Effective use of recycled waste PET in cementitious grouts for developing sustainable semi-flexible pavement surfacing using artificial neural network (ANN)

Khan, Muhammad Imran; Sutanto, Muslich Hartadi; Khan, Kaffayatullah; Iqbal, Mudassir; Napiah, Madzlan; Zoorob, Salah E.; Klemeš, Jiří Jaromír; Bokhari, Awais; Rafiq, Waqas

doi:10.1016/j.jclepro.2022.130840

Cited by 60 publications

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“…The regression slope of the line trending from plotting experimental results on the X-axis and predicted results on the Y-axis was investigated in this section regarding the performance of the developed models ( Figure 6 ). A similar type of analysis in evaluating AI models has been previously practised by numerous researchers [ 36 , 37 , 38 ]. While exploring the non-linear capabilities of ANN for the compressive strength of polyethylene-terephthalate-incorporated cementitious grouts, Khan, et al [ 36 ], found this slope equal to 1.01 and 0.90 for the training and testing data, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…A similar type of analysis in evaluating AI models has been previously practised by numerous researchers [ 36 , 37 , 38 ]. While exploring the non-linear capabilities of ANN for the compressive strength of polyethylene-terephthalate-incorporated cementitious grouts, Khan, et al [ 36 ], found this slope equal to 1.01 and 0.90 for the training and testing data, respectively. A value of this slope more significant than 0.80 indicated agreement between the experimental and predicted results reported in the previous studies [ 39 , 51 ].…”

Section: Resultsmentioning

confidence: 99%

“…Moreover, basic simplified assumptions have been made in establishing these empirical relations [ 32 ]. Artificial intelligence (AI) is widely used in engineering problems in order to find the optimal solution for regression and classification problems [ 33 , 34 , 35 , 36 , 37 , 38 ]. AI models are not only trained using an appreciable number of experimental observations but also validated by employing a new data set [ 39 ].…”

Section: Introductionmentioning

confidence: 99%

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GEP Tree-Based Prediction Model for Interfacial Bond Strength of Externally Bonded FRP Laminates on Grooves with Concrete Prism

et al. 2022

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Reinforced concrete structures are subjected to frequent maintenance and repairs due to steel reinforcement corrosion. Fiber-reinforced polymer (FRP) laminates are widely used for retrofitting beams, columns, joints, and slabs. This study investigated the non-linear capability of artificial intelligence (AI)-based gene expression programming (GEP) modelling to develop a mathematical relationship for estimating the interfacial bond strength (IBS) of FRP laminates on a concrete prism with grooves. The model was based on five input parameters, namely axial stiffness (Eftf), width of FRP plate (bf), concrete compressive strength (fc′), width of groove (bg), and depth of the groove (hg), and IBS was considered the target variable. Ten trials were conducted based on varying genetic parameters, namely the number of chromosomes, head size, and number of genes. The performance of the models was evaluated using the correlation coefficient (R), mean absolute error (MAE), and root mean square error (RMSE). The genetic variation revealed that optimum performance was obtained for 30 chromosomes, 11 head sizes, and 4 genes. The values of R, MAE, and RMSE were observed as 0.967, 0.782 kN, and 1.049 kN for training and 0.961, 1.027 kN, and 1.354 kN. The developed model reflected close agreement between experimental and predicted results. This implies that the developed mathematical equation was reliable in estimating IBS based on the available properties of FRPs. The sensitivity and parametric analysis showed that the axial stiffness and width of FRP are the most influential parameters in contributing to IBS.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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GEP Tree-Based Prediction Model for Interfacial Bond Strength of Externally Bonded FRP Laminates on Grooves with Concrete Prism

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“…Therefore, a large number of papers dedicated to PET recycling were published, being focused on physical (thermal extrusion and pelletization) or chemical processes (aiming to decompose PET into depolymerized oligomers) [ 5 ]. Since PET degradation naturally occurs after prolonged exposures (to temperature, humidity, radiation or bacteria) [ 6 ], various approaches aimed to accelerate these processes, such as depolymerization at 110 °C for 5 h [ 7 ], melting and plasticization under shear and tension flows [ 8 ], or gamma rays’ exposure [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

DMA Investigation of the Factors Influencing the Glass Transition in 3D Printed Specimens of Shape Memory Recycled PET

