Excellence in Engineering Since 1946
Strand Associates, Inc. ( ) Lessons Learned Out In The Collection System Tom Brankamp, PE OWEA State Collection Systems Committee Specialty Conference May 18, 2016
Why Asset Management? Aging infrastructure Limited budgets Deferred maintenance Sewer collapse closes railroad crossing in Oshkosh (May 2016) Sewer collapses in Jeffersonville IN (Sept. 2015) Sewer collapse blamed for northwest Ohio sinkhole (July 2013) Sewer collapse causes lane closure on Lexington Road in Louisville (July 2011) Sewer line collapses in front of OH fire station (June 2015) Unknown $
What is Asset Management? The business side of utility work: Operating and maintaining an asset from the end of installation through the end of it s useful life (or the beginning of replacement).
For Today: Inventory and condition assessment work for sewer collection systems Have a Plan Use of Technology QA/QC of the Collected Data Lessons Learned Project Examples
The Philosophy: Quality data is the foundation for sound engineering and operational decisions. Bad data leads to bad decisions which leads to bad projects and that s a waste of your time and money! Every decision you make is based on the best available information. You will use your data every day through your O&M activities and future projects.
As Owners, Some Things to Think About: Do you just want to get your system functional? Where are the problem areas in my system? Do you have a lot of I/I to get out of the system? What are your critical assets? What is you long term vision for your system? How much money to you want to spend collecting additional data? Where should you focus your investigations? Who will be your champion? Who are your vital resources? How will you document all of this? What else do you want to do with the data? No one understands your system better than you!
To Be Successful, You Need A Plan Begin with the end in mind Be involved in the planning Perform a pilot study Have a QA/QC program Tweak process and procedures Keep your pulse on the field work Technology can be very helpful, but
You Need to Know What you want to collect Why you want to collect it What you are going to do with the data after it is collected Potential uses of the data: Infrastructure mapping Illicit discharge detection program Preventative maintenance Computerized maintenance management system (CMMS) Regional master planning Watershed modeling Design project Consider the useful life of the data
Ultimately, you want High quality data for intended uses To maximize the effectiveness of your data collection effort To minimize you number of trips to the field To collect the data right the first time
Data Collection Goals Only collect data that addresses your needs Don t collect data you don t need Make sure your view of condition is the same Encourage detailed comments Collect data necessary to tell the story Get pictures! How would you describe these?
How would you describe these?
How would you describe these?
22 pipes? Or 1 culvert?
The process evolves! Perform a pilot study to test your plan Evaluate to see what worked, didn t work
Use of Technology: Which is better- the old way or the new way? Start with the data you have, get what you need Spreadsheets, databases, GIS Advantages of technology Facilitates field navigation Standardizes responses Consistent data using pick-lists Allows quicker review of the data Allows quicker QC of the data Makes data available to users quicker Computer generated reports Technology streamlines data collection Data is compatible with GIS, CMMS
Graphical Reporting
Technology Lessons Learned Electronics are not fail proof Plan to have problems Keep spare equipment on hand Back up data often Use a pilot area as a test of the entire process Don t overcomplicate the process May collect too much data because you can
Example: Manhole Assessment Maintenance I/I Structural Deterioration
Manhole Assessment Overview Manhole Assessment and Certification Program (NASSCO) Level 1 Inspection- provides basic information Level 2 Inspection- much more detailed Uses defect coding system found in PACP Goal: Standardized process which results in uniformity of observations Project: MH rehab project Purpose: Functional understanding of system @ reasonable level of effort Do not need all of the data in Level 2 Make cleaning, rehab recommendations in field Note health and safety issues Take photos of unique situations Plan on 15 minutes per manhole (average)
Manhole Inspection Form
Example: Simplified Structural Rating RATING CODE CATEGORY MANHOLE / JUNCTION BOX 10 Excellent New Condition 9 Very Good Straight lines between bricks or joints 8 Good Minor misalignment at joints; no settlement 7 Satisfactory Minor misalignment and / or settlement 6 Fair Generally fair; additional minor misalignment and / or settlement still functioning as intended 5 Poor Marginal; significant settlement and / or misalignment 4 Serious Significant misalignment, loss of material has occurred 3 Critical Structure not functioning as intended 2 Failing Structure partially collapsed or collapse is imminent 1 Failed Structure collapsed
Other Types of Field Investigations and Assessments Same Approach, Different Tools Smoke Testing Storm Dye Testing Illicit Connections Investigations Private Property Dye Testing Laser Scanning Potholing (SUE) CCTV Goals: Cost-effectively identify / isolate problems Get enough data to make recommendations Prioritize if need is greater than your budget By sewershed By future project areas By maintenance logs By pipe sizes By critical assets
QA/QC of Collected Data The processes you put in place before, during, and after field data collection help to your ensure data is of high quality for the intended uses. Accuracies and Tolerances Set before you start Be sure they are achievable Top-side vs. confined space entries Don t guess just to have an answer Cannot determine and Difficult to measure are acceptable answers The physical features will not change significantly over time.
Example: QA/QC of MH Data (55,000 structures) X, Y & Z Coordinates Invert Depth Pipe Diameter, Width and Height Tolerance (GPS) Not greater than +/- 0.1 (Digitized) Not greater than +/- 15 Not greater than +/- 0.1 The measurements for these attribution are considered either right or wrong for the purpose of accuracy Objective vs. Subjective Data Accuracy Percentages
What Consultant Did (55,000 structures) Automated scripts to verify logic (132 of them!) Voids in the data Correctness of data Avoid contradictory and inconsistent data Depths across structure Negative slopes Visual checks of maps Inferred pipes Independent field checks Error trends by crew Consistency between field crews Make sure measurements are repeatable
What Client Did Combined independent field checks: Statistically random selection of structures Client and consultant staff field QC Field investigations to verify connectivity Automated programs to check data Flow direction and connectivity Comments review Duplicate structure IDs At the end of the day- it s your data!
There are a lot of tools in the toolbox, but to be successful, you need a plan Begin with the end in mind Be involved in the planning Perform a pilot study Have a QA/QC program Tweak process and procedures Keep your pulse on the field work Technology can be very helpful, but
Questions? Tom Brankamp Project Manager Tom.Brankamp@Strand.com Strand Associates, Inc. (513) 861-5600
Excellence in Engineering Since 1946