Advanced Artificial Intelligence Solutions for Modern Engineering Applications

Topic Information

Dear Colleagues,

Artificial intelligence plays an increasingly important role in modern engineering by enabling automated analysis, intelligent monitoring, and data-driven decision-making across complex systems. This Topic aims to highlight recent advances in artificial intelligence techniques and their effective application to contemporary engineering problems, with an emphasis on practical relevance and real-world impact.

We invite original research and review papers that present innovative AI models, algorithms, and frameworks, as well as studies demonstrating their use in monitoring, inspection, diagnosis, prediction, and optimization tasks. Application areas may include, but are not limited to, infrastructure and structural monitoring, industrial systems, transportation, energy and environmental monitoring, smart cities, manufacturing, robotics, and autonomous systems.

Contributions that address challenges such as data efficiency, model robustness, interpretability, scalability, and deployment in real operational environments are particularly encouraged. By bringing together diverse perspectives and application domains, this Topic seeks to provide a focused yet broad overview of how advanced artificial intelligence can enhance the performance, reliability, and sustainability of modern engineering applications.

Dr. Qasim Zaheer
Prof. Dr. Shi Qiu
Prof. Dr. Mohammad AlHamaydeh
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
AI ai	5.0	6.9	2020	19.2 Days	CHF 1800	Submit
Applied Sciences applsci	2.5	5.5	2011	16 Days	CHF 2400	Submit
Buildings buildings	3.1	4.4	2011	15.1 Days	CHF 2600	Submit
Electronics electronics	2.6	6.1	2012	16.4 Days	CHF 2400	Submit
ISPRS International Journal of Geo-Information ijgi	2.8	7.2	2012	33.1 Days	CHF 1900	Submit
Sensors sensors	3.5	8.2	2001	17.8 Days	CHF 2600	Submit
Smart Cities smartcities	5.5	14.7	2018	25.2 Days	CHF 2000	Submit

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Published Papers (5 papers)

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All AI Applied Sciences Buildings Electronics IJGI Sensors Smart Cities

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25 pages, 1848 KB  

Open AccessArticle

Multi-Stage State Assessment of Breakers Based on TCWGAN-GP and XGBoost Under Insufficient Samples

by Lixia Sun, Ling Wang, Jiahao Wang and Zijia Liu

Sensors 2026, 26(10), 3112; https://doi.org/10.3390/s26103112 - 14 May 2026

Viewed by 313

Abstract

The increasing randomness and volatility of renewable energy resources have raised higher demands for circuit breakers. Utilizing monitoring data enables more accurate condition assessment; however, the imbalance between fault and normal samples hampers the performance of machine-learning-based assessment. To address the overfitting and [...] Read more.

The increasing randomness and volatility of renewable energy resources have raised higher demands for circuit breakers. Utilizing monitoring data enables more accurate condition assessment; however, the imbalance between fault and normal samples hampers the performance of machine-learning-based assessment. To address the overfitting and limited diversity of traditional oversampling methods, this paper proposes a Transformer-conditioned CWGAN-GP (TCWGAN-GP) model to generate multi-class fault samples for data augmentation. The generator of the proposed model takes random noise and class labels as input to capture the distribution characteristics of real fault samples. By combining a transformer-based generator to model inter-feature dependencies among 14 monitoring indicators and a WGAN-GP training objective with gradient penalty, the proposed approach improves training stability and synthetic sample quality. Moreover, a three-stage state assessment method based on XGBoost is developed to sequentially assess health status, fault category, and fault severity. Results demonstrate that the proposed method in the paper outperforms conventional data augmentation methods and single-stage classifiers in terms of accuracy, recall, F1-score, and online prediction efficiency. Specifically, the proposed three-stage model achieves an overall assessment accuracy of 93.10%, outperforming the single-stage XGBoost framework. In terms of online efficiency, the initial anomaly detection stage requires only 0.0041 s per sample, which is a substantial reduction compared to the 0.0241 s required by the single-stage model. Furthermore, compared to traditional Random Oversampling (ROS) and SMOTE, the TCWGAN-GP augmentation yields superior evaluation performance on fully balanced datasets, achieving a recall rate of 91.26% and an F1-score of 92.61%. Overall, the proposed TCWGAN-GP and three-stage XGBoost method contributes to addressing data imbalance challenges and improving the accuracy of circuit breaker state assessment. Full article

(This article belongs to the Topic Advanced Artificial Intelligence Solutions for Modern Engineering Applications)

