knowledge management and its research implications in construction management and education...
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Knowledge Management And Its Research Implications In Construction
Management and Education
Presented by Ken-Yu Lin, PhDAssistant Professor
Dept. of Construction ManagementCollege of Built Environments
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About the Speaker• 1993-1997 B.S., CE, NTU• 1997-1999 M.S., CE, NTU• 2000-2005 Ph.D., CEE, U of I
Fulbright Exchanged Scholar• 2005-2006 Consultant, Ming-Jian Company• 2006-2007 Post-doc, CE, NTU• 2008 to date Assistant Professor, CM, UW
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University of Washington
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University of Washington• Fall 2008 Enrollment – 41,405 students
• Ranked Top 16 of World Universities by Shanghai Jiao Tong University (2008)
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University Total No. of Student
Total No. of Undergraduate
Students
No. of Graduate Students
NCKU (2007)
21,184 10,451 10,733
UW (2008) 41,405 29,304 12,101
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Dept. of Construction Management
• College of Built Environments– Architecture– Construction Management
• 10 full-time faculty members• 13 lecturers• 30-ish industry advisory council members
– Landscape Architecture– Urban Design and Planning
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Dept. of Construction Management
• Pacific Northwest Center for Construction Research and Education – Virtual Construction Lab
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Dept. of Construction Management
• Pacific Northwest Center for Construction Research and Education – Methods and Materials Lab
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Dept. of Construction Management
• Pacific Northwest Center for Construction Research and Education - Education Lab & Collaboration Suites
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CM Graduate Programs
• M.S. Program in Construction Management• Distance Learning M.S. Program in
Construction Engineering• Ph.D. Program in the Built Environments
Last revised 12.21.2008 9
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CM Graduate Programs• M.S. in CM
– Core courses (9 credits)– CM focus areas (27 or 33 credits)
• Integrated Project Delivery Systems• Sustainable Built Environment• Infrastructure Development• International Construction• Virtual Design and Construction
– Thesis or research paper (9 or 3 credits)
Last revised 12.21.2008 10
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CM Graduate Programs• Ph.D. Program in the Built Environments
– Core Courses (21 credits)• History, Theory, and Ethics• Colloquium-Practicum• Research Methods and Design
– Areas of Study (30 credits)• Sustainable Systems and Prototypes• Computational Design and Research
– Examination/Dissertation (30 credits)
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What is Knowledge?• Data, Information, Knowledge
– “Данные”– “82%”
• Valuable information for the human mind (Davenport, 1997)
• Information only becomes knowledge when it is put into a logical and understandable context which can be verified and recalled from human experiences (Gunnlaugsdottir, 2003)
12Last revised 07.15.2009
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What is Knowledge Management?• Knowledge Classification
(Nonaka and Takeuchi, 1995)– Explicit
• Knowledge that can be articulated in formal languages
– Tacit• Subtle level of understanding often rooted in practice,
expressed through skillful execution, and transmitted by apprenticeship
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Knowledge-assisted Information Retrieval
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Data Mining
Ontology Knowledge Sharing and Reuse
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Data Mining for Construction
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Data Mining for Construction
• Fort Wayne, IN - Flood Control Project*– Four phases– Significant project delay (54%) for the
6”-42” drainage pipeline installation– Main sub-activities
• Excavating the ground• Installing pipelines• Erosion protection• Backfilling compacted material
16* Source - Soibelman, L, and Kim, H., “Knowledge Discovery for Project Delay Analysis” Bauingenieur, Springer VDI Verlag, February 2005.
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Data Mining for Construction
Original DataPre-Processing DataPost-Processing DataPre-computing the Data
Data Base Systems
AI Tools
Visualization Tools
Assess Model
Decision Support
Expert Knowledge
Understand and Define
the Problem
CollectData
Enhance Data
Clean Data
Mine
the Data
Data Analysis
Evaluate the
Result
Select Data
Attribute
Explore Data
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Figure 1. Data mining framework
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Data Mining for Construction
Weather was not the main cause
Inaccurate site survey appeared to be moreinfluential
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Figure 2. Analysis for the Data Mining Example Project
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Data Mining for Construction
• Predicting Activity Duration– Adjusting the duration for 320-unit drainage pipe
installation with 10 available workers• Empirical Data from RS Means
– Industry productivity average = 10 units/worker/day– 3.2 days
• Neural Network prediction– 4.96 - 6.86 days
• Monte Carlo Simulation– 3.51 – 6.85 days
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Knowledge-assisted Information Retrieval
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Data Mining
Ontology Knowledge Sharing and Reuse
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Knowledge-assisted Information Retrieval
Translucent roof panels
Fiberglass
Skylight
BT
RTRT
UF
Translucent roof assemblies
UF
LegendUF: Use-for TermBT: Broader TermRT: Related Term
Natural light
Daylighting panels
Translucent roof systems
UF
Figure 3. An example knowledge representation
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Knowledge-assisted Information Retrieval
Figure 4. Illustration of the crawling and ranking strategy
Query expansion by semantic components
Query expansionby lexical components
