Prediction model

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Predictive neural stem cell differentiation using single-cell images based on deep learning

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 1 1 Download

Comprehensive characterization and modeling of planar anisotropy in stainless steel SUS 304 for accurate earing profile prediction in circular cup drawing processes

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 1 1 Download

Predicting load-deflection of composite concrete bridges using machine learning models

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 2 1 Download
Predicting the maximum load capacity of circular RC columns confined with Fibre-Reinforced Polymer (frp) using machine learning model

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
RIME-RF-RIME: A novel machine learning approach with SHAP analysis for predicting macroscopic permeability of porous media

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 1 1 Download
Efficient structural analysis: Predicting punching shear strength in two-way concrete slabs using gradient boosting machine learning

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 2 1 Download
Investigation of support vector machines with different kernel functions for prediction of compressive strength of concrete

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
Effect of curing temperature on the mechanical characteristics of cement-treated soils: A review

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 1 1 Download
Using GA-ANFIS machine learning model for forecasting the load bearing capacity of driven piles

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
Prediction of shear strength of corrosion reinforced concrete beams using Artificial Neural Network

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
Artificial intelligence approach to predict the dynamic modulus of asphalt concrete mixtures

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
Application of an artificial neural network and QCM sensor coated with γ-Fe2O3 nanoparticles for estimation of SO2 gas sensing characteristics

The basic characteristics of sensor were investigated, and these experimental data were used for a machine learning. The results of the model validation proved to be a reliable way between the experiment and prediction values.

10p viengfa 28-10-2024 1 1 Download
Forecast of surface chloride concentration of concrete utilizing ensemble decision tree boosted

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
Prediction of compressive strength of concrete at high heating conditions by using artificial neural network-based Bayesian regularization

In this study, an artificial neural networkbased Bayesian regularization (ANN) model is proposed to predict the compressive strength of concrete. The database in this study includes 208 experimental results synthesized from laboratory experiments with 9 input variables related to temperature change and design material composition.

12p viengfa 28-10-2024 1 1 Download
Optimizing the architecture of the artificial neural network by genetic algorithm to improve the predictability of pile bearing capacity based on CPT results

This paper presents the results of applying the Artificial Neural Network (ANN) model in determining pile bearing capacity. The traditional methods used to calculate the bearing capacity of piles still have many disadvantages that need to be overcome such as high cost, complicated calculation, time-consuming.

8p viengfa 28-10-2024 1 1 Download
Estimation of the bond strength between FRP and concrete using ANFIS and hybridized ANFIS machine learning models

Adaptive Neuro-Based Fuzzy Inference System (ANFIS) and Particle Swarm Optimization (PSO) algorithms were utilized to produce numerical tools for predicting the bond strength between the concrete surface and carbon fiber reinforced polymer (CFRP) sheets.

11p viengfa 28-10-2024 2 2 Download
Prediction of California Bearing Ratio (CBR) of stabilized expansive soils with agricultural and industrial waste using light gradient boosting machine

Light Gradient Boosting Machine is a new machine learning technique developed by Microsoft corporation which has been proposed in the present study to determine the CBR of stabilized expansive soils. Model performance of the ML model are evaluated by different criteria such as correlation coefficient R, root mean square error RMSE and mean absolute error MAE.

8p viengfa 28-10-2024 3 2 Download
Machine learning models for real-time traffic prediction: A case study in urban traffic management

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 2 2 Download
Development of effective XGB model to predict the Axial Load Capacity of circular CFST columns

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 1 1 Download
Forecasting construction price index using artificial intelligence models: Support vector machines and radial basis function neural network

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