Artificial Neural Network and Nonlinear Models for Gelling Time and Maximum Curing Temperature Rise in Polymer Grouts

Demircan, Emrah; Harendra, Sivaram; Vipulanandan, C.

doi:10.1061/(asce)mt.1943-5533.0000172

Cited by 42 publications

(11 citation statements)

References 9 publications

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“…In addition, the model parameters are denoted by the letters a, b, c, d, e, f, g, and h. The proposed Equation (2) can be viewed as an extension of Equation (1) because all variables can be adjusted linearly; however, while all variables can influence CS and interact with one another, this is not always the case. As a result, in order to estimate the CS accurately, the model should always be adapted [ 36 ]. In this study, the above equation was used to predict the CS of FA-based geopolymer mortar with parameters considered for geopolymer mortar included, modifying Equation (2) to produce Equation (3).…”

Section: Methodsmentioning

confidence: 99%

Statistical Methods for Modeling the Compressive Strength of Geopolymer Mortar

et al. 2022

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In recent years, geopolymer has been developed as an alternative to Portland cement (PC) because of the significant carbon dioxide emissions produced by the cement manufacturing industry. A wide range of source binder materials has been used to prepare geopolymers; however, fly ash (FA) is the most used binder material for creating geopolymer concrete due to its low cost, wide availability, and increased potential for geopolymer preparation. In this paper, 247 experimental datasets were obtained from the literature to develop multiscale models to predict fly-ash-based geopolymer mortar compressive strength (CS). In the modeling process, thirteen different input model parameters were considered to estimate the CS of fly-ash-based geopolymer mortar. The collected data contained various mix proportions and different curing ages (1 to 28 days), as well as different curing temperatures. The CS of all types of cementitious composites, including geopolymer mortars, is one of the most important properties; thus, developing a credible model for forecasting CS has become a priority. Therefore, in this study, three different models, namely, linear regression (LR), multinominal logistic regression (MLR), and nonlinear regression (NLR) were developed to predict the CS of geopolymer mortar. The proposed models were then evaluated using different statistical assessments, including the coefficient of determination (R2), root mean squared error (RMSE), scatter index (SI), objective function value (OBJ), and mean absolute error (MAE). It was found that the NLR model performed better than the LR and MLR models. For the NLR model, R2, RMSE, SI, and OBJ were 0.933, 4.294 MPa, 0.138, 4.209, respectively. The SI value of NLR was 44 and 41% lower than the LR and MLR models’ SI values, respectively. From the sensitivity analysis result, the most effective parameters for predicting CS of geopolymer mortar were the SiO2 percentage of the FA and the alkaline liquid-to-binder ratio of the mixture.

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Section: Methodsmentioning

confidence: 99%

Statistical Methods for Modeling the Compressive Strength of Geopolymer Mortar

et al. 2022

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“…Approximately 70% of the collected data was used as training data to train the network. The data set was tested with 15% of the total data, and the remaining data were used to validate the trained network (Demircan et al, 2011).…”

Section: Arti Cial Neural Network (Ann)mentioning

confidence: 99%

Proposing Several Model Techniques Including ANN and M5P-tree to Forecast the Stress at the Failure of Geopolymer Concrete Mixtures Incorporated Nano-silica

Ahmed

Mohammed

2022

Preprint

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Geopolymers are innovative cementitious materials that can completely replace traditional Portland cement composites and have a lower carbon footprint than Portland cement. Recent efforts have been made to incorporate various nanomaterials, most notably nano-silica (nS), into geopolymer concrete (GPC) to improve the composite's properties and performance. Compression strength (CS) is one of the essential properties of all types of concrete composites, including geopolymer concrete. As a result, creating a credible model for forecasting concrete CS is critical for saving time, energy, and money, as well as providing guidance for scheduling the construction process and removing formworks. This paper presents a large amount of mixed design data correlated to mechanical strength using empirical correlations and neural networks. Several models, including artificial neural network, M5P-tree, linear regression, nonlinear regression, and multilogistic regression models were utilized to create models for forecasting the CS of GPC incorporated nS. In this case, about 207 tested CS values were collected from literature studies and then analyzed to promote the models. For the first time, eleven effective variables were employed as input model parameters during the modeling process, including the alkaline solution to binder ratio, binder content, fine and coarse aggregate content, NaOH and Na2SiO3 content, Na2SiO3/NaOH ratio, molarity, nS content, curing temperatures, and ages. The developed models were assessed using different statistical tools such as RMSE, MAE, SI, OBJ value, and R2. Results revealed that the ANN model estimated the CS of GPC incorporated nS more accurately than the other models. On the other hand, the alkaline solution to binder ratio, molarity, NaOH content, curing temperature, and ages were those parameters that have significant influences on the CS of GPC incorporated nS.

