AIMC Topic: Steel
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Leveraging machine learning for monitoring afforestation in mining areas: evaluating Tata Steel's restoration efforts in Noamundi, India.
Environmental monitoring and assessment
Jun 27, 2025
Mining activities have long been associated with significant environmental impacts, including deforestation, habitat degradation, and biodiversity loss, necessitating targeted strategies like afforestation to mitigate ecological damage. Tata Steel's ...
A hybrid PSO-FFNN approach for optimized seismic design and accurate structural response prediction in steel moment-resisting frames.
PloS one
Jun 2, 2025
The first steel is the most prevalent material used in building. Steel's intrinsic hardness and durability make it appropriate for different uses, but its greater adaptability makes it ideal for seismic design. The brittle fracture occurred in welded...
Performance evaluation of machine learning techniques in surface morphology and corrosion prediction for A286 3D printed micro-lattice structures.
PloS one
May 7, 2025
The development of lightweight, corrosion-resistant metallic lattice structures has gained significant attention in aerospace, defense, and structural applications, where material durability and weight optimization are critical. This study investigat...
Hazard source detection of longitudinal tearing of conveyor belt based on deep learning.
PloS one
Apr 6, 2023
Belt tearing is the main safety accident of belt conveyor. The main cause of tearing is the doped bolt and steel in the conveying belt. In this paper, the bolt and steel are identified as the Hazard source of tear. In this paper, bolt and steel are d...
Artificial Intelligence-Driven Image Analysis of Bacterial Cells and Biofilms.
IEEE/ACM transactions on computational biology and bioinformatics
Feb 3, 2023
The current study explores an artificial intelligence framework for measuring the structural features from microscopy images of the bacterial biofilms. Desulfovibrio alaskensis G20 (DA-G20) grown on mild steel surfaces is used as a model for sulfate ...
The Prediction of Steel Bar Corrosion Based on BP Neural Networks or Multivariable Gray Models.
Computational intelligence and neuroscience
Jan 5, 2023
The corrosion of steel bars in concrete has a significant impact on the durability of constructed structures. Based on the gray relational analysis (GRA) of the accelerated corrosion data and practical engineering data using MATLAB, a back propagatio...
Integrated Damage Location Diagnosis of Frame Structure Based on Convolutional Neural Network with Inception Module.
Sensors (Basel, Switzerland)
Dec 30, 2022
Accurate damage location diagnosis of frame structures is of great significance to the judgment of damage degree and subsequent maintenance of frame structures. However, the similarity characteristics of vibration data at different damage locations a...
A Novel CNN-LSTM Hybrid Model for Prediction of Electro-Mechanical Impedance Signal Based Bond Strength Monitoring.
Sensors (Basel, Switzerland)
Dec 16, 2022
The recent application of deep learning for structural health monitoring systems for damage detection has potential for improvised structure performance and maintenance for long term durability, and reliable strength. Advancements in electro-mechanic...
Frame Structure Fault Diagnosis Based on a High-Precision Convolution Neural Network.
Sensors (Basel, Switzerland)
Dec 2, 2022
Structural health monitoring and fault diagnosis are important scientific issues in mechanical engineering, civil engineering, and other disciplines. The basic premise of structural health work is to be able to accurately diagnose the fault in the st...
Loosening Identification of Multi-Bolt Connections Based on Wavelet Transform and ResNet-50 Convolutional Neural Network.
Sensors (Basel, Switzerland)
Sep 9, 2022
A high-strength bolt connection is the key component of large-scale steel structures. Bolt loosening and preload loss during operation can reduce the load-carrying capacity, safety, and durability of the structures. In order to detect loosening damag...
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