Semantic Enrichment for BIM

SeeBridge meeting @ Technion

March, 2017

WP4 Review

Virtual Construction Lab

What is semantic enrichment in SeeBridge

© 2017, Dr. Ling Ma 

WP4 in the SeeBridge

Requirement of the enrichment result

Produce feature-based enrichment rules

Algorithm for identification of model features and rule-based model processing

© 2017, Dr. Ling Ma 

MVD development

Mapping entities

Mapping properties

Mapping constraints

© 2017, Dr. Ling Ma 

Rule engine of SeeBIM 1.0

Rules:

  • IF a & b -> X
  • IF b & c -> Y

Advantage:

  • No computer programing skill is required

Disadvantage:

  • Lack of rigor

​Fact:

IF a & b & c -> ?

© 2017, Dr. Ling Ma 

SeeBIM 2.0

Features F1 F2 F3 F4 F5 F6
Type A y y x y n n
Type B n n y x y n
Type C y y n y x n
Type D n n y y y x
  • ‘y’:= Yes
  • ‘n’:= No
  • ‘x’:= Not always

Find the 'unique' string (skip the comparison with ambiguous 'x' )

y n y x n
y x y y n

Not unique

© 2017, Dr. Ling Ma 

SeeBIM 2.0 - pairwise

Physical contact ?

Orthogonality

Length comparison

Width comparison

Elevation comparison

...

Find the 'unique' string (skip the comparison with ambiguous 'x' )

  • ‘y’:= Yes;
  • ‘n’:= No;
  • ‘x’:= Not always;
y n y x n
y x y y n

Not unique

© 2017, Dr. Ling Ma 

SeeBIM 2.0 - an example rule set for bridge object classification

Sacks, R., L. Ma, R. Yosef, A. Borrmann, S. Daum. and U. Kattel, Enhancement of a Semantic Enrichment Tool for Building Information Modeling. Journal of Computing in Civil Engineering, under review.

© 2017, Dr. Ling Ma 

SeeBIM 2.0 - another thought

Feature1 Feature2 Feature3 Feature4 Feature5
Type A n y X n y
Type B n y y n y
  • ‘y’:= Yes;
  • ‘n’:= No;
  • ‘x’:= Not always;

Rules:

Fact:

Obj 1 n y y n y
Obj 2 n y n n y
PB>PA
PA>PB

© 2017, Dr. Ling Ma 

Feature Matching

Longitudinal Transverse Vertical Convex ...
Primary Girder 1 -1 -1 -1
Transverse Beam -1 1 -1 1
...

Primary Girder = (1,-1,-1,-1)

Longitudinal Transverse Vertical Convex
Object 1 -1 1 -1 1
Object 2 1 -1 -1 -1
Object 3 1 -1 -1 1

Object 1 = (-1,1,-1,1)

Primary Girder Transverse Beam
Object 1 180 degree 0 degree
Object 2 0 degree 180 degree
Object 3 60 degree 90 degree

© 2017, Dr. Ling Ma 

Pairwise Feature Matching

f1 f2 f3
E1,E1 1 1 1
E1,E2 -1 0 0
E1,E3 -1 -1 0
E2,E1 0 0 0
E2,E2 1 1 0
E2,E3 0 0 -1
E3,E1 -1 -1 -1
E3,E2 0 -1 0
E3,E3 0 0 -1
f1 f2 f3
O1,O2 1 1 1
O1,O3 -1 1 -1
O1,O4 -1 -1 1
O1,O5 1 1 1
O2,O1 1 1 1
O2,O3 -1 -1 1
O2,O4 1 1 1
O2,O5 1 1 1
O3,O1 1 1 1
O3,O2 -1 -1 1
O3,O4 -1 -1 1
O3,O5 1 1 -1
O4,O1 1 1 -1
O4,O2 1 1 -1
O4,O3 -1 -1 -1
O4,O5 -1 -1 1
O5,O1 -1 1 -1
O5,O2 -1 1 -1
O5,O3 -1 1 -1
O5,O4 1 -1 1

3 types of bridge elements (e.g., girder, sheer key etc.)

