This work provides a data-oriented overview of the rapidly growing research field covering
machine learning (ML) applied to predicting electrochemical corrosion. Our main aim was to …

The purpose of this article is to present a structured review of publications utilizing machine
learning methods to aid in engineering risk assessment. A keyword search is performed to …

This paper develops and employs an ensemble machine learning (ML) model for prediction
of surface chloride concentration (C s) of concrete, which is an essential parameter for …

The main objective of this paper is to develop accurate novel frameworks for the estimation
of the maximum pitting corrosion depth in oil and gas pipelines based on data-driven …

To quantify the progress of corrosion damage and develop pipeline integrity management
strategies, it is necessary to establish a reliable corrosion growth model. Due to the …

Although civil engineering problems are often characterized by significant levels of
complexity, they are generally approached and solved by combining several practitioners' …

High-entropy alloys (HEAs) have attracted tremendous attention in various fields due to
unique microstructures and many excellent properties. For particular applications, an in …

Accurate corrosion assessment of reinforced concrete (RC) structures is expected to
improve the service life and durability of structures. However, traditional evaluation methods …

Water quality is one of the most critical issues in reservoir management owing to its strong
effects on the natural environment and human life. This study establishes a machine …

Suspension bridges are critical components of transport infrastructure around the world.
Therefore, their operating conditions should be effectively monitored to ensure their safety …