knowledge management and its research implications in construction management and education...

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

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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• Pacific Northwest Center for Construction Research and Education – Virtual Construction Lab

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• Pacific Northwest Center for Construction Research and Education – Methods and Materials Lab

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• 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

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

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

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

Data Mining for Construction

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• 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.


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


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


• 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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Data Mining



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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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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• 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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• Performance : # of Relevant Documents Found

24Figure 6. Performance evaluation in terms of the # of relevant

documents found


• Performance: # of Manufacturers Found

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Figure 7. Performance evaluation in terms of the # of manufacturers found


Last revised 07.15.2009 26Figure 8. Information Acquisition Took Kit


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Data Mining


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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Data Mining


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?

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

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

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

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

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

Thank You

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