5 objects in the model

3 types of features

E1,E1 E1,E2 E1,E3 E2,E1 E2,E2 E2,E3 E3,E1 E3,E2 E3,E3
O1,O2 1 3 -3 1 -5 2 -3 2 1
O1,O3 -5 1 -3 2 -3 -3 2 2 -2
O1,O4 -2 3 -3 5 3 -1 -5 -2 -2
O1,O5 -1 5 1 5 2 1 3 2 1
O2,O1 -3 4 2 0 1 -1 -4 3 -5
O2,O3 -1 3 5 -5 0 0 -3 4 -2
O2,O4 1 3 -4 4 0 4 -1 5 -5
O2,O5 -5 -2 -1 0 1 3 2 0 1
O3,O1 -3 5 0 -2 -3 1 5 -1 5
O3,O2 4 1 -2 3 -2 3 1 0 -1
O3,O4 -2 5 -2 0 -5 4 5 2 -2
O3,O5 -2 -5 -4 3 -2 -3 1 1 -2
O4,O1 -4 1 0 -4 4 -5 -5 1 -4
O4,O2 5 2 5 -5 3 -4 -4 -2 -5
O4,O3 -5 0 3 1 4 -1 2 5 1
O4,O5 -1 -3 3 -5 3 2 -5 -5 -5
O5,O1 1 5 2 5 1 5 0 0 1
O5,O2 -3 -4 0 4 5 0 1 -4 2
O5,O3 -5 0 5 3 -1 4 2 4 2
O5,O4 3 5 -5 5 1 -4 -1 3 2

obj1?=element1

2n(m-1) relations

         m    :     n

© 2017, Dr. Ling Ma 

Experiement

© 2017, Dr. Ling Ma 

SeeBIM 3.0

Machine learning:

Classifier: 3 * Feature1 ^ Feature2 + Feature3 / Feature4

Feature1 Feature2 Feature3 Feature4 Type
Obj 1 1 0 20.5 a Type A
Obj 2 -1 1 40 b Type B
Feature1 Feature2 Feature3 Feature4 Type
Obj 1,001 ... ... ... ... Type ?
Obj 1,002 ... ... ... ... Type ?

© 2017, Dr. Ling Ma 

SeeBIM 4.0?

Object Type
Obj 1 Type A
Obj 2 Type B
Obj 3 Type C

Deep learning:

Object Type
Obj 10,001 Type ?
Obj 10,002 Type ?
Obj 10,003 Type ?

© 2017, Dr. Ling Ma 

Enrichment of other information

© 2017, Dr. Ling Ma 

Supplementing information:

  • Bridge grids
  • Span
  • Assembly

 

Filling in missing objects or fixing their shape caused by occlusion. such as:

  • Bearings
  • Girders
  • Transverse beams

Demo

© 2017, Dr. Ling Ma 

Demo 2

© 2017, Dr. Ling Ma 

Enhancement of the Semantic Enrichment Engine

Dr. Ling Ma

Technion - Faculty of Civil and Environmental Engineering

SeeBridge WP4 Mid-term Report

Georgia Tech

Additional Data

© 2016, Dr. Ling Ma 

Manual input & load from other source

Enhanced Geometry and Spatial Feature

© 2016, Dr. Ling Ma 

Spatial Relationship Matrix

© 2016, Dr. Ling Ma 

Transverse beam & Abutment

Uniqueness Checking

© 2016, Dr. Ling Ma 

Element pairs are unique in terms of the relational features are unique

Other Attempts

© 2016, Dr. Ling Ma 

Rule-based object identification

Similarity by feature matching

(Determinate result)

(Result with confidence)

Run-time file enrichment

Document-oriented database

Schema-free BIM model

Cloud service

(Deliver a model)

(Information sharing and collaboration)

