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A BIM Integrated Framework to Predict
Schedule Delays in Manufactured and
Modular Construction
Master’s Plan B Report
By
S...
Two truths and a lie about me
• I took acting classes and have been in a
commercial
• When I was a kid, I wanted to be a P...
Outline
• Research Goals
• Research Methods
• Framework Development
• Demonstration with a case scenario
• Research Findin...
Introduction
• Literature has demonstrated the effect of risk
management on a construction project
• Scheduling errors and...
Problem Statement
• Most of the previous literature demonstrated the
need of manual inputs in regard to domain
knowledge
•...
Research Gap
• No guidelines, specification on generation,
accumulation and storage of digitalized
construction project da...
Existing Practices
Risk Management in Construction
Identify Analyze Respond
Qualitative & Evaluate
Surveys Based on Cost &...
Research Goals
• Define the data structure of Virtual Design and
Construction (VDC) technology to streamline operation
wor...
Research Methods
• Literature Review
• Framework Development
• Case Demonstration
4/21/2017 Master's Research- Sahil Navla...
Ideology
4/21/2017 Master's Research- Sahil Navlani 10
DEFINE MEASURE ANALYZE IMPROVE CONTROL
Business & Data Data Data Ev...
Framework Development
4/21/2017 Master's Research- Sahil Navlani 11
As-
Planned
Schedule
As-Built
Schedule
Knowledge Base
...
Framework Features
• Based on the Lean six sigma DMAIC techniques
which is a iterative process improvement cycle.
• Employ...
Framework Workflow
4/21/2017 Master's Research- Sahil Navlani 13
Data Transformation from structuring to
Warehousing
4/21/2017 Master's Research- Sahil Navlani 14
Case Demonstration
4/21/2017 Master's Research- Sahil Navlani 16
Case scenario: Modular Construction
• A speciality modular construction firm
considered to simulate conceptual schedules
•...
Case Description
As-built Duration (days) Variation
Case 1 35 None
Case 2 42 None
Case 3 39 2 out of 6 window sizes
change...
Data Structuring
• Baselined to LOD 300
• The Model Element is graphically represented within the
Model as a specific syst...
Data Warehousing
• IFC file interface to filter the attribute export for
specific building objects
• The exported workshee...
Predictive Modeling
• Feature Engineering
• Classification Algorithm
4/21/2017 Master's Research- Sahil Navlani 21
Cross-validation & Interpretation of Results
• Percentage split
• Result model
4/21/2017 Master's Research- Sahil Navlani ...
Research Findings and Contribution
4/21/2017 Master's Research- Sahil Navlani 23
Research Findings and Contribution
• As AEC industry is advancing in the new era of
technological advancements, Data Analy...
Limitations and Future Research
4/21/2017 Master's Research- Sahil Navlani 25
Limitations
• Resiliency in the construction industry
• Lack of Digitalized data
• Validation
4/21/2017 Master's Research-...
Future Research
• Framework extensible to other domains in the
construction industry
• Extending applications of Data Anal...
Questions & Discussions
4/21/2017 Master's Research- Sahil Navlani 28
Thank You
4/21/2017 Master's Research- Sahil Navlani 29
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A BIM-integrated framework to predict schedule delays in Construction

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The construction industry has been encountering critical issues in project delays. Fortunately, technological advances, such as the building information modeling (BIM), offer potential solutions.
This report aims to establish a BIM-integrated framework that can be used to provide data-driven scheduling decisions for construction management. The study explores the intersection of the construction and data analytics domains. The framework captures operational data from a BIM model and put them into a machine learning algorithm to facilitate prediction.
(This article is an excerpt from my Master's research at Michigan State University and is a piece of intellectual property, Please provide appropriate references while citing the work. Use the link https://goo.gl/827Zs8 to access the full report.)

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A BIM-integrated framework to predict schedule delays in Construction

