In the last few decades, the concrete industry has been massively expanded with the adoption of various kinds of binding materials. As a substitute to cement and in an effort to relieve ecofriendly difficulties linked with cement creation, the utilization of industrial waste as cementitious material can sharply reduce the amount of trash disposed of in lakes and landfills. With respect to the mechanical properties, durability and thermal behavior, ground-granulated blast-furnace slag (GGBS) delineates a rational way to develop sustainable cement and concrete. Apart from environmental benefits, the replacement of cement by GGBS illustrates an adequate way to mitigate the economic impact. Although many researchers concentrate on utilizing GGBS in concrete production, knowledge is scattered, and additional research is needed to better understand relationships among a wide spectrum of key questions and to more accurately determine these preliminary findings. This work aims to shed some light on the scientific literature focusing on the use and effectiveness of GGBS as an alternative to cement. First and foremost, basic information on GGBS manufacturing and its physical, chemical and hydraulic activity and heat of hydration are thoroughly discussed. In a following step, fresh concrete properties, such as flowability and mechanical strength, are examined. Furthermore, the durability of concrete, such as density, permeability, acid resistance, carbonation depth and dry shrinkage, are also reviewed and interpreted. It can be deduced that the chemical structure of GGBS is parallel to that of cement, as it shows the creditability of being partially integrated and overall suggests an alternative to Ordinary Portland Cement (OPC). On the basis of such adjustments, the mechanical strength of concrete with GGBS has shown an increase, to a certain degree; however, the flowability of concrete has been reduced. In addition, the durability of concrete containing GGBS cement is shown to be superior. The optimum percentage of GGBS is an essential aspect of better performance. Previous studies have suggested different optimum percentages of GGBS varying from 10 to 20%, depending on the source of GGBS, concrete mix design and particle size of GGBS. Finally, the review also presents some basic process improvement tips for future generations to use GGBS in concrete.

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A Comprehensive Review on the Ground Granulated Blast Furnace Slag (GGBS) in Concrete Production

The current practice of concrete is thought to be unsuitable because it consumes large amounts of cement, sand, and aggregate, which causes depletion of natural resources. In this study, a step towards sustainable concrete was made by utilizing recycled concrete aggregate (RCA) as a coarse aggregate. However, researchers show that RCA causes a decrease in the performance of concrete due to porous nature. In this study, waste glass (WG) was used as a filler material that filled the voids between RCA to offset its negative impact on concrete performance. The substitution ratio of WG was 10, 20, or 30% by weight of cement, and RCA was 20, 40, and 60% by weight of coarse aggregate. The slump cone test was used to assess the fresh property, while compressive, split tensile, and punching strength were used to assess the mechanical performance. Test results indicated that the workability of concrete decreased with substitution of WG and RCA while mechanical performance improved up to a certain limit and then decreased due to lack of workability. Furthermore, a statical tool response surface methodology was used to predict various strength properties and optimization of RCA and WG.

https://www.mdpi.com/1996-1944/15/2/430/pdf?version=1642142887

Concrete with Partial Substitution of Waste Glass and Recycled Concrete Aggregate

Today, it's getting harder to find natural resources for concrete production. Utilization of the waste materials not just helps in getting them used in concrete, cement, and other construction materials, but also has various secondary advantages, for example, saving in energy, decrease in landfill cost, and protecting climate from pollution. Considering this in the development of modern structural design, utilizing waste materials instead of natural aggregate is a good option to make concrete that is sustainable and eco-friendly. The present research aims to find the impact of adding glass fiber into sustainable concrete made with silica fume, as a partial replacement of cement, and coconut shell added with different ratios as a replacement of coarse aggregate, on concrete mechanical and durability aspects. Various blends were made, with coconut shell as a substitution of coarse aggregates with different ratios. Portland cement was substituted with silica fume at 5%, 10%, 15%, and 20% by cement weight in all concrete blends. The volume ratios of glass fibers utilized in this study were 0.5%, 1.0%, 1.5% and 2.0%. Adding glass fibers increases concrete density to some extent and then marginally reduces the density of coconut shell concrete. When the percentage of glass fibers increases, the compressive, flexural and split tensile strength of coconut shell concrete also increases. From the lab results and SEM images of the present research display that glass fibers might be utilized in coconut shell concrete to enhance its mechanical and durability attributes, to accomplish sustainable concrete with acceptable strength with ease.

/pdf/a-step-towards-sustainable-glass-fiber-reinforced-concrete-vjy5eam07r.pdf

A step towards sustainable glass fiber reinforced concrete utilizing silica fume and waste coconut shell aggregate

Compressive and flexural strength are the crucial properties of a material. The strength of recycled aggregate concrete (RAC) is comparatively lower than that of natural aggregate concrete. Several factors, including the recycled aggregate replacement ratio, parent concrete strength, water–cement ratio, water absorption, density of the recycled aggregate, etc., affect the RAC’s strength. Several studies have been performed to study the impact of these factors individually. However, it is challenging to examine their combined impact on the strength of RAC through experimental investigations. Experimental studies involve casting, curing, and testing samples, for which substantial effort, price, and time are needed. For rapid and cost-effective research, it is critical to apply new methods to the stated purpose. In this research, the compressive and flexural strengths of RAC were predicted using ensemble machine learning methods, including gradient boosting and random forest. Twelve input factors were used in the dataset, and their influence on the strength of RAC was analyzed. The models were validated and compared using correlation coefficients (R2), variance between predicted and experimental results, statistical tests, and k-fold analysis. The random forest approach outperformed gradient boosting in anticipating the strength of RAC, with an R2 of 0.91 and 0.86 for compressive and flexural strength, respectively. The models’ decreased error values, such as mean absolute error (MAE) and root-mean-square error (RMSE), confirmed the higher precision of the random forest models. The MAE values for the random forest models were 4.19 MPa and 0.56 MPa, whereas the MAE values for the gradient boosting models were 4.78 MPa and 0.64 MPa, for compressive and flexural strengths, respectively. Machine learning technologies will benefit the construction sector by facilitating the evaluation of material properties in a quick and cost-effective manner.

