2013 ASPRS Track, Modeling Asset Condition Using LIDAR and GIS Data by Colin Hobson

Modeling Asset Condition Using LIDAR and GIS Data

Colin Hobson Director, North America Open Spatial Corporation

This presentation outlines the data and techniques used to provide a prioritized rating value and map of the condition and improvement priority of the collection system infrastructure as part of the development of an Asset Management Information Plan (AMIP) for the City of Albany

Overview and purpose

Conceptual model and data sources

COND_CATEGORY

A - 10 or more years

B - 5-10 years

C - 1 - 5 years

OBSERVED CONDITION Structural condition Ops and maint. condition

PROBABILITY (OF FAILURE) Age Material

CONSEQUENCE Pipe Diameter Environmental proximity Construction Critical crossings

RISK SCORE

CONDITION SCORE

Prob.

Con

seq.

Cond.

Ris

k

TOTAL SCORE

Build Asset Intelligence

Paper maps As builts & projects

CAD drawings

Seamless, connected, attributed network

Modeling, Online maps, mobile apps

Management WO’s, Planning

• Structural

• Ops and Maint

• Age • Material • Diameter

• Condition from CCTV

• Derived from CMMS Score based on maintenance and repairs required

• Derived from as-builts,

field notes and stored in GIS database

Data sources

• Build attributed asset inventory in Munsys using – Existing CAD/DWG maps – Paper as-builts – Field check and updates – In the office

• Checking • Imagery • Google

• Autodesk Civil 3D and Munsys for LIDAR surface and flow analysis

• Autodesk InfraWorks

How was the data gathered and checked

Reference and locate as-built documents

Reference and locate as-built documents

Update directly from as-builts

Track and find referenced as-built documents

Show and list all referenced as-builts for the project area (solid red area)

Search, find and hyperlink in a browser

Search, find and hyperlink in a browser

Selected objects

Summary list of objects

Individual details and hyperlink

Search, find and hyperlink in a browser

As-built document opened from Hyperlink

Fully attributed asset inventory in Munsys

Error checking using 3D visualization

From asset inventory to 3D visualization

Example factors

LIDAR data used for flow checking between sewer and storm systems: Surface loaded in Autodesk Civil 3D with Sewer and Storm data

LIDAR data used for flow checking between sewer and storm systems: Water surface flows displayed

LIDAR data used for flow checking between sewer and storm systems: Flow from Sewer to Storm recorded – distance, ID and type of asset

Proximity factors

Using the model – data manipulation Linked GIS Source data Calculated values and derived categories

Using the model – calibration categories Set category ranges and factors – view changes dynamically

Using the model – scores from matrices

Using the model – data listing of score, priority and repair and replace costs for each pipe segment

Output on the Map – Repair categories

Tangible value – repair priorities

Conceptual model and data sources

COND_CATEGORY

A - 10 or more years

B - 5-10 years

C - 1 - 5 years

OBSERVED CONDITION Structural condition Ops and maint. condition

PROBABILITY (OF FAILURE) Age Material

CONSEQUENCE Pipe Diameter Environmental proximity Construction Critical crossings

RISK SCORE

CONDITION SCORE

Prob.

Con

seq.

Cond.

Ris

k

TOTAL SCORE

Thank you

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