Machine learning for structural engineering: A state-of-the-art review

Structures, Год журнала: 2022, Номер 38, С. 448 - 491

Опубликована: Фев. 15, 2022

Язык: Английский

---

Revealing the nature of metakaolin-based concrete materials using artificial intelligence techniques

Construction and Building Materials, Год журнала: 2022, Номер 322, С. 126500 - 126500

Опубликована: Янв. 22, 2022

Язык: Английский

---

Soft computing-based models for the prediction of masonry compressive strength

Engineering Structures, Год журнала: 2021, Номер 248, С. 113276 - 113276

Опубликована: Окт. 5, 2021

Язык: Английский

---

A novel integrated approach of augmented grey wolf optimizer and ANN for estimating axial load carrying-capacity of concrete-filled steel tube columns

Construction and Building Materials, Год журнала: 2022, Номер 337, С. 127454 - 127454

Опубликована: Май 1, 2022

Язык: Английский

---

Predicting uniaxial compressive strength of rocks using ANN models: Incorporating porosity, compressional wave velocity, and schmidt hammer data

Ultrasonics, Год журнала: 2024, Номер 141, С. 107347 - 107347

Опубликована: Май 20, 2024

Язык: Английский

---

State-of-the-art AI-based computational analysis in civil engineering

Journal of Industrial Information Integration, Год журнала: 2023, Номер 33, С. 100470 - 100470

Опубликована: Май 5, 2023

Язык: Английский

---

Development of hybrid gradient boosting models for predicting the compressive strength of high-volume fly ash self-compacting concrete with silica fume

Structures, Год журнала: 2024, Номер 66, С. 106850 - 106850

Опубликована: Июль 8, 2024

Язык: Английский

---

AI-powered GUI for prediction of axial compression capacity in concrete-filled steel tube columns

Neural Computing and Applications, Год журнала: 2024, Номер unknown

Опубликована: Сен. 21, 2024

Язык: Английский

---

Predicting the web crippling capacity of cold-formed steel lipped channels using hybrid machine learning techniques

Engineering Structures, Год журнала: 2024, Номер 309, С. 118061 - 118061

Опубликована: Апрель 22, 2024

Cold-Formed Steel Lipped (CFSL) channels are susceptible to a localized failure mechanism known as web crippling, triggered by concentrated loads or reactions applied the of section. These induce buckling and distortion in web, ultimately leading member's collapse. It is challenging task accurately determine crippling capacity CFSL channel due its complexity various influencing factors. This paper presents hybrid soft computing techniques for predicting subjected two flange load cases. The developed combine Artificial Neural Networks (ANN) with either Genetic Algorithms (GA) Particle Swarm Optimization (PSO) improve computational efficiency accuracy. finite element models validated experimental results then employed generate database, which used train machine learning models, including ANN, GA-ANN, PSO-ANN. Analysis undertaken on reliability existing design formulas determining channels. shown that PSO-ANN model outperforms other terms prediction codes not reliable estimating However, proposed yields good correlation analysis results. A user- friendly graphical interface tool practical cold-formed steel lipped

Язык: Английский

---

Evaluation of the ultimate eccentric load of rectangular CFSTs using advanced neural network modeling

Engineering Structures, Год журнала: 2021, Номер 248, С. 113297 - 113297

Опубликована: Окт. 4, 2021

Язык: Английский

---