  1. 1. A BIM Integrated Framework to Predict Schedule Delays in Manufactured and Modular Construction Master’s Plan B Report By Sahil Navlani Construction Management Master’s Candidate 4/21/2017 1
  2. 2. Two truths and a lie about me • I took acting classes and have been in a commercial • When I was a kid, I wanted to be a Pilot • I’ve been into 3 major motor-crashes. 4/21/2017 Master's Research- Sahil Navlani 2 More about me • Indian, Civil Engineer growing into Construction management • Firm beliefs in passion, innovation, hard-work & rationalism
  3. 3. Outline • Research Goals • Research Methods • Framework Development • Demonstration with a case scenario • Research Findings and Contribution • Limitations and Future Research 4/21/2017 Master's Research- Sahil Navlani 3
  4. 4. Introduction • Literature has demonstrated the effect of risk management on a construction project • Scheduling errors and contractor delays have been categorized as some of the most frequent and impactful project management risks • Several methods have been proposed in forms of Monte-Carlo simulation, Bayesian belief networks, time series analysis to mitigate construction schedule inconsistency. 4/21/2017 Master's Research- Sahil Navlani 4
  5. 5. Problem Statement • Most of the previous literature demonstrated the need of manual inputs in regard to domain knowledge • Correlation factors, weights are sought out from seasoned professionals to mitigate schedule delay risks • The most common method of collection is through surveys and questionnaires 4/21/2017 Master's Research- Sahil Navlani 5
  6. 6. Research Gap • No guidelines, specification on generation, accumulation and storage of digitalized construction project data. • No existing methods to capture expert knowledge in the construction domain • Missing workflows for knowledge reuse in the construction industry 4/21/2017 Master's Research- Sahil Navlani 6
  7. 7. Existing Practices Risk Management in Construction Identify Analyze Respond Qualitative & Evaluate Surveys Based on Cost & Schedule Analysis Experience Resource Constraints Experience Cost Implications Mutual Agreement Schedule Implications Gut-Feeling Knowledge Data Analysis Data-Driven Decisions Base & Analytics Expert Judgement Proposed Framework 4/21/2017 Master's Research- Sahil Navlani 7
  8. 8. Research Goals • Define the data structure of Virtual Design and Construction (VDC) technology to streamline operation workflow for project risk knowledge management. • Develop an analytic framework to predict schedule data for data-driven assistance to facilitate construction schedule decision making. • Demonstration of the framework through a case study. 4/21/2017 Master's Research- Sahil Navlani 8
  9. 9. Research Methods • Literature Review • Framework Development • Case Demonstration 4/21/2017 Master's Research- Sahil Navlani 9
  10. 10. Ideology 4/21/2017 Master's Research- Sahil Navlani 10 DEFINE MEASURE ANALYZE IMPROVE CONTROL Business & Data Data Data Evaluation Deployment Understanding Preparation Modelling  What Data is available?  What Data is needed? What data is important beneficial to Project Risk management?  Data stored according to the prescribed data structure.  Preparation of Data Warehouse.  Discovery of hidden trends in the prepared datasets.  Perform predictive modeling on the prepared datasets.  Application of data analytic algorithms.  Mapping the outcomes for further qualitative input to the schedule. Figure 1 Superimposed DMAIC and CRISP-DM process flow
  11. 11. Framework Development 4/21/2017 Master's Research- Sahil Navlani 11 As- Planned Schedule As-Built Schedule Knowledge Base  Delay activities  Delay duration  Delay Reason  Person in-charge  Activity parameters i.e. dimensions, area, building level etc.  Project parameters i.e. project location, type etc. Qualitative Inputs Scheduler’s experience Assumed/asse ssed risk ratios Quantitative Inputs Delay duration Delay Reasons Person in-charge Delayed project
  12. 12. Framework Features • Based on the Lean six sigma DMAIC techniques which is a iterative process improvement cycle. • Employs data analytics techniques for passive knowledge capture • Leverages the Building Information Modeling practice, to facilitate risk management by defining the Data Structuring and Warehousing methods. 4/21/2017 Master's Research- Sahil Navlani 12
  13. 13. Framework Workflow 4/21/2017 Master's Research- Sahil Navlani 13
  14. 14. Data Transformation from structuring to Warehousing 4/21/2017 Master's Research- Sahil Navlani 14
  15. 15. Case Demonstration 4/21/2017 Master's Research- Sahil Navlani 16
  16. 16. Case scenario: Modular Construction • A speciality modular construction firm considered to simulate conceptual schedules • The aforementioned project with the same floor plan and little variation in building objects was developed. • Modular housing construction was chosen for demonstration, pertaining to their little to no variability in the building objects, systems and floor plans while leveraging the construction schedule for facilitating the learning of the4/21/2017 Master's Research- Sahil Navlani 17
  17. 17. Case Description As-built Duration (days) Variation Case 1 35 None Case 2 42 None Case 3 39 2 out of 6 window sizes changed to be smaller Case 4 38 Wall thickness increased, door size decreased and the roof systems changed to EPDM 4/21/2017 Master's Research- Sahil Navlani 18
  18. 18. Data Structuring • Baselined to LOD 300 • The Model Element is graphically represented within the Model as a specific system, object or assembly in terms of quantity, size, shape, location, and orientation. Non-graphic information may also be attached to the Model Element. • Data loaded using project and shared parameters 4/21/2017 Master's Research- Sahil Navlani 19
  19. 19. Data Warehousing • IFC file interface to filter the attribute export for specific building objects • The exported worksheets will be compiled using a Macro enabled excel workbook 4/21/2017 Master's Research- Sahil Navlani 20
  20. 20. Predictive Modeling • Feature Engineering • Classification Algorithm 4/21/2017 Master's Research- Sahil Navlani 21
  21. 21. Cross-validation & Interpretation of Results • Percentage split • Result model 4/21/2017 Master's Research- Sahil Navlani 22
  22. 22. Research Findings and Contribution 4/21/2017 Master's Research- Sahil Navlani 23
  23. 23. Research Findings and Contribution • As AEC industry is advancing in the new era of technological advancements, Data Analytics proves to be viable and Feasible. • The research’s major contribution is exploratory conjunction within the AEC and Data Analytics industry. Research accomplishes the goals set forth by proposing a functional framework for implementation. 4/21/2017 Master's Research- Sahil Navlani 24
  24. 24. Limitations and Future Research 4/21/2017 Master's Research- Sahil Navlani 25
  25. 25. Limitations • Resiliency in the construction industry • Lack of Digitalized data • Validation 4/21/2017 Master's Research- Sahil Navlani 26
  26. 26. Future Research • Framework extensible to other domains in the construction industry • Extending applications of Data Analytics in the construction industry • Text mining • Process mining 4/21/2017 Master's Research- Sahil Navlani 27
  27. 27. Questions & Discussions 4/21/2017 Master's Research- Sahil Navlani 28
  28. 28. Thank You 4/21/2017 Master's Research- Sahil Navlani 29

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