AIMC Topic: Steel

Clear Filters Showing 11 to 20 of 23 articles

Research on Mechanical and Carbonization Properties of Hybrid Fiber Iron Tailings Concrete Based on Deep Learning.

Computational intelligence and neuroscience Jun 9, 2022
Iron tailings sand is a kind of mineral waste, and open-air storage is a common treatment method for iron tailings, which not only has a huge impact on the ecological environment but also occupies a lot of land resources. Therefore, the preparation o...
Deep Learning Steel Environment Engineering Iron
View on PubMed DOI

Corroded Bolt Identification Using Mask Region-Based Deep Learning Trained on Synthesized Data.

Sensors (Basel, Switzerland) Apr 27, 2022
The performance of a neural network depends on the availability of datasets, and most deep learning techniques lack accuracy and generalization when they are trained using limited datasets. Using synthesized training data is one of the effective ways...
Deep Learning Steel Neural Networks, Computer
View on PubMed DOI

Digital Twins-Based Impact Response Prediction of Prestressed Steel Structure.

Sensors (Basel, Switzerland) Feb 20, 2022
Civil infrastructure O&M requires intelligent monitoring techniques and control methods to ensure safety. Unfortunately, tedious modeling efforts and the rigorous computing requirements of large-scale civil infrastructure have hindered the developmen...
Machine Learning Steel
View on PubMed DOI

Development and comprehensive evaluation of a new spring-steel-driven glove for grasping assistance during activities of daily living.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine Aug 23, 2021
Millions of people suffer from a decline in grip strength and hand function due to conditions such as chronic disease, injuries, and aging. Hand function decline results in difficulties with performing activities of daily living, where grasping, lift...
Hand Strength Robotics Humans Steel Hand Activities of Daily Living
View on PubMed DOI

Pixel-Level Fatigue Crack Segmentation in Large-Scale Images of Steel Structures Using an Encoder-Decoder Network.

Sensors (Basel, Switzerland) Jun 16, 2021
Fatigue cracks are critical types of damage in steel structures due to repeated loads and distortion effects. Fatigue crack growth may lead to further structural failure and even induce collapse. Efficient and timely fatigue crack detection and segme...
Steel Humans Neural Networks, Computer Image Processing, Computer-Assisted Fatigue
View on PubMed DOI

A New Steel Defect Detection Algorithm Based on Deep Learning.

Computational intelligence and neuroscience Mar 17, 2021
In recent years, more and more scholars devoted themselves to the research of the target detection algorithm due to the continuous development of deep learning. Among them, the detection and recognition of small and complex targets are still a proble...
Rotation Steel Deep Learning Neural Networks, Computer Algorithms
View on PubMed DOI

A Novel Hybrid Model Based on a Feedforward Neural Network and One Step Secant Algorithm for Prediction of Load-Bearing Capacity of Rectangular Concrete-Filled Steel Tube Columns.

Molecules (Basel, Switzerland) Jul 31, 2020
In this study, a novel hybrid surrogate machine learning model based on a feedforward neural network (FNN) and one step secant algorithm (OSS) was developed to predict the load-bearing capacity of concrete-filled steel tube columns (CFST), whereas th...
Neural Networks, Computer Machine Learning Databases, Factual Engineering Construction Industry Models, Theoretical Steel Weight-Bearing Construction Materials
View on PubMed DOI

Large-area, high-resolution characterisation and classification of damage mechanisms in dual-phase steel using deep learning.

PloS one May 8, 2019
High performance materials, from natural bone over ancient damascene steel to modern superalloys, typically possess a complex structure at the microscale. Their properties exceed those of the individual components and their knowledge-based improvemen...
Steel Deep Learning Materials Testing
View on PubMed DOI

Prediction of hearing loss among the noise-exposed workers in a steel factory using artificial intelligence approach.

International archives of occupational and environmental health Nov 29, 2014
PURPOSE: Prediction of hearing loss in noisy workplaces is considered to be an important aspect of hearing conservation program. Artificial intelligence, as a new approach, can be used to predict the complex phenomenon such as hearing loss. Using art...
Steel Cross-Sectional Studies Auditory Threshold Metallurgy Male Occupational Diseases Audiometry Noise, Occupational Neural Networks, Computer Occupational Exposure Iran Hearing Loss, Noise-Induced Adult Humans Predictive Value of Tests
View on PubMed DOI

ZFD-Net: Zinc flower defect detection model of galvanized steel surface based on improved YOLOV5.

PloS one Jan 1, 2025
Due to the complex factory environment, zinc flower defects and galvanized sheet background are difficult to distinguish, and the production line running speed is fast, the existing detection methods are difficult to meet the needs of real-time detec...
Neural Networks, Computer Algorithms Surface Properties Steel Zinc Models, Theoretical
View on PubMed DOI
Popular Topics
Recent Journals