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“…ANN is a powerful simulation software designed for data analysis and computation to think as a human brain in terms of processing and analyses. This machine learning tool is widely used in construction engineering for predicting the future behavior of several numerical problems [60,167,168]. ANN model is generally divided into three main layers, which are input, hidden, output layers.…”

Section: Artificial Intelligence Network (Ann)mentioning

confidence: 99%

“…The collected data set (a total of 510 data) has been divided into three parts for the training purpose of the designed ANN. Around 70 th percent of the collected data was used as a trained data for training the network, 15 th percent of overall data was used for testing the dada set, and the rest of the remaining data was used to validate the trained network [167]. The designed ANN was trained and tested for various hidden layers to determine optimal network structure based on the fitness of the predicted compression strength of FA-GPC with the fc′ of the real collected data.…”

Section: Artificial Intelligence Network (Ann)mentioning

confidence: 99%

Compressive Strength of Geopolymer Concrete Composites: Modeling and Comprehensive Systematic Review

Ahmed

Mohammed

2021

Preprint

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The growing concern about global climate change and its adverse impacts on societies is putting severe pressure on the construction industry as one of the largest producers of greenhouse gases. Given the environmental issues associated with cement production, geopolymer concrete has emerged as a sustainable construction material. Geopolymer concrete is cementless concrete that uses industrial or agro by-product ashes as the main binder instead of ordinary Portland cement; this leads to being an eco-efficient and environmentally friendly construction material. Compressive strength is one of the most important mechanical property for all types of concrete composites including geopolymer concrete, and it is affected by several parameters like an alkaline solution to binder ratio (l/b), fly ash (FA) content, SiO2/Al2O3 (Si/Al) of the FA, fine aggregate (F) and coarse aggregate (C) content, sodium hydroxide (SH) and sodium silicate (SS) content, ratio of sodium silicate to sodium hydroxide (SS/SH), molarity (M), curing temperature (T), curing duration (CD) inside the oven and specimen ages (A). In this regard, a comprehensive systematic review was carried out to show the effect of these different parameters on the compressive strength of the fly ash-based geopolymer concrete (FA-GPC). In addition, multi-scale models such as Artificial Neural Network (ANN), M5P-tree (M5P), Linear Regression (LR), and Multi-logistic Regression (MLR) models were developed to predict the compressive strength of FA-GPC composites. For the first time, in the modeling process, twelve effective parameters including l/b, FA, Si/Al, F, C, SH, SS, SS/SH, M, T, CD, and A were considered the modeling input parameters. Then, the efficiency of the developed models was assessed by various statistical assessment tools like Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Scatter Index (SI), OBJ value, and the Coefficient of determination (R2). Results show that the curing temperature, sodium silicate content, and ratio of the alkaline solution to the binder content are the most significant independent parameters that influence on the compressive strength of the FA-GPC, and the ANN model has better performance for predicting the compressive strength of FA-GPC in compared to the other developed models.

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Artificial Neural Network and Nonlinear Models for Gelling Time and Maximum Curing Temperature Rise in Polymer Grouts

Cited by 42 publications

References 9 publications

Statistical Methods for Modeling the Compressive Strength of Geopolymer Mortar

Statistical Methods for Modeling the Compressive Strength of Geopolymer Mortar

Proposing Several Model Techniques Including ANN and M5P-tree to Forecast the Stress at the Failure of Geopolymer Concrete Mixtures Incorporated Nano-silica

Compressive Strength of Geopolymer Concrete Composites: Modeling and Comprehensive Systematic Review

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