The Root of Object Identification

© 2016, Dr. Ling Ma 

Feature Matching

© 2016, Dr. Ling Ma 

Longitudinal Transverse Vertical Convex ...
Primary Girder 1 -1 -1 -1
Transverse Beam -1 1 -1 1
... 0

Primary Girder = (1,-1,-1,-1)

Longitudinal Transverse Vertical Convex
Object 1 -1 1 -1 1
Object 2 1 -1 -1 -1
Object 3 1 -1 -1 1

Object 1 = (-1,1,-1,1)

Primary Girder Transverse Beam
Object 1 180 degree 0 degree
Object 2 0 degree 180 degree
Object 3 60 degree 90 degree

Pairwise Features

© 2016, Dr. Ling Ma 

Closer to the transverse ?

Pairwise feature matrix

Pairwise Feature Matching

© 2016, Dr. Ling Ma 

f1 f2 f3
E1,E1 1 1 1
E1,E2 -1 0 0
E1,E3 -1 -1 0
E2,E1 0 0 0
E2,E2 1 1 0
E2,E3 0 0 -1
E3,E1 -1 -1 -1
E3,E2 0 -1 0
E3,E3 0 0 -1
f1 f2 f3
O1,O2 1 1 1
O1,O3 -1 1 -1
O1,O4 -1 -1 1
O1,O5 1 1 1
O2,O1 1 1 1
O2,O3 -1 -1 1
O2,O4 1 1 1
O2,O5 1 1 1
O3,O1 1 1 1
O3,O2 -1 -1 1
O3,O4 -1 -1 1
O3,O5 1 1 -1
O4,O1 1 1 -1
O4,O2 1 1 -1
O4,O3 -1 -1 -1
O4,O5 -1 -1 1
O5,O1 -1 1 -1
O5,O2 -1 1 -1
O5,O3 -1 1 -1
O5,O4 1 -1 1

3 types of bridge elements (e.g., girder, sheer key etc.)

5 objects in the model

3 types of features

E1,E1 E1,E2 E1,E3 E2,E1 E2,E2 E2,E3 E3,E1 E3,E2 E3,E3
O1,O2 1 3 -3 1 -5 2 -3 2 1
O1,O3 -5 1 -3 2 -3 -3 2 2 -2
O1,O4 -2 3 -3 5 3 -1 -5 -2 -2
O1,O5 -1 5 1 5 2 1 3 2 1
O2,O1 -3 4 2 0 1 -1 -4 3 -5
O2,O3 -1 3 5 -5 0 0 -3 4 -2
O2,O4 1 3 -4 4 0 4 -1 5 -5
O2,O5 -5 -2 -1 0 1 3 2 0 1
O3,O1 -3 5 0 -2 -3 1 5 -1 5
O3,O2 4 1 -2 3 -2 3 1 0 -1
O3,O4 -2 5 -2 0 -5 4 5 2 -2
O3,O5 -2 -5 -4 3 -2 -3 1 1 -2
O4,O1 -4 1 0 -4 4 -5 -5 1 -4
O4,O2 5 2 5 -5 3 -4 -4 -2 -5
O4,O3 -5 0 3 1 4 -1 2 5 1
O4,O5 -1 -3 3 -5 3 2 -5 -5 -5
O5,O1 1 5 2 5 1 5 0 0 1
O5,O2 -3 -4 0 4 5 0 1 -4 2
O5,O3 -5 0 5 3 -1 4 2 4 2
O5,O4 3 5 -5 5 1 -4 -1 3 2

obj1?=element1

2n(m-1) relations

         m    :     n

© 2016, Ling Ma 

Matching Result

Similarity Instead of Determinate Result

O1 E1 E3 E2
O2 E2 E1 E2
O3 E2 E1 E3
O4 E2 E1 E3
O5 E1 E3 E2
1st Candidate 2nd Candidate 3rd Candidate

