Effect of nano-CuO on engineering and microstructure properties of fibre-reinforced mortars incorporating metakaolin: experimental and numerical studies

In this study, the effects of nano-CuO (NC) on engineering properties of fibre-reinforced mortars incorporating metakaolin (MK) were investigated. The effects of polypropylene fibre (PP) were also examined. A total of twenty-six mixtures were prepared. The experimental results were compared with numerical results obtained by adaptive neuro-fuzzy inference system (ANFIS) and Primal Estimated sub-GrAdient Solver for SVM (Pegasos) algorithm. Scanning Electron Microscope (SEM) was also employed to investigate the microstructure of the cement matrix. The mechanical test results showed that both compressive and flexural strengths of cement mortars decreased with the increase of MK content, however the strength values increased significantly with increasing NC content in the mixture. The water absorption of samples decreased remarkably with increasing NC particles in the mixture. When PP fibres were added, the strengths of cement mortars were further enhanced accompanied with lower water absorption values. The addition of 2 wt % and 3 wt % nanoparticles in cement mortar led to a positive contribution to strength and resistance to water absorption. Mixture of PP-MK10NC3 indicated the best results for both compressive and flexural strengths at 28 and 90 days. SEM images illustrated that the morphology of cement matrix became more porous with increasing MK content, but the porosity reduced with the inclusion of NC. In addition, it is evident from the SEM images that more cement hydration products adhered onto the surface of fibres, which would improve the fibre–matrix interface. The numerical results obtained by ANFIS and Pegasos were close to the experimental results. The value of R2 obtained for each data set (validate, test and train) was higher than 0.90 and the values of mean absolute percentage error (MAPE) and the relative root mean squared error (PRMSE) were near zero. The ANFIS and Pegasos models can be used to predict the mechanical properties and water absorptions of fibre-reinforced mortars with MK and NC

Innovating Two-Stage Concrete with Improved Rheological, Mechanical and Durability Properties

Two-stage concrete (TSC), also known as preplaced aggregate concrete, is a special type of concrete that is produced using a unique procedure which differs from that of conventional concrete. TSC is distinguished by its high coarse aggregate content and exceptional placement technique, whereby aggregates are first pre-placed in the mold then injected with a special grout. The preplacement of aggregates saves substantial energy since only the grout needs mechanical mixing; the grout is self-leveling and needs no vibration and no mechanical compaction. However, TSC applications are still limited despite substantial advancement of modern concrete technology. Therefore, there is a need to explore new possibilities and applications for TSC through adjusting and improving its properties. The objective of this study is to advance the TSC technology through the use of supplementary cementitious materials (SCMs), fibre reinforcement, capturing its sustainability features to develop novel pavements with very high recycled content, and establishing models with predictive capability for its engineering properties. Therefore, the fresh and hardened properties of grout mixtures incorporating various SCMs, including fly ash (FA), silica fume (SF) and metakaolin (MK) were investigated. An attempt was made to identify the optimum water-to-binder (w/b) ratio and the high-range water-reducing admixture (HRWRA) dosages for grout mixtures that meet the recommended efflux time (i.e. 35-40 ± 2 sec) according to ACI 304.1. Moreover, the effects of various SCMs at different dosages on the development of TSC mechanical properties were investigated. Likewise, the performance of TSC made with single, binary and ternary binders exposed to different environments conducive to physical and chemical sulfate attack was explored. The negative influence of fibres on the workability of conventional concrete is eliminated in TSC since the coarse aggregates and fibres are preplaced in the formwork and then injected with a flowable grout. This allows using fibre dosages beyond the practical levels typically adopted in conventionally mixed concrete. Therefore, the mechanical performance of two-stage steel fibre-reinforced concrete (TSSFRC) made with different dosages of steel fibres having various lengths was explored for the first time. The high coarse aggregate content endows TSC with superior volume stability, making it an ideal contender for pavements and sidewalks, which typically suffer from shrinkage and thermal cracking. In this study, the preplaced material consists of recycled concrete aggregate and scarp tire rubber granules along with scrap tire steel wire fibres, while the grout uses high-volume fly ash. The performance of such a “green” TSC pavement construction technology was explored. Finally, the experimental results were used to create a database which was utilized for developing fuzzy logic (FL) models as a means of predicting the grout flowability (i.e. efflux time and spread flow) and the mechanical properties (i.e. compressive and tensile strength) of a variety of two-stage concrete (TSC) mixtures. Results indicated that grouts made with water-to-binder ratio (w/b) = 0.45 can achieve the recommended grout flowability for successful TSC production. Moreover, TSC grout properties highly depended on the type and dosage of SCM used. The grout flowability was significantly enhanced as the FA dosage was increased, while the compressive strength was decreased. Partially replacing cement with 10% SF or 10% MK reduced the grout flowability and enhanced its compressive strength. Moreover, the binder composition has a great influence on the TSC mechanical properties. Empirical relationships between the properties of the grout and those of the corresponding TSC were proposed, offering a potential tool for estimating TSC properties based on primary grout properties. Furthermore, the ease of using a high dosage of pre-placed fibres in TSSFRC allowed achieving exceptional engineering properties for the pre-placed aggregate concrete. Indeed, TSSFRC can easily be produced with 6% steel fibre dosage, which makes it an innovative option and a strong contender in many construction applications. Fully immersed TSC specimens incorporating FA or MK in sodium sulfate solution exhibited high sulfate resistance. Surprisingly, TSC specimens incorporating SF deteriorated significantly due to thaumasite formation. Under physical sulfate attack exposure, TSC specimens incorporating FA and/or SF incurred severe surface scaling at the evaporative front, while those made with MK exhibited high resistance to surface scaling. A novel eco-efficient technology for the construction of pavements and sidewalks was proposed. The results demonstrate the feasibility of TSC eco-efficient technology to produce durable and cost-effective sidewalks and pavements, offering ease of placement and superior sustainability features. Finally, the performance of the developed FL models was evaluated using error and statistical analyses. The results indicate that the FL models can offer a flexible, adaptable and reasonably accurate tool for predicting the TSC grout flowability and mechanical properties. The findings of this study should provide a leap forward in establishing the TSC technology as a strong contender in many construction applications. It contributes to taking the TSC from a basic technology to a more modern system that benefits from advancements in concrete technology through the use of SCMs, chemical admixtures and fibre reinforcement. In particular, in a new context that values sustainability and “green” construction technology, this study has proven TSC to be exceptional in its ability to use recycled materials without the drawbacks observed in normal concrete technology. These findings should contribute to enhancing the understanding of the TSC behaviour, paving the way for its wider implementation in today’s concrete industry