Pooling
Grow Trim
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Knowledge-assisted Information Retrieval
• P-Norm (Salton et. al., 1983)
Goal (1,1)(0,1)
(1,0)(0,0)
Daylighting
Panels
d1
d3 d2
Null (0,0)
(0,1)
(1,0)
Daylighting
Panels
(1,1)
d1
d2
Figure 5. Illustration of the ranking model
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Knowledge-assisted Information Retrieval
• Performance : # of Relevant Documents Found
24Figure 6. Performance evaluation in terms of the # of relevant
documents found
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Knowledge-assisted Information Retrieval
• Performance: # of Manufacturers Found
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Figure 7. Performance evaluation in terms of the # of manufacturers found
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Knowledge-assisted Information Retrieval
Last revised 07.15.2009 26Figure 8. Information Acquisition Took Kit
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Knowledge-assisted Information Retrieval
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Data Mining
Ontology Knowledge Sharing and Reuse
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What is Ontology?• According to Gruber 1993
– A formal, explicit specification of conceptualization
– An agreement to use a vocabulary in a way that is consistent with respect to the theory specified by an ontology
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(Method by Uschold and Gruninger, 1996) (Method by Noy and McGuinness, 2001)
Identify Purpose and Scope
Ontology Capture
Identify Concepts
Produce Definition of concepts
Identify Entities and Relations
Ontology Coding
Determine the domain and scope
Reuse existing ontologies
Enumerate important terms
Define the classes and the class hierarchy
Define the property of classes
Define the facets of the slots
Create instances
Figure 9. Ontology Development Procedure Exemplars
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Objective 1. Domain definition
Task 1.1 Determining green purchasing processesTask 1.2 Identifying needed informationTask 1.3 Specifying how project stakeholders gather and utilize the information
Step 1.1 Mapping purchasing procedure using the process mapping methodStep 1.2 Designing and implementing questionnaire survey
Objective 2. Green purchasing semantic model development
Task 2.1 Reusing existing ontologies
Task 2.2 Building green purchasing taxonomiesTask 2.3 Specifying concept restrictions and enumerations
Task 2.4 Applying taxonomy concepts to model the green purchasing information needs
Step 2.1 Defining model scope
Step 2.2 Exploiting existing ontologies Step 2.3 Deriving concepts/terminologies Step 2.4 Structuring concept hierarchies Step 2.5 Assigning concept restrictions and enumerationsStep 2.6 Semantic modeling
Step 2.7 Model coding and visualization
Objective 3. Model evaluation and improvement
Task 3.1 Completeness evaluation
Task 3.3 Conciseness evaluation
Task 3.2 Consistency evaluation
Step 3.1 Scope and scenario verificationStep 3.2 Logic checking via reasonerStep 3.3 Interviews and surveys with AEC practitioners
Step 3.4 Feedback and model revision
Task 3.4 Iterative model revision
Figure 10. Green Purchasing Ontology Development Procedure
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Figure 11. Developing Domain Ontology Using Domain Handbooks
(*Source: H.T. Lin, S.H. Hsieh, K.W. Chou and K.Y. Lin, “A Statistical Method for Constructing Engineering Domain Ontology through Extraction of Knowledge from Domain Handbooks”, Advanced Engineering Informatics, under review. )
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Knowledge-assisted Information Retrieval
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Data Mining
Ontology Knowledge Sharing and Reuse
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KM in CM Education
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• Drowning in Data– How Could Construction Students Find Useful Online
Resources for Learning and Researching Needs?
• Knowledge Is Not Power, Sharing Is– How Can One Student Share the Captured Construction
Online Resources with Other Students Who Have Similar Learning or Researching Interests?
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KM in CM Education
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AAAA
BBBB
CCCC
Resource XResource X Concept ZConcept Z
Resource YResource Y
Figure 15. Learning through socialization
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KM in CM Education
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Figure 16. Sustainable e-learning model
Resource Repository
Online Resources
Domain Knowledge
Setting the Contexts
Knowledge Coding
Socializing
Searching
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KM in CM Education• A Pilot Study
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Wiring Components: Hot, Neutral, Ground
Current Kills, Not the Voltage
Breaker for Equipment Protection
Basic Electrical Concepts: Volts,
Amps, Ohms
Tool Maintenance and Inspection
Common Hazards: Shocks, Burns,
Explosions, Fire
Reverse Polarity
Grounding
Using GFCI Outlets for Personnel
Protection
Safety Prevention
Figure 17. e-Learning exemplar on construction S&H
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KM in CM Education
• Feasibility Study– Electrical safety in
construction– Students selected 10
concepts to plot mind maps and then furnished each concept with 3 online resources
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Figure 18. Data Processing Interface
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KM in CM Education
• Preliminary Data Analysis– Responses from 28 Students Were Analyzed– 717 Online Resources Were Identified– 363 Online Resources Were Reused– Most Cited
• OSAH, Wikipedia, YouTube, WA L&I, and CDC/NIOSH
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KM in CM Education
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Criteria Number of LinksEffective (before adjustment) 717Cited by more than one student (before adjustment)
363 (= 50% of 717)
Effective (after adjustment) 599Cited by more than one student (after adjustment)Unique
154 (= 25% of 599)
528
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Thank You
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