Making Intelligent Asset Decisions Using a GIS …June 20, 2012 Separating the Forest from the...

June 20, 2012

Separating the Forest from the Trees: Making Intelligent Asset Decisions Using a GIS Decision Tool

Marc A. Lehmann, PE

Agenda

• Project location

• Project description

• Brief history

• Methodology description

• Closure

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Project Location

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Project Description

• Detailed studies to identify the locations and causes of:

– Sewage overflows from manholes

– Sewage system surcharging

– Sewage backup into basements

• Mitigate and/or eliminate for a selected performance target

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Decision Tree History

• 2005: Method developed for the Livingston/James Project

• 2009: Method adopted and used for a major municipality in Minnesota

• 2009: Ported to C# for inclusion in ArcGIS 10 platform

• 2010: Method used on the Northwest Alum Creek Project

• 2011: Method to be used in later phase in Medellin, Colombia

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So what is the Decision Tree?

• GIS-based decision making dashboard incorporating:

– PACP CCTV Data

– Hydraulic and hydrologic data

– Spatial GIS data (topography, surface development, etc.)

• Enabling:

– Flexible and reproducible decision making

– Viewing CCTV footage while overlaying recommendations

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Collapsed View

1. Definitions

2. Decisions

3. Prioritization

5. Data Queries/Review

4. Cost Estimating

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5-Step Methodology

• Step 1: Define problems

• Step 2: Decision making

• Step 3: Prioritization

• Step 4: Cost comparison

• Step 5: Data queries and review

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Step 1: Define the Problem

• Roots

• Infiltration

– Runners and gushers

• Pipe sags

• Pipe cracks and fractures

• Structural Level 5 defects

Paper ID #

• Relies on PACP incident codes as a point of beginning

• How much is too much?

• Which observations are more important to others?

– Roots vs. structural defects

– Grease vs. sags

Paper ID #

Step 2: Decision Making

Paper ID #

The Method

Decision Making Example

Step 3: Prioritization

• Surface development

– Under a major structure?

– Near a hospital?

– Service area?

• Flow regime

– Low velocity?

– Under capacity?

– High I/I?

• Depth

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Step 4: Cost Comparison

• Level of service

– What is desired?

– What is required?

– Does the system meet LOS?

• Available resources

– Does program burden existing funding?

– Does the program need to be adjusted?

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Paper ID #

Step 5: Data Queries

• Where are improvements located?

• How can these improvements be programmed?

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Closure

• Paradigm shift from “Find & Fix”

• Methodology is transparent

– Assumptions are imbedded directly into the tree

– Removes subjectivity

• Methodology is flexible

– Any quantifiable “finding” can be incorporated

– Impact of changes can be seen very quickly

• Basis of improvements is reproducible

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For More Information

• Marc A. Lehmann, PE, CDM Smith

LehmannMA@CDMSmith.com

• C. Timothy Fallara, PE, City of Columbus

CTFallara@columbus.gov

• Rodney Moeller, PE, CDM Smith

MoellerRA@CDMSmith.com

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