Sustainable design of self-consolidating green concrete with partial replacements for cement through neural-network and fuzzy technique

In order to achieve a sustainable mix design, this paper evaluates self-consolidating green concrete (SCGC) properties by experimental tests and then examines the design parameters with an artificial intelligence technique. In this regard, cement was partially replaced in different contents with granulated blast furnace slag (GBFS) powder, volcanic powder, fly ash, and micro-silica. Moreover, fresh and hardened properties tests were performed on the specimens. Finally, an adaptive neuro-fuzzy inference system (ANFIS) was developed to identify the influencing parameters on the compressive strength of the specimens. For this purpose, seven ANFIS models evaluated the input parameters separately, and in terms of optimization, twenty-one models were assigned to different combinations of inputs. Experimental results were reported and discussed completely, where furnace slag represented the most effect on the hardened properties in binary mixes, and volcanic powder played an effective role in slump retention among other cement replacements. However, the combination of micro-silica and volcanic powder as a ternary mix design successfully achieved the most improvement compared to other mix designs. Furthermore, ANFIS results showed that binder content has the highest governing parameters in terms of the strength of SCGC. Finally, when compared with other additive powders, the combination of micro-silica with volcanic powder provided the most strength, which has also been verified and reported by the test results

Pre-bcc: a novel integrated machine learning framework for predicting mechanical and durability properties of blended cement concrete

Partially replacing ordinary Portland cement (OPC) with low-carbon supplementary cementitious materials (SCMs) in blended cement concrete (BCC) is perceived as the most promising route for sustainable concrete production. Despite having a lower environmental impact, BCC could exhibit performance inferior to OPC in design-governing functional properties. Hence, concrete manufacturers and scientists have been seeking methods to predict the performance of BCC mixes in order to reduce the cost and time of experimentally testing all alternatives. Machine learning algorithms have been proven in other fields for treating large amounts of data drawing meaningful relationships between data accurately. However, the existing prediction models in the literature come short in covering a wide range of SCMs and/or functional properties. Considering this, in this study, a non-linear multi-layered machine learning regression model was created to predict the performance of a BCC mix for slump, strength, and resistance to carbonation and chloride ingress based on any of five prominent SCMs: fly ash, ground granulated blast furnace slag, silica fume, lime powder and calcined clay. A database from>150 peer-reviewed sources containing>1650 data points was created to train and test the model. The statistical performance was found to be comparable to that of existing models (R = 0.94–0.97). For the first time, the model, Pre-bcc, was also made available online for users to conduct their own prediction studies.Peer ReviewedPostprint (published version

