Prediction of compressive strength of concrete incorporated with jujube seed as partial replacement of coarse aggregate: a feasibility of Hammerstein–Wiener model versus support vector machine

Adamu, Musa; Haruna, Sadi İbrahim; Malami, Salim Idris; Ibrahim, Mahdi; Abba, S.I.; Ibrahim, Yasser E.

doi:10.1007/s40808-021-01301-6

Cited by 23 publications

(7 citation statements)

References 46 publications

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“…Aggregates, construction water, exposure to the sea, or the usage of deicing salts during the winter are some of the sources of chloride contamination [7]. The eld of civil engineering has seen tremendous advancements in the application of advanced arti cial intelligence (AI) technologies, especially machine learning, in addressing challenges in a number of domains, including geotechnical engineering [20,21], pavement structures [22], structural engineering [23,24], Composite structural elements [25], material science [26][27][28][29][30], and tra c engineering [31][32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Exploring Optimization Strategies for Support Vector Machine -Based Half Cell Potential Prediction

Murthy,

Pandey,

Gandhi

2024

Preprint

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Purpose This study aims to evaluate the predictive performance of Support Vector Machine (SVM) models in estimating HCP values based on input parameters, employing Bayesian Optimization, Grid Search, and Random Search optimization techniques. Study Design/Methodology Using a dataset containing 1134 rows and six columns, Principal Component Analysis (PCA) is utilized to reduce dimensionality while preserving 95% of the explained variance. Input parameters such as temperature, age, relative humidity, and X and Y lengths are considered, with HCP as the output parameter. Various performance metrics including RMSE, R-squared, MSE, MAE, prediction speed, and training time are analyzed to evaluate model effectiveness. Findings The study reveals variations in predictive performance across different optimization methods, with RMSE values ranging from 18.365 to 30.205 and R-squared values spanning from 0.88 to 0.96. Additionally, differences in training times, prediction speeds, and model complexities are observed, highlighting the trade-offs between model accuracy and computational efficiency. Originality This study contributes to the understanding of SVM model efficacy in HCP prediction, emphasizing the importance of optimization techniques, model complexity, and dimensionality reduction methods such as PCA. The comprehensive evaluation of performance metrics across different optimization methods provides valuable insights for researchers and practitioners in the field.

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Section: Introductionmentioning

confidence: 99%

Exploring Optimization Strategies for Support Vector Machine -Based Half Cell Potential Prediction

Murthy,

Pandey,

Gandhi

2024

Preprint

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“…Many civil engineering problems have been solved using ML algorithms. These include geotechnical engineering [ 17 , 18 ], pavement structures [ 19 ], structural engineering [ 20 , 21 , 22 , 23 ], composite structural elements [ 24 ], material science [ 25 , 26 , 27 , 28 , 29 ], and traffic engineering [ 30 , 31 , 32 , 33 ]. Moreover, previous research has been conducted to predict the Dcl in concrete using an AI-based approach [ 34 , 35 , 36 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Chloride Diffusion Coefficient in Concrete Modified with Supplementary Cementitious Materials Using Machine Learning Algorithms

Fuhaid

Alanazi

2023

Materials

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The chloride diffusion coefficient (Dcl) is one of the most important characteristics of concrete durability. This study aimed to develop a prediction model for the Dcl of concrete incorporating supplemental cementitious material. The datasets of concrete containing supplemental cementitious materials (SCMs) such as tricalcium aluminate (C3A), ground granulated blast furnace slag (GGBFS), and fly ash were used in developing the model. Five machine learning (ML) algorithms including adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), support vector machine (SVM), and extreme learning machine (ELM) were used in the model development. The performance of the developed models was tested using five evaluation metrics, namely, normalized reference index (RI), coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE). The SVM models demonstrated the highest prediction accuracy with R2 values of 0.955 and 0.951 at the training and testing stage, respectively. The prediction accuracy of the machine learning (ML) algorithm was checked using the Taylor diagram and Boxplot, which confirmed that SVM is the best ML algorithm for estimating Dcl, thus, helpful in establishing reliable tools in concrete durability design.

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“…Modeling of the jacketed chemical reactor is carried out using the neural networks, and the controller action is carried out based on the minimization of quadratic performance criteria. Adamu et al 12 had predicted the estimation of turbidity in water treatment plants using Hammerstein–Wiener and neural network techniques. The measurement of the scattering of light when light is incident on a liquid sample is known as turbidity.…”

Section: Introductionmentioning

confidence: 99%

Wiener-Neural-Network-Based Modeling and Validation of Generalized Predictive Control on a Laboratory-Scale Batch Reactor

Kumbhare

Yadav

et al. 2022

ACS Omega

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Batch reactors are large vessels in which chemical reactions take place. They are mostly found to be used in process control industries for processes such as reactant mixing, waste treatment of leather byproducts, and liquid extraction. Modeling and controlling of these systems are complex due to their highly nonlinear nature. The Wiener neural network (WNN) is employed in this work to predict and track the temperature profile of a batch reactor successfully. WNN is different from artificial neural networks in various aspects, mainly its structure. The brief methodology that was deployed to complete this work consisted of two parts. The first part is modeling the WNN-based batch reactor using the provided input–output data set. The input is feed given to the reactor, and the reactor temperature needs to be maintained in line with the optimal profile. The objective in this part is to train the neural network to efficiently track the nonlinear temperature profile that is provided from the data set. The second part is designing a generalized predictive controller (GPC) using the data obtained from modeling the reactor to successfully track any arbitrary temperature profile. Therefore, this work presents the experimental modeling of a batch reactor and validation of a WNN-based GPC for temperature profile tracking.

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Prediction of compressive strength of concrete incorporated with jujube seed as partial replacement of coarse aggregate: a feasibility of Hammerstein–Wiener model versus support vector machine

Cited by 23 publications

References 46 publications

Exploring Optimization Strategies for Support Vector Machine -Based Half Cell Potential Prediction

Exploring Optimization Strategies for Support Vector Machine -Based Half Cell Potential Prediction

Prediction of Chloride Diffusion Coefficient in Concrete Modified with Supplementary Cementitious Materials Using Machine Learning Algorithms

Wiener-Neural-Network-Based Modeling and Validation of Generalized Predictive Control on a Laboratory-Scale Batch Reactor

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