Visual Sensing and Analytics for Construction and Infrastructure Management

2015 CII Annual Conference August 3–5 • Boston, Massachusetts

Visual Sensing and Analytics for Construction and

Infrastructure Management

Academic Committee Annual Conference Speaker

General research question

• What are different types of visual sensing technologies?

– Current and coming in the horizon

• How do they perform for construction/infrastructure data

collection/situation awareness needs?

• What types of analytics can be performed on this data?

• How do they help in supporting a variety of construction and

infrastructure management decisions?

Panel

Lincoln Wood, Manager,

Virtual Design &

Construction, Turner Construction

Daniel Huber,

Senior Systems Scientist at Robotics Institute,

Carnegie Mellon Univ.

Mani Golparvar-Fard,

Assist. Prof of Civil & Env.

Eng.& Computer Scien.,

University of Illinois

Burcu Akinci,

Paul Christiano Prof. of Civil & Env. Eng.,

Carnegie Mellon Univ.

Image based sensing and analytics for

construction

Terrestrial scanners and analytics for construction and

infrastructure management

Aerial Robots for Infrastructure

Management

Visual Sensing and analytics current usages

IMAGE BASED SENSING AND

ANALYTICS FOR CONSTRUCTION

Smooth flow of production in construction

• Identifying different forms of waste

• 25-50% waste in coordination labor and equipment

and in managing, moving, and installation material.

• Cost overrun and delays

• 90% of projects exhibit average 28% higher cost than

their forecasted cost.

• What do I need for minimizing waste?

• Awareness on “who does what task, and where”

Opportunity- 5D BIM for progress analysis

Turner Construction

Operation-Level 4D (3D + Schedule) 5D (3D + Schedule + Cost)

PB

Extend the application of BIM/CIM primarily used for clash prevention

and constructability review as

a basis for monitoring work in progress

Opportunities for Reality capture (Images & Videos)

Drones equipped with cameras

Icarusaerials – U of Illinois Collaboration, 2015, FL

Autonomous Image Data Collection

Automatic creation of flight path

Opportunity- 4D As-built Models + 4D BIM

$500M Sacramento King’s Stadium Sacramento, California

Turner Construction

Automated Detection of Progress Deviations

Color-coding BIM elements based on traffic light color metaphor

Components ahead of schedule Components behind schedule

Weekly Work Plan Updates

Video-based Activity Analysis via Crowdsourcing

Worker with Role “A” is conducting Activity “B” with Tool “C”

LIDAR FOR CONSTRUCTION

Opportunity – LIDAR for reality capture

Hand-held laser scanners Terrestrial scanning

Mobile ground scanning Scanning with Drones Airborne lidar http://cenews.com/article/8332/mobile_laser_scanning

http://www.geoconnexion.com/news/optech-to-deliver-state-of-the-art-airborne-lidar-and-thermal-imaging-solut/_

Increased spatial

coverage and efficiency Increased precision and

Opportunity – Integration of Virtual with Reality

Context

Assessing the capabilities of 3D imaging technologies

Edge detection and

Boundary effects

Surface flatness

Points to BIM

Sensor / Data

Construction decision support

AERIAL ROBOTS FOR

INFRASTRUCTURE MANAG.

ARIA – The Aerial Robotic Infrastructure Analyst

• Benefits

– Go in difficult to reach places – Comprehensive monitoring – Reduced footprint

– Offline access

• Challenges

– Difficult to get hands on

ARIA Research Objectives

Algorithms transform 3D and imagery from the MAV into a high-level semantic model, and finally a finite element model.

Rapid infrastructure modeling and analysis

The robot acts as an inspector’s apprentice, learning to accomplish inspection tasks with

various levels of autonomy.

Robotic inspection assistant

A visualization environment provides an immersive virtual infrastructure representation to aid in inspection and assessment tasks.

Immersive inspection and assessment

VISUAL SENSING CURRENT

USAGES

Problem

• We are great planners but we are poor at adjusting as we go

Switch primary focus from Office to field to field to office

As-planned vs. as-built comparison and documentation requires images/videos to be located within BIM environment

Data Requirements

• Reliable/Trust

• Relevant- that impacts 3 week look-ahead plan

• Speed (must be at the pace of the conversation/meeting)

Location-based monitoring for tasks not associated with physical elements Highlighting as-risk locations with

Integrated point cloud and BIM

Task Readiness Level = R Location Entropy = TL

Social

• Patchwork or small batch is OK (crowd sourced with "infill" capture)

• Ownership of the builder / data collector

Conclusion

• Visual Sensing and Analytics can

– Reduce the gap between as-built and as-planned data

– Minimize the challenges for data collection, synthesis, and analytics

– Enable root-cause assessment on potential/actual deviations

– Detect and communicate at-risk locations

– In case of videos, provided detailed information on work efficiency

– During operation, provides up-to-date information on condition and

Future directions

•

Autonomous data collection:

Robotic vehicles path-planning and

data collection with engineered precision

•

Data processing and management:

Data cleaning without

removing artifacts, data fusion, big data management

•

Analytics:

Real-time awareness of work status and availability of

resources, more reliable weekly work plans, enhanced QA/QC and

safety monitoring, accurate and up-to-date as-is modeling

•

Communication and Visualization:

Real-time project controls,