Artificial Neural Network and NLR techniques to predict the rheological properties and compression strength of cement past modified with nanoclay

Mohammed, Ahmed; Rafiq, Serwan; Mahmood, Wael; Al-Darkazalir, Hind; Noaman, Riyadh; Qadir, Warzer; Ghafor, Kawan

doi:10.1016/j.asej.2020.07.033

Cited by 47 publications

(15 citation statements)

References 54 publications

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“…A sensitivity analysis was conducted for the models to classify and evaluate the input parameter that mainly impacts the CS prediction of SCC containing RP aggregates (Mohammed et al 2020a ). To do so, the MLR model was selected because all the input variables should have a value greater than zero in this model, and it means that the effect of different variables on the CS prediction could be more evident.…”

Section: Results and Analysismentioning

confidence: 99%

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Soft computing techniques to predict the compressive strength of green self-compacting concrete incorporating recycled plastic aggregates and industrial waste ashes

Faraj

Mohammed

Omer

et al. 2022

Clean Techn Environ Policy

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Rapid urbanization and industrialization with corresponding economic growth have increased concrete production, leading to resource depletion and environmental pollution. The mentioned problems can be resolved by using recycled aggregates and industrial waste ashes as natural aggregate and cement replacement in concrete production. Incorporating different by-product ashes and recycled plastic (RP) aggregates are viable options to produce sustainable self-compacting concrete (SCC). On the other hand, compressive strength is an essential characteristic among other evaluated properties. As a result, establishing trustworthy models to forecast the compressive strength of SCC is critical to saving cost, time, and energy. Furthermore, it provides valuable instruction for planning building projects and determining the best time to remove the formwork. In this study, four alternative models were suggested to predict the compressive strength of SCC mixes produced by RP aggregates: the artificial neural network (ANN), nonlinear model, linear relationship model, and multi-logistic model. To do so, an extensive set of data consisting of 400 mixtures were extracted and analyzed to develop the models, various mixture proportions and curing times were considered as input variables. To test the effectiveness of the suggested models, several statistical evaluations, including coefficient of determination ( R 2 ), scatter index, root mean squared error (RMSE), mean absolute error (MAE), and Objective (OBJ) value were utilized. Compared to other models, the ANN model performed better to forecast the compressive strength of SCC mixes incorporating RP aggregates. The RMSE, MAE, OBJ, and R 2 values for this model were 5.46 MPa, 2.31 MPa, 4.26 MPa, and 0.973, respectively. Graphical Abstract

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Section: Results and Analysismentioning

confidence: 99%

“…Mohammed et al ( 2020a ) established multiscale approaches to simulate the CS of concrete containing high quantities of fly ash. In their study, 450 samples were utilized for modeling.…”

Section: Introductionmentioning

confidence: 99%

Soft computing techniques to predict the compressive strength of green self-compacting concrete incorporating recycled plastic aggregates and industrial waste ashes

Faraj

Mohammed

Omer

et al. 2022

Clean Techn Environ Policy

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“…In recent years, the development and study of self-compacted cement grout modified with polymeric admixtures have accelerated, intending to produce high-performance cement grout. Polymeric admixtures have long been used in the cement [19,20], cement grout [21,22], and concrete industries [23,24] to speed upsetting times, reduce water content, and improve cement's physical and mechanical characteristics [25]. Various approaches for modeling material properties have been used to predict the flowability [20,21] and strength of concrete [22,23] drilling fluids and mortar [24,25], including numerical simulations, statistical methods, and recently established systematic methods such as regression analysis, M5-Ptree multilinear, and ANN model [26].…”

Section: Introductionmentioning

confidence: 99%

“…Polymeric admixtures have long been used in the cement [19,20], cement grout [21,22], and concrete industries [23,24] to speed upsetting times, reduce water content, and improve cement's physical and mechanical characteristics [25]. Various approaches for modeling material properties have been used to predict the flowability [20,21] and strength of concrete [22,23] drilling fluids and mortar [24,25], including numerical simulations, statistical methods, and recently established systematic methods such as regression analysis, M5-Ptree multilinear, and ANN model [26]. Validating experimental results using different model techniques has been successfully used to estimate material properties using linear, nonlinear, and intelligent neural networks in various fields such as soils [26], rocks [27], hydraulic fracturing [28], oil well cement [29], and concrete to save time and reduce project costs [30].…”

