Prediction of compressive strength of nano-silica modified engineering cementitious composites exposed to high temperatures using hybrid deep learning models

Tanyildizi, Harun

doi:10.1016/j.eswa.2023.122474

Cited by 4 publications

(1 citation statement)

References 80 publications

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“…When the NRMSE = 0, this indicates perfect prediction, while NRMSE = 1 indicates statistical prediction. The peak signal-to-noise ratio (PSNR) is given in Equation ( 17) [36,37].…”

Section: Performance Metricsmentioning

confidence: 99%

Prediction of Losses Due to Dust in PV Using Hybrid LSTM-KNN Algorithm: The Case of Saruhanlı

Tanyıldızı Ağır

2024

Sustainability

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Sustainable and renewable energy sources are of great importance in today’s world. In this respect, renewable energy sources are used in many fields of technology. In order to minimize dust on PV panels and ensure their sustainability, power losses due to dust must be estimated accurately. In this way, the efficiency of a sustainable energy source will increase and serious economic savings can be achieved. In this study, a hybrid deep learning model was designed to predict losses caused by dust in PV panels installed in the Manisa Saruhanlı district. The hybrid deep learning model consists of Long Short-Term Memory (LSTM) and K-Nearest-Neighbors (KNN) algorithms. The performance of the proposed hybrid deep learning model was compared with LSTM and KNN algorithms. Sensitivity analysis was performed to statistically evaluate the prediction results. The input variables of the models were time, sunshine duration, humidity, ambient temperature and solar radiation. The output variable was the losses caused by dust in the PV panels. Hybrid LSTM-KNN, LSTM and KNN models predicted losses caused by dust in PV panels with 98.22%, 95.51% and 61.49% accuracy. The hybrid LSTM-KNN model predicted losses caused by dust in PV panels with higher accuracy than other models. Using LSTM and KNN algorithms together improved the performance of the hybrid deep learning model. With sensitivity analysis, it was found that solar radiation is the most important variable affecting the losses caused by dust in PV panels.

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“…When the NRMSE = 0, this indicates perfect prediction, while NRMSE = 1 indicates statistical prediction. The peak signal-to-noise ratio (PSNR) is given in Equation ( 17) [36,37].…”

Section: Performance Metricsmentioning

confidence: 99%

Prediction of Losses Due to Dust in PV Using Hybrid LSTM-KNN Algorithm: The Case of Saruhanlı

Tanyıldızı Ağır

2024

Sustainability

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Deep learning–based prediction of compressive strength of eco-friendly geopolymer concrete

Tanyildizi

2024

Environ Sci Pollut Res

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The greenhouse gases cause global warming on Earth. The cement production industry is one of the largest sectors producing greenhouse gases. The geopolymer is produced with synthesized by the reaction of an alkaline solution and the waste materials such as slag and fly ash. The use of eco-friendly geopolymer concrete decreases energy consumption and greenhouse gases. In this study, the fc (compressive strength) of eco-friendly geopolymer concrete was predicted by the deep long short-term memory (LSTM) network model. Moreover, the support vector regression (SVR), least squares boosting ensemble (LSBoost), and multiple linear regression (MLR) models were devised to compare the forecast results of the deep LSTM algorithm. The input variables of the models were used as the mole ratio, the alkaline solution concentration, the curing temperature, the curing days, and the liquid-to-fly ash mass ratio. The output variable of the proposed models was chosen as the compressive strength (fc). Furthermore, the effects of the input variable on the fc of eco-friendly geopolymer concrete were determined by the sensitivity analysis. The fc of eco-friendly geopolymer concrete was predicted by the deep LSTM, LSBoost, SVR, and MLR models with 99.23%, 98.08%, 78.57%, and 88.03% accuracy, respectively. The deep LSTM model forecasted the fc of eco-friendly geopolymer concrete with higher accuracy than the SVR, LSBoost, and MLR models. The sensitivity analysis obtained that the curing temperature was the most important experimental variable that affected the fc of geopolymer concrete.

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ANN-based predictive mimicker for the constitutive model of engineered cementitious composites (ECC)

Malik,

Khokhar,

Hammad

et al. 2024

Construction and Building Materials

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Prediction of compressive strength of nano-silica modified engineering cementitious composites exposed to high temperatures using hybrid deep learning models

Cited by 4 publications

References 80 publications

Prediction of Losses Due to Dust in PV Using Hybrid LSTM-KNN Algorithm: The Case of Saruhanlı

Prediction of Losses Due to Dust in PV Using Hybrid LSTM-KNN Algorithm: The Case of Saruhanlı

Deep learning–based prediction of compressive strength of eco-friendly geopolymer concrete

ANN-based predictive mimicker for the constitutive model of engineered cementitious composites (ECC)

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