Conclusion

© 2016, Dr. Ling Ma 

Machine learning for more complex cases

Tested on synthetic cases

Semantic Enrichment for Bridges

SeeBridge WP4 Mid-term Report

Georgia Tech

Dr. Ling Ma

Technion - Faculty of Civil and Environmental Engineering

Why Enrichment is Important to BIM

© 2016, Dr. Ling Ma 

Enrichment Tasks

© 2016, Dr. Ling Ma 

© 2016, Dr. Ling Ma 

Why Enrichment is Important in SeeBridge

© 2016, Dr. Ling Ma 

Role in the SeeBridge

Requirement of the enrichment result

Produce feature-based enrichment rules

Algorithm for identification of model features and rule-based model processing

MVD Development Discussion

Technion + TUM + buildingSMART + AEC 3

Dr. Ling Ma

Technion - Faculty of Civil and Environmental Engineering

© 2016, Dr. Ling Ma 

Workflow

  • Method: mvdXML
    • ​Object typing
    • Property/Quantity sets
    • Exchange requirements
    • Relationship?
  • Tool: IFCDoc
    • App for mvdXML dev
    • Concept rule checking?
    • ​Documentation

IDM development

MVD development

  • SeeBridge element types
    • Physical elements
    • Spatial elements
    • Defects elements
  • Property sets of elements
  • Element relationships
    • Composition
    • Containment
    • Grouping
  • Info Exchange Requirements

Object Typing

IfcTypeObject

TypeEnum

  • Share PropertySet
  • Complex rules
    • 2 PredefinedType
      • USERDEFINED in TypeObject
      • NOTDEFINED / $ in Object
    • ElementType in TypeObject
  • Not all the element has type object
    • IfcSurfaceFeature (defect)
      • no Type
      • has TypeEnum
  • Binding to Object (e.g. IfcBeam)

© 2016, Dr. Ling Ma 

Bridge Element Property Set

  • Define the property set for bridge elements and element types

Element Type

Element Family

Element Property Set

Element Property

© 2016, Dr. Ling Ma 

Bridge Element Typing

  • Select appropriate object types for bridge elements

and many more...

 

 

?

© 2016, Dr. Ling Ma 

Property/Quantity Set Definition

Concept Rule on TypeEnum 

  • QuantitySet of PredefinedType
  • PropertySet of PredefinedType

© 2016, Dr. Ling Ma 

Exchange Requirement

Mandatory in both import & export

Existence:

  • Property/Quantity Set of PredefinedType ?
  • Property/Quantity in PropertySet (not $)?

<TemplateRules operator="and">

 <TemplateRule Parameters="PredefinedType=GIRDER;PropertySetName=Pset_SeeBridgeGirderPropertySet;Properties=IfcPropertySingleValue;SingleValuePropertyName=P_SeeBridgeGirderProperty1;SingleValue=IfcText;" />

<TemplateRule Parameters="PredefinedType=GIRDER;PropertySetName=Pset_SeeBridgeGirderPropertySet;Properties=IfcPropertyBoundedValue;BoundedValuePropertyName=P_SeeBridgeGirderProperty2;SetPointValue=IfcReal;" />

<TemplateRule Parameters="PredefinedType=GIRDER;PropertySetName=Pset_SeeBridgeGirderPropertySet;Properties=IfcPropertyEnumeratedValue;EnumerationValuePropertyName=P_SeeBridgeGirderProperty3;EnumerationValue=IfcText;" />

</TemplateRules>

© 2016, Dr. Ling Ma 

MVD Documentation (Deliverable)

© 2016, Dr. Ling Ma 

Thanks!

SeeBridge Status Report

MVD Develement

Dr. Ling Ma

Technion - Faculty of Civil and Environmental Engineering

© 2016, Ling Ma 

Content in IDM

  • List the bridge element types
  • Define the property sets of the bridge elements
  • Identify the composition, connectivity, grouping relationships among the physical and conceptual bridge elements
  • List the bridge defect types and property sets
  • Specify the exchange requirements for these information items (Mandatory, optional, N/A)

© 2016, Ling Ma 

Bridge Element Typing

  • Select appropriate object types for bridge elements

and many more...

 

 

?

