The document presents a framework for integrating building information modeling (BIM) with data analytics to predict schedule delays in modular and manufactured construction. It aims to develop a data-driven framework to assist construction schedule decision making. The framework is based on lean six sigma techniques and employs data analytics on BIM data structured according to a defined data model. A case study demonstrates applying the framework to predict delays on a modular housing project. The research contributes an exploratory conjunction of construction and data analytics and proposes a functional framework for implementation.

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. 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.
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More about me
• Indian, Civil Engineer growing into Construction
management
• Firm beliefs in passion, innovation, hard-work &
rationalism

3. Outline
• Research Goals
• Research Methods
• Framework Development
• Demonstration with a case scenario
• Research Findings and Contribution
• Limitations and Future Research
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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.
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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
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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
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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
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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.
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9. Research Methods
• Literature Review
• Framework Development
• Case Demonstration
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10. Ideology
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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. Framework Development
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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. 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.
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13. Framework Workflow
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14. Data Transformation from structuring to
Warehousing
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15. Case Demonstration
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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. 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
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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
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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
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20. Predictive Modeling
• Feature Engineering
• Classification Algorithm
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21. Cross-validation & Interpretation of Results
• Percentage split
• Result model
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22. Research Findings and Contribution
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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.
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24. Limitations and Future Research
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25. Limitations
• Resiliency in the construction industry
• Lack of Digitalized data
• Validation
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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
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27. Questions & Discussions
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28. Thank You
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