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65 articles for “damage detection”
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IoT-Based Structural Health Monitoring and Damage Detection in Fiber Reinforced Polymer Composite Structures
Abstract: Applications of fiber-reinforced polymer (FRP) composite in the aerospace, civil infrastructure and renewable energy systems are increasing due to the fact that the composite possesses high ratio of strength to weight and can resist corrosion. However, processes of internal damages such as the cracking of the matrix, delamination and fiber fracture, are likely to take place without being visible on the surface and therefore a periodic check of the structure …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 1076–1100 Read article
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Optimization of Structural Health Monitoring Using Artificial Neural Network and Comparison with Traditional Method: A Comprehensive Review
Abstract: Structural health monitoring (SHM) has a critical role in ensuring civil infrastructure safety, reliability, and durability through real-time, condition-based monitoring. Traditional SHM systems employ hundreds of sensors such as accelerometers, strain gauges, and displacement transducers for monitoring vast amounts of data for structural inspection, but do not effectively manage complicated nonlinear data. This research paper, “Optimization of Structural Health Monitoring Using Artificial Neural Network and Comparison with Traditional Methods,” investigates …
Published in Journal of Structural Engineering and Management · Vol. 13, Issue 1, 2026 · pp. 23–33 Read article
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A Review on Integrated Acoustic Emission and Piezoelectric Sensing for Real-Time Damage Characterization of Polymer Composite-Enhanced Concrete: Advances, Challenges, and Future Perspective
Abstract: Polymer composite reinforced concrete has been identified as an efficient material system that can enhance the mechanical properties, durability, and service life of modern structures. The combination of fiber reinforced polymers (FRPs), polymer modifiers, and hybrid composite reinforcements increases structural effectiveness. However, these systems are still vulnerable to damage processes, including matrix cracking, fiber breaking, interfacial debonding, and delamination. Thus, there is a need for structural health monitoring (SHM) strategies …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 930–939 Read article
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Artificial Neural Network Based Prediction of Impact Loads and Thickness in CFRP and GFRP Composite Laminates
Abstract: Recent technological advancements, particularly the integration of neural networks, have facilitated a predictive approach to complex engineering problems, especially those involving composite materials with directional properties. The scarcity of literature on predicting impact damage using experimental and ultrasonic flaw detection data motivated this study. Experimental assessment of impact damage on carbon fiber/epoxy (CFRP) and glass fiber/epoxy (GFRP) composites was conducted using low-velocity drop weight impact testing. Damage assessment employed an …
Published in International Journal of Fracture Mechanics and Damage Science · Vol. 2, Issue 1, 2024 · pp. 34–45 Read article
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Role of Artificial Intelligence in Structural Health Monitoring-A Brief Evaluation
Abstract: Artificial intelligence (AI) refers to the capacity of a machine or a computer to ‘think’ or reason in the way a human would, utilizing experience, learned facts, and flexible rules to solve problems that may not fit the standard outlines for a normal algorithm. From this follows the utilization of AI in various sectors, such as the information technology (IT) industry, media, healthcare and medicine, logistics, environmental sustainability, finance, business, …
Published in Journal of Structural Engineering and Management · Vol. 13, Issue 1, 2026 · pp. 34–39 Read article
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Detection and Control of Adulterants in Milk
Abstract: Milk is one of the most essential and widely consumed staple foods, valued for its rich nutritional composition, including proteins, fats, vitamins, and minerals necessary for human growth and health. However, the increasing demand for milk, coupled with economic incentives, has led to the widespread issue of milk adulteration, posing serious concerns for food safety and public health. Adulteration involves the addition of harmful or inferior substances to milk, either …
Published in Research and Reviews : Journal of Dairy Science and Technology · Vol. 15, Issue 1, 2026 · pp. 1–6 Read article
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Development of a Generative AI Model for Early Detection and Prevention of Electrical Faults in Thermal Power Plants
