Search

The phosphate industry produces a hazardous byproduct called phosphogypsum (PG). A rising global stockpiling of PG poses severe threats to public health and the environment. Fortunately, recycling this material may be a risk-free and environmentally beneficial answer to this problem. It is necessary to examine its current application and future growth prospects in civil engineering to increase the PG utilization rate. This paper summarizes previous research on reusing PG in building materials. This review article is divided into four essential sections. Firstly, the chemical properties of PG and their treatment methods before using PG in any application are reviewed extensively. Secondly, the strength properties of bricks made with PG and their microstructural characteristics through scanning electron microscope (SEM) and X-ray diffraction (XRD) were reviewed. The third part of this article is about using PG as a substitution for cement and studies the setting time, compressive strength, SEM and XRD of cement mortar. The final section is PG-based concrete and studies concrete specimens' workability, compressive strength, SEM and XRD. The last three sections extensively focused on the microstructural characteristics of the building materials. Following a comprehensive literature search, it was determined that PG could be utilized in the construction industry. Several studies have been performed on using PG in building materials such as bricks, concrete and other materials. However, PG utilization in large-scale practical applications needs better social and political awareness. More money is needed for research and development to free the economy and technology from their shackles. The findings of this review will serve as a foundation for further research and practical applications of PG in environmentally friendly processes.

Commercial powder activators are commonly used to produce one-part alkali-activated binders (AAB) despite their manufacturing method using more energy and generating more CO2 emissions. To address this concern, the study employed a mechanical process to synthesize an agricultural waste-based powder activator, derived from rice husk ash (RHA) and NaOH powder at six different ratios. Paste samples were prepared by blending the derived activator with ground granulated blast furnace slag (GGBFS) precursor in a 1:4 ratio. Fresh properties and compressive strength (range of 33–77 MPa) was evaluated. Besides, microstructural analysis and ecological factors were estimated, including embodied CO2 emissions and energy analysis, and the approach employed in this investigation significantly reduced both embodied CO2 emissions and embodied energy requirements compared to conventional practices. The result indicates that the activator powder synthesized in this study can produce AAB with qualities comparable to Portland cement.

Geopolymer (GMR) paste with micro-fibres and nanoparticles is an excellent alternative for reducing cement production for environmental sustainability. Therefore, the performance of fibre-reinforced (FR) GMR pastes containing nanoparticles must be further explored. The goal of this study is to incorporate varying amounts of nano titanium dioxide (NTD) into micro carbon-FR fly ash-based GMR pastes to increase microstructure, impact strength, toughness, fracture, and mechanical performance. Four different dosages of NTD, ranging from 1 to 4% by the weight of the mix to cast GMR mixes including a constant dosage of micro carbon fibre (CF) (0.5% by weight of mix). A reference mix with 0.5% micro-CF having no dosage of NTD was also manufactured. Scanning Electron Microscopy (SEM) was used to assess the internal structure of GMR. The average findings of six specimens from each paste were taken to determine the different features of GMR pastes. The outcomes of this investigation revealed that using 3% NTD in a carbon-FR-GMR mix produced the best results in terms of hardness, impact strength, and compressive stress while using 2% NTD produced the best results in terms of flexural stress and fracture toughness of pastes. The compressive and flexural strengths of GMR improved by 23% and 40% while the impact strength and hardness were improved by 53% and 10%, respectively.

High-strength concrete (HSC) is vulnerable to strength loss when exposed to high temperatures or fire, risking the structural integrity of buildings and critical infrastructures. Predicting the compressive strength of HSC under high-temperature conditions is crucial for safety. Machine learning (ML) techniques have emerged as a powerful tool for predicting concrete properties. Accurate prediction of the compressive strength of HSC is important as HSC can experience strength losses of up to 80% after exposure to temperatures of 800°C–1000°C. This study evaluates the efficacy of ML techniques such as Extreme Gradient Boosting, Random Forest (RF), and Adaptive Boosting for predicting the compressive strength of HSC. The results of this study demonstrate that the RF model is the most efficient for predicting the compressive strength of HSC, exhibiting the R2 value of 0.98 and lower mean absolute error and root mean square error values than the other applied models. Furthermore, Shapley Additive Explanations analysis highlights temperature as the most significant factor influencing the compressive strength of HSC. This article provides valuable insights into the timely and effective determination of the compressive strength of HSC under high-temperature conditions, benefiting both the construction industry and academia. By leveraging ML techniques and considering the critical factors that influence the compressive strength of HSC, it is possible to optimize the design and construction process of HSC and enhance its resilience to high-temperature exposure.