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24 pages, 10505 KB  

Open AccessArticle

Design and De-Icing Performance Evaluation of a Stay-Cable De-Icing Robot

by Yaoyao Pei, Xinyan Yu, Lei Xi, Yuzhen Zhao and Feng Gao

Appl. Sci. 2026, 16(10), 4605; https://doi.org/10.3390/app16104605 - 7 May 2026

Viewed by 291

Abstract

In winter, ice readily accretes on the HDPE sheath of stay cables, creating shedding hazards and exacerbating wind-induced vibrations, thereby threatening bridge and traffic safety. Cable-climbing de-icing devices have been proposed to replace manual operations, yet their performance is often limited by climbing [...] Read more.

In winter, ice readily accretes on the HDPE sheath of stay cables, creating shedding hazards and exacerbating wind-induced vibrations, thereby threatening bridge and traffic safety. Cable-climbing de-icing devices have been proposed to replace manual operations, yet their performance is often limited by climbing instability caused by abrupt changes in cable-surface friction. This study develops a quadrotor-driven stay-cable de-icing device that integrates an arc-shaped milling wheel with an embedded heating module to realize thermo-mechanically coupled de-icing. The device climbs via rotor-generated aerodynamic lift and performs continuous top-down de-icing using gravity-assisted motion together with rotor thrust. Laboratory tests and ANSYS LS-DYNA explicit dynamic simulations are conducted to quantify the effects of clamping force and axial thrust on the ice removal ratio in a purely mechanical mode. In addition, a three-stage experimental campaign—temperature-rise, thermo-mechanical de-icing, and thermal-balance tests—is carried out to verify heating feasibility and to examine the roles of heating power and initial wheel temperature. The results indicate that, under purely mechanical de-icing, the ice removal ratio increases monotonically with clamping force and thrust but gradually approaches saturation. Under thermo-mechanical de-icing, higher heating power and initial temperature improve removal performance. Notably, thermo-mechanical de-icing under low thrust achieves a higher removal level than purely mechanical de-icing under high loads, demonstrating improved effectiveness and engineering practicality. An initial equivalence relationship between mechanical parameters and temperature is established to support further optimization. Full article

(This article belongs to the Topic Advanced Artificial Intelligence Solutions for Modern Engineering Applications)

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34 pages, 7482 KB  

Open AccessReview

Machine Learning for Leakage Diagnosis in Building Pipe Networks: A Review

by Mingyu Chang, Haosen Qin and Zhengwei Li

Buildings 2026, 16(10), 1855; https://doi.org/10.3390/buildings16101855 - 7 May 2026

Viewed by 283

Abstract

Pipe networks are essential components of modern building infrastructure, including heating, ventilation, and air conditioning (HVAC) water systems, water distribution networks (WDNs), and district heating and cooling (DHC) systems. Leakage in these systems can lead to increased energy consumption, loss of thermal efficiency, [...] Read more.

Pipe networks are essential components of modern building infrastructure, including heating, ventilation, and air conditioning (HVAC) water systems, water distribution networks (WDNs), and district heating and cooling (DHC) systems. Leakage in these systems can lead to increased energy consumption, loss of thermal efficiency, and unstable system operation, thereby affecting indoor environmental quality and overall building performance. Despite differences in scale and application, similar leakage mechanisms are also observed in other pipe network systems, such as oil and gas pipelines and liquid cooling networks. These shared characteristics motivate a unified analytical perspective across different applications. This review provides a systematic analysis of leakage diagnosis methods, with a focus on machine learning (ML) approaches. The results indicate that ML methods have become a dominant research direction due to their ability to capture nonlinear relationships and process high-dimensional data. However, their effectiveness is often constrained by the limited availability of labeled leakage data, sensitivity to dynamic operating conditions, and insufficient physical interpretability. This review provides a structured framework for understanding ML-based leakage diagnosis and offers insights into the integration of data-driven and physics-based approaches for pipe network systems. In addition, the potential role of reinforcement learning (RL) is briefly discussed as an emerging direction for handling dynamic and adaptive scenarios. Compared with ML-based methods, RL has not yet been systematically explored in leakage diagnosis and remains at an early stage of development. This review synthesizes current methodologies, identifies key challenges, and outlines future research directions. Full article

(This article belongs to the Topic Advanced Artificial Intelligence Solutions for Modern Engineering Applications)

