Prediction of compressive strength of lightweight concrete made with partially replaced cement by animal bone ash using artificial neural network

Aliyu, Daha Shehu; Malami, Salim Idris; Anwar, Faiz Habib; Farouk, Mahmud Murtala; Labbo, Muwaffaq Sufyan; Abba, S.I.

doi:10.1109/icmeas52683.2021.9692317

Cited by 8 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the growth of the wind turbine system across the globe the common vision in the present power network. Studies have now gone AI and ML for the prediction system in science and engineering (Aliyu et al, 2021;Asnake Metekia et al, 2022;Ibrahim et al, 2022;Manzar et al, 2022;Nourani et al, 2018;Sammen et al, 2021;Usman et al, 2022;Yassin et al, 2022;Farrar & Ali, 2023). Development in wind estimating will be a welcoming tool; when it comes to the electricity square, there will be a need for accurate WS predictions that will always fascinate for a different intention.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Wind Speed Estimation for Renewable Energy Optimization in Urban Environments: A Case Study in Kano State, Nigeria

A. M.,

J. M.,

J. K.

et al. 2024

Advanced Journal of Science, Technology and Engineering

View full text Add to dashboard Cite

Climate change always had a massive effect on worldwide cities. which can only be decreased through considering renewable energy sources (wind energy, solar energy). However, the need to focus on wind energy prediction will be the best solution to the world electricity petition. Wind power (WP) estimating techniques have been used for diverse literature studies for many decades. The hardest way to improve WP is its nature of differences that make it a tough undertaking to forecast. In line with the outdated ways of predicting wind speed (WS), employing machine learning methods (ML) has become an essential tool for studying such a problem. The methodology used for this study focuses on sanitizing efficient models to precisely predict WP regimens. Two ML models were employed “Gaussian Process Regression (GPR), and Feed Forward Neural Network (FFNN)” for WS estimation. The experimental methods were used to focus the WS prediction. The prophecy models were trained using a 24-hour’ time-series data driven from Kano state Region, one of the biggest cities in Nigeria. Thus, investigating the (ML) forecast performance was done in terms of coefficient of determination (R²), linear correlation coefficient (R), Mean Square Error (MSE), and Root Mean square error (RMSE). Were. The predicted result shows that the FFNN produces superior outcomes compared to GPR. With R²= 1, R = 1, MSE = 6.62E-20, and RMSE = 2.57E-10

show abstract

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Wind Speed Estimation for Renewable Energy Optimization in Urban Environments: A Case Study in Kano State, Nigeria

A. M.,

J. M.,

J. K.

et al. 2024

Advanced Journal of Science, Technology and Engineering

View full text Add to dashboard Cite

show abstract

“…With a lower MAE and greater R2, the ANFIS model fared better than the ANN model. Not even GPC prediction has been carry out type of concrete where also (Aliyu et al, 2021;Haruna et al, 2021;Jibril, Zayyan, et al, 2023;Malami et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

New random intelligent chemometric techniques for sustainable geopolymer concrete: low-energy and carbon-footprint initiatives

Jibril,

Malami,

Jibrin

et al. 2023

Asian J Civ Eng

Self Cite

View full text Add to dashboard Cite

The construction industry, being a significant contributor to greenhouse gas emissions, facing considerable attention and demand on account of the increasing global apprehension regarding climate change and its adverse impacts on environments. Geopolymer shows itself as a viable and sustainable alternative to the Portland cement binder in civil infrastructure applications, offering a low-energy, low-carbon footprint solution. This study evaluates five models: Random Forest (RF), Robust Linear Regression (RL), Recurrent Neural Network (RNN), Response Surface Methodology (RSM), and Regression Tree (RT). The RL and RT models were utilized in the prediction of GPC Compressive strength (CS), employing the Matlab R19a regression learner APP. The RNN model was implemented using the Matlab R19a toolkit. Furthermore, the RF model was developed using R studio version 4.2.2 programming code, and the RSM model was constructed using the Minitab 18 toolbox. EViews 12 software was utilized for both pre-processing and post-processing of the data. Additionally, it was employed to convert the non-stationary data into stationary data in order to obtain accurate results. The input variables included SiO2/Na2O (S/N), Na2O (N), Water/Binder Ratio (W/B), Curing Time (CT), Ultrasonic Pulse Velocity (UPV), and 28-day Compressive Strength (Mpa) (CS) as the target variable. The findings of the study indicate that the RMS-M3 model exhibited superior performance compared to all other models, demonstrating a high level of accuracy. Specifically, the Pearson correlation coefficient (PCC) was calculated to be 0.994, while the mean absolute percentage error (MAPE) was found to be 0.708 during the verification phase.

show abstract

An improved prediction of high-performance concrete compressive strength using ensemble models and neural networks

Muhammad,

Aminu,

Mahmoud

et al. 2024

AI Civ. Eng.

View full text Add to dashboard Cite

Traditional methods for proportioning of high-performance concrete (HPC) have certain shortcomings, such as high costs, usage constraints, and nonlinear relationships. Implementing a strategy to optimize the mixtures of HPC can minimize design expenses, time spent, and material wastage in the construction sector. Due to HPC's exceptional qualities, such as high strength (HS), fluidity and resilience, it has been broadly used in construction projects. In this study, we employed Generalized Regression Neural Network (GRNN), Nonlinear AutoRegressive with exogenous inputs (NARX neural network), and Random Forest (RF) models to estimate the Compressive Strength (CS) of HPC in the first scenario. In contrast, the second scenario involved the development of an ensemble model using the Radial Basis Function Neural Network (RBFNN) to detect inferior performance of standalone model combinations. The output variable was the 28 Days CS in MPa, while the input variables included slump (S), water-binder ratio (W/B) %, water content (W) kg/m3, fine aggregate ratio (S/a) %, silica fume (SF)%, and superplasticizer (SP) kg/m3. An RF model was developed by using R Studio; GRNN and NARX-NN models were developed by using the MATLAB 2019a toolkit; and the pre- and post-processing of data was carried out by using E-Views 12.0. The results indicate that in the first scenario, the Combination M1 of the RF model outperformed other models, with greater prediction accuracy, yielding a PCC of 0.854 and MAPE of 4.349 during the calibration phase. In the second scenario, the ensemble of RF models surpassed all other models, achieving a PCC of 0.961 and MAPE of 0.952 during the calibration phase. Overall, the proposed models demonstrate significant value in predicting the CS of HPC.

show abstract

Prediction of compressive strength of lightweight concrete made with partially replaced cement by animal bone ash using artificial neural network

Cited by 8 publications

References 15 publications

Machine Learning-Based Wind Speed Estimation for Renewable Energy Optimization in Urban Environments: A Case Study in Kano State, Nigeria

Machine Learning-Based Wind Speed Estimation for Renewable Energy Optimization in Urban Environments: A Case Study in Kano State, Nigeria

New random intelligent chemometric techniques for sustainable geopolymer concrete: low-energy and carbon-footprint initiatives

An improved prediction of high-performance concrete compressive strength using ensemble models and neural networks

Contact Info

Product

Resources

About