Numerıcal Modelıng And Experımental Evaluatıon Of Shrınkage Of Concretes Incorporatıng Fly Ash And Sılıca Fume

Rötre genellikle sertleşmiş betonun önemli bir özelliği olarak ele alınır. Kuruma sürecinde boşluk yapısında bulunan serbest ve emilmiş su kaybedilir. Betonun rötresi kısıtlandığı zaman betonda olşan gerilmelere bağlı olarak çatlak oluşumu gözlenir. Bu çatlaklardan zararlı maddelerin geçmesiyle betonun dayanım ve dayanıklılıgında azalma olur. Bu çalışman ilk aşamasinda genetik programlama ve yapay sinir ağları yöntemleri kullanılarak rötre tahmin modelleri geliştirilmiştir. Modellerin eğitimi ve test edilmesi için literatürden veri toplanmıştır. Çalışmanın ikinci aşamasında ise uçucu kül ve silis dumanı içeren betonlar hazırlanarak kırk günlük kuruma sürecinde rötreleri ölçülmüştür. En yüksek rötre değerleri en çok mineral katkı içeren betonlarda gözlenmiştir. Bunların yanı sıra deneysel çalışmada elde edilen sonuçlar tahmin modellerinin verdikleriyle karşılaştırılmışlardır. YSA ile elde edilen değerlerin GP ile elde edilenlere göre gerçeğe daha yakın oldukları görülmüştür

Influence of Water Binder Ratio and Chemical Admixture on the Properties of Self- Compacting Concrete with composite Cement- Fly Ash binder

This paper describes an experimental investigation to study the combined effect of water binder ratio and chemical admixture on mechanical properties of self- compacting concrete (SCC) prepared using composite fly ash–cement binder. For this purpose, the mixture proportioning for SCC was based upon creating a high-degree of flowability by using High-Range Water-Reducing Admixtures (HRWRA) combined with Viscosity Modifying Admixture (VMA) to ensure homogeneity of the mixture. The flowability test results showed that the spread for all mixes was within the specified range recommended by EFNARC 2005 and EN 206. The J Ring height for all SCC mixes was observed to be between 17-20 mm, which was within the specified limits of EFNARC 2005. A visual stability index has been provided to all SCC mixes for qualitative assessment of the flowability indexes. The cementing efficiency factor of fly ash, adopted in the presented work, restores the cementitious content in the mix. At 0.36 w/b ratio, the cube compressive strength at 28 days was almost 51MPa when 2.2% HRWRA with VMA was added to the mix. Through the different flowability test results, an effort has been made to develop a correlation between different flowability parameters using regression analysis in MINITAB software. An empirical formula in the form of basic equations suggested by CEB-FIP and ACI 363R -92 to express the relationship between split tensile strength and compressive strength of SCC has also been proposed

Forecasting The Compressive Strength Of Self-Compacting Concretes Containing Mineral Admixtures By Artificial Neural Networks

This research was conducted to design an artificial neural network for predicting the compressive strength of self compacting concrete containing mineral admixtures. This prediction is divided into feed forward back propagation and reverse neural network model. The first part the model can predict the SCC compressive strength not only on experimental data but also on the every desired mineral admixture mix proportions. The network is able to pass the following way reversely. In other words, the network is acting as two-way routes. The first is the way which the starting point is amount of mineral admixtures (as input data) and the end point is the SCC compressive strength at 28 and 90 day (as desired output), the return way is vice versa

M5' and Mars Based Prediction Models for Properties of Self-Compacting Concrete Containing Fly Ash

The main purpose of this paper is to predict the properties (mechanical and rheological) of the self-compacting concrete (SCC) containing fly ash as cement replacement by using two decision tree algorithms: M5′ and Multivariate adaptive regression splines (Mars). The M5′ algorithm as a rule based method is used to develop new practical equations while the MARS algorithm besides its high predictive ability is used to determine the most important parameters. To achieve this purpose, a data set containing 114 data points related to effective parameters affect on SSC properties is used. A gamma test is employed to determine the most effective parameters in prediction of the compressive strength at 28 days, the V-funnel time, the slump flow, and the L-box ratio of SCC. The results from this study suggests that tree based models perform remarkably well in predicting the properties of the self-compacting concrete containing fly ash as cement replacement.&nbsp

