Data driven approach

This study develops a decision support system, named Academic Quality Assurance, for supporting the analyzing of data collected from multiple semesters. The system focuses on assisting users in filtering data based on semesters, courses, departments, and programs, and then visualizing the data, providing comparisons.

13p viling 11-10-2024 2 0   Download

In this study, we propose a novel approach using the oscillation characteristics of the RMS current as the input to machine learning models, combined with the confident learning technique. Using the oscillation characteristics obtained by taking a discrete Fourier transform (DFT) of the RMS current as model input, we aim to reduce the computational requirements of the machine learning models.

12p viling 11-10-2024 1 0   Download

The most rigorous effective medium approximations for elastic moduli are elaborated for matrix composites made from an isotropic continuous matrix and isotropic inclusions associated with simple shapes such as circles or spheres. In this paper, we focus specially on the effective elastic moduli of the heterogeneous composites with arbitrary inclusion shapes.

13p vifilm 24-09-2024 2 1   Download

This study develops a probabilistic data-driven approach using the Multiple Layer Perceptron network coupled with the Dropout mechanism to perform the pushover analysis of reinforced concrete (RC) frame structures, predicting base shear, lateral displacement, as well as their relationship between the two formers.

11p vifilm 24-09-2024 4 1   Download

Using a data-driven approach to study and predict the shear strength of slender steel fiber reinforced concrete beams has great applicability for the design and construction process. Based on the data-driven approach, an Artificial Neural Network (ANN) model with some hyperparameters optimized by Particle Swarm Optimization (PSO) algorithm is successfully built.

12p vifaye 20-09-2024 2 1   Download

The main objective of this paper is to use the data-driven approach to predict and study the factors affecting the compressive strength of steel fiber concrete. Therefore, six machine learning (ML) models were evaluated against a database of 166 samples and ten input variables, including Cement content, Water content, Silica fume content, Steel fiber content, Coarse aggregate content, Sand content, Superplasticizer content, Fiber diameter, Fiber length, Fly ash content.

15p vifaye 20-09-2024 2 1   Download