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Sava²,

Lohan

et al. 2022

Polymers

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Polyethylene terephthalate (PET) is used worldwide for packing, and for this reason, it is the main material in plastic waste. The paper uses granules of recycled PET (R-PET) as raw material for producing filaments for 3D printing, subsequently used for printing the test specimens in different ways: longitudinally and at angles between 10° and 40° in this direction. Both the filaments and the printed specimens experience thermally driven shape memory effect (SME) since they have been able to recover their straight shape during heating, after being bent to a certain angle, at room temperature (RT). SME could be reproduced three times, in the case of printed specimens, and was investigated by cinematographic analysis. Then, differential scanning calorimetry (DSC) was used, in R-PET granules, filaments and 3D printed specimens, to emphasize the existence of glass transition, which represents the governing mechanism of SME occurrence in thermoplastic polymers, as well as a recrystallization reaction. Subsequently, the paper investigated the 3D printed specimens by dynamic mechanical analysis (DMA) using a dual cantilever specimen holder. Temperature (DMA-TS) and isothermal scans (DMA-Izo) were performed, with the aim to discuss the variations of storage modulus and loss modulus with temperature and time, respectively.

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“…Note that, similar to the case of ANFIS modeling, the results of the training dataset (R = 0.90) are superior to those of the validation datasets (R = 0.86), implying that the results of modeling with ANFIS are comparatively better. At higher R values, a strong correlation can be observed between the input parameters [ 55 ]. Furthermore, the RMSE and MAE performance indices show small differences in the training and validation datasets, that is, (7.22 and 7.68) and (5.74 and 6.06), respectively.…”

Section: Resultsmentioning

confidence: 99%

Compressive Strength Estimation of Fly Ash/Slag Based Green Concrete by Deploying Artificial Intelligence Models

et al. 2022

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Cement production is one of the major sources of decomposition of carbonates leading to the emission of carbon dioxide. Researchers have proven that incorporating industrial wastes is of paramount significance for producing green concrete due to the benefits of reducing cement production. The compressive strength of concrete is an imperative parameter to consider when designing concrete structures. Considering high prediction capabilities, artificial intelligence models are widely used to estimate the compressive strength of concrete mixtures. A variety of artificial intelligence models have been developed in the literature; however, evaluation of the modeling procedure and accuracy of the existing models suggests developing such models that manifest the detailed evaluation of setting parameters on the performance of models and enhance the accuracy compared to the existing models. In this study, the computational capabilities of the adaptive neurofuzzy inference system (ANFIS), gene expression programming (GEP), and gradient boosting tree (GBT) were employed to investigate the optimum ratio of ground-granulated blast furnace slag (GGBFS) and fly ash (FA) to the binder content. The training process of GEP modeling revealed 200 chromosomes, 5 genes, and 12 head sizes as the best hyperparameters. Similarly, ANFIS hybrid subclustering modeling with aspect ratios of 0.5, 0.1, 7, and 150; learning rate; maximal depth; and number of trees yielded the best performance in the GBT model. The accuracy of the developed models suggests that the GBT model is superior to the GEP, ANFIS, and other models that exist in the literature. The trained models were validated using 40% of the experimental data along with parametric and sensitivity analysis as second level validation. The GBT model yielded correlation coefficient (R), mean absolute error (MAE), and root mean square error (RMSE), equaling 0.95, 3.07 MPa, and 4.80 MPa for training, whereas, for validation, these values were recorded as 0.95, 3.16 MPa, and 4.85 MPa, respectively. The sensitivity analysis revealed that the aging of the concrete was the most influential parameter, followed by the addition of GGBFS. The effect of the contributing parameters was observed, as corroborated in the literature.

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Effective use of recycled waste PET in cementitious grouts for developing sustainable semi-flexible pavement surfacing using artificial neural network (ANN)

Cited by 60 publications

References 50 publications

GEP Tree-Based Prediction Model for Interfacial Bond Strength of Externally Bonded FRP Laminates on Grooves with Concrete Prism

GEP Tree-Based Prediction Model for Interfacial Bond Strength of Externally Bonded FRP Laminates on Grooves with Concrete Prism

DMA Investigation of the Factors Influencing the Glass Transition in 3D Printed Specimens of Shape Memory Recycled PET

Compressive Strength Estimation of Fly Ash/Slag Based Green Concrete by Deploying Artificial Intelligence Models

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