A PSO-ANN Intelligent Hybrid Model to Predict the Compressive Strength of Limestone Fillers Roller Compacted Concrete (RCC) to Build Dams

Chakali, Youcef; Sadok, Ahmed Hadj; Tahlaiti, Mahfoud; Nacer, T.

doi:10.1007/s12205-021-1531-6

Cited by 13 publications

(1 citation statement)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results obtained thus suggest that the hybrid PSO-ANN model is a reliable and promising method for multi-criteria predictions, which may have significant implications for various industrial applications. Moreover, these results are in agreement with previous work by Ramesh et al (2022) and Chakali et al (2021), confirming the validity and reliability of the hybrid PSO-ANN approach for multi-criteria predictions.…”

Section: Prediction With the Rsmsupporting

confidence: 91%

A combination of the particle swarm optimization-artificial neurons network algorithm and response surface method to optimize energy consumption and cost during milling of the 2017A alloy

Bousnina,

Hamza,

Yahia

2023

Energy Exploration & Exploitation

View full text Add to dashboard Cite

This research aims to predict the cost and energy consumption associated with pocket and groove machining using the hybrid particle swarm optimization-artificial neurons network (PSO-ANN) algorithm and the response surface method (RSM). A parametric study was conducted to determine the best predictions by adjusting the swarm population size (pop) and the number of neurons (n) in the hidden layer. The results showed that machining strategies and sequences can have a significant impact on energy consumption, reaching a difference of 99.25% between the minimum and maximum values. The cost ( Ctot) and energy consumption ( Etot) values with the PSO-ANN algorithm increased significantly by 99.99% and 92.41%, respectively, compared to the RSM model. The minimum mean square error values for Etot and Ctot with the PSO-ANN models are 3.0499 × 10−5 and 4.6296 × 10−10, respectively. This study highlights the potential of the hybrid PSO-ANN algorithm for multi-criteria prediction and highlights the potential for improved machining of 2017A alloy.

show abstract

Section: Prediction With the Rsmsupporting

confidence: 91%

A combination of the particle swarm optimization-artificial neurons network algorithm and response surface method to optimize energy consumption and cost during milling of the 2017A alloy

Bousnina,

Hamza,

Yahia

2023

Energy Exploration & Exploitation

View full text Add to dashboard Cite

show abstract

Artificial intelligence techniques in advanced concrete technology: A comprehensive survey on 10 years research trend

Kazemi

2023

Engineering Reports

View full text Add to dashboard Cite

Advanced concrete technology is the science of efficient, cost‐effective, and safe design in civil engineering projects. Engineers and concrete designers are generally faced with the slightest change in the conditions or objectives of the project, which makes it challenging to choose the optimal design among several ones. Besides, the experimental examination of all of them requires time and high costs. Hence, an efficient approach is to utilize artificial intelligence (AI) techniques to predict and optimize real‐world problems in concrete technology. Despite the large body of publications in this field, there are few comprehensive surveys that conduct scientometric analysis. This paper provides a state‐of‐the‐art review that lists, summarizes, and categorizes the most widely used machine learning methods, meta‐heuristic algorithms, and hybrid approaches to concrete issues. To this end, 457 publications are considered during the recent decade with a scientometric approach to highlight the annual trend/active journals/top researchers/co‐occurrence of key title words/countries' participation/research hotspots. In addition, AI techniques are classified into distinct clusters using VOSviewer clustering visualization to identify the application scope and their relationship through the link strength. The findings can be a beacon to help researchers utilize AI techniques in future research on advanced concrete technology.

show abstract

An Intelligent Model to Predict the Compactness of Granular Mixtures Used in Conventional (CC) and Roller-Compacted Concrete (RCC)

Sadok,

Chakali,

Tahlaiti

et al. 2024

Advances in Science, Technology &Amp; Innovation

View full text Add to dashboard Cite

A PSO-ANN Intelligent Hybrid Model to Predict the Compressive Strength of Limestone Fillers Roller Compacted Concrete (RCC) to Build Dams

Cited by 13 publications

References 42 publications

A combination of the particle swarm optimization-artificial neurons network algorithm and response surface method to optimize energy consumption and cost during milling of the 2017A alloy

A combination of the particle swarm optimization-artificial neurons network algorithm and response surface method to optimize energy consumption and cost during milling of the 2017A alloy

Artificial intelligence techniques in advanced concrete technology: A comprehensive survey on 10 years research trend

An Intelligent Model to Predict the Compactness of Granular Mixtures Used in Conventional (CC) and Roller-Compacted Concrete (RCC)

Contact Info

Product

Resources

About