Predictive modeling

In this study, we explore the potential of graph neural networks (GNNs), in combination with transfer learning, for the prediction of molecular solubility, a crucial property in drug discovery and materials science. Our approach begins with the development of a GNN-based model to predict the dipole moment of molecules.

8p viling 11-10-2024 1 1   Download

The process of neural stem cell (NSC) differentiation into neurons is crucial for the development of potential cell-centered treatments for central nervous system disorders. However, predicting, identifying, and anticipating this differentiation is complex. In this study, we propose the implementation of a convolutional neural network model for the predictable recognition of NSC fate, utilizing single-cell brightfield images.

7p viengfa 28-10-2024 2 2   Download

Early prediction of ovarian cancer has not been given much attention, the application of combined models in clinical practice is not widespread, and the calculation of these models is still difficult due to the complexity and multiple variables.

8p viengfa 23-10-2024 6 1   Download

This research provides a comprehensive investigation of the planar anisotropy in stainless steel SUS 304. Constitutive modeling approaches, employing both associated and non-associated flow rules, using quadratic functions have been applied in the simulation process.

10p viengfa 28-10-2024 2 2   Download

The main objective of this study is to predict accurately the loaddeflection of composite concrete bridges using two popular machine learning (ML) models namely Random Tree (RT) and Artificial Neural Network (ANN). Data from 83 track loading tests conducted on various bridges in Vietnam were collected and analyzed.

9p viengfa 28-10-2024 3 2   Download

This article conducts an exhaustive investigation into the utilization of machine learning (ML) methods for forecasting the maximum load capacity (MLC) of circular reinforced concrete columns (CRCC) using Fiber-Reinforced Polymer (FRP). Extreme Gradient Boosting (XGB) algorithm is combined with novel metaheuristic algorithms, namely Sailfish Optimizer and Aquila Optimizer, to fine-tune its hyperparameters.

18p viengfa 28-10-2024 2 2   Download

Predicting the macroscopic permeability of porous media is critical in various scientific and engineering applications. This study proposes a novel model that combines Random Forest (RF) and rime-ice (RIME) optimization algorithm, denoted RIME-RF-RIME, to predict permeability based on six key features covering fluid phase dimensions, geometric characteristics, surrounding phase permeability, and media porosity.

14p viengfa 28-10-2024 2 2   Download

This study delves into the application of machine learning (ML), specifically a Gradient Boosting (GB) model, for predicting the punching shear strength (PSS) of two-way reinforced concrete flat slabs.

16p viengfa 28-10-2024 3 2   Download

In this study, our primary aim is to assess and compare the efficacy of Support Vector Machines (SVM) employing various kernel functions: linear (LIN), polynomial (POL), Radial Basis Function (RBF), and sigmoid (SIG) in predicting the compressive strength of concrete.

14p viengfa 28-10-2024 2 2   Download

The present paper also includes strength prediction models that consider the influence of curing temperature. In addition, the Thermogravimetry analysis (TGA) used to determine the chemically bound water content in cement-treated soil and the X-ray diffraction (XRD) test to explain the chemical mechanism in cement-treated soil are also mentioned.

18p viengfa 28-10-2024 4 2   Download

This study introduces and evaluates the Long-term Traffic Prediction Network (LTPN), a specialized machine learning framework designed for realtime traffic prediction in urban environments.

12p viengfa 28-10-2024 3 2   Download

The Axial Load Capacity (ALC) of Concrete-Filled Steel Tubular (CFST) structural members is regarded as one of the most crucial technical factors for the design of these composite structures.

17p viengfa 28-10-2024 2 2   Download

The results of this study would be useful in quickly and accurately predicting CPI to the management agencies, investors, construction contractors to pre-plan the construction investment costs. This will also help in suitably adjusting changing construction cost with time.

11p viengfa 28-10-2024 2 2   Download

The study is a complement to the studies of the tensile strength of cement paste backfill gradually becoming complete. Evaluate the reliability of the proposed model and analyze the influence of the components on the tensile strength. At the same time, the study is also interested in the influence of the components.

9p viengfa 28-10-2024 4 2   Download

This study focuses on optimizing the hyperparameters of the XGB model by a grid search to find an optimal XGB predictive model. In addition, the effect of input parameters on the SCCRF's CS is studied using Shapley Additive exExplanations (SHAP) values technique.

14p viengfa 28-10-2024 6 2   Download

Rubberized concrete is a material that is both ecologically friendly and sustainable, and it has been finding more and more usage in building applications recently. In this study, a machine learning model, namely LightGBM, is developed to predict rubberized concrete's compressive strength (CS) using 11 input parameters.

18p viengfa 28-10-2024 3 1   Download

This paper is aimed to apply hybrid machine learning model namely GA-ANFIS, which is a combination of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Genetic Algorithm (GA), for the prediction of total bearing capability of driven piles.

8p viengfa 28-10-2024 3 2   Download

In this study, we propose a machine learning technique for estimating the shear strength of CRC beams across a range of service periods. To do this, we gathered 158 CRC beam shear tests and used Artificial Neural Network (ANN) to create a forecast model for the considered output.

12p viengfa 28-10-2024 3 2   Download

This paper develops an Artificial Neural Network (ANN) model based on 96 experimental data to forecast the dynamic modulus of asphalt concrete mixtures. This study applied the repeated KFold cross-validation technique with 10 folds on the training data set to make the simulation results more reliable and find a model with more general predictive power.

9p viengfa 28-10-2024 5 2   Download

This study proposes the application of Ensemble Decision Tree Boosted (EDT Boosted) model for forecasting the surface chloride concentration of marine concrete Cs. A database of 386 experimental results was collected from 17 different sources covering twelve variables was used to build and verify the predictive power of the EDT model.

12p viengfa 28-10-2024 5 2   Download