Experimental investigation and prediction of strength development of GGBFS-, LFS- and SCBA-based green concrete using soft computing techniques

Rani, K. Sasi Kala; Suthar, Manju; Sihag, Parveen; Boora, Amardeep

doi:10.1007/s12517-021-08869-4

Cited by 11 publications

(3 citation statements)

References 32 publications

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“…Employing detailed measurement data in calibrating a hydrodynamic model facilitates the identification of the distribution of 'n' values [18]. Over the past decades, many researchers have turned to modeling approaches to analyze and estimate Manning's 'n' value, noting significantly improved outcomes compared to traditional statistical methods [19,20,21,22,23,24,25,26,27]. In the central region of the Hindu Kush Himalayas, the majority of river basins experience peak flows during the monsoon, driven by approximately 75% of the annual rainfall, significantly impacting flow dynamics and consequently altering Manning's roughness.…”

Section: Introductionmentioning

confidence: 99%

Estimation of hydraulic parameter (Manning’s roughness coefficient) in mountainous river at middle stage of Hindu Kush Himalaya region

Lamichhane,

Devkota,

Dawadi

et al. 2023

Jnl Innov. Engin. Educ.

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Manning's roughness coefficient (n) holds significant importance within a hydrodynamic model, yet its value is notably subject to variation, influenced by both time and specific site conditions. Determining the appropriate value for 'n' is a challenging endeavour, particularly in natural watercourses, given the multitude of factors that impact this coefficient. The research unveils findings from a hydraulic model, examining the fluctuation of Manning’s roughness coefficient concerning discharge, thereby influencing the flow depth in the mountainous areas of Nepal situated within the middle stage of the Hindu Kush Himalaya region. This study applied the unsteady flow model HEC-RAS to three comparable reaches in Nepal—the Bagmati River, Kamala River, and Kankai River—to determine the Manning’s coefficient. Through the calibration method, which involves aligning the value to accurately replicate observed data, a suitable Manning’s roughness coefficient "n" was identified. The findings indicate that this coefficient varies notably with reduced discharge and flow depth.

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Section: Introductionmentioning

confidence: 99%

Estimation of hydraulic parameter (Manning’s roughness coefficient) in mountainous river at middle stage of Hindu Kush Himalaya region

Lamichhane,

Devkota,

Dawadi

et al. 2023

Jnl Innov. Engin. Educ.

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show abstract

“…Several researchers have resorted to usage of modelling approaches for analyzing the field and laboratory data, and have found significantly better results as compared to conventional statistical methods during the last few decades (Giustolisi 2004;Azmathullah et al 2005;Azamathulla et al 2010;Singh et al 2018;Mohanty et al 2019;Singh Nain et al 2019;Sihag et al 2020;Rani et al 2021;Sihag et al 2021). Several researchers have analyzed the data obtained on downstream of hydraulic structures and scour around using ANN (Azmathullah et al 2005;Azamathulla et al 2008).…”

Section: Graphical Abstract Introductionmentioning

confidence: 99%

Prediction of Manning's coefficient of roughness for high-gradient streams using M5P

Sihag

Singh²,

Said

et al. 2021

Water Supply

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The coefficient of Manning's roughness (n) has been generally implemented in the determination of depth and discharge in open channels and canals. This study unravels the novel idea and potential of Random Forest (RF), M5P, and Random Tree (RT) approaches to evaluate and predict the coefficient of Manning's roughness for hydraulic designing. To achieve this purpose, 42 observations are collected for high-gradient streams in Colorado, USA. All the observations are from boulder-bed, cobble and high gradient (S > 0.002 m/m) streams for within bank flows. In order to ascertain the best model, the above-mentioned approaches are evaluated and compared using performance evaluation indices such as mean absolute error (MAE), coefficient of correlation (CC), and root mean square error (RMSE). Outcomes of performance evaluation indices revealed that the proposed pruned M5P approach outperformed other applied models for predicting the coefficient of Manning's roughness for hydraulic designing with CC = 0.7858, 0.7910, RMSE = 0.0195, 0.0195, and MAE = 0.0157, 0.0165 for model development and validation period, correspondingly. Furthermore, Taylor diagram and Box plot also suggest that M5P based approach works better than RF and RT based approaches for predicting the coefficient of Manning's roughness for high-gradient streams using the given data set.

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“…At the terminal (leaf) nodes, the M5P model tree is a two-fold decision tree with linear equations that may represent continuous mathematical features. Trimming is done to limit the chance of overfitting [8]…”

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confidence: 99%

Comparison between Random forest and M5P to predict the compressive strength of concrete modified with solid wastes.

Ali

Suthar

2023

IOP Conf. Ser.: Earth Environ. Sci.

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The efficacy of two machine learning algorithms to predict the concrete compressive strength is investigated in this research. For this objective, a vast amount of experimental data from numerous academic research articles was statically evaluated and modelled. In all, 265 observations were considered in this investigation 70% of the data was used for training, while the remaining 30% was used for testing. The data used in this paper was divided randomly. Cement, ground granulated blast furnace slag, limestone powder, fly ash, rice husk ash, fine aggregate, coarse aggregate, silica fume, superplasticizer, water, viscosity modifying admixture, and coarse aggregate were among the 11 input parameters employed in the study. The concrete compressive strength was taken as the output parameter of the model. To assess the models’ prediction abilities, correlation coefficient (CC), the mean absolute error (MAE) and root mean square error (RMSE) values were employed. From the results it was concluded that RF-based model outperforms the M5P Model. The CC, RMSE and MAE values for RF are 0.9867, 3.7887 and 2.9244 for training and 0.8668, 9.1723 and 5.8552 for testing stage respectively.

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Experimental investigation and prediction of strength development of GGBFS-, LFS- and SCBA-based green concrete using soft computing techniques

Cited by 11 publications

References 32 publications

Estimation of hydraulic parameter (Manning’s roughness coefficient) in mountainous river at middle stage of Hindu Kush Himalaya region

Estimation of hydraulic parameter (Manning’s roughness coefficient) in mountainous river at middle stage of Hindu Kush Himalaya region

Prediction of Manning's coefficient of roughness for high-gradient streams using M5P

Comparison between Random forest and M5P to predict the compressive strength of concrete modified with solid wastes.

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