Application of optimization‐based regression analysis for evaluation of frost durability of recycled aggregate concrete

Esmaeili‐Falak, Mahzad; Sarkhani Benemaran, Reza

doi:10.1002/suco.202300566

“…According to the adaptive neuro-fuzzy inference system ( ANFIS ) successful application reports and the aim of this study, it is worth scrutinizing the potential and applicability of this model in various fields [23][24][25][26][27]. Several studies have recently been developed employing different types of machine learning-based algorithms [28][29][30][31][32][33][34][35][36][37][38][39][40][41]. The application of theANFISwas reported successfully in several publications in single or hybrid forms [42].…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization-Based Fuzzy System Application on Deformation of Geogrid-Reinforced Soil Structures

Dou

2024

Int J Comput Intell Syst

0

View full text Add to dashboard Cite

When it comes to the geosynthetic-reinforced soil structures’ structural design, the analysis of deformation is of the highest relevance. In spite of this, the academic literature lays a substantial amount of attention on the potential of artificial intelligence approaches in efficiently addressing the many challenges that are faced in geotechnical engineering. The investigation of the possible use of approaches based on machine learning for the purpose of forecasting the geogrid-reinforced soil structures’ deformation ($${\text{Dis}}$$ Dis ) was the major focus of this study. These efforts are made to reduce the time and cost of numerical modeling. This study aimed to enable computers to learn patterns and insights from data to make accurate predictions or decisions about unseen data. This paper introduces novel systems that coupled the Beluga whale optimizer ($${\text{BWH}}$$ BWH ), Henry gas solubility optimization ($${\text{HGSO}}$$ HGSO ), gannet optimization algorithm ($${\text{GOA}}$$ GOA ), and Harris hawks optimizer ($${\text{HHO}}$$ HHO ) with adaptive neuro-fuzzy inference system ($${\text{ANFIS}}$$ ANFIS ). A dataset was created by gathering 166 finite element analyses accomplished in the literature. Between four $${\text{ANFIS}}$$ ANFIS systems, the integrated one with $${\text{GOA}}$$ GOA got the largest accuracy value, accounting for 0.9841 and 0.9895 in the train and test stages, better than $${\text{ANF}}_{{{\text{BWH}}}}$$ ANF BWH , followed by $${\text{ANF}}_{{{\text{HH}}}}$$ ANF HH . It was seen from $${\text{U}}_{{{95}}} { }$$ U 95 that the $${\text{ANF}}_{{{\text{GOA}}}} { }$$ ANF GOA scenario exhibits the least level of uncertainty in comparison to other models, hence demonstrating its greater capacity for generalization. Between four $${\text{ANFIS}}$$ ANFIS systems, the most accurate system with the lowest $${\text{OBJ}}$$ OBJ value is $${\text{ANF}}_{{{\text{GOA}}}}$$ ANF GOA at 2.6098, followed by $${\text{ANF}}_{{{\text{BWH}}}}$$ ANF BWH at 2.8002. Sensitivity analysis depicts that removing the surcharge $$\left( q \right)$$ q parameter from the input group has a considerable negative impact on output accuracy.

show abstract

“…Machine learning ( ML ), a subset of artificial intelligence that simulates the human brain's functioning, is capable of inferring novel information nonlinearly from past data via adaptive learning [17][18][19][20][21][22][23][24]. Additionally, as learning data increases, the machine learning ( ML)-based models' efficiency may be enhanced progressively, keeping them current with the strict precision demands for complicated engineering challenges [25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Bearing capacity prediction of the concrete pile using tunned ANFIS system

Gu,

Liao,

Cheng

2024

J. Eng. Appl. Sci.

1

0

View full text Add to dashboard Cite

The design process for pile foundations necessitates meticulous deliberation of the calculation pertaining to the bearing capacity of the piles. The primary objective of this work was to investigate the potential use of Coot bird optimization ($${\text{CBO}}$$ CBO ) techniques in predicting the load-bearing capacity of concrete-driven piles. Despite the availability of several suggested models, the investigation of Coot bird optimization ($${\text{CBO}}$$ CBO ) for estimating the pile-carrying capacity has been somewhat neglected in this research. This work presents and validates a unique approach that combines the Coot bird optimization ($${\text{CBO}}$$ CBO ) model with the Multi-layered perceptron ($${\text{MLP}}$$ MLP ) neural network and adaptive neuro-fuzzy inference system ($${\text{ANFIS}}$$ ANFIS ). The findings of 472 different driven pile static load tests were put in a database. The proposed framework's building, validation, and testing stages were each accomplished utilizing the training set (70%), validation set (15%), and testing set (15%) of the dataset, respectively. According to the findings, $${{\text{MLP}}}_{{\text{CBO}}}$$ MLP CBO and $${{\text{ANFIS}}}_{{\text{CBO}}}$$ ANFIS CBO both offer remarkable possibilities for accurately predicting the pile-bearing capacity of a given structure. The $${R}^{2}$$ R 2 values for $${{\text{ANFIS}}}_{{\text{CBO}}}$$ ANFIS CBO during the training stage were 0.9874, while during the validating stage, they were 0.9785, and during the testing stage they were 0.987. After considering various kinds of performance studies and contrasting them with existing literature, it has been concluded that the $${{\text{ANFIS}}}_{{\text{CBO}}}$$ ANFIS CBO model provides a more appropriate calculation of the bearing capacity of concrete-driven piles.

show abstract

Utilizing ensemble machine learning and gray wolf optimization to predict the compressive strength of silica fume mixtures

Javid,

Toufigh

2024

Structural Concrete

1

0

View full text Add to dashboard Cite

The concrete compressive strength is essential for the design and durability of concrete infrastructure. Silica fume (SF), as a cementitious material, has been shown to improve the durability and mechanical properties of concrete. This study aims to predict the compressive strength of concrete containing SF by dual‐objective optimization to determine the best balance between accurate prediction and model simplicity. A comprehensive dataset of 2995 concrete samples containing SF was collected from 36 peer‐reviewed studies ranging from 5% to 30% by cement weight. Input variables included curing time, SF content, water‐to‐cement ratio, aggregates, superplasticizer levels, and slump characteristics in the modeling process. The gray wolf optimization (GWO) algorithm was applied to create a model that balances parsimony with an acceptable error threshold. A determination coefficient (R2) of 0.973 demonstrated that the CatBoost algorithm emerged as a superior predictive tool within the boosting ensemble context. A sensitivity analysis confirmed the robustness of the model, identifying curing time as the predominant influence on the compressive strength of SF‐containing concrete. To further enhance the applicability of this research, the authors proposed a web application that facilitates users to estimate the compressive strength using the optimized CatBoost algorithm by following the link: https://sf-concrete-cs-prediction-by-javid-toufigh.streamlit.app/.

show abstract

Application of optimization‐based regression analysis for evaluation of frost durability of recycled aggregate concrete

Cited by 24 publications

References 73 publications

Optimization-Based Fuzzy System Application on Deformation of Geogrid-Reinforced Soil Structures

Optimization-Based Fuzzy System Application on Deformation of Geogrid-Reinforced Soil Structures

Bearing capacity prediction of the concrete pile using tunned ANFIS system

Utilizing ensemble machine learning and gray wolf optimization to predict the compressive strength of silica fume mixtures

Contact Info

Product

Resources

About