相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。Implementing ensemble learning methods to predict the shear strength of RC deep beams with/without web reinforcements
De-Cheng Feng et al.
ENGINEERING STRUCTURES (2021)
A machine learning-based time-dependent shear strength model for corroded reinforced concrete beams
Bo Fu et al.
JOURNAL OF BUILDING ENGINEERING (2021)
Data-driven machine-learning-based seismic failure mode identification of reinforced concrete shear walls
Sujith Mangalathu et al.
ENGINEERING STRUCTURES (2020)
Machine learning and nonlinear models for the estimation of fundamental period of vibration of masonry infilled RC frame structures
Aristotelis E. Charalampakis et al.
ENGINEERING STRUCTURES (2020)
Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach
Sujith Mangalathu et al.
ENGINEERING STRUCTURES (2020)
Application of pool-based active learning in reducing the number of required response history analyses
Jalal Kiani et al.
COMPUTERS & STRUCTURES (2020)
On the application of machine learning techniques to derive seismic fragility curves
Jalal Kiani et al.
COMPUTERS & STRUCTURES (2019)
Stripe-based fragility analysis of multispan concrete bridge classes using machine learning techniques
Sujith Mangalathu et al.
EARTHQUAKE ENGINEERING & STRUCTURAL DYNAMICS (2019)
Machine Learning-Based Failure Mode Recognition of Circular Reinforced Concrete Bridge Columns: Comparative Study
Sujith Mangalathu et al.
JOURNAL OF STRUCTURAL ENGINEERING (2019)
Rapid seismic damage evaluation of bridge portfolios using machine learning techniques
Sujith Mangalathu et al.
ENGINEERING STRUCTURES (2019)
Machine learning algorithms for structural performance classifications and predictions: Application to reinforced masonry shear walls
Ahmad Siam et al.
STRUCTURES (2019)
Classification of failure mode and prediction of shear strength for reinforced concrete beam-column joints using machine learning techniques
Sujith Mangalathu et al.
ENGINEERING STRUCTURES (2018)
Artificial neural network based multi-dimensional fragility development of skewed concrete bridge classes
Sujith Mangalathu et al.
ENGINEERING STRUCTURES (2018)
Critical uncertainty parameters influencing seismic performance of bridges using Lasso regression
Sujith Mangalathu et al.
EARTHQUAKE ENGINEERING & STRUCTURAL DYNAMICS (2018)
Bridge classes for regional seismic risk assessment: Improving HAZUS models
Sujith Mangalathu et al.
ENGINEERING STRUCTURES (2017)
ANCOVA-based grouping of bridge classes for seismic fragility assessment
Sujith Mangalathu et al.
ENGINEERING STRUCTURES (2016)
Prediction of the Fundamental Period of Infilled RC Frame Structures Using Artificial Neural Networks
Panagiotis G. Asteris et al.
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE (2016)
The FP4026 Research Database on the fundamental period of RC infilled frame structures
Panagiotis G. Asteris
DATA IN BRIEF (2016)
Use of response surface metamodels to generate system level fragilities for existing curved steel bridges
Junwon Seo et al.
ENGINEERING STRUCTURES (2013)
Empirical estimate of fundamental frequencies and damping for Italian buildings
Maria Rosaria Gallipoli et al.
EARTHQUAKE ENGINEERING & STRUCTURAL DYNAMICS (2009)
Parameters affecting the fundamental period of RC buildings with infill walls
Mehmet Metin Kose
ENGINEERING STRUCTURES (2009)
Estimation of the fundamental vibration period of existing RC buildings in turkey utilizing ambient vibration records
Kadir Guler et al.
JOURNAL OF EARTHQUAKE ENGINEERING (2008)
A tutorial on support vector regression
AJ Smola et al.
STATISTICS AND COMPUTING (2004)
分享论文
