The document outlines a meetup focused on applying analytics in construction management, introducing key terminologies and practical applications across various construction areas. It emphasizes the importance of collaboration between industry and academia to upskill professionals and discusses the use of data science for cost estimation, risk analysis, and project performance. Future initiatives include workshops, hackathons, and community engagement activities to further the adoption of analytics in the construction sector.

1. Application of Analytics in
Construction
Management
An Introductory session

2. Problem definition
Introduction
01
Understand terminologies
Definitions
02
Investigate practical applications in various areas of construction
Practical Applications
03
Discuss Future Strategies
The Meetup Strategy
04
Agenda

3. Introduction

4. Hassan Emam
About me

5. Historical Perspective

6. Why bother?
Data and Analytics Facility for National Infrastructure

7. Data Explosion
Source: Adham, K. Big Data Analysis and Storage

8. • Knowledge Sharing
• Interdisciplinary interaction
• Networking
• Bridging the gap between the industry and academia
• Share success cases
• Upskilling construction practitioners in the field of Analytics
• Upskill data scientist and Analytics professionals with the
opportunities in the construction industry
• Collaboration and joint efforts to disrupt current practices in the
construction industry
Objectives of this Meetup

9. Definition

10. Definitions
Business Case
Analysis
Qualitative
Analytics
Preliminary Data
Report
Business
Intelligence
Forecasting
Optimisation of
operations
Business
Data
Science
PresentPast Future

11. Definitions
Business Case
Analysis
Qualitative
Analytics
Preliminary
Data Report
Dashboard
Reporting
Forecasting
Optimisation
of operations
Business
Artificial
Intelligence
Machine
Learning
Data
Science/
Analytics
PresentPast Future
Business
Intelligence

12. Types of Data
Data Types
Structured Data
Continuous Categorical
Big Data
Later

13. Predictive Analytics
Predictive Analytics
Supervised
Regression Classification
Unsupervised
Clustering
Reinforcement

14. Linear Regression

15. Non linear regression

16. Classification

17. Big Data
Big Data
Volume
Velocity
Variety
Variability
Value
Veracity

18. Big Data Analytics
Big Data Analytics
Computer
Vision
Natural
Language
Processing
Audio
Recognition

19. Neural Networks

20. Practical Applications

21. Data Science Process

22. Analytics Effort
Source: Forbes

23. Data in Construction Context
Cost Estimating
Change
Management Cost Control
Finance (ERP) Risk Analysis
Programme
Management
Performance &
Reporting

24. Data Streamlining using BIM
Cost Estimating
Change
Management Cost Control
Finance (ERP) Risk Analysis
Programme
Management
Performance &
Reporting
BIM

25. Analytics Applications in Construction
Discipline Applications
Health and Safety PPE detection,
Hazards warning,
Near miss reporting
Pose estimation for detecting
Security Face recognition
ANPR
Automated detection of intruders
Theft detection
Quality Compliance check, defects inspection, process validation
Stakeholders Quantification of Community engagement effectiveness
Duration prediction for consents
Environment Emission prediction
Recycling using CV and Robots
Ecology Surveys

26. Analytics Applications in Construction
Discipline Applications
Controls and Reporting BI tools to automate reporting
Performance monitoring
Automated progress capturing
Accurate forecasting
Schedule duration estimation
Cost estimation prediction
Procurement Work winning
Review bidding companies
Commercial Management of Contracts
Correspondence Monitoring
Engineering Design Compliance Review
Design Recommendations
Recommendations for design
HR Recruitment skills analysis
Interview video analysis
Absenteeism prediction

27. Data Science for H&S

28. Security

29. Performance Monitoring

30. Planning and Controls

31. Community Engagement

32. Procurement
RFP
• Request for proposal
Requirements
• Using natural language processing (NLP) requirements are
extracted
Mapping to
offering
• Identified requirements are mapped to offerings
Pricing
• Using predictive algorithm, estimation of price is
performed
Outcome
• Predicting bidding outcome with high level of confidence
Win/No Win

33. Our Community Strategy

34. Next Steps
Our plan
• Monthly Meetup
• Survey for other plans
• Workshops
• Hackathons
• Training Sessions
• Open Data Initiative (ODI)

35. • This community is owned by everyone. Hence, we are expecting
community.
• Promoting the community to your contacts
• Propose Speakers
• Volunteers for organising
• Venues to host upcoming events.
Next event is planned on 9th July. Details to follow
Your Contributions

36. Q&A

37. Amsterdam  Birmingham  Brisbane  Dublin Hong
Kong  London  Perth  Sydney
https://twitter.com/hassanemam
https://www.linkedin.com/in/hassanemam/
Connect with Me
https://www.meetup.com/Birmingham-Project-Management-Meetup/