Predictive BIM with Integrated Bayesian Inference of Deterioration Models as a Four-Dimensional Decision Support Tool
Abstract
:1. Introduction
1.1. Digitalization in Construction
1.2. Modeling and Probabilistic Analysis of Durability
2. Data and Methods
2.1. Models, Assumptions, and Constraints
2.2. Automation of Bayesian Inference
2.3. Implementation in BIM
- A
- Input
- 1
- select components to be evaluated:
- these components can be any objects with flat surfaces and do not need to be a certain object type;
- there must be diagnosis data implemented in those objects for the script to run properly, as the calibration requires evidence;
- all available evidence (diagnosis data) in these components will be combined into one sample.
- 2
- select plane (can be any pickable object surface) as the reference for the z-axis;
- 3
- geometries (such as width, height, etc.) of selected components are extracted;
- 4
- previously implemented objects carrying the concrete cover (measured with linear scans during the building diagnosis) as properties available in the selected components are extracted;
- 5
- previously implemented objects carrying the carbonation depths and chloride profiles as properties available in the selected components are extracted;
- 6
- grouping of concrete cover in different rebar layers.
- B
- Analysis
- 1
- Python environment with SMILE, the configuration of Bayesian networks (sample size, number of bins), and further input such as critical chloride threshold:
- the user is required to define the values for certain configuration parameters via Dynamo as additional input if the default values are not used;
- the user is required to navigate to the folder in which the Python environment with SMILE was installed so that Revit can add this folder to its system path and assess the functionality of SMILE.
- 2
- monitoring data as a CSV data set, if available;
- 3
- analysis of concrete cover (mean, standard deviation, quantiles);
- 4
- Bayesian inference, using all available diagnosis data as evidence;
- 5
- analysis of rebar location (eccentricity, tilting);
- 6
- reliability assessment, including prognoses of carbonation and chloride ingress, as well as calculation of probabilities of failure (depassivation) and reliability indices;
- 7
- assessment of repair methods.
- C
- Output
- 1
- export of analysis results as CSV, TXT, XDSL;
- 2
- creation of additional BIM objects (flat layers) hovering over the component surface containing the most relevant analysis data that are IFC compatible.
3. Results
3.1. Calibration of Carbonation Model
3.2. Calibration of Chloride Model
3.3. Bayesian Inference in BIM
4. Discussion
4.1. Automated Bayesian Inference with Iteration
4.2. Predictive BIM as Decision Support Tool
5. Conclusions and Outlook
- The automation of iterative Bayesian inference leads to significantly more efficient calibrations, as the processing times can be reduced by 99.7% for carbonation and 98.8% for chloride ingress compared to the conventional approach with similar precision.
- The precision of the calibration process can be influenced by adjustable termination criteria, allowing individual configurations to be adjusted to project requirements.
- As the deterioration models are calibrated according to the actual carbonation depths and chloride contents, the need for models for the combined effect of carbonation and chloride ingress, as well as alternative binders or concrete compositions, diminishes. The influence of these aspects is incorporated automatically, as well as the actual concrete cover per component.
- The functionality of BIM can be extended significantly by combining BIM with Python according to wrappers. This way, the interoperability of several programs can be achieved while the user only operates one piece of software containing the combined functionality of all the implemented software.
- By exporting the Bayesian networks and analysis results as CSV, TXT, and XDSL files, the workflow is transparent and traceable. The final results of the assessment are stored in automatically created BIM objects serving as data layers on the corresponding components. These objects are IFC compatible, allowing interoperability with other BIM software via enriched IFC files.
