Effect of basalt and polypropylene fibers on crumb rubber mortar with Portland cement and calcium aluminate cement binders: Strength and artificial neural network prediction model

Author(s):  
Saghar Baghban ◽  
Kim Hung Mo ◽  
Zainah Ibrahim ◽  
Mohammed KH Radwan ◽  
Syed Nasir Shah

This paper aims to study the influence of basalt fiber (BF) and polypropylene fiber (PPF) in crumb rubber (CR) mortar made of two different types of cement, including ordinary Portland cement (OPC) and calcium aluminate cement (CAC). CR was used to partially (5%, 10%, 15%, and 20% by volume) replace the fine aggregate in OPC and CAC mortars. BF and PPF were added (0.1%, 0.3%, and 0.5% by total volume) in the CR mortars. The consistency, density, compressive, and flexural strength of cement mortars were investigated. The use of CAC cement slightly increased the consistency; however, the results showed that the CR replacement and the addition of both fiber types tend to reduce the consistency in OPC and CAC mortars. Significant reduction in the density of fiber-added CR mortar was found with increasing CR content, whereas the influence of both PPF and BF was minimal. The fiber-added CR mortar made of both binder and fiber types in general exhibited a reducing trend in the 28 days compressive strength when increasing CR and fiber contents. Nevertheless, an enhancement in the compressive strength of CAC mortar with 20% CR was found with the addition of 0.1% of both fibers. The use of CR and addition of the fibers generally decreased the flexural strength of mortar made of both binder types; however, the addition of 0.3% BF in mortars containing 15–20% CR positively affected the flexural performance. Finally, the artificial neural network (ANN) approach demonstrated the ability to predict the compressive strength of fiber-added CR mortars. The model showed a considerably insignificant mean square error (MSE) of 1.4–1.5 and high plot regression (R) results of 0.97–0.98.

2019 ◽  
Vol 138 (6) ◽  
pp. 4561-4569 ◽  
Author(s):  
Wiesława Nocuń-Wczelik ◽  
Katarzyna Stolarska

Abstract The studies focused on the kinetics of early hydration in the high-calcium aluminate cement (CAC 70)—by-pass cement kiln dusts (BPCKD)—mixtures. For this purpose, the mixtures of cement with this additive or with some potential constituents of dusts were produced. The microcalorimeter was applied to follow the kinetics of hydration. The investigations with the aim of finding the relationship between the components of initial mixtures and the modification of hydration process were carried out. The rheological properties were characterized, and the chemical shrinkage characteristics were produced. The phase assemblage characterization and microscopic observations were done as well. In case of the high-calcium aluminate-based binders, the modification of setting process was observed; the rheological properties and chemical shrinkage were affected too. The acceleration of heat evolution—the shortening of so-called induction period in the presence of BPCKD additive—was observed. The results were compared to those obtained for the CAC with ordinary Portland cement additive. The results of calorimetric measurements are discussed in terms of the chemical and phase assemblage of this additive as compared to the Portland cement clinker precursors and potassium chloride—the solid and liquid components of the dust.


2018 ◽  
Vol 4 (12) ◽  
pp. 3005 ◽  
Author(s):  
Chioma Temitope Gloria Awodiji ◽  
Davis Ogbonnaya Onwuka ◽  
Chinenye Okere ◽  
Owus Ibearugbulem

In this research work, the levernberg Marquardt back propagation neural network was adequately trained to understand the relationship between the 28th day compressive strength values of hydrated lime cement concrete and their corresponding mix ratios with respect to curing age. Data used for the study were generated experimentally. A total of a hundred and fourteen (114) training data set were presented to the network. Eighty (80) of these were used for training the network, seventeen (17) were used for validation, and another seventeen (17) were used for testing the network's performance. Six (6) data set were left out and later used to test the adequacy of the network predictions. The outcome of results of the created network was close to that of the experimental efforts. The lowest and highest correlation coefficient recorded for all data samples used for developing the network were 0.901 and 0.984 for the test and training samples respectively. These values were close to 1. T-value obtained from the adequacy test carried out between experimental and model generated data was 1.437. This is less than 2.064, which is the T values from statistical table at 95% confidence limit. These results proved that the network made reliable predictions. Maximum compressive strength achieved from experimental works was 30.83N/mm2 at a water-cement ratio of 0.562 and a percentage replacement of ordinary portland cement with hydrated lime of 18.75%. Generally, for hydrated lime to be used in making structural concrete, ordinary portland cement percentage replacement with hydrated lime must not be up to 30%. With the use of the developed artificial neural network model, mix design procedure for hydrated lime cement concrete can be carried out with lesser time and energy requirements, when compared to the traditional method. This is because, the need to prepare trial mixes that will be cured, and tested in the laboratory, will no longer be required.


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