Abstract: Electrical faults in thermal power plants can lead to severe equipment damage, production downtime, and safety hazards if not detected in advance. This study presents the development of a Generative Artificial Intelligence (GenAI) model for the early detection and prevention of electrical faults using predictive analytics. The proposed framework integrates Generative Adversarial Networks (GANs) with deep learning (CNN) and machine learning algorithms (Random Forest, Logistic Regression) to enhance data diversity, …
Published in International Journal of Energy and Thermal Applications · Vol. 4, Issue 1, 2026 · pp. 1–10 Read article
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CNN-Based Diagnosis of Skin Cancer from Dermoscopic Images
Abstract: Skin cancer has become one of the diseases widely spread over the globe, with melanoma becoming a severe threat to one’s health. Detection of such diseases at the initial stage saves an individual from drastic damage. Using a Convolutional Neural Network (CNN) for detecting skin cancer through image classification as benign or malignant provides significant support to dermatological practice and reduces dependence solely on subjective visual examination. Dermatologists often face …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 15, Issue 1, 2026 · pp. 37–42 Read article
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Multi-Sensor System for Underwater Pothole Detection to Enhance Road Safety During Monsoon Seasons
Abstract: Monsoon seasons across India and similar tropical regions severely compromise road safety by causing water accumulation that conceals dangerous potholes beneath stagnant pools, leading to frequent vehicle damage, tire punctures, and fatal accidents. Traditional detection methods relying on smartphone accelerometers, ultrasonic sensors, or machine vision fail under flooded conditions due to acoustic signal reflection at water surfaces and optical distortions from glare and turbidity. This research proposes an innovative multi-sensor …
Published in International Journal of Mechanical Dynamics and Systems Analysis · Vol. 4, Issue 1, 2026 · pp. 25–32 Read article
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Autonomous Fire Suppression Robot Based on Arduino
Abstract: Fire hazards continue to pose serious risks in homes, workplaces, and industrial facilities, making rapid detection and early suppression crucial for minimizing damage and protecting lives. This paper presents the design and development of an autonomous fire suppression robot built around an Arduino- based control system. The robot is engineered to independently detect, approach, and extinguish small fires with minimal human interaction. It incorporates multiple sensing components, including flame sensors …
Published in Journal of Advancements in Robotics · Vol. 13, Issue 1, 2026 · pp. 26–32 Read article
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Automated Fault Identification and Tracking in Power Transmission Networks Using GPS
Abstract: Power transmission lines are vulnerable to faults caused by environmental factors, aging infrastructure, and external disturbances such as falling trees or animal contact. Prompt and accurate detection and location of these faults are essential to ensure uninterrupted power delivery, reduce equipment damage, and minimize downtime. This project presents an automatic fault detection and location system using GPS technology integrated with a microcontroller-based sensing module. The proposed system continuously monitors voltage …
Published in Journal of Power Electronics and Power Systems · Vol. 15, Issue 3, 2025 · pp. 37–43 Read article
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Arduino Fire Guardian: Empowering Fire Safety with Robotics
Abstract: Identification of early fires is crucial in preventing losses. Fires often result in significant damage due to the lack of timely detection. Detecting and extinguishing fires early can protect lives and property. Robotics has become popular due to many advances in technology. A properly equipped robot will detect the fire. In the situation of a fire, a mounted robot will be directed to extinguish it. Equipped with sensors and a …
Published in Journal of Thermal Engineering and Applications · Vol. 11, Issue 1, 2024 · pp. 20–27 Read article
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Automated Plant Disease Detection and Treatment Advisor Using Artificial Intelligence
Abstract: Automated plant disease detection and treatment advisors using artificial intelligence represent a significant advancement in modern agriculture. The identification of plant leaf diseases is essential to maintaining food security and agricultural output. Machine learning models, particularly deep learning algorithms like convolutional neural networks (CNNs), are trained on labeled datasets containing images of healthy and diseased plants. These models learn to classify images into different disease categories with high accuracy. Convolutional …
Published in International Journal of Cheminformatics · Vol. 1, Issue 2, 2023 · pp. 1–7 Read article