© 2016, Ling Ma 

Bridge Element Property Set

  • Define the property set for bridge elements and element types

Element Type

Element Family

Element Property Set

Element Property

© 2016, Ling Ma 

Bridge Element Relationship

  • Aggregation (IfcRelAggregates)
    • element composition: physical element <> physical elements/parts
    • spatial composition: spatial element <> subspace
  • Spatial Container (IfcRelContainedInSpatialStructure
    • spatial container <> physical elements
  • Grouping (IfcRelAssignsToGroup)
    • system <> subsystems

© 2016, Ling Ma 

Damage Typing

  • IsDeclaredBy
  • IsDefinedBy
  • Decomposes

A physical element instance

A property set

An element type

© 2016, Ling Ma 

Work items

  • Mapping bridge elements in IDM to Ifc type objects
  • Convert the property set in IDM to Ifc format
  • Mapping the relationship and conceptual elements to IFC elements
  • Define the constraints on information items in MVD

SeeBridge Status Report

Enrichment Engine

Dr. Ling Ma, Uri Kattel, Raz Yosef

Technion - Faculty of Civil and Environmental Engineering

© 2016, Ling Ma 

Shape Matching

© 2016, Ling Ma 

Operators

  1. Dimension: [x,y,z]
  2. Axis: [[x1,y1,z1],[x2,y2,z2],[x3,y3,z3]]
  3. Viewpoint coordinate system
    • Origin: [x,y,z]
    • Orientation: [[x1,y1,z1],[x2,y2,z2],[x3,y3,z3]]
  4. Topology:
    • Touch: Boolean 
    • Disjoint: Boolean 
    • Euclidean Relation: [Left?,Right?,Above?,Below?]

© 2016, Ling Ma 

Operators

Near

Overlap

Touch

© 2016, Ling Ma 

Operators

  • Three beams in the model
  • Two are non-axis-aligned
  • Get the dimensions
  • Get the orientations

© 2016, Ling Ma 

Operators

Wider

Perpendicular

Parallel

Longer

Bigger

© 2016, Ling Ma 

Inference Rule

© 2016, Ling Ma 

Domain Knowledge

A B C D E
A 0 3 0 2 1
B 2 0 0 3 1
C 0 0 0 0 4
D 1 4 0 0 0
E 1 2 4 0 0

Matrix 1: Perpendicular

A B C D E
A 0 0 1 0 0
B 0 0 1 0 0
C 2 1 0 2 0
D 0 0 1 0 2
E 0 0 0 2 0

Matrix 2: Parallel

A B C D E
A 0 1 4 1 2
B 0 0 0 3 1
C 0 0 0 3 4
D 0 0 0 0 2
E 0 0 0 0 0

Matrix 3: Above

Rating score of being the determinant relationship for identifying A

© 2016, Ling Ma 

Truth

1 2 3 4 5 6 7 8
1 0 1 0 0 1 0 0 1
2 0 0 1 0 1 0 1
3 0 0 1 1 0 1
4 0 0 1 0 0
5 0 1 1 0
6 0 1 0
7 0 0
8 0

Matrix 1: Perpendicular

Matrix 3: Above

1 2 3 4 5 6 7 8
1 0 0 1 1 0 0 0 1
2 1 0 0 1 0 1 0 1
3 0 0 0 0 1 1 0 1
4 0 0 0 0 0 1 0 0
5 1 0 0 1 0 1 1 0
6 1 0 0 1 0 0 1 0
7 0 0 1 0 0 0 0 0
8 0 0 0 0 1 0 1 0

© 2016, Ling Ma 

Matching of Relations

1
2
3
4
5
6
7
8
A
B
C
D
E

Element A's Feature

© 2016, Ling Ma 

Work items

  • Test the operators on bridge model provided by WP3
  • Complete the relationship matrices
  • Develop the matching algorithm

SeeBIM 2.0 : a Cloud-based Platform for Information Collaboration on BIM model

SeeBridge Workshop @ Cambridge

Dr. Ling Ma

Technion - Faculty of Civil and Environmental Engineering

ionlny0irvononeinyhSnfrctuoeao1eTvcopuirU2tatHbgienongesngrhd5eilzgeg9nlayne1rsfdiec
Technology global 2015 engineering University...
Huazhong University of Science and Technology ranked top 19 global university for engineering in 2015
Huazhong University of Science and Technology ranked top 19 global university for engineering in 2015

Goal?

