Aircraft structures are exposed to impact damage caused by debris and hail during their service life. One of the design concerns in composite structures is the resistance of layered surfaces to damage, which occurs from impacts with various foreign objects. Therefore, the impact localization and damage quantification of impacts should be studied and considered to address flight safety and to reduce costs associated with a regularly scheduled visual inspection. Since the structural components of the aircraft are large scale, visual inspection and monitoring are challenging and subject to human error. This paper presents a promising solution that can automatically detect and localize an impact that may occur during flight. To achieve this goal, acoustic emission (AE) is employed as an impact monitoring approach. Random forest and deep learning were adopted for training the source location models. An AE dataset was collected by conducting an impact experiment on a full-size thermoplastic aircraft elevator in a laboratory environment. A dataset consisting of AE parametric features and a dataset consisting of AE waveforms were assigned to a random forest classifier and deep learning network for the investigation of their applicability of impact source localization. The results obtained were compared using the source localization approach in previous research using a conventional artificial neural network. The analysis of results shows the random forest and deep learning leads to better event localization performance. In addition, the random forest model can provide the importance of features. By deleting the least important features, the storage required to save the input and the computing time for the random forest is greatly reduced, and an acceptable localization performance can still be obtained.

This paper provides a comprehensive literature review on the application of digital twins (DTs) and the development of artificial intelligence (AI), big data and building information management (BIM). Driven by the Internet of Things (IoT), big data analytics and artificial intelligence, DT technology has become a transformative force in Industry 4.0. It enables real-time simulation, analysis, and optimization of industrial systems throughout their lifecycle, leading to significant improvements in operational efficiency and decision-making processes. This review explores the various applications, challenges, and prospects of DTs in the aerospace, manufacturing, construction, and power industries. The key challenges discussed include data management, model complexity, cybersecurity, and standardization. This review highlights the importance of addressing these challenges to realize the full potential of DT technology in various industries while emphasizing the need for high-quality data, accurate modeling, robust security measures, and standardized evaluation criteria. As DT technology continues to evolve, it will play a key role in advancing smart, resilient and efficient industrial systems.

The effect of steel reinforcement on the distribution of alkali-silica reaction (ASR) damage in concrete blocks is studied by leveraging acoustic emission sensing with a data-driven approach. The innovation lies in deriving damage contours caused by ASR based on a minimal sensor array. Through this approach, damage progression can be traced in time and event distribution can be visualized. A gap in the current literature, namely evaluating ASR progress in concrete structures with different internal restraint using acoustic emission (AE), is addressed. Unsupervised pattern recognition is utilized to study the effect of the temporal damage condition. In the confined specimen, the distribution of AE events in the midwidth region of the specimen is concentrated and close to the normal distribution. The surface cracks are mostly oriented along the specimen length and in the midwidth region. However, in the unconfined specimen, the distribution of AE events is more uniform, and cracks are randomly distributed.