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The effectiveness of surrogate models in predicting the long-term behavior of varying compressive strength ranges of recycled concrete aggregate for a variety of shapes and sizes of specimens

Ibrahim Aisha Kamal, Dhahir Hyfaa Yasin, Mohammed Ahmed Salih, Omar Helen Ali, Sedo Ammar Hassan

Archives of Civil and Mechanical Engineering

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2023

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Vol. 23, no. 1

art. no. e61, 2023

EN

One of the most fundamental developments in improving the mechanical properties of concrete is the introduction of recycled coarse aggregate, which offers an environmentally preferable substitute for traditional waste management techniques. Using recycled coarse aggregate and a small number of mix proportions for the concrete components, a few studies looked at the mechanical properties of concrete. To assess the impact of recycled coarse aggregate on the long-term compressive strength of concrete at various mix proportions and different compressive strength ranges, this study analyzed four models, including linear regression (LR), nonlinear regression (NLR), pure quadratic (PQ), and full quadratic (FQ). Three datasets training, testing, and validating, each containing 314 data points culled from various studies, were used to apply the models. The recycled coarse aggregate (RA) density ranged from 0 to 1240 kg/m3, and the curing time (t) varied from 1 to 90 days. While the predicted compressive strength of the models ranged between 5 and 75 MPa, the compressive strength of the data gathered from the experimental work of several studies ranged from 8 to 78 MPa. The models’ accuracy was assessed using several metrics, including the coefficient of determination (R2), the root-mean-square error (RMSE), the scatter index (SI), the objective (OBJ), and the mean absolute error (MAE).

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Effectiveness of nano-SiO2 on the mechanical, durability, and microstructural behavior of geopolymer concrete at different curing ages

Ahmed Hemn Unis, Mohammed Azad A., Mohammed Ahmed Salih

Archives of Civil and Mechanical Engineering

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2023

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Vol. 23, no. 2

art. no. e129, 2023

EN

The invention and development of new binding construction materials to replace conventional Portland cement are now essential from the perspective of environmental concerns. Geopolymers are a potential solution to this problem. Geopolymers are innovative cementitious materials with the potential to replace Portland cement in manufacturing concrete composites. Nanomaterials offer novel features and performances to geopolymer composites by enhancing the composite's microstructural characteristics by forming extra calcium-silicate-hydrate (C-S-H), sodium-alumino-silicate-hydrate (N-A-S-H), and calcium-alumino-silicate-hydrate (C-A-S-H) gels, as well as the filling nano-pores in the matrix. In this study, extensive experimental laboratory works have been conducted on around 250 geopolymer concrete (GPC) specimens to investigate the effects of adding different dosages (1, 2, 3, and 4%) of nano-silica (NS) on the fresh, compressive strength, splitting tensile strength, flexural strength, stress-strain behaviors, modulus of elasticity, water absorption, rapid chloride permeability, resistance to an acidic environment, and microstructural properties like scanning electron microscopy (SEM) and X-ray diffraction (XRD) of geopolymer concrete composites. As a result of the addition of NS, it was found that the largest improvement in compressive strength was occurred at 3% NS, which was 6.3, 13.4, 20.5, 21, and 21.9% at 3, 7, 28, 90, and 180 days, respectively, compared to the control GPC mixture. Also, the maximum improvement in water absorption was nearly similar for 2 and 3% of NS content, which was 32.2 and 38% at 28 and 90 days, respectively, compared to the control GPC mixture. Furthermore, according to SEM observations, the addition of NS improved the microstructural characteristics of the GPC specimens due to the formation of additional geopolymerization products, as revealed by XRD analyses. However, the fresh characteristics of the geopolymer concrete mixtures are reduced due to the addition of NS to the GPC mixtures.

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

Mahmood Wael, Mohammed Ahmed Salih, Sihag Parveen, Asteris Panagiotis G., Ahmed Hawreen

Archives of Civil and Mechanical Engineering

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2022

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Vol. 22, no. 1

art. no. e19, 2022

EN

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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Hybrid MARS-, MEP-, and ANN-based prediction for modeling the compressive strength of cement mortar with various sand size and clay mineral metakaolin content

Abdalla Aso, Mohammed Ahmed Salih

Archives of Civil and Mechanical Engineering

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2022

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Vol. 22, no. 4

art. no. e194, 2022

EN

In this study, several mathematical, soft computing, and machine learning modeling tools are used to develop a dependable model for forecasting the compressive strength of cement mortar modified with metakaolin (MK) additive and predicting the effect of MK and a maximum diameter of the fine aggregate (MDA) on the compressive strength of the mortar. In this regard, 230 datasets were collected from literature with a wide-ranging mix of proportion and curing time. Water to binder ratio (w/b) ranged between 0.36 and 0.6 (by the weight of dry cement), sand to binder ratio 2 to 3, metakaolin content 0–30%, and curing time up to 90 days. Multivariate regression spline (MARS), multiexpression programming (MEP), nonlinear regression (NLR), and artificial neural network (ANN) models were used. Several assessment tools were utilized to quantify the performance of the proposed models, such as coefficient of determination (R2), root mean squared error (RMSE), mean absolute error (MAE), scatter index (SI), and Taylor diagram. Based on the modeling result, the performance of the MARS model is better than MEP, NLR, and ANN models with high R2 and low RMSE and MAE. The MARS, MEP, and ANN excellently predicted the compressive strength based on the scatter index. The parametric analysis of MK and MDA revealed that the ANN model successfully predicted the influence of the mentioned model inputs and optimum MK content for improving long- and short-term compressive strength.