(Rank by US news 2015 and Thomson Reuters)

© 2015, Dr. Ling Ma 

Previous work

© 2015, Dr. Ling Ma 

Previous work 

SeeBIM

Michael Belsky, PhD, “A FRAMEWORK FOR SEMANTIC ENRICHMENT OF IFC MODELS”, Civil and Env. Eng., Technion. 2015

© 2015, Dr. Ling Ma 

© 2015, Dr. Ling Ma 

Previous work 

Ease the information retrieval

SeeBIM

Michael Belsky, PhD, “A FRAMEWORK FOR SEMANTIC ENRICHMENT OF IFC MODELS”, Civil and Env. Eng., Technion. 2015

Extend the schema

© 2015, Dr. Ling Ma 

Horizontal/Vertical Element ? ->

                                    bounding box ->

                                             major axis ->

                                                     parallel/perpendicular to the ground

© 2015, Dr. Ling Ma 

Previous work 

Rationalize Domain Knowledge

Ease the information retrieval

SeeBIM

Michael Belsky, PhD, “A FRAMEWORK FOR SEMANTIC ENRICHMENT OF IFC MODELS”, Civil and Env. Eng., Technion. 2015

Extend the operand and operator

Extend the schema

© 2015, Dr. Ling Ma 

SeeBIM 2.0

© 2015, Dr. Ling Ma 

© 2015, Dr. Ling Ma 

As-damaged model

© 2015, Dr. Ling Ma 

© 2015, Dr. Ling Ma 

© 2015, Dr. Ling Ma 

© 2015, Dr. Ling Ma 

© 2015, Dr. Ling Ma 

Next?

© 2015, Dr. Ling Ma 

©2016, Dr. Ma, Ling 

  1. Develop domain ontology for bridge inspection
  2. Upload BIM model, query data
  3. Develop APIs

You can join us

© 2015, Dr. Ling Ma 

Semantic Enrichment for Bridges

Dr. Ling Ma

Technion - Faculty of Civil and Environmental Engineering

© 2016, Dr. Ling Ma 

Conditions of the Highway Bridges

One in nine of the bridges in the US are structurally deficient

FHWA needs $20.5 billion annually to eliminate the bridge deficient backlog by 2028, but $12.8 billion is being spent

607,380 National Bridges in the US !!!

Over two billion trips are taken daily across these bridges

© 2016, Dr. Ling Ma 

Bridge Inspection - Current Practice

14,700 bridges in Georgia

42 Inspectors in GDOT

Descriptive Inspection Report

AASHTOWare Bridge Management System (BMS)

Good?
Satisfactory?
Desirable?

Bridge Risk Prediction

© 2016, Dr. Ling Ma 

Bridge Management System

3D Semantically rich bridge model (Bridge Information Model)

+

=

McGuire, B., et al., (2016) Bridge Information Modeling for Inspection and Evaluation. Journal of Bridge Engineering

Average age: 42 years

Time consuming

Infravation - SeeBridge

© 2016, Dr. Ling Ma 

© 2016, Dr. Ling Ma 

Data Collection

Videogrammetry

(GoPro4 black)

Mobile laser scanning

(Trimble MX7)

Terrestrial laser scanning

(Leica c10)

© 2016, Dr. Ling Ma 

Georgia (3)

Data Collection

Cambridge (10)

Haifa (1)

3D Reconstruction

© 2016, Dr. Ling Ma 

Semantic Enrichment

© 2016, Dr. Ling Ma 

Bridge Defects

© 2016, Dr. Ling Ma 

Copy of SeeBridge

By Ling Ma (Lorin)

Copy of SeeBridge

Research Sharing - Ling Ma

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