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1. BDC Dataset: A Comprehensive Dataset for Automated Build Damage Classification NSTL国家科技图书文献中心

Xing Zi |  Yunxiao Shi... -  《Advanced Data Mining and Applications,Part I》 -  International Conference on Advanced Data Mining and Applications - 2025, - 91~104 - 共14页

摘要: the Build Damage Classification (BDC) Dataset, an |  distinct sub-datasets for building damage classification |  demand for efficient, automated methods of damage |  datasets specifically designed for house damage |  classification tasks. To address this gap, this paper presents
关键词: Contrastive learning |  Supervised learning |  Attention mechanism |  Property damage classification benchmark |  Infrastructure condition assessments |  Natural disasters

2. CNN-based damage classification of soybean kernels using a high-magnification image dataset NSTL国家科技图书文献中心

Chauhan, Isparsh |  Kekre, Siddharth... -  《Journal of Food Measurement and Characterization》 - 2025,19(5) - 3471~3495 - 共25页

摘要: detailed classification of damage in soybean kernels |  classification. This study demonstrates the use of a machine |  eight distinct damage classes: healthy, heat damage |  (HD), immature damage (IMD), mold damage (MD | ), purple mottled and stained (PMS), stinkbug damage (SBD
关键词: Soybean kernel damage |  Convolutional neural networks (CNN) |  High-magnification image dataset |  EfficientNet-B0 |  Dataset imbalance |  Agricultural quality assessment

3. Damage classification and segmentation in extended shear tab connection using convolutional neural networks and transfer learning NSTL国家科技图书文献中心

Priti R.,Satarkar |  Pradnya R.,Dixit... -  《Asian Journal of Civil Engineering》 - 2025,26(1) - 221~236 - 共16页

摘要:-101 and VGG-16 are considered for crack and damage | Abstract The joint is one of the most critical |  parts of a building structure. In steel buildings |  extended shear tab (EST) connection is becoming an |  attractive alternative for light to moderate end shear
关键词: Steel structures |  Extended shear tab connection |  Finite element analysis |  Convolutional neural network |  Transfer learning

4. FFTCA: a Feature Fusion Mechanism Based on Fast Fourier Transform for Rapid Classification of Apple Damage and Real-Time Sorting by Robots NSTL国家科技图书文献中心

Xiang, Pengjun |  Pan, Fei... -  《Food and bioprocess technology》 - 2025,18(2) - 1631~1655 - 共25页

摘要: apple damage classification network, Fast Fourier |  damage during the production process. Such damage not |  apple damage and perform sorting in real time |  lightweight classification networks. Experimental results |  show that this network has better classification
关键词: Apple damage classification |  Fast Fourier Transform |  Smart agriculture |  Deep learning |  Secondary sorting

5. Feature Selection Voting Strategies and Hyperparameter Tuning in a Boosting Classification NSTL国家科技图书文献中心

Nicole Dalia Cilia |  Giovanni Fanara... -  《Pattern Recognition,Part II》 -  International Conference on Pattern Recognition - 2025, - 123~142 - 共20页

摘要: damage classification processes. The findings |  performance of boosting classification. As a case study, the |  damage in concrete structures. By leveraging boosting |  reliable methods for assessing damage in reinforced |  effectiveness of the approach in accurately identifying damage
关键词: Boosting techniques |  Machine learning |  Optimization strategies |  Predictive modeling |  Feature selection |  Damage classification

6. Transfer learning in bridge monitoring: Laboratory study on domain adaptation for population-based SHM of multispan continuous girder bridges NSTL国家科技图书文献中心

Valentina Giglioni |  Jack Poole... -  《Mechanical Systems & Signal Processing》 - 2025,224(Feb.) - 112151.1~112151.18 - 共18页

摘要: associated to various environmental conditions and damage |  conditions and to the same pseudo-damage scenarios, is |  demonstrate the possibility of effectively exchanging damage |  labels to perform novelty detection and damage |  classification across the population via domain adaptation
关键词: Bridge monitoring |  Population-based SHM |  Transfer learning |  Domain adaptation |  Damage classification |  Experimental bridge model

7. Classification of Damage on Wind Turbine Blades Using Automatic Machine Learning and Pressure Coefficient NSTL国家科技图书文献中心

Javier A. Carmona-Tr... |  Leonardo Trujillo... -  《Expert systems》 - 2025,42(4) - e70024.1~e70024.26 - 共26页

摘要: environments WTBs face significant challenges, since damage |  detect WTB damage is to use machine learning, but |  detect these types of damage. Results show the | Wind turbine blades (WTB) are critical |  components of wind energy systems. Operating in harsh
关键词: automl |  erosion |  fault diagnosis |  machine learning |  renewable energy |  structural health monitoring |  wind turbine blades

8. Smart railways: AI-based track-side monitoring for wheel flat identification NSTL国家科技图书文献中心

Mohammadi, Mohammadr... |  Mosleh, Araliya... -  《Proceedings of the Institution of Mechanical Engineers,Part F.Journal of rail and rapid transit》 - 2025,239(3) - 272~289 - 共18页

摘要:) classification of wheel damage based on its severity using k |  wheel flat damage, resorting to wayside monitoring |  the damage to pinpoint the location of the defective |  flat detection, localization, and damage severity |  classification regardless of the number of defective wheels and
关键词: Wheel flat detection |  wayside condition monitoring |  train-track interaction |  damage localization |  damage classification |  unsupervised learning |  artificial intelligence

9. A Comprehensive Dataset for a Population of Experimental Bridges Under Changing Environmental Conditions for PBSHM NSTL国家科技图书文献中心

Valentina Giglioni |  Jack Poole... -  《Dynamics of Civil Structures,Vol.2》 -  IMAC Conference and Exposition on Structural Dynamics - 2025, - 59~68 - 共10页

摘要: (SHM) to assess damage in real time. However, the |  considering various environmental conditions and damage |  environmental chamber. Multiple damage scenarios are also |  introduced to enable the investigation of damage detection |  and classification methods for both conventional SHM
关键词: Machine learning |  Structural health monitoring |  Population-based SHM |  Bridge monitoring |  Damage classification

10. An improved automatic image labeling and classification algorithm for multi-mode damage quantification of 2.5D woven composites based on deep learning strategy NSTL国家科技图书文献中心

Zheng J. |  Qian K.... -  《Composites science and technology》 - 2025,259(Jan.5) - 1.1~1.15 - 共15页

摘要: classification algorithm can achieve a damage identification |  automatic image labeling and classification algorithm |  unseen CT images and separate the damage and different |  bending damage accumulation predominantly manifests as |  interface debonding, representing 51.93 % of the damage
关键词: 2.5D woven composites |  Automatic labeling |  Bending performance |  Damage quantification |  Deep learning
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