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36 pages, 3661 KB  

Open AccessArticle

Intelligent Temperature Control Using Artificial Neural Networks in an IoT-Enabled Cyber-Physical Hot-Air Drying System: Analysis of Drying Kinetics and Thermal Efficiency

by Juan Manuel Tabares-Martinez, Adriana Guzmán-López, Micael Gerardo Bravo-Sánchez, Francisco Villaseñor-Ortega, Juan José Martínez-Nolasco and Alejandro Israel Barranco-Gutierrez

AI 2026, 7(5), 157; https://doi.org/10.3390/ai7050157 - 30 Apr 2026

Viewed by 953

Abstract

This study aims to develop and experimentally evaluate an artificial neural network-based temperature control strategy for hot-air carrot drying within an IoT-enabled cyber-physical system. The experimental setup employs an Arduino Mega 2560 equipped with AM2302 (air temperature sensor), MLX90614 (infrared surface temperature sensor), [...] Read more.

This study aims to develop and experimentally evaluate an artificial neural network-based temperature control strategy for hot-air carrot drying within an IoT-enabled cyber-physical system. The experimental setup employs an Arduino Mega 2560 equipped with AM2302 (air temperature sensor), MLX90614 (infrared surface temperature sensor), and SHT35 (relative humidity sensor), an HX711 load cell, and a WS68 anemometer, with cloud communication provided by an ESP8266 module for remote monitoring via Wi-Fi. The neural controller, implemented using the Arduino Neurona library, regulates the dryer temperature in real time, enabling drying kinetics analysis under ANN-based thermal control to investigate its capability to maintain thermal stability. Three initial loads (2, 4, and 6 kg) were analyzed to determine the thermal efficiency. In the dehydration experiments, the 2 kg load reached a final moisture content of 10% in 4.4 h, consuming 1390 kJ with a thermal efficiency of 83%. The 4 kg load exhibited the best time–energy balance (6.6 h, 1850.0 kJ, 88%), while the 6 kg load achieved the highest efficiency (8.1 h, 2250.0 kJ, 91%). These results demonstrate the effectiveness of neural-network-based control implemented on low-cost microcontrollers to enhance thermal efficiency in food dehydration processes. Full article

(This article belongs to the Topic Advanced Artificial Intelligence Solutions for Modern Engineering Applications)

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22 pages, 1687 KB  

Open AccessArticle

Data-Driven Offline Compensation of Robotic Welding Trajectories Using 3D Optical Metrology in Industrial Manufacturing

by Alexandru Costinel Filip, Dorian Cojocaru and Ionel Cristian Vladu

Appl. Sci. 2026, 16(5), 2510; https://doi.org/10.3390/app16052510 - 5 Mar 2026

Viewed by 544

Abstract

The geometric variability of industrial components represents a persistent challenge in robotic arc welding, particularly in high-volume manufacturing environments where parts are positioned in fixtures based on nominal CAD assumptions. Even moderate deviations in dimensions or seating conditions can lead to weld defects, [...] Read more.

The geometric variability of industrial components represents a persistent challenge in robotic arc welding, particularly in high-volume manufacturing environments where parts are positioned in fixtures based on nominal CAD assumptions. Even moderate deviations in dimensions or seating conditions can lead to weld defects, rework, and reduced process capability when conventional offline programming is employed. This paper presents an applied industrial workflow for adaptive robotic welding trajectory correction that integrates full-field 3D optical metrology with a data-driven deep reinforcement learning (DRL) model. Prior to welding, each component is scanned using a structured-light 3D system, and critical geometric deviations are extracted relative to the nominal CAD model. These deviations define a compact state representation that is mapped, via a trained DRL agent, to corrective translational and rotational adjustments of the welding trajectory. Importantly, all trajectory corrections are computed offline, ensuring compatibility with standard industrial robot controllers and avoiding real-time computational overheads. The proposed approach is validated using real production data from an industrial batch of 5000 components characterized by significant dimensional variability and limited process capability. Experimental results demonstrate a reduction in welding defects exceeding 90%, elimination of rework associated with improper part positioning, and an improvement of the overall process performance to a sigma level of 5.219. The results show that combining 3D optical metrology with learning-based trajectory adaptation enables robust compensation of part-level geometric deviations without mechanical fixture modifications. The proposed method provides a practical and scalable solution for improving welding quality in manufacturing environments affected by upstream variability and imperfect part positioning. Full article

(This article belongs to the Topic Advanced Artificial Intelligence Solutions for Modern Engineering Applications)

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