance prediction to realize the real-time dynamic evolution of prediction. The method proposed in this study is applied in the life cycle management of the Dalian highway-crossing tunnel in Shanghai, China. A dataset covering 2010–2019 is collected for real-time prediction of the pavement performance. The prediction accuracy evaluation shows that the mean absolute error (MAE) is 0.1314, the root mean squared error (RMSE) is 0.0386, the mean absolute percentage error (MAPE) is 5.10%, and the accuracy is 94.90%. Its overall performance is better than a single model. The results verify that the prediction method based on digital twin and MTSS is feasible and effective in the highway tunnel pavement performance prediction.

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Section: Introductionmentioning

confidence: 99%

Prediction of Highway Tunnel Pavement Performance Based on Digital Twin and Multiple Time Series Stacking

Zhang

et al. 2020

Advances in Civil Engineering

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“…BIM models richly describe buildings through classes of objects that contain their 3D geometry and other characteristics. Quantity can be measured automatically from BIM models by extracting geometric data and semantic properties of each building element [11][12][13][14][15].…”

Section: Manualmentioning

confidence: 99%

Cost Comparison of a Building Project by Manual and BIM

et al. 2020

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This article focuses on comparison between Manual/Traditional and Building Information Modelling (BIM) software based approaches for cost comparison. Centre line method for manual and Revit software for BIM based approaches are used in this research. The principal objectives of this research are to calculate quantities by Manual Centre line method, then to make the cost estimation fast, accurate, efficient, and errorless by using BIM software Rivet, and finally the comparison study of manual based and BIM / Software based estimation. For manual approach, quantities are calculated by multiplying the measurements of length, breadth, and height. Then to get the total quantities the deduction is subtracted from the quantities and final BOQ is prepared for which estimated cost of building is calculated. For BIM based approach, 3D model from 2D floor plan of building is prepared in Revit software, then to estimate the cost of building structure elements, sheets of quantities are generated in the schedule option of the view tab in the software. The Percentage difference between Manual and BIM / Revit Software estimation in brick work, RCC Slab, Plaster Work, PCC for Flooring, Floor Tile Work, Skirting, Paint Work, False Ceiling, Doors and Aluminum Work is 4.57, 2.61, 7.58, 3.27, 1.87, 6.73, 8.03, 1.87, and 0.00% respectively. The total cost difference between manual and BIM based estimation approach comes out to be 4.8%. It is thus concluded that the BIM-assisted estimates have better performance over traditional/manual estimating methods.

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“…According to [37], in increasing maintenance performance, the building information modelling (BIM) approach is implemented and developed through 3D modelling with aims to manage and maintain facilities, where the model is integrated with information related to previous facility maintenance works and their workers. The maintenance management can then run effectively by achieving the reduction of maintenance costs through energy and resource savings, as well as increasing asset value.…”

Section: Iiviixii Hubungan Bim Dan Kinerja Pemeliharaanmentioning

confidence: 99%