Comparative seismic analysis of symmetrical and asymmetrical G + 7 structures using STAAD.Pro: insights into performance and material efficiency

Asian Journal of Civil Engineering, Год журнала: 2025, Номер unknown

Опубликована: Янв. 14, 2025

Язык: Английский

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Efficient Cooling Capability in Microchannel Heat Sink Reinforced with Y-shaped Fins: Based on Artificial Neural Network, Genetic Algorithm, Pareto front, and Numerical Simulation

Case Studies in Thermal Engineering, Год журнала: 2025, Номер unknown, С. 105936 - 105936

Опубликована: Фев. 1, 2025

Язык: Английский

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State-of-the-art review on the use of AI-enhanced computational mechanics in geotechnical engineering

Artificial Intelligence Review, Год журнала: 2024, Номер 57(8)

Опубликована: Июль 5, 2024

Abstract Significant uncertainties can be found in the modelling of geotechnical materials. This attributed to complex behaviour soils and rocks amidst construction processes. Over past decades, field has increasingly embraced application artificial intelligence methodologies, thus recognising their suitability forecasting non-linear relationships intrinsic review offers a critical evaluation AI methodologies incorporated computational mechanics for engineering. The analysis categorises four pivotal areas: physical properties, mechanical constitutive models, other characteristics relevant Among various analysed, ANNs stand out as most commonly used strategy, while methods such SVMs, LSTMs, CNNs also see significant level application. widely algorithms are Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Machines (SVM), representing 35%, 19%, 17% respectively. extensive is domain accounting 59%, followed by applications at 16%. efficacy intrinsically linked type datasets employed, selected model input. study outlines future research directions emphasising need integrate physically guided adaptive learning mechanisms enhance reliability adaptability addressing multi-scale multi-physics coupled problems geotechnics.

Язык: Английский

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Long-term stability forecasting for energy storage salt caverns using deep learning-based model

Energy, Год журнала: 2025, Номер unknown, С. 134854 - 134854

Опубликована: Фев. 1, 2025

Язык: Английский

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Performance characterisation of machine learning models for geotechnical axial pile load capacity estimation: an enhanced GPR-based approach

Geomechanics and Geoengineering, Год журнала: 2025, Номер unknown, С. 1 - 42

Опубликована: Фев. 24, 2025

Язык: Английский

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Comprehensive technical risk indices and advanced methodologies for power system risk management

Electric Power Systems Research, Год журнала: 2025, Номер 244, С. 111534 - 111534

Опубликована: Фев. 21, 2025

Язык: Английский

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Colon Cancer Disease Diagnosis Based on Convolutional Neural Network and Fishier Mantis Optimizer

Diagnostics, Год журнала: 2024, Номер 14(13), С. 1417 - 1417

Опубликована: Июль 2, 2024

Colon cancer is a prevalent and potentially fatal disease that demands early accurate diagnosis for effective treatment. Traditional diagnostic approaches colon often face limitations in accuracy efficiency, leading to challenges detection In response these challenges, this paper introduces an innovative method leverages artificial intelligence, specifically convolutional neural network (CNN) Fishier Mantis Optimizer, the automated of cancer. The utilization deep learning techniques, CNN, enables extraction intricate features from medical imaging data, providing robust efficient model. Additionally, bio-inspired optimization algorithm inspired by hunting behavior mantis shrimp, employed fine-tune parameters enhancing its convergence speed performance. This hybrid approach aims address traditional methods leveraging strengths both nature-inspired enhance effectiveness diagnosis. proposed was evaluated on comprehensive dataset comprising images, results demonstrate superiority over approaches. CNN–Fishier Optimizer model exhibited high sensitivity, specificity, overall distinguishing between non-cancer tissues. integration algorithms with techniques not only contributes advancement computer-aided tools but also holds promise disease, thereby facilitating timely intervention improved patient prognosis. Various CNN designs, such as GoogLeNet ResNet-50, were capture associated diseases. However, inaccuracies introduced feature data classification due abundance features. To issue, reduction implemented using algorithms, outperforming alternative Genetic Algorithms simulated annealing. Encouraging obtained evaluation diverse metrics, including accuracy, F1-Score, which found be 94.87%, 96.19%, 97.65%, 96.76%, respectively.

Язык: Английский

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Hygrothermal modeling in mass timber constructions: Recent advances and machine learning prospects

Journal of Building Engineering, Год журнала: 2024, Номер 96, С. 110500 - 110500

Опубликована: Авг. 23, 2024

Язык: Английский

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Optimal Power Flow using PSO algorithms based on Artificial Neural Networks

IEEE Access, Год журнала: 2024, Номер 12, С. 154778 - 154795

Опубликована: Янв. 1, 2024

Язык: Английский

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Artificial neural network to characterize spatially varying quantity through random field approach

Neural Computing and Applications, Год журнала: 2025, Номер unknown

Опубликована: Янв. 4, 2025

Язык: Английский

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