Experimental and numerical analyses of buried box culverts in trenches using geofoam

Moradi, Gholam; Hassankhani, Ebrahim; Halabian, A H

doi:10.1680/jgeen.19.00288

Cited by 23 publications

(3 citation statements)

References 37 publications

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“…27 However, it should be mentioned that the mentioned experimental research may be time-consuming and costly. [28][29][30][31] Accordingly, various artificial intelligencebased models have been utilized to assess the efficiency of the RAC, as regards AI-based techniques have been employed in other fields of civil engineering. [32][33][34][35][36] For instance, artificial neural networks and fuzzy logic techniques have been employed so effectively to estimate the splitting tensile and compressive mechanical behavior of concrete.…”

Section: Introductionmentioning

confidence: 99%

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

Esmaeili‐Falak,

Sarkhani Benemaran

2024

Structural Concrete

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Concrete constructed using recycled aggregates in place of natural aggregates is an efficient approach to increase the construction sector's sustainability. To improve recycled aggregate concrete () technologies in permafrost, it is essential to certify the stability in frost‐induced conditions. The main goal of this study was to use support vector regression () methods to forecast the frost durability () of on the basis of durability agent value in cold climates. Herein, three optimization methods called Ant lion optimization (), Grey wolf optimization (), and Henry Gas Solubility Optimization () were employed for indicating optimal values of key parameters. The results depicted that all developed models have capability in predicting the of in cold regions. The values of as a comprehensive index depicted that the model has the higher value at 0.0571 as the weakest model, then at 0.0312 recognized as the second‐highest model, and finally the system at 0.0224 mentioned as outperformed model. and approaches were likewise capable of accurately forecasting the of in cold regions, but the created method outperformed them all when taking into account the explanations and justifications.

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Section: Introductionmentioning

confidence: 99%

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

Esmaeili‐Falak,

Sarkhani Benemaran

2024

Structural Concrete

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“…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]. Numerous studies have shown how well ML-based models work in solving issues associated to civil engineering, such as forecasting the mechanical characteristics (compressive/ tensile strength/shear) of hardened concrete [31][32][33][34], the ultimate bond strength of corroded reinforcement and surrounding concrete [35,36], the bearing capability of piles [37,38], the pulling capability of ground anchors [39][40][41], and others.…”

Section: Introductionmentioning

confidence: 99%

Bearing capacity prediction of the concrete pile using tunned ANFIS system

Gu,

Liao,

Cheng

2024

J. Eng. Appl. Sci.

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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.

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“…At present, in order to reduce costs and time spent, artificial intelligence with experimental data is used in civil engineering and related fields [14][15][16][17][18][19][20][21][22][23]. Investigating the literature review demonstrates that, soft computing and artificial intelligence has a major contribution in recent studies in the field of civil engineering sciences and especially geotechnical engineering [24][25][26][27][28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Novel hybrid models to predict the capillary water absorption values of building stones

Yang

Wang³

2022

Eng. Res. Express

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The water that moves upward inside the stone is affected by capillary water absorption (CWA). This upward trend in historic and old buildings causes rapid destruction. In the simple regression model, the relationship between CWA with resistance (compressive strength CS) and its physical characteristics, such as (water absorption by weight ( W_a), surface porosity (n), P-wave velocity (V_p), dry unit weight (ρ_d)) can be extracted. Several samples were collected from several rocks. To aim the prediction design, SVR and ANFIS models were proposed, in which two main ANFIS variables and the optimal quantity of SVR’s decisive variables were indicated via linking with a multi-verse optimizer (MVO) algorithm. So as to develop models, the gathered data records for estimating CWA from published articles were separated into two parts after being sorted randomly. Numerous stones containing igneous, metamorphic, and travertine were gathered from Turkey and Anatolia to use in experimental affairs. The outputs of hybrid models for predicting the CWA of building stones depict that both models have the justifiable capability in the prediction process, indicating the acceptable correlation between recorded and predicted values of CWA. All in all, it is proved that the MVO-SVR has the best performance among the comprising approaches.

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Experimental and numerical analyses of buried box culverts in trenches using geofoam

Cited by 23 publications

References 37 publications

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

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

Bearing capacity prediction of the concrete pile using tunned ANFIS system

Novel hybrid models to predict the capillary water absorption values of building stones

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