/pdf/machine-learning-prediction-models-to-evaluate-the-strength-11nnigyu.pdf

Machine Learning Prediction Models to Evaluate the Strength of Recycled Aggregate Concrete

Industrial waste has been rapidly increased day by day because of the fast-expanding population which inappropriately dumps the waste resulting in environmental pollutions. It has been recommended that the disposal of industrial waste would be greatly reduced if it could be incorporated into concrete production. The basic objective of this investigation is to examine the characteristics of concrete using marble slurry as binding material in proportions 5%, 10%, 15%, 20%, 25%, and 30% by weight of cement. Many properties have been reviewed in the current paper; the results observed from the various studies depict that replacement of marble slurry to a certain extent enhances strength properties of the concrete but simultaneously decreases the slump value with the increase of replacement level of marble slurry.

Mechanical properties of sustainable concrete modified by adding marble slurry as cement substitution

Self-compacting concrete (SCC) is a special type of concrete that is highly flowable, nonsegregating
and spread into place by its own weight, completely filling the formwork even in the
presence of dense reinforcement and then encapsulating the rebar without the need for any additional
compaction. This research was carried out to evaluate the effects of bentonite clay and wheat straw
ash as a partial substitution for cement in SSC. Bentonite clay and wheat straw ash were added
in proportion of 0%, 5.0%, 10%, 15%, and 20% of the weight of the cement. Fresh characteristics
were evaluated based on its passing ability and flowability using slump flow, slump T50, L-box,
and V-funnel tests. After 7 days, 14 days, and 28 days of curing, cylinders of standard size were cast
and tested for compressive and split tensile strength. The test results indicate that bentonite clay and
wheat straw ash decrease the passing ability and filling ability of SCC. Furthermore, the concrete
specimens’ tests indicate that wheat straw ash and bentonite clay additions of up to 10% and 15% of
the weight of the cement tend to improve the compressive and split tensile strength of hardened SCC.
Response surface methodology (statistical models) is used to optimize the combined dosage of wheat
straw ash and bentonite clay and is verified through experimental tests. It can also be suggested that
bentonite and wheat straw ash are successfully neutralized in concrete instead of cement.

https://www.mdpi.com/2071-1050/13/2/824/pdf

A Step towards Sustainable Self-Compacting Concrete by Using Partial Substitution of Wheat Straw Ash and Bentonite Clay Instead of Cement

The waste disposal crisis and development of various types of concrete simulated by the construction industry has encouraged further research to safely utilize the wastes and develop accurate predictive models for estimation of concrete properties. In the present study, sugarcane bagasse ash (SCBA), a by-product from the agricultural industry, was processed and used in the production of green concrete. An advanced variant of machine learning, i.e., multi expression programming (MEP), was then used to develop predictive models for modeling the mechanical properties of SCBA substitute concrete. The most significant parameters, i.e., water-to-cement ratio, SCBA replacement percentage, amount of cement, and quantity of coarse and fine aggregate, were used as modeling inputs. The MEP models were developed and trained by the data acquired from the literature; furthermore, the modeling outcome was validated through laboratory obtained results. The accuracy of the models was then assessed by statistical criteria. The results revealed a good approximation capacity of the trained MEP models with correlation coefficient above 0.9 and root means squared error (RMSE) value below 3.5 MPa. The results of cross-validation confirmed a generalized outcome and the resolved modeling overfitting. The parametric study has reflected the effect of inputs in the modeling process. Hence, the MEP-based modeling followed by validation with laboratory results, cross-validation, and parametric study could be an effective approach for accurate modeling of the concrete properties.

https://www.mdpi.com/2071-1050/13/5/2867/pdf

Performance Evaluation of Soft Computing for Modeling the Strength Properties of Waste Substitute Green Concrete

In this research, multiexpression programming (MEP) has been employed to model the compressive strength, splitting tensile strength, and flexural strength of waste sugarcane bagasse ash (SCBA) concrete. Particle swarm optimization (PSO) algorithm was used to fine-tune the hyperparameter of the proposed MEP. The formulation of SCBA concrete was correlated with five input parameters. To train and test the proposed model, a large number of data were collected from the published literature. Afterward, waste SCBA was collected, processed, and characterized for partial replacement of cement in concrete. Concrete specimens with varying proportion of SCBA were prepared in the laboratory, and results were used for model validation. The performance of the developed models was then evaluated by statistical criteria and error assessment tests. The result shows that the performance of MEP with PSO algorithm significantly enhanced its accuracy. The essential input variables affecting the output were revealed, and the parametric analysis confirms that the models are accurate and have captured the essential properties of SCBA. Finally, the cross validation ensured the generalized capacity and robustness of the models. Hence, the adopted approach, i.e., MEP-based modeling with PSO, could be an effective tool for accurate modeling of the concrete properties, thus directly contributing to the construction sector by consuming waste and protecting the environment.

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Muhammad Sohaib Khan Niazi

Papers

A Step towards Sustainable Self-Compacting Concrete by Using Partial Substitution of Wheat Straw Ash and Bentonite Clay Instead of Cement

Performance Evaluation of Soft Computing for Modeling the Strength Properties of Waste Substitute Green Concrete

Machine Learning-Based Modeling with Optimization Algorithm for Predicting Mechanical Properties of Sustainable Concrete