DEVELOPMENT AND APPLICATION OF LIFE-CYCLE PREDICTIVE MODELING FOR UNDERGROUND INFRASTRUCTURE
Abstract:Sewers represent a very substantial and crucial part of our underground infrastructure and a high level of investment is needed to maintain them. Sewers are generally out of sight and, as long as they continue to function reasonably well, they are also out of mind – that is, until any structural, hydraulic or functional problems arise. The criticality of any sewer defect is a combination of the risk of failure and the consequences of it. One of the primary reasons that these systems are failing at an unmanageable rate is that many were installed in response to the post-World War II economic and population boom experienced in North America. These systems are now reaching the ends of their design and service lives (generally fifty years), and the deterioration will accelerate as we continue to use these systems in the upcoming decades. Therefore, a systematic approach is necessary to fully understand, evaluate, and predict the conditions of this underground infrastructure. The present state-of-the-art does not allow the utility manager to determine which pipe distresses should be repaired immediately to stop further degradation, or which distresses are stable and could be left for a future repair.
Due to implementation of new requirements from the U.S. Environmental Protection Agency (EPA), municipalities are finding themselves more and more in need of detailed information regarding their collection systems. In the past, this information has often been archived in filing cabinets in the form of videotapes and log sheets, where accessing the information was difficult and time consuming.
A four year study in sewer prediction modeling was performed at Trenchless Technology Center at Louisiana Tech University to develop a systematic sanitary sewer management system that can help sewer managers evaluate and develop a cost-effective maintenance program that avoids frequent crisis management. The preliminary work in sewer prediction modeling reported in Kathula's Master's thesis (Kathula, 2000) has laid the groundwork for this research effort to develop a field-tested sewer-deterioration prediction model. The proposed prediction model incorporates knowledge and experience of infrastructure experts who were asked to predict the deterioration of sewer pipes over a period of time. In this research a detailed questionnaire was mailed to municipalities in the Gulf Coast region of the United States. The survey participants included 33 municipal officials and experts from Alabama, Florida, Louisiana, Mississippi and Texas. These regional data were used to develop a regional sewer deterioration prediction model.
This paper presents the sewer deterioration prediction models that were developed for the five common degradation states – cracks, open joints, displaced joints, corrosion and deformation for both clay and concrete sewers.
The combination of these five degradation sequence states with the mean time for any state to degrade to the collapse state can provide the sewer manager with objective criteria for ranking repairs (Figure 1).
This study used the basic principles of probability-based Markovian Chain methods to develop a distress transition matrix for sewer pipes. The sewer deterioration prediction models were verified by using the field CCTV data collected from municipalities. The predictive model developed in this research has the ability to:
To predict the service and structural distresses of clay and concrete pipelines for any given number of years, based on any initial condition of the pipeline.
To predict failures before they occur.,
To identify the remaining life of a deteriorated pipeline, so that proper allocation of scarce rehabilitation resources can be prioritized.
To prioritize rehabilitation targets according to the risk level they pose.
Therefore, an effective and proven predictive model is possible that will allow the utility manager to apply life-cycle-costing economics to the utility with confidence.
Assessment of current conditions and prediction of future performance are essential steps of any infrastructure management systems. The assessment, interpretation, prediction and life-cycle costs models will assist asset managers in improved performance modeling of the sewer infrastructure, and will be a step towards preventive rather than reactive maintenance.
Document Type: Research Article
Publication date: January 1, 2002
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