Capturing an Architectural Knowledge Base Utilizing Rules Engine Integration for Energy and Environmental Simulations

Transcription

1 Capturing an Architectural Knowledge Base Utilizing Rules Engine Integration for Energy and Environmental Simulations Holly T. Ferguson Charles F. Vardeman II Aimee P. C. Buccellato

2 Contents Research Problem Green Scale (GS) Tool Development LCA/EE/BEAM GS Interface Overarching Research Goals Application Challenges Data Structures Geometry Tagging Systems Introducing Rule Engines Example Before Rules Example Including Rules Extending Work for Crowdsourcing Application Extension Architecture Extension Results of Testing Rules Model Test Set Performance Rule Types Varying Search Complexity Future Opportunities and Goals References Acknowledgements

3 Research Problem Modern Design Tools Future Design Tools Material databases

4 Research Problem Modern Design Tools Material databases Future Design Tools More scalable, reliable, and accessible

5 Research Problem Modern Design Tools Material databases Future Design Tools More scalable, reliable, and accessible Structured information

6 Research Problem Modern Design Tools Material databases Structured information Future Design Tools More scalable, reliable, and accessible Well-structured information can lead to robust decision support

7 Research Problem Modern Design Tools Material databases Structured information Future Design Tools More scalable, reliable, and accessible Well-structured information can lead to robust decision support Influence architectural simulations

8 Research Problem Modern Design Tools Material databases Structured information Influence architectural simulations Future Design Tools More scalable, reliable, and accessible Well-structured information can lead to robust decision support Influence architectural simulations earlier in the design process

9 Research Problem Modern Design Tools Material databases Structured information Influence architectural simulations Future Design Tools More scalable, reliable, and accessible Well-structured information can lead to robust decision support Influence architectural simulations earlier in the design process API-based systems with material data silos

10 Research Problem Modern Design Tools Material databases Structured information Influence architectural simulations API-based systems with material data silos Future Design Tools More scalable, reliable, and accessible Well-structured information can lead to robust decision support Influence architectural simulations earlier in the design process Integrating distributed API-based systems for enriched/extensible tools

11 Research Problem Modern Design Tools Material databases Structured information Influence architectural simulations API-based systems with material data silos Future Design Tools More scalable, reliable, and accessible Well-structured information can lead to robust decision support Influence architectural simulations earlier in the design process Integrating distributed API-based systems for enriched/extensible tools Proprietary Data Sets

12 Research Problem Modern Design Tools Material databases Structured information Influence architectural simulations API-based systems with material data silos Proprietary Data Sets Future Design Tools More scalable, reliable, and accessible Well-structured information can lead to robust decision support Influence architectural simulations earlier in the design process Integrating distributed API-based systems for enriched/extensible tools Data sources can be from crowdsourcing, external databases, and various design documents Integration with the Semantic Web via Linked Data principles

13 Green Scale (GS) Tool Development LCA/EE/BEAM GS Interface Example Assembly A Example Assembly B Calculated by GST Thermal Model (BEAM) LCA/EE A Heat Flux A LCA/EE A Example Plan View Heat Flux B LCA/EE B Calculated by GST Thermal Model (BEAM) LCA/EE B Among these and potentially other metrics, the GS Tool seeks to find a balance between operational and embodied energies

14 Green Scale (GS) Tool Development LCA/EE/BEAM GS Interface ) Assembly Options to Compare 2) Comparative Analysis Visualization 3) Output Reports and Calculation Totals 4) Material Confidence Calculations and %s

15 Overarching Research Goals

16 Application Challenges Data Tagging Rule Categories gbxml Zone 1 Space 1 Surface 1 Surface 2 Surface 3 Surface 4 Surface 5 Surface 6 Openings Doors Windows Constructions su-5 Layers Materials Window Details su-1 su-3

17 Application Challenges Data Tagging Rule Categories

18 Application Challenges Data Tagging Rule Categories Material Choice Rules (ex. When Data is Missing) Geometry Discrepancy Rules (ex. When Values are Wrong) Combinations of Rules Architectural Best Practice Rules (ex. When Adjustments happen from human knowledge)

19 Introducing Rule Engines Before Rules After Rules Without Tags, how to ID properties for accurate calculations and how to optimize when there are 1000s of DB choices per a given preference set for: minimizing cost, aesthetics, manufacturing, shipping, energy, distances, etc. Solution: 1) Hundreds of conditional statements 2) OR use a Rules Engine (Logic Programming)

20 Introducing Rule Engines Before Rules After Rules Now, ONE Compound Rule using PyKE can process any number of material types via Inferencing over a decoupled Keyword Set These keyword sets are decoupled in.krb,.kfb, &.kqb files

21 Extending Work for Crowdsourcing Application Ext. Architecture Ext. New Rule-Entering Procedure: A. Begin Rule Engine B. Choose Rule Type C. Choose Rule Name D. Select Types of Answer E. Select Data Needed F. Select Answer Format

