A Predicting pipe failures with CMMS/GIS/Modeling

Department of public works and engineering
DPW&Es budget for 1997 was over $600 M. Of this total, approximately $200 M was the capital expenditure program for wastewater facilities. Some of this money will be applied to emergency repair and unscheduled maintenance.

The Department of Public Works maintains extensive facilities, including water distribution, wastewater, and storm water runoff systems. Table 1 shows the relative sizes of these three systems. In an effort to help streamline and optimize the management process, RJN Group, Inc. was contracted by the City of Houston to develop a Geographic Information Management System (GIMS). This system will serve multiple purposes, including map maintenance and publishing and maintenance management. In addition, it will contain the predictive modeling component for use by the DPW&E. Predictive modeling has proven to be effective in a variety of fields related to infrastructure management including water pump, station maintenance, water main breakage, and pavement assessment. GIMS’ predictive modeling component will be used to determine failure likelihoods for Houston’s underground infrastructure system components.

TABLE 1. Estimated element lengths and counts for GIMS facility database components.

Facility	Miles	Elements
Water Distribution Mains	5600	295,000
Water Distribution Valves		148,000
Water Hydrants		148,000
Waste Water Lines	4980	105,000
Waste Water Manholes		105,000
Waste Water Services		750,000
Storm Water Lines	3800	40,000
Storm Water Manholes		40,000
Storm Inlets		160,000
Total:		1,791,000

City of Houston has a dynamic growth rate. Houston continues to annex areas that are unincorporated, thereby increasing its size and population on an on-going basis.

Data requirements for predictive modeling
The development of a decision support system that provides predictive modeling information requires several basic data services. A database of factors related to the failure of each system component must be designed, developed and maintained. The database management system must support database development (data collection and maintenance), access to the data via several methods, and interactive data query and display mechanisms.

A critical factor in developing an accurate model is having historical maintenance data. That is why it is imperative to develop an inventory of the existing underground infrastructure and track the maintenance history for these structures. The most efficient way to accomplish this is by means of a Computerized Maintenance Management System (CMMS).

CMMS database development
Data items describing each facility’s components must be continually captured, stored and maintained. Each of the facility components to be modeled will require access to numerous attributes for each actual component in the system. These attributes fall into several data categories: tabular, spatial, topological, and temporal. Tabular attributes include age, warranty date, and pipe type, and others. Spatial, topological and temporal attributes are important when predicting failure potential and failure rates. Spatial attributes include information concerning positional coincidence with other entities such as soil properties, and intersections with fault lines. Topological information includes properties of those features that are adjacent to the feature being modeled. Finally, temporal data will include historical maintenance records with time stamps, allowing repair dates and trends to be modeled.

In GIMS; attribute management is provided by a combination of R.TN’sCMMS called CASS WORKS and ESRI’S Arc/Info GIS. The facility management system is built on top of the Oracle relational database management system (RDBMS), and maintains the tabular factors such as length, diameter, and pipe material. The GIS supports direct connections to the RDBMS, and will supply the spatial and topological model parameters including soil type, and number of breaks within a certain proximity to a specified feature. The temporal attributes can be viewed as calculated values based on tabular data.

Data server tools
The predictive model tools will require access to the database via several different methods. SQL based queries can be performed in either the GIS, the facility management system, or using the RDBMS tools directly. The facility management system is designed to allow easy development of complex SQL queries, which can isolate sets of records for modeling based on tabular data criteria. Using a GIS allows for the creation of spatial and topological oriented data access methods. The results of the model runs must be stored back into the database for subsequent inspection and analysis.

Data presentation
The modeling results must be displayed using effective methods. Those results which are tabular in nature (e.g. sorted lists of facilities in need of repair) are handled in CASS WORKS. The GIS provides graphic mechanisms to view the results. Thematic mapping of a facility’s failure likelihood values is easily performed. This allows the user to display different failure levels graphically. Visual inspection of the spatial distribution of the thematic display may provide insight into patterns related to facility failure.

Predictive modeling tools
Predictive models in GIMS will supply a variety of tools to the planner. These include descriptive statistical methods, a variety of queries, potential use of expert systems and neural networks, predictive statistical analysis, and spatial analysis, all of which will be described in this paper. Accuracy verification is critical to the successful development of any of these tools. Verification is dependent on the continued development of a large historical database, which is in the process of being populated. Tabular data screens have been developed to manage facility maintenance data and work order processing, including historical repair data.

Descriptive statistical methods
Statistical methods can enhance the information in the database by creating variable transformations, descriptive values and basic relationships between component attributes and failure. For instance, summary statistics (mean, standard deviation, median, etc.) can be computed for interval and ratio data types. In addition, linear regressions can be run to determine the correlations between factors and failures. For example, age and pipe diameter has been correlated with failure rates of water mains, even though this is not necessarily a consistently reliable indicator. These values can be added to the database and become available to the planner for subsequent modeling efforts. These new data, combined with existing tabular data will support the execution of powerfid exploratory queries.

Queries
Given a rich set of facility attributes, summary and descriptive statistics, powerful queries can be run to provide insight into attribute/failure relationships. Queries considered to be standard will be built directly into the interface to allow push button execution. It would also be desirable to allow the user to develop customized queries. This can readily be accomplished for SQL type queries. However, queries retrieving data that is not tabular are more difficult due to the lack of a query language that supports both spatial and tabular attributes. By providing tools, a data dictionary and access to the GIS programming language, users will be able to develop new queries. Following are some examples of the type of queries that could be posed:

Select water mains where warranty is expired.
Select storm lines where age is greater than 2 standard deviations away from the mean age.
Select large diameter valves connected to cast iron pipes.
Select waste lines that have been repaired more than once in the last 5 years.