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Fungus Detection System
Abstract: This project identifies fungal pathogens in wet soil with sensors and gives an instant result through an Android app. It helps farmers avoid crop loss, increase yield, and encourage eco-friendly farming practices by enabling early detection and evidence-based decision-making for soil health management. Soil condition is most important to agriculture and environmental health. Having fungus in pre-maturity soil analysis can prevent crop damage and increase the yield. This project seeks …
Published in Journal of Microcontroller Engineering and Applications · Vol. 12, Issue 2, 2025 · pp. 30–34 Read article
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Alzheimer’s Disease Classification Based on Transfer Learning of New-CNN Model
Abstract: The long-term, irreversible brain disorder “Alzheimer’s disease (AD)” currently has no known cure. Nonetheless, current medications may impede their advancement. Globally, those over 65 are the primary population affected by Alzheimer’s disease. Accurate detection of this condition requires early diagnosis. Because there are so many people who come with an ailment, manual diagnosis by health specialists is laborious and prone to error. Early detection of AD is a difficult undertaking …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 15, Issue 3, 2025 · pp. 16–23 Read article
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Early Flood Detection and Avoidance Using IoT
Abstract: This project presents an advanced flood alert system powered by IoT technology, designed to enhance public safety and minimize flood-related damage in high-risk areas. The system uses various sensors to keep track of environmental conditions, especially changes in water levels. These sensors are strategically placed in critical zones to detect early indicators of flooding, such as sudden increases in water level, surface runoff, and river overflow. The gathered information is …
Published in International Journal of Data Structure Studies · Vol. 3, Issue 2, 2025 · pp. 1–5 Read article
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Enhancing Glaucoma Diagnosis with Deep Learning: A Study Using ResNet-50 and DenseNet-121
Abstract: Glaucoma is a leading cause of irreversible blindness worldwide, mainly resulting from progressive optic nerve damage, often related to elevated intraocular pressure. Early detection is essential to prevent vision loss, but traditional diagnostic methods rely on specialized equipment and trained professionals, making large-scale screening difficult. This study uses a publicly available fundus imaging dataset to explore the effectiveness of deep learning models for glaucoma detection. These datasets provide medical images, …
Published in International Journal of Brain Sciences · Vol. 2, Issue 2, 2025 · pp. 9–18 Read article
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Enhancing Road Safety: A Robotic System for Automatic Pothole Identification and Filling
Abstract: Innovative technologies called automatic pothole detection systems are made to automatically locate and identify potholes on road surfaces. These systems incorporate many components, including sensors such as cameras, LiDAR, and accelerometers, to acquire data on the road conditions. Potholes on roads provide serious risks to passing cars and pedestrians, which can result in collisions, damage to cars, and deterioration of the infrastructure. Automatic pothole detection systems, which make use of …
Published in Journal of Instrumentation Technology & Innovations · Vol. 14, Issue 3, 2024 · pp. 1–6 Read article
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Smart Detection of Wild Animals in Residential Area
Abstract: In recent years, wild animals have been increasingly spotted in residential areas, posing risks to both humans and animals. The presence of these animals can cause accidents, damage to property, or even harm to the animals themselves. Therefore, it is important to have an effective method to detect wild animals in residential areas and ensure safety for everyone involved. This research focuses on creating a smart detection system that can …
Published in Journal of Instrumentation Technology & Innovations · Vol. 15, Issue 3, 2025 · pp. 44–50 Read article
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Enhancing Space Safety: A Review of CubeSat Constellations for Tracking Orbital Debris
Abstract: Space debris includes any human-made object in orbit or idle. Debris reentering the atmosphere or orbiting the Earth are examples of these things. Debris can include satellites or launch vehicles, dysfunctional spacecraft, remains from rockets and airplanes from crashes or explosions, debris from spacecraft and vehicles that were purposefully expelled during separation or operation, etc. The danger of collisions rises as the number of items in the path increases. Over …
Published in Research & Reviews : Journal of Space Science & Technology · Vol. 13, Issue 3, 2024 · pp. 27–43 Read article