An Efficient Reliability Assessment of a Composite Panel at Different Elevated Temperatures Based on Surrogate Modeling DOI
Mohsen Kouhi, Alireza Mojtahedi

Опубликована: Янв. 1, 2024

Язык: Английский

Data-driven wind-induced response prediction for slender civil infrastructure: Progress, challenges and opportunities DOI
Yiming Zhang, Haoqing Li, Hao Wang

и другие.

Structures, Год журнала: 2025, Номер 74, С. 108650 - 108650

Опубликована: Март 14, 2025

Язык: Английский

Процитировано

2
Optimizing coastal groundwater quality predictions: A novel data mining framework with cross-validation, bootstrapping, and entropy analysis DOI
Abu Reza Md. Towfiqul Islam, Md. Abdullah-Al Mamun, Mehedi Hasan

и другие.

Journal of Contaminant Hydrology, Год журнала: 2024, Номер 269, С. 104480 - 104480

Опубликована: Дек. 10, 2024

Язык: Английский

Процитировано

6
Constrained Bayesian optimization for engineering bridge design DOI Creative Commons

Heine Røstum,

Sébastien Gros, Ketil Aas-Jakobsen

и другие.

Structural and Multidisciplinary Optimization, Год журнала: 2025, Номер 68(1)

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
A Unified Surrogate Framework for Data-Driven Reliability Analysis of Mechanical Systems from Low to Multi-DOF DOI Creative Commons
Lun Shao,

Alexandre Saidi,

Abdelmalek Zine

и другие.

Vibration, Год журнала: 2025, Номер 8(1), С. 7 - 7

Опубликована: Фев. 20, 2025

This paper proposes a unified reliability analysis framework for mechanical and structural systems equipped with Tuned Mass Dampers (TMDs), encompassing single-degree-of-freedom (1-DOF), two-degrees-of-freedom (2-DOF), ten-degrees-of-freedom (10-DOF) configurations. The methodology integrates four main components: (i) probabilistic uncertainty modeling mass, damping, stiffness, (ii) Latin Hypercube Sampling (LHS) to efficiently explore parameter variations, (iii) Monte Carlo simulation (MCS) estimating failure probabilities under stochastic excitations, (iv) machine learning models, including Random Forest (RF), Gradient Boosting (GB), Extreme (XGBoost), Neural Networks (NNs), predict responses probabilities. results demonstrate that ensemble methods, such as RF XGBoost, provide high accuracy can effectively identify important features. perform well capturing nonlinear behavior, although careful tuning is required prevent overfitting. further extended 10-DOF structure, the confirm learning-based models are highly effective large-scale analysis. These findings highlight synergy between methods data-driven in enhancing of TMD uncertain inputs.

Язык: Английский

Процитировано

0
Enhancing floating offshore wind turbine systems through multi-scale coupled modeling DOI
Solomon Evro,

Jacquelyn Veith,

Akinmoladun Akinwale

и другие.

Sustainable Energy Technologies and Assessments, Год журнала: 2025, Номер 77, С. 104299 - 104299

Опубликована: Апрель 14, 2025

Язык: Английский

Процитировано

0
Prioritized Experience Replay-based Adaptive Hybrid Method for Aerospace Structural Reliability Analysis DOI
Jiongran Wen,

Baiyang Zheng,

Cheng‐Wei Fei

и другие.

Aerospace Science and Technology, Год журнала: 2025, Номер unknown, С. 110257 - 110257

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0
Numerical simulation of soil-structure interaction on the seismic response of reinforced concrete buildings DOI
Yasser M. Kadhim, Abdulkhalik J. Abdulridha

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

Опубликована: Май 21, 2025

Язык: Английский

Процитировано

0
IMR-HACSM: Hybrid adaptive coordination surrogate modeling-based improved moving regression approach for cascading reliability evaluation DOI

Hui-Kun Hao,

Cheng Lu,

Hui Zhu

и другие.

Computer Methods in Applied Mechanics and Engineering, Год журнала: 2024, Номер 435, С. 117680 - 117680

Опубликована: Дек. 18, 2024

Язык: Английский

Процитировано

0
DeepFEA: Deep learning for prediction of transient finite element analysis solutions DOI
Georgios Triantafyllou, Panagiotis G. Kalozoumis, George Dimas

и другие.

Expert Systems with Applications, Год журнала: 2024, Номер unknown, С. 126343 - 126343

Опубликована: Дек. 1, 2024

Язык: Английский

Процитировано

0
Evolutionary game theory-based finite element model updating of a moveable cable-stayed footbridge DOI Creative Commons
Javier Fernando Jiménez‐Alonso, Suzana Ereiz, Ivan Duvnjak

и другие.

Journal of Civil Structural Health Monitoring, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 4, 2024

Abstract Evolutionary game theory allows determining directly the solution of maximum likelihood finite element model updating problem via transformation a bi-objective optimization into problem. The formulation as avoids computation Pareto front and subsequent decision-making problem, selection best among elements front. For this purpose, each term function is considered player that interacts collaboratively or non-collaboratively with other during game. One main advantages method different global algorithm can be associated player. In manner, higher performance in expected linking between objective (a player) for its minimization. study, advantage analysed detail. process real footbridge, Viana do Castelo has been benchmark. As algorithms, nature-inspired computational algorithms have considered. solved using two methods: (i) conventional together method; (ii) an evolutionary method. result, highlighted. Additionally, influence noted.

Язык: Английский

Процитировано

0