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

Asian Journal of Civil Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 14, 2025

Language: 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, Journal Year: 2025, Volume and Issue: unknown, P. 105936 - 105936

Published: Feb. 1, 2025

Language: Английский

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

Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(8)

Published: July 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.

Language: Английский

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

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 134854 - 134854

Published: Feb. 1, 2025

Language: 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, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 42

Published: Feb. 24, 2025

Language: Английский

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

Electric Power Systems Research, Journal Year: 2025, Volume and Issue: 244, P. 111534 - 111534

Published: Feb. 21, 2025

Language: Английский

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Optimizing Solar Water-Pumping Systems Using PID-Jellyfish Controller with ANN Integration

Electronics, Journal Year: 2025, Volume and Issue: 14(6), P. 1172 - 1172

Published: March 17, 2025

This study presents a novel approach to improving the efficiency and reliability of solar water pumping systems by integrating proportional–integral–derivative (PID) controller with Jellyfish Algorithm (PID-JC) artificial neural networks (ANN). Solar water-pumping are gaining attention due their sustainable eco-friendly nature; however, performance is often limited fluctuating irradiance varying demand. To address these challenges, Monte Carlo simulations were employed account for system uncertainties. Traditional PID controllers, although widely used, struggle adapt effectively dynamic environmental conditions. The proposed utilizes an ANN predict demand patterns based on historical data, enabling real-time adjustments pump operations through PID-JC. inspired adaptive behavior jellyfish in environments. PID-JC adjusts parameters dynamically predictions, optimizing performance. Simulation experimental results conducted Mrada City, Northeastern Libya, demonstrated significant improvements delivery, energy consumption, compared conventional controllers. PID-JC’s ability diverse conditions ensures robust across various geographical locations seasonal changes. Additionally, comparisons other optimization algorithms, such as Firefly Golden Eagle Optimization, show that outperforms them 6.30% improvement cost function 28.13% reduction processing time Firefly, 26.81% 20.69% Optimization.

Language: Английский

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

Diagnostics, Journal Year: 2024, Volume and Issue: 14(13), P. 1417 - 1417

Published: July 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.

Language: Английский

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

Journal of Building Engineering, Journal Year: 2024, Volume and Issue: 96, P. 110500 - 110500

Published: Aug. 23, 2024

Language: Английский

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

Neural Computing and Applications, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 4, 2025

Language: Английский

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