- The combination of timely and spatially resolved, reliable deterioration models can be used to establish predictive BIM for maintenance and repair as a resource-efficient way of working.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
AAM | Alkali-Activated Materials |
AI | Artificial Intelligence |
BIM | Building Information Modeling |
BMS | Bridge Monitoring System |
CSV | Comma-Separated Values |
DT | Digital Twin |
DTC | Digital Twin Construction |
GUI | Graphical User Interface |
IFC | Industry Foundation Classes |
IoT | Internet of Things |
shBIM | Structural Health BIM |
SHM | Structural Health Monitoring |
TR IH | Technical Standard “Maintenance of Concrete Structures” |
TXT | text file format |
XDSL | GeNIe’s native file format |
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Parameter | Lower Bound | Upper Bound | Unit |
---|---|---|---|
0 | 100 | % | |
0 | 5 | – | |
0 | 20,000 | (mm/a)/(kg/m) | |
1 | 1000 | (mm/a)/(kg/m) | |
0 | 0.01977 | kg/m |
Parameter | Lower Bound | Upper Bound | Unit |
---|---|---|---|
0 | 5 | wt% | |
0 | 50 | mm | |
0 | 10,000 | K | |
233.15 | 333.15 | K | |
0 | 200 | mm/a | |
a | 0 | 1 | – |
Software | Version | Developer | Location | Function |
---|---|---|---|---|
Revit | 22.0.2.392 | Autodesk | San Rafael, USA | BIM author software |
BIMvision | 2.25.2 | Datacomp | Cracow, Poland | BIM viewer |
Dynamo | 2.10.1.4002 | Autodesk | San Rafael, USA | (Visual) Programming in BIM |
Python | 3.8.3 | Python Software Foundation | Wilmington, USA | Programming language |
GeNIe | 4.0.2304.0 | BayesFusion | Pittsburgh, USA | Bayesian network software |
PySMILE | 2.0.8 | BayesFusion | Pittsburgh, USA | Python wrapper for GeNIe |
Depth | Evidence | Calculation | Relative Deviation |
---|---|---|---|
in mm | in wt% | in wt% | in % |
0–15 | 1.915 | 1.463 | −23.60 |
15–30 | 0.294 | 0.404 | 37.37 |
30–45 | 0.022 | 0.053 | 140.0 |
Parameter | Lower Bound | Upper Bound | Mean | Standard Deviation | Unit |
---|---|---|---|---|---|
43.75 | 92.97 | 70.00 | 9.81 | % | |
0.352 | 2.813 | 1.525 | 0.543 | – | |
1230 | 9844 | 5030 | 1851 | (mm/a)/(kg/m) | |
1 | 562.9 | 330.1 | 143.3 | (mm/a)/(kg/m) | |
0 | 0.00352 | 0.00045 | 0.00027 | kg/m | |
1.421 | 1.914 | 1.645 | 0.100 | wt% | |
3.434 | 8.759 | 5.760 | 1.156 | mm | |
2500 | 4463 | 3337 | 440 | K | |
265.8 | 278.7 | 272.8 | 2.8 | K | |
47.59 | 89.67 | 67.82 | 8.32 | mm/a | |
a | 0.374 | 0.461 | 0.418 | 0.019 | – |
Selection | Applicative Elements | Applicative Area | ||
---|---|---|---|---|
- | # | % | m | % |
group | 0 of 1 | 0.0 | 0.0 of 44.9 | 0.0 |
component | 4 of 6 | 66.7 | 33.1 of 44.9 | 73.8 |
sides | 17 of 24 | 70.8 | 31.6 of 44.9 | 70.4 |
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Share and Cite
Morgenstern, H.; Raupach, M. Predictive BIM with Integrated Bayesian Inference of Deterioration Models as a Four-Dimensional Decision Support Tool. CivilEng 2023, 4, 185-203. https://doi.org/10.3390/civileng4010012
Morgenstern H, Raupach M. Predictive BIM with Integrated Bayesian Inference of Deterioration Models as a Four-Dimensional Decision Support Tool. CivilEng. 2023; 4(1):185-203. https://doi.org/10.3390/civileng4010012
Chicago/Turabian StyleMorgenstern, Hendrik, and Michael Raupach. 2023. "Predictive BIM with Integrated Bayesian Inference of Deterioration Models as a Four-Dimensional Decision Support Tool" CivilEng 4, no. 1: 185-203. https://doi.org/10.3390/civileng4010012
APA StyleMorgenstern, H., & Raupach, M. (2023). Predictive BIM with Integrated Bayesian Inference of Deterioration Models as a Four-Dimensional Decision Support Tool. CivilEng, 4(1), 185-203. https://doi.org/10.3390/civileng4010012