Developing a level-1B qualifiable CNN for in-situ ultrasonic damage classification of aerospace composite structures: An in-depth evaluation on the end-to-end process of developing a data-driven tool

This paper examines the end-to-end development process for a Convolution Neural Network (CNN) based damage classification tool for ultrasonic inspection of aerospace-grade composite structures. The recent advent of Artificial Intelligence (AI) and Machine Learning (ML) has piqued the interest of the aerospace industry since it has the potential...

The use of new data sources in bridge maintenance decision-making: On the effect of using new data sources during decision making moments of bridge maintenance experts through a serious game based experiment

The current practice of bridge maintenance decision-making is informed by subjective visual inspection data with a long time interval between measurements, which creates inefficiencies and possible loss of value in the maintenance entity’s capabilities. New opportunities from technology-related innovations for measurement devices are available...

In-situ Non-destructive Stiffness Assessment of FRC Materials

Fiber Reinforced Composite (FRC) materials are gaining great popularity in marine structures because of their excellent strength-to-weight ratio, low density, and provided freedom in the design process. However, the use of FRC materials comes along with relatively large uncertainties in material properties and structural integrity after...

Feasibility study of a non-contact, non-destructive testing method to assess the structural integrity of synthetic mooring ropes

Governments are looking more and more to invest in renewable energy sources due to the energy transition that is currently taking place. One of the many renewable energy sources is wind energy which is increasingly positioned at sea. Wind turbines in deep parts of the ocean can be placed on floating structures which are often moored to the sea...