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Data-driven shear strength prediction of steel fiber reinforced concrete beams using machine learning approach

Engineering structures, 2021-04, Vol.233, p.111743, Article 111743

2021

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Classifying earthquake damage to buildings using machine learning

Earthquake spectra, 2020-02, Vol.36 (1), p.183-208

2020

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Predicting the dissolution kinetics of silicate glasses using machine learning

Journal of non-crystalline solids, 2018-05, Vol.487, p.37-45

2018

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Enhanced fragility analysis of buried pipelines through Lasso regression

Acta geotechnica, 2020-02, Vol.15 (2), p.471-487

2020

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The effect of rupture directivity, distance and skew angle on the collapse fragilities of bridges

Bulletin of earthquake engineering, 2021-11, Vol.19 (14), p.5843-5869

2021

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Fragility analysis of gray iron pipelines subjected to tunneling induced ground settlement

Tunnelling and underground space technology, 2018-06, Vol.76, p.133-144

2018

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Simplified evaluation of pipe strains crossing a normal fault through the dissipated energy method

Engineering structures, 2018-07, Vol.167, p.393-406

2018

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Classification of failure mode and prediction of shear strength for reinforced concrete beam-column joints using machine learning techniques

Engineering structures, 2018-04, Vol.160, p.85-94

2018

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Artificial neural network based multi-dimensional fragility development of skewed concrete bridge classes

Engineering structures, 2018-05, Vol.162, p.166-176

2018

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Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach

Engineering structures, 2020-09, Vol.219, p.110927, Article 110927

2020

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Adaptive sampling approach based on Jensen-Shannon divergence for efficient reliability analysis

Journal of Central South University, 2021-08, Vol.28 (8), p.2407-2422

2021

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Data-driven machine-learning-based seismic failure mode identification of reinforced concrete shear walls

Engineering structures, 2020-04, Vol.208, p.110331, Article 110331

2020

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Data-driven machine learning approaches for predicting slump of fiber-reinforced concrete containing waste rubber and recycled aggregate

Construction & building materials, 2024-02, Vol.417, p.135369, Article 135369

2024

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Implementing ensemble learning methods to predict the shear strength of RC deep beams with/without web reinforcements

Engineering structures, 2021-05, Vol.235, p.111979, Article 111979

2021

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Rapid seismic damage evaluation of bridge portfolios using machine learning techniques

Engineering structures, 2019-12, Vol.201, p.109785, Article 109785

2019

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Deep learning-based classification of earthquake-impacted buildings using textual damage descriptions

International journal of disaster risk reduction, 2019-05, Vol.36, p.101111, Article 101111

2019

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Machine-learning interpretability techniques for seismic performance assessment of infrastructure systems

Engineering structures, 2022-01, Vol.250, p.112883, Article 112883

2022

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Basin effects on tall bridges in Seattle from M9 Cascadia scenarios

Engineering structures, 2022-06, Vol.260, p.114252, Article 114252

2022

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Critical uncertainty parameters influencing seismic performance of bridges using Lasso regression

Earthquake engineering & structural dynamics, 2018-03, Vol.47 (3), p.784-801

2018

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Probabilistic curvature-and-drift limit states predictive models of high-strength bridge columns

Engineering structures, 2023-11, Vol.295, p.116859, Article 116859

2023

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Explainable machine learning models for predicting the axial compression capacity of concrete filled steel tubular columns

Construction & building materials, 2022-11, Vol.356, p.129227, Article 129227

2022

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Stripe‐based fragility analysis of multispan concrete bridge classes using machine learning techniques

Earthquake engineering & structural dynamics, 2019-09, Vol.48 (11), p.1238-1255

2019

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A comparative analytical study on the fragility assessment of box-girder bridges with various column shapes

Engineering structures, 2017-12, Vol.153, p.460-478

2017

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Fragility functions for highway RC bridge under various flood scenarios

Engineering structures, 2022-06, Vol.260, p.114244, Article 114244

2022

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Bridge classes for regional seismic risk assessment: Improving HAZUS models

Engineering structures, 2017-10, Vol.148, p.755-766

2017

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Data-driven shear strength prediction of steel fiber reinforced concrete beams using machine learning approach

Classifying earthquake damage to buildings using machine learning

Predicting the dissolution kinetics of silicate glasses using machine learning

Enhanced fragility analysis of buried pipelines through Lasso regression

The effect of rupture directivity, distance and skew angle on the collapse fragilities of bridges

Fragility analysis of gray iron pipelines subjected to tunneling induced ground settlement

Simplified evaluation of pipe strains crossing a normal fault through the dissipated energy method

Classification of failure mode and prediction of shear strength for reinforced concrete beam-column joints using machine learning techniques

Artificial neural network based multi-dimensional fragility development of skewed concrete bridge classes

Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach

Adaptive sampling approach based on Jensen-Shannon divergence for efficient reliability analysis

Data-driven machine-learning-based seismic failure mode identification of reinforced concrete shear walls

Data-driven machine learning approaches for predicting slump of fiber-reinforced concrete containing waste rubber and recycled aggregate

Implementing ensemble learning methods to predict the shear strength of RC deep beams with/without web reinforcements

Rapid seismic damage evaluation of bridge portfolios using machine learning techniques

Deep learning-based classification of earthquake-impacted buildings using textual damage descriptions

Machine-learning interpretability techniques for seismic performance assessment of infrastructure systems

Basin effects on tall bridges in Seattle from M9 Cascadia scenarios

Critical uncertainty parameters influencing seismic performance of bridges using Lasso regression

Probabilistic curvature-and-drift limit states predictive models of high-strength bridge columns

Explainable machine learning models for predicting the axial compression capacity of concrete filled steel tubular columns

Stripe‐based fragility analysis of multispan concrete bridge classes using machine learning techniques

A comparative analytical study on the fragility assessment of box-girder bridges with various column shapes

Fragility functions for highway RC bridge under various flood scenarios

Bridge classes for regional seismic risk assessment: Improving HAZUS models

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