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[3DIR] BIM Search Engine: Exploiting Interrelations between Objects when Assessing Relevance

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A Presentation for a paper presented at ICCCBE 2018 in Tampere, Finland, June 2018 (17th International Conference on Computing in Civil and Building Engineering)

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[3DIR] BIM Search Engine: Exploiting Interrelations between Objects when Assessing Relevance

  1. 1. Dr Peter Demian Reader in Building Information Management ICCCBE 2018, Tampere, Finland, June 2018 BIM Search Engine: Exploiting Interrelations between Objects when Assessing Relevance
  2. 2. Outline • Introduction to 3DIR project • Related research • Graph Theory for studying 3D models • Method: Relevance formulations • Results • Conclusions
  3. 3. Problem addressed by 3DIR: Finding information • Formulate query • Identify relevant information from index • Present a ranked list of search results
  4. 4. …but if our information is linked to a 3D artefact (…BIM) There might be a better way to: • Formulate queries • Identify relevant information • Present search results
  5. 5. Related Research • BIM/CAD: more information in models • Information Retrieval • Topology, Graph Theory • Literature reviewed in paper Important because we wish to exploit interrelations
  6. 6. Graph Theory for studying 3D models V3D 1 Roof 2 Door 3 Wall Vi 4 Name: Red roof Type: Roof 5 Name: West door Type: Door Material: Glass 6 Name: South wall Type: External Wall Material: Concrete 7 This document is a reinforcement schedule for the South wall (external). En a 1,4 b 2,5 c 3,6 d 3,7 Et e 1,2 Touching f 1,3 Intersecting g 2,3 Hosting Roof Wall Door Reinforcement schedule for wall 4 2 3 1 5 6 7 a b c d e f g
  7. 7. Method 1/2 • Revit model from industry partner • Ground+three-floor office • 7k 3D objects, 20k “info” objects • Test queries: single keyword or multiple keyword • Relevant items for each query identified by human expert • Measures of Recall and Precision used to assess system’s retrieval performance • Holistic/contextual search relevance measures take account of related items (other properties of that 3D object, related 3D objects or “neighbours”)
  8. 8. Method 2/2: Relevance Measures Name Equation Rationale “Vi” Relevance S(V3D) Standard Vi Lucene score “Vi+V3D” Relevance C1S(V3D) + C2S(V3D) Also accounting for relevance of 3D object as a whole “Vi+V3D+N” Relevance C3S(V3D) + C4S(V3D) + C5S(V3D-N) Also accounting for relevance of Neighbours “Vi+V3D+N+NN” Relevance C6S(V3D) + C7S(V3D) + C8S(V3D-N) + C9S(V3D- NN) Also accounting for relevance of Neighbours- of-Neighbours
  9. 9. Results 1/2: Single Keyword Queries Query → Query 1a Query 1b Query 1c Query Terms glazing glazed glaz* Relevant Vi items (according to human expert) 9 3092 3101 Vi items retrieved by 3DIR 8 250 (3DIR maximum) 250 (3DIR maximum) “Vi” Relevance performance 3DIR successfully retrieved 8 of the 9 relevant items. The precision was 1 at all recall levels. 3DIR has a maximum of 250 search hits, which means the maximum possible recall is 0.08, and this was achieved using this basic relevance measure. Precision was 1 at all levels. As expected, the set of relevant items for this query is the union of the relevant sets for Queries 1a and 1b. The results were roughly the same as for Query 1b. “Vi+V3D” Relevance performance The ranking of search hits did not change from above. Although there were minor differences to the items retrieved and their rankings, the maximum of 250 search hits and the large number of relevant items meant that maximum precision was still 0.08, again with no irrelevant items retrieved. Roughly the same as for Q1b. “Vi+V3D+N” Relevance performance The ranking of search hits did not change from above. Same as above: slightly different search hits and ranking, but no change in recall and perfect precision. Roughly the same as for Q1b. “Vi+V3D+N+NN” Relevance performance The ranking of search hits did not change from above. Same as above: slightly different search hits and ranking, but no change in recall and perfect precision. Roughly the same as for Q1b.
  10. 10. Results 2/2: Multiple Keyword Queries Query 2 Query Terms internal wall door glaz* Relevant Vi items (according to human expert) 238 Items retrieved 250 (3DIR maximum) Maximum Recall 0.567 Average Precision (averaged over 250 retrieved search hits) 0.871 Relevance Measure → Performance Criterion ↓ “Vi” Relevance “Vi+V3D” Relevance “Vi+V3D+N” Relevance “Vi+V3D+N+NN” Relevance Top Rank of Irrelevant Retrieved Search Hit 134 134 134 134 Bottom Rank of Relevant Retrieved Search Hit 150 250 250 250
  11. 11. Conclusions • 3DIR imposed a limit of maximum 250 search hits, which obscured results • Innovation presented here did not affect retrieval of results, only inking • Useful effect of scattering relevance measures • Measures of Recall and Precision not sensitive enough to measure benefit or our proposes • Graph theoretic formulation is a useful theoretical lens for studying and developing BIM search engines
  12. 12. THANK YOU Peter Demian P.Demian@lboro.ac.uk 3DIR project website: http://www.3dir.org/ Free 3DIR add-in-in for Revit available from the Autodesk App Store

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