Predictive models for concrete properties using machine learning and deep learning approaches: A review
Concrete is one of the most widely used materials in various civil engineering applications.
Its global production rate is increasing to meet demand. Mechanical properties of concrete …
Its global production rate is increasing to meet demand. Mechanical properties of concrete …
Artificial intelligence, machine learning, and deep learning in structural engineering: a scientometrics review of trends and best practices
Artificial Intelligence (AI), machine learning (ML), and deep learning (DL) are emerging
techniques capable of delivering elegant and affordable solutions which can surpass those …
techniques capable of delivering elegant and affordable solutions which can surpass those …
Perspectives of 2D MXene tribology
The large and rapidly growing family of 2D early transition metal carbides, nitrides, and
carbonitrides (MXenes) raises significant interest in the materials science and chemistry of …
carbonitrides (MXenes) raises significant interest in the materials science and chemistry of …
Influence of data splitting on performance of machine learning models in prediction of shear strength of soil
The main objective of this study is to evaluate and compare the performance of different
machine learning (ML) algorithms, namely, Artificial Neural Network (ANN), Extreme …
machine learning (ML) algorithms, namely, Artificial Neural Network (ANN), Extreme …
Development of deep neural network model to predict the compressive strength of rubber concrete
This paper presents an innovative development process of a Deep Neural Network model to
predict the compressive strength of rubber concrete. To this goal, a rubber concrete …
predict the compressive strength of rubber concrete. To this goal, a rubber concrete …
Prediction of axial load capacity of rectangular concrete-filled steel tube columns using machine learning techniques
This work aims to develop a novel and practical equation for predicting the axial load of
rectangular concrete-filled steel tubular (CFST) columns based on soft computing …
rectangular concrete-filled steel tubular (CFST) columns based on soft computing …
Machine learning approach for investigating chloride diffusion coefficient of concrete containing supplementary cementitious materials
VQ Tran - Construction and Building Materials, 2022 - Elsevier
Chloride diffusion coefficient is an important durability indicator in durability design of
concrete structure according to performance-based approach. However, this indicator is …
concrete structure according to performance-based approach. However, this indicator is …
Machine learning-based prediction of CFST columns using gradient tree boosting algorithm
Among recent artificial intelligence techniques, machine learning (ML) has gained
significant attention during the past decade as an emerging topic in civil and structural …
significant attention during the past decade as an emerging topic in civil and structural …
Evaluation of the ultimate eccentric load of rectangular CFSTs using advanced neural network modeling
In this paper an Artificial Neural Network (ANN) model is developed for the prediction of the
ultimate compressive load of rectangular Concrete Filled Steel Tube (CFST) columns, taking …
ultimate compressive load of rectangular Concrete Filled Steel Tube (CFST) columns, taking …
A novel feature selection approach based on tree models for evaluating the punching shear capacity of steel fiber-reinforced concrete flat slabs
When designing flat slabs made of steel fiber-reinforced concrete (SFRC), it is very important
to predict their punching shear capacity accurately. The use of machine learning seems to …
to predict their punching shear capacity accurately. The use of machine learning seems to …