22 Extending Work for Crowdsourcing Application Ext. Architecture Ext.

23 Extending Work for Crowdsourcing Application Ext. Architecture Ext.

24 Results of Testing Rules Models Performance Types Complexity

25 Results of Testing Rules Models Performance Types Complexity

26 Results of Testing Rules Models Performance Types Complexity Material Choice Geometry Discrepancy Architectural Best-Practice Combinations

27 Processing (seconds) Results of Testing Rules Models Performance Types Complexity Material String Searches Number of Conditional Statements per 1 Rule in the Simplest Form If-Else Statements (seconds) Rule Inferencing (seconds)

28 Future Opportunities and Goals Overall Goal: To make Smarter Data to help the Architectural Community! Proof of Concept: Rules are successful and work as intended. New Possibilities and Improvements Using the Rules Engine Layer: Speed (after initial overhead is overcome) worthwhile for what is gained, Decouples Rules from the Source Code with a Flexible Framework, Can Connect to other Linked Data and use LD Techniques, Linked Data used with REST type interfacing and web services, Uncertainty Measures and Data Confidence can be considered, Ontology Pattern Development and Alignment, Provides Layer for proper Ontology Integration with the Semantic Web, Provides link mechanisms to Decision Theory Frameworks, and Multi-Criteria Decision Analysis will now be possible/effective. Implications and Next Steps: Simulation Tools will be able to use Knowledge from Hundreds of Sources, Allows Crowd-Sourcing to gather Experience Knowledge, and Resulting Knowledge Base can Help the Larger Community.

29 Acknowledgements My Advisors: Jarek Nabrzyski and Charles Vardeman II (Center for Research Computing, Notre Dame) The GS Research Project has been made possible by Center for Research Computing at the University of Notre Dame, the University of Notre Dame Office of the VP of Research Faculty Research Support Program 2012 Regular Grant, and the University of Notre Dame Center for Sustainable Energy Sustainable Energy Initiative Program and undergraduate research support by the Slatt Foundation.

30 References 1. Bertin, B. et al. Semantic modelling of dependency relations between Life Cycle Analysis processes pp Bertin, B., et al. A Semantic Approach to Life Cycle Assessment Applied on Energy Environmental Impact Data Management. ACM:EDBT Bertin, B., et al. Carbon DB: A semantic life cycle inventory database pp Buccellato, A.P.C., Duke, R.P., Veenstra, K. E. Material Matters. Smart and Sustainable Built Environments (SASBE) Brazil. 5. Buildings and their Impact on the Environment: A Statistical Summary. Technical report, April C-Series Reports. Technical report, January Chu, M.L. et al. SAFEgress: A Flexible Platform to Study the Effect of Human and Social Behaviors on Egress Performance. ACM: SimAUD Emissions of Greenhouse Gases in the US Technical Report DOE/ EIA-0573(2007) Frederiksen, B. Applying Expert System Technology to Code Reuse with Pyke. PyCon: Chicago Furber, C., Hepp, M. Using SPARQL and SPIN for Data Quality Management on the Semantic Web. SpringerLink: BIS. Volume 47, 2010, pp Hastie, T., Tibshirani, R., Friedman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics, 2 nd Ed Independent Statistics and Analysis: U.S. Energy Information Administration. (2014) Available from: Ishizaka, A., Nemery, P. Multi-criteria Decision Analysis: Methods and Software. John Wiley & Sons, Ltd. August, Kim, K., et al. Semantic Material Name Matching System for Building Energy Analysis. Elsevier: Automation in Construction pp Kraines, S., Guo, W. A System for Ontology-Based Sharing of Expert Knowledge in Sustainability Science. Data Science Journal: Vol pp Oh, Y. et al. Intelligent Critiquing of Design Sketches. CDG, Carnegie Mellon University. AAAIFS Sander, M., et al. Ontology-Based Translation of Natural Language Queries to SPARQL. NLABD: AAAI Fall Symposium Su, C. J., et al. Ontological Knowledge Engine and Health Screening Data Enabled Ubiquitous Personalized Physical Fitness (UFIT). Sensors The Green Scale, Green Scale Research Project. (2015) Available from: U. S. Department of Energy and Annual Energy Review Technical Report DOE/ EIA-0384 (2007) Vardeman, C. et al. An Ontology Design Pattern for Material Transformation. WOP2014 Patterns Yang, Q.Z., Zhang, Y. Semantic Interoperability in Building Design: Methods and Tools. Elsevier: Computer-Aided Design pp Yu, N., et al. Thermal Model of Green Scale Digital Design and Analysis Tool for Sustainable Buildings (BEAM). Aerospace and Mechanical Engineering Internal Report: University of Notre Dame