Proportion and performance evaluation of fly ash-based geopolymer and its application in engineered composites

It is well known that the use of Portland cement (PC) in concrete construction is causing severe environmental issues primarily due to vast quantity of carbon dioxide released to the atmosphere during the manufacture of PC. On the other hand, disposal of industrial solid wastes such as fly ash and slag in landfills is creating another threat to the environment. The development of a fly ash geopolymer binder, produced from the reaction of fly ash and alkaline solution, may replace Portland cement as a construction material and at the same, reduce the disposal of fly ash in landfills. This dissertation reports the efforts in optimizing mix proportion, predictive modeling on early age properties, shrinkage control and mechanical performance of an engineered composite made with fly ash-based geopolymer. This dissrtation consists of four papers: (1) Optimization of Mix Design Parameters on Thermal, Setting and Stiffening Behaviors of High Calcium Fly Ash Geopolymer; (2) Prediction of Strength, Setting Time and Heat Generation of Fly Ash Geopolymer Using Artificial Neural Network; (3) The Effects of Activator and Shrinkage Reducing Admixture on Shrinkage Behavior of Fly Ash Geopolymer, and (4) The Effect of Slag on Mechanical Properties of Engineered Geopolymer Composite. Due to the lack of knowledge to optimize the mix proportion of fly ash based geopolymer in the published literature, Paper 1 is focused on the effects of design parameters including SiO2/Na2O mole ratio (Module), solute (NaOH and Na2SiO3) mass concentration on the fresh and hardened properties (i.e., setting time, compressive strength and heat of hydration). The knowledge gained from this study is expected to assist in the optimization of the mix proportions for thefly ash geopolymer. Results from Paper 1 have shown that modules less than 1.5, concentrations between 40% and 50%, L/F ratios less than 0.40, and higher curing temperature, such as 50oC, were preferred to synthesize a geopolymer system using high calcium fly ash. In Paper 2, an artificial neural network (ANN) approach was applied to analyze the complexity between geopolymer properties and various parameters forgeopolymer mix proportion design. The predictive models for setting time and compressive strength of geopolymer were established for the ease of mix design. Paper 2 concluded that ANN was an effective tool for parametric study of the properties of fly ash geopolymer. The effects of geopolymer mix design parameters on setting time, compressive strength and heat generation were discussed in accordance with the prediction profiler generated by the ANN models. The proposed model can be used as a guidance for high calcium fly ash geopolymer mix design in the future. Shrinkage of cement-based materials is a major cause of cracking. The work discussed in Paper 3 was to characterize the shrinkage behavior (e.g., free drying shrinkage and restrained ring shrinkage) of fly ash-based geopolymer in comparison with that of PC paste. The effects of activator (Module and Concentration) and shrinkage reducing admixture (SR) on the shrinkage behavior of fly ash-based geopolymer have been explored. In addition, the flowability of the geopolymer using a mini slump test and compressive strength test were also carried out. The results indicate that the fly ash geopolymer has comparable flowability properties as compared to that of PC. SR slightly decreased flowability of PC and fly ash geopolymer. It was also found that the drying shrinkage of fly ash geopolymer was of similar magnitude to that of PC, but was not due to mass loss for fly ash geopolymer. The SR significantly reduced the drying shrinkage of fly ash geopolymer up to 52% as well as in PC. The SR decreased the restrained shrinkage up to 16%, delayed the cracking time, reduced the crack width and lowered the cracking potential for both PC and fly ash geopolymer. The fly ash geopolymer mixtures had lower cracking potential than PC. The effects of Module and Concentration on drying shrinkage and restrained ring shrinkage were also concluded. The last paper (Paper 4) investigated the mechanical performance of fly ash-based geopolymer in a fiber reinforced composite, namely an engineered geopolymer composite (EGC). Fly ash was replaced with slag in the geopolymer. The physical and chemical interactions of these two cementitious materials have resulted in a high strength (up to 110 MPa) and workable EGC. The mechanical properties including compressive strength, tensile strength, tensile strain capacity, toughness, elasticity, flexural bending strength, ductility and pullout bond strength were assessed. Experimental results in Paper 4 revealed that all EGCs exhibited strain hardening behavior. Twenty percent slag addition improved the engineering strength most. However, as slag addition increased, the tensile strain capacity, ultimate deflections, toughness and ductility decreased. In addition, bond strength can be estimated precisely based on the compressive strength of EGCs