Experimental and Informational Modeling Study of Sustainable Self-Compacting Geopolymer Concrete

Abstract

Self-compacting concrete (SCC) became a strong candidate for various construction applications owing to its excellent workability, low labor demand, and enhanced finish-ability, and because it provides a solution to the problem of mechanical vibration and related noise pollution in urban settings. However, the production of Portland cement (PC) as a primary constituent of SCC is energy-intensive, contributing to about 7% of global carbon dioxide (CO2) emissions. Conversely, the use of alternative geopolymer binders (GBs) in concrete can significantly reduce the energy consumption and CO2 emissions. In addition, using GBs in SCC can produce unique sustainable concrete with unparallel engineering properties. In this outlook, this work investigated the development of some eco-efficient self-compacting geopolymer concretes (SCGCs) obtained by incorporating different dosages of fly ash (FA) and ground blast furnace slag (GBFS). The structural, morphological, and mechanical traits of these SCGCs were examined via non-destructive tests like X-ray diffraction (XRD) and scanning electron microscopy (SEM). The workability and mechanical properties of six SCGC mixtures were examined using various measurements, and the obtained results were analyzed and discussed. Furthermore, an optimized hybrid artificial neural network (ANN) coupled with a metaheuristic Bat optimization algorithm was developed to estimate the compressive strength (CS) of these SCGCs. The results demonstrated that it is possible to achieve appropriate workability and mechanical strength through 50% partial replacement of GBFS with FA in the SCGC precursor binder. It is established that the proposed Bat-ANN model can offer an effective intelligent method for estimating the mechanical properties of various SCGC mixtures with superior reliability and accuracy via preventing the need for laborious, costly, and time-consuming laboratory trial batches that are responsible for substantial materials wastage.

Performance of Self-Compacted Geopolymer Concrete Containing Fly Ash and Slag as Binders

“…However, ACI 363 and Eurocode CEB-FIB are the closest to the real values for G50 and G100 mixtures, which range between 9.0 and 13% lower than what was predicted. Faridmehr et al [53] investigated SCGC made with the combined use of FA and slag. They also found that the proposed relationship by ACI 318 between compressive and splitting tensile strength does not correctly estimate the splitting tensile values of SCGC.…”

Strain Hardening of Polypropylene Microfiber Reinforced Composite Based on Alkali-Activated Slag Matrix

“…In study of mixture compositions the following results were obtained: the use of microfiber in the amount of 2% by volume or more [51][52][53][54][55]; the use of rheologically active mineral additives in cement matrices with a superplasticizer to ensure workability at low W/C ratio and at high microfiber amount [56,57]; the use of mineral additives that improve workability and setting time in alkali-activated slag matrices [58][59][60][61]; the use of quartz sand with a low fineness modulus as a fine aggregate to obtain a homogeneous matrix and ensure a dense and uniform contact of matrix with the microfiber surface [62][63][64][65][66][67][68]. Manufacturing technology takes into account: the sequence of addition of components into a mixture [69][70][71][72][73]; mixing modes [62,[74][75][76][77].…”

“…An increase in density can be achieved by reducing the water-to-cement ratio using a superplasticizer and rheologically active mineral additives. High filling with microfiber can be achieved by justifying the choice of the type of concrete mixer [68][69][70][71], the sequence of addition of components [72][73][74], and the speed and duration of mixing [62,[75][76][77]. A laboratory automatic mixer for mortars from Tinius Olsen was used to prepare the mixtures (Figure 2).…”

Compressive Strength Prediction of Alkali‐Activated Slag Concretes by Using Artificial Neural Network (ANN) and Alternating Conditional Expectation (ACE)

“…Error increases with the increase of MAE and RMSE values. Calculations of MAE and RMSE are given in formulae (8) and ( 9) according to the literature [2][3][4][5][6]. Furthermore, agreement between predicted and measured values together with coefficient of determination was also applied to better assess the applicability of the proposed networks.…”

“…Finally, mean absolute error (MAE), RMSE, and mean absolute percentage error (MAPE) of 0.042, 0.094, and 0.001 were obtained, respectively, exhibiting that the model had high accuracy. Faridmehr et al [4] also developed an ANN model for compressive strength of alkali-activated fly ash-slag self-compacting concretes, where contents of fly ash and slag, as well as curing age were taken as input parameters. Within the total 6 groups of data based on their own experiment, 70% and 30% of the data were used for training and testing, respectively.…”