Leveraging machine learning for pipeline condition assessment
Pipeline condition assessment is a cost-effective method to determine the status of pipeline
structure and predict failure probability. Although 100% inspection may not be feasible for …
structure and predict failure probability. Although 100% inspection may not be feasible for …
Comparison of artificial intelligence techniques to failure prediction in contaminated insulators based on leakage current
A Medeiros, A Sartori, SF Stefenon… - Journal of Intelligent …, 2022 - content.iospress.com
Contamination in insulators results in an increase in surface conductivity. With higher
surface conductivity, insulators are more vulnerable to discharges that can damage them …
surface conductivity, insulators are more vulnerable to discharges that can damage them …
Accurate prediction of band gap of materials using stacking machine learning model
The prediction of the band gap of semiconductor materials using machine learning has
gradually progressed in recent years. However, the performance of such prediction still …
gradually progressed in recent years. However, the performance of such prediction still …
Efficacy of Tree-Based Models for Pipe Failure Prediction and Condition Assessment: A Comprehensive Review
This paper provides a comprehensive review of tree-based models and their application in
condition assessment and prediction of water, wastewater, and sewer pipe failures. Tree …
condition assessment and prediction of water, wastewater, and sewer pipe failures. Tree …
A data-driven approach for pipe deformation prediction based on soil properties and weather conditions
The health condition of infrastructure including water transmission and distribution mains
has a great impact on the quality of human life. The performance of these water …
has a great impact on the quality of human life. The performance of these water …
A new implementation of stacked generalisation approach for modelling arsenic concentration in multiple water sources
The current study proposes an effective machine learning model based on a stacked
generalisation technique for predicting arsenic content in water sources (groundwater …
generalisation technique for predicting arsenic content in water sources (groundwater …
Meta-learner methods in forecasting regulated and natural river flow
Monthly river flow forecasting has a vital role in many water resource management activities,
especially in extreme events such as flood and drought. Therefore, experts need a reliable …
especially in extreme events such as flood and drought. Therefore, experts need a reliable …
Prediction of breaks in municipal drinking water linear assets
Improper asset management practices increase the probability of water main failures due to
inactive intervention actions. The annual number of breaks of each pipe segment is known …
inactive intervention actions. The annual number of breaks of each pipe segment is known …
Automobile insurance claim occurrence prediction model based on ensemble learning
J Si, H He, J Zhang, X Cao - Applied Stochastic Models in …, 2022 - Wiley Online Library
The generalized linear model (GLM) is a widely used method in traditional automobile
insurance loss prediction. Ensemble learning algorithms have recently shown promising …
insurance loss prediction. Ensemble learning algorithms have recently shown promising …
Development of a fuzzy inference performance rating system for water pipelines using a comprehensive list of input variables
Fuzzy logic model or fuzzy inference system is effective in modeling heuristic expert
knowledge which is delivered as inexact statements with uncertainty, ambiguity, and even …
knowledge which is delivered as inexact statements with uncertainty, ambiguity, and even …