Self-compacting concrete (SCC) was developed to overcome the challenges of concrete placement in dense or congested reinforcement structure, where the concrete can flow under its own weight to fill the densely reinforced structure. However, production of SCC mostly involves the use of high cement to achieve the desired strength. Therefore, to reduce the needed amount of cement, pozzolanic materials such as fly ash can be used to partially replace cement. However, fly ash has been reported to decrease the strengths of concrete especially at early ages. In this study, a self-compacting concrete (SCC) was developed with fly ash as a basic replacement material considering the efficiency of fly ash and incorporating graphene oxide (GO) as a cement additive to counteract the negative effect of fly ash. Response surface methodology (RSM) was utilized for designing the experiments, investigating the effects of fly ash and GO on SCC properties, and developing mathematical models for predicting mechanical properties of SCC. The ranges of fly ash and graphene oxide were 16.67 to 35% and zero to 0.05%, respectively. Statistical analysis was performed by using Design Expert software (version 11.0, Stat Ease Inc., Minneapolis, MS, USA). The results showed that fly ash had a positive effect while GO had a negative effect on the workability of SCC. The incorporation of fly ash alone decreased the compressive strength (CS), splitting tensile strength (STS) and flexural strength (FS), and additionally, increased the porosity of SCC. The addition of GO to fly ash-based SCC reduced its porosity and enhanced its mechanical strengths which was more pronounced at early ages. The developed models for predicting the mechanical strengths of fly ash-based SCC containing GO have a very high degree of correlation. Therefore, the models can predicts the strengths of SCC using fly ash and GO as the variables with a high level of accuracy. The findings show that based on the EFNARC guidelines, up to 35% of fly ash can be used to replace cement in SCC to achieve a mix with satisfactory flowability and deformability properties

This study has endeavored to produce eco-friendly coal ash-incorporated recycled aggregate concrete (FRAC) by utilizing wastewater effluents for environmental sustainability. The mechanical and durability efficiency of the FRAC manufactured were explored using different kinds of effluent by performing a series of tests at various ages. The considered kinds of effluent for the mixing of FRAC were collected from a service station, as well as fertilizer, textile, leather, and sugar factories. Scanning electron microscopy (SEM) was utilized to judge the microstructural behavior of the constructed concrete compositions. The outcomes revealed that using textile factory effluent in the manufacturing of FRAC depicted peak compressive and split tensile strength improvements of 24% and 16% compared to that of the FRAC manufactured using potable water. The application of leather factory effluent for the manufacturing of FRAC portrayed the highest water absorption (13% better than the control mix). The application of fertilizer effluent in the manufacture of FRAC presented the greatest mass loss (19% enhanced than the control mix) due to H2SO4 solution intrusion and the ultimate chloride ion migration (16 mm at twenty-eight days of testing). The summation of coal ash improved the mechanical behavior of the concrete and also caused a reduction in its durability loss of. The SEM analysis depicted that the textile factory effluent presented the most densified microstructure with the development of ettringite needles and CSH gel having refined the ITZ.

This research work is devoted to the experimental investigation of both rheological and mechanical properties of self-compacting concrete (SCC) produced with waste galvanized copper wire fiber and rice husk ash (RHA). In the study, three different volume fractions of 0.5 p to 0.75 percent, 1 percent of scrap copper wire fiber as reinforcing material, and 2 percent RHA as cement replacement were used. To evaluate the fresh characteristics of SCC, the slump flow, J-ring, and V-funnel experiments were conducted for this investigation. Compressive strength, splitting tensile strength, and flexural strength of the concrete were conducted to assess the hardened properties. The test was carried out to compare each characteristic of plain SCC with this modified SCC mixture, containing RHA as pozzolanic materials and copper fiber as reinforcing material. Incorporating copper fiber in the SCC leads to a drop in fresh properties compared to plain SCC but remains within an acceptable range. On the other hand, the inclusion of 2% RHA makes the SCC more viscous. Although adding 2% RHA and 1% copper wire in SCC provide the highest strength, this mix has an unacceptable passing ability. The SCC mix prepared with 2% RHA and 0.75% copper fiber is suggested to be optimum in terms of the overall performance. According to this study, adding metallic fiber reinforcement like copper wire and mineral admixture like RHA can improve the mechanical properties of SCC up to a certain level.

The utilization of waste industrial materials such as Blast Furnace Slag (BFS) and Fly Ash (F. Ash) will provide an effective alternative strategy for producing eco-friendly and sustainable concrete production. However, testing is a time-consuming process, and the use of soft machine learning (ML) techniques to predict concrete strength can help speed up the procedure. In this study, artificial neural networks (ANNs) and decision trees (DTs) were used for predicting the compressive strength of the concrete. A total of 1030 datasets with eight factors (OPC, F. Ash, BFS, water, days, SP, FA, and CA) were used as input variables for the prediction of concrete compressive strength (response) with the help of training and testing individual models. The reliability and accuracy of the developed models are evaluated in terms of statistical analysis such as R2, RMSE, MAD and SSE. Both models showed a strong correlation and high accuracy between predicted and actual Compressive Strength (CS) along with the eight factors. The DT model gave a significant relation to the CS with R2 values of 0.943 and 0.836, respectively. Hence, the ANNs and DT models can be utilized to predict and train the compressive strength of high-performance concrete and to achieve long-term sustainability. This study will help in the development of prediction models for composite materials for buildings.