Artificial Neural Networks (ANN)

Accurate forecasting of the electrical load is a critical element for grid operators to make well-informed decisions concerning electricity generation, transmission, and distribution. In this study, an Extreme Learning Machine (ELM) model was proposed and compared with four other machine learning models including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU).

10p viengfa 28-10-2024 2 1   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

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

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 2 2   Download

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 2 2   Download

This study presents the application of six single algorithm-based models of artificial intelligence, such as artificial neural network (ANN), support vector machine (SVM), classification and regression trees (CART), linear regression (LR), general linear model (GENLIN), and automatic Chisquared interaction detection (CHAID) to predict the residual flexural capacity of corroded RC structures.

12p vibecca 01-10-2024 1 1   Download

Artificial neural network (ANN), a powerful technique, has been used widely over the last decades in many scientific fields including engineering problems. However, the backpropagation algorithm in ANN is based on a gradient descent approach.

15p vibecca 01-10-2024 2 1   Download

This study seeks to develop a prediction model to estimate the bond strength of FRP bars in concrete, utilizing an extended dataset from 1010 pull-out tests. Initially, the study evaluates the applicability of several bond strength formulas from existing codes.

16p vibecca 01-10-2024 3 0   Download

Recently, researches have been used Artificial Neural Network (ANN) to predict the early-age thermal cracking of rectangle piers. But ANN has not resulted for different types of concrete piers. This article presents an evaluation of the early-age thermal characteristics of mass concrete piers with four distinct cross-sectional shapes.

11p vibecca 01-10-2024 3 1   Download

In this paper, an artificial neural network (ANN) model was applied to forecast PM2.5 at the Coc Sau open–pit coal mine (Northern Vietnam) with fine–tuning parameters. It aims to provide the feasibility and insights into controlling air quality in open–pit mines using artificial intelligence techniques.

8p viyoko 01-10-2024 0 0   Download

This article presents the results of improving an artificial neural network (ANN) to predict the tool wear in high-speed dry turning of SKD11 steel. The original ANN was a backpropagation (BPN) model with the Gradient Descent algorithm (GD).

15p viyoko 01-10-2024 4 1   Download

An accurate prediction of the future condition of structural components is essential for planning the maintenance, repair, and rehabilitation of bridges. As such, this paper presents an application of Artificial Neural Networks (ANN) to predict future deck condition for highway bridges in the State of Alabama, the United States.

11p vifilm 24-09-2024 2 1   Download

This paper describes a method to predict the fire resistance ratings of the wooden floor assemblies using Artificial Neural Networks. Experimental data collected from the previously published reports were used to train, validate, and test the proposed ANN model.

12p vifilm 24-09-2024 1 1   Download

In this paper, therefore, given experimental data STCC columns, the authors used four useful AI models to estimate the axial strength in the STCC columns. Particularly, artificial neural networks (ANNs), support vector regression (SVR), linear regression (LR), and M5P were applied in this study.

14p vifilm 24-09-2024 2 1   Download

The main aim of this paper is to propose a risk assessment framework using Artificial Neural Network (ANN) technique. Three main phases of the proposed framework are risk management phase, ANN training phase and framework application phase.

12p viyoko 24-09-2024 1 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

This paper is aimed at quickly predicting the dynamic behavior of functionally graded plates using nontraditional computational approaches consisting of artificial neural networks (ANN) and extreme gradient boosting (XGBoost).

11p vifaye 20-09-2024 3 1   Download

Bài viết này trình bày một phương pháp đánh giá xác suất tin cậy của kết cấu bằng cách sử dụng kết hợp mạng Nơ-ron nhân tạo (Artificial Neural Network - ANN) và MCS, sau đó áp dụng phương pháp này để đánh giá độ tin cậy của cột thép tiết diện chữ I thay đổi với các tham số đầu vào ngẫu nhiên.

13p vicarlos 16-05-2024 3 0   Download

The purpose of this article is to analyze the performance of companies in the slaughterhouse industry in health and safety issues. The research method is quantitative modeling. The main research technique uses a mixed method based on multi-attribute utility method (MAUT) and artificial neural networks (ANN). The research object is 34 slaughterhouse companies located in Southern Brazil. Then, we ranked the companies and modeled their decision trees using the MAUT method.

9p longtimenosee10 26-04-2024 6 2   Download