Section: Introductionmentioning

confidence: 99%

Interpreting the experimental results of compressive strength of hand-mixed cement-grouted sands using various mathematical approaches

Mahmood

Mohammed

Sihag

et al. 2021

Archiv.Civ.Mech.Eng

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Using two different test standards (ASTM and BS), the influence of five different sizes of sand on the ultimate stress (MPa) of hand-mixed cement-grouted sands modified with polymer is discussed in this study. The characteristics of cement-grouted sands modified with polymer up to 0.16% (percent weight of dry cement) were evaluated and measured in fresh and hardened conditions. Adding polymer decreased the water/cement ratio ( w∕c ) from 0.6 to 0.5, and it kept the flow time of the cement-based grout in the range of 18 to 23 s recommended by ASTM standard. Using mix proportion and curing time, adding polymer significantly increased the prismatic and cylindrical compressive strength (MPa) by 113 to 577% and 53 to 459%. Several mathematical approaches such as linear regression (LR), Nonlinear regression (NLR), multilinear regression (MLR), Artificial neural network (ANN), and M5P-tree were used to predict the compression strength of cement-grouted sand with a different size of sand, w/c, polymer content, and curing age. Based on the scatter index (SI), objective function (OBJ) assessments, and training and testing datasets, the compressive strength of the cement-grouted sands can be predicted well using NLR and ANN models. The compression strength tested using the BS standard was 71% higher than the compression strength of the same mix tested using the ASTM standard.

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“…Artificial Neural Network (ANN) is inconsistent and inclined to develop strange connections among system variables, even if they are unaware of the type of interaction [7][8][9][10][11], which do not depend on the underlying mechanism assumptions [12][13][14][15]. In the last two decades, the use of Soft Computing (SC) methods to solve complex problems has been proposed by many researchers [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]. In this work, response of both complex and simple reinforced concrete members at ULR has been assessed by using CDC, CFP, and ANN techniques.…”

Section: Introductionmentioning

confidence: 99%

Knowledge-Based Prediction of Load-Carrying Capacity of RC Flat Slab through Neural Network and FEM

Ahmad

Arshid

Mahmood

et al. 2021

Mathematical Problems in Engineering

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The present research work aims to compare the results for predicting the ultimate response of Reinforced Concrete (RC) members using Current Design Codes (CDCs), an alternative method based on the Compressive Force Path (CFP) method, and Artificial Neural Network (ANN). For this purpose, the database of 145 samples of RC Flat Slab with the simple supported condition under concentrated load is developed from the latest published work. All the cases studied were Square Concrete Slabs (SCS). The critical parameters used as input for the study were column dimension, cs, depth of the slab, ds, shear span ratio, a v s / d , longitudinal percentage steel ratio, ρls, yield strength of longitudinal steel, fyls, the compressive strength of concrete, fcs, and ultimate load-carrying capacity, Vus. Seven ANN models were trained using different combinations of input parameters and different points of hidden neurons with different activation functions. The results exhibited that SCS-4 was the most optimized ANN model, having the maximum value of R (89%) with the least values of MSE (0.62%) and MAE (6.2%). It did not only reduce the error but also predicted accurate results with the least quantity of input parameters. The predictions obtained from the studied models (i.e., CDCs, CFP, and ANN) exhibited that results obtained using the ANNs model correlated well with the experimental data. Furthermore, the FEM results for the selected cases show the closer result to the ANN predictions.

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Artificial Neural Network and NLR techniques to predict the rheological properties and compression strength of cement past modified with nanoclay

Cited by 47 publications

References 54 publications

Soft computing techniques to predict the compressive strength of green self-compacting concrete incorporating recycled plastic aggregates and industrial waste ashes

Soft computing techniques to predict the compressive strength of green self-compacting concrete incorporating recycled plastic aggregates and industrial waste ashes

Interpreting the experimental results of compressive strength of hand-mixed cement-grouted sands using various mathematical approaches

Knowledge-Based Prediction of Load-Carrying Capacity of RC Flat Slab through Neural Network and FEM

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