The tool also creates the basic information and methodology for ongoing condition assessment of the asset.
Each project will require to be addressed in terms of pipe type and location
Quantitative Data will be obtained from existing records such as previous inspections and investigation
Physical Data available from utility records
Environmental Data from observation and utility records
Operational Data from utility records
Observation Data can be obtained from surface inspection to detected inferential indicators of corrosion, leakage, external threats, consequences of failure.
It is not normally necessary for any additional quantitative site investigation to be undertaken to develop the audit.
Initial analysis based on this information will identify gaps and missing information that allow the definition of an intervention program for determining the Likelihood of Failure and for Consequences of Failure. The RSG AIM™ AI tool is versatile and can benefit various types of pipe networks, particularly those requiring detailed and ongoing assessment for maintenance and risk management. Here are some types of pipe networks that could benefit from such a tool:
Municipal Water Supply Networks: These systems often cover large areas and serve many users, making efficient management crucial.
Sewage and Wastewater Systems: These networks are subject to various environmental and operational stresses, requiring regular monitoring.
Gas Distribution Networks: Safety is paramount in these systems, and predictive maintenance can prevent leaks and failures.
Oil and Petrochemical Pipelines: These networks are typically spread over vast distances and can greatly benefit from AI-driven analysis and predictive maintenance.
Industrial Process Piping: Factories and plants use complex piping systems that need constant monitoring to ensure smooth operations.
District Heating Systems: These systems can be optimized for efficiency using AI tools to analyze and predict maintenance needs.
The AI tool’s ability to integrate different data types (quantitative, physical, environmental, operational, and observational) makes it particularly useful for comprehensive network analysis. This can lead to improved decision-making regarding maintenance, repairs, and upgrades, ultimately enhancing the reliability and longevity of the infrastructure.
Handling Different Pipe Materials
The RSG AIM™ AI tool, with its foundation in Bayesian logic, is designed to handle different pipe materials by incorporating material-specific properties into its analysis:
Ductile Iron and Cast Iron: The tool considers their susceptibility to corrosion and the impact of age on their integrity.
PVC and Plastic: Analyzes different stress responses and durability factors.
Steel: Assesses vulnerability to corrosion and fatigue.
Concrete: Evaluates propensity for cracking and structural integrity.
Copper: Assesses long-term durability and resistance to corrosion.
For each material, the tool uses existing data on material behavior, failure modes, and life expectancy to predict maintenance needs and potential failures. It also factors in environmental conditions, operational data, and physical inspections to provide a comprehensive analysis tailored to the specific characteristics of each pipe material.
Handling Joint Types and Connections
The RSG AIM™ AI tool handles different joint types and connections by analyzing data specific to the joint’s characteristics and the materials involved:
Data Analysis: Identifies the type of joint, such as welded, flanged, or threaded connections, using a neural network trained on a large dataset.
Material Consideration: Factors in the material data to assess the joint’s integrity.
Environmental Impact: Considers environmental factors that could affect the joints, such as temperature changes, ground movement, or chemical exposure.
Operational Data: Uses operational data, such as pressure cycles and flow rates, to assess the stress on joints and predict potential failure points.
Physical Inspection Data: Incorporates observation data from surface inspections to identify issues at the joints, such as corrosion or leakage.
By integrating these data points, the AI tool provides a comprehensive assessment of the joints and connections within the pipe network, leading to informed decisions about maintenance, repair, or replacement needs.