Development and characterization of cotton and cotton fabric reinforced geopolymer composites

Sustainable geopolymer composites reinforced with natural cotton fibres have been developed and their mechanical and durability properties are evaluated in this research. Results showed that the mechanical properties (flexural strength, flexural modulus, fracture toughness, compressive strength, impact strength and hardness) of woven cotton fabric-reinforced geopolymer composites were superior to those of geopolymer composites with short cotton fibres. Exposure to water and elevated temperatures (200 to 1000°C) severely reduced the mechanical properties of the composites

Artificial intelligence-based prediction of strengths of slag-ash-based geopolymer concrete using deep neural networks

The construction and building industry, one of the greatest emitters of greenhouse gases, is under tremendous pressure because of the growing concern about global climate change and its detrimental effects on societies. Given the environmental problems connected to cement production, geopolymer concrete has become a viable alternative. In addition, if the concrete strength results failed to meet the specified strength after being cast, modifications are impossible. Thus, it is particularly desirable to predict strength prior to casting concrete. This study presents the first effort in applying deep neural networks (DNN) of AI techniques to predict the mechanical strengths (GGBFS) of geopolymer concrete (GPC) produced from corncob ash and ground granulated blast furnace slag. The mixes were activated with 12–16 M of alkali solutions at ambiently cured conditions for 7–90 days. Following that, back propagation learning algorithms were created for forecasting the concrete strengths based on concrete mix proportions. The mechanical strengths estimated by the DNN were verified by laboratory testing results. Results revealed that GGBFS, mix grade, curing days, and alkali precursor are variables that govern the mechanical strengths of the GGBFS-CCA-GPC. Forecasting the mechanical properties of GPC produced using DNN shows that the relationship between the input and output arguments could be most accurately predicted by a 10–20–20–20-1 network topology, evident by approximately 99% correlation coefficient between the actual and predictive values for compressive and flexural strengths. However, the 10–17–17–17-1 network architecture showed the best DNN for predicting split tensile strength, with a 97% correlation coefficient between the actual and projected values. This study demonstrated that the DNN techniques are efficient in predicting the mechanical strengths of GPC based on the mix proportions. Application of these techniques will greatly advance concrete quality assurance

Potential application of artificial intelligence to the alpha and gamma radiation from agricultural byproducts used as building and construction materials

Recycled agricultural wastes are being used in the building and construction sector as cement additives as a result of the environmental impact of cement production. Agricultural byproducts, on the other hand, are naturally occurring radioactive elements that could expose people and the environment to radiation dangers. As a result, this research assesses the radiological characteristics of agricultural byproducts utilized as building and construction materials with special attention to their activity concentrations (226Ra series, 232Th series, and 40K isotopes). The levels of alpha and gamma radiation were measured via the activity concentrations. Alpha and gamma radiation (output data) and activity concentrations (input data) were trained using artificial intelligence techniques, and the model's effectiveness was evaluated. In terms of the metrics of the model, the linear regression algorithm outperformed other algorithms. Finally, none of the agricultural byproducts studied are at risk from alpha and gamma radiation. Thus, the findings provide the reference information needed to build a framework for radiation monitoring of surveyed agricultural byproducts

Potential application of artificial intelligence to the alpha and gamma radiation from agricultural byproducts used as building and construction materials

Recycled agricultural wastes are being used in the building and construction sector as cement additives as a result of the environmental impact of cement production. Agricultural byproducts, on the other hand, are naturally occurring radioactive elements that could expose people and the environment to radiation dangers. As a result, this research assesses the radiological characteristics of agricultural byproducts utilized as building and construction materials with special attention to their activity concentrations (226Ra series, 232Th series, and 40K isotopes). The levels of alpha and gamma radiation were measured via the activity concentrations. Alpha and gamma radiation (output data) and activity concentrations (input data) were trained using artificial intelligence techniques, and the model's effectiveness was evaluated. In terms of the metrics of the model, the linear regression algorithm outperformed other algorithms. Finally, none of the agricultural byproducts studied are at risk from alpha and gamma radiation. Thus, the findings provide the reference information needed to build a framework for radiation monitoring of surveyed agricultural byproducts

Cement-based concrete modified with Vitellaria Paradoxa ash: A lifecycle assessment

Building sustainable concrete requires an increasing demand for technology, innovation, and alternative binders to cement. The building sector is technologically driven toward sustainable construction materials and their relationship with the environment. Thus, this study designed three grades of cement-based concrete strengths (C 25, C 30, and C 40) modified with an alternative binder, shea nutshell ash (Vitellaria Paradoxa Ash, VPA). The binder (VPA) was varied at 0–20 wt% of Portland limestone cement (PLC) cured at 28 days, examining the compressive strength cost attained sustainability. Moreover, the embodied energy (EE), global warming potential (GWP) and global temperature potential (GTP) of the concrete compositions were evaluated using the inventory of carbon and energy (ICE) method within the confine of cradle-to-site. Also, the sustainability index (Si) and economic index (Ei) of the concrete mixes were assessed. The results revealed that VPA-cement-based concrete yielded a lesser EE, GWP, GTP, Si, and Eci than the control concrete (Portland limestone cement concrete, PLCC), indicating VPA-cement-based concrete is more sustainable than PLCC. Notwithstanding, an optimum replacement of 15 wt% PLC with VPA is recommended to satisfy all assessments earlier stated for all concrete strength grades. Therefore, these findings can be beneficial in attaining a cleaner built environment and sustainable production. Finally, VPA has proved to be a sustainable building material

Modelling the strength of cashew nutshell ash-cement-based concrete

This study models the flexural strength of concrete modified with cashew nutshell ash (CNA) using reactivity index concepts. Cashew nutshell was valorised and its ash was used at 5, 10, 15 and 20 wt.% of cement via mix proportions of concrete grades C 25–40 targeted at 25–40 MPa at 28 days. The reactivity indexes (RIs) were evaluated based on the oxide compositions and chemical moduli of both the cement and CNA. The design mix parameters, water/binder and binder/aggregate ratios, and RIs were applied to model the flexural strength at 7–90 days’ curing. The results revealed that, at 28 and 90 days’ curing, the developed models yielded high precisions at 91 and 84% R2, respectively, hence indicating a good agreement. Moreover, there was a good relationship between these developed models and previous studies. Therefore, the flexural strength of concrete incorporating supplementary cementitious materials (SCMs) can be efficiently predicted via these developed models; this would save both time and money when carrying out the experimental works

Potential application of artificial intelligence to the alpha and gamma radiation from agricultural byproducts used as building and construction materials

Recycled agricultural wastes are being used in the building and construction sector as cement additives as a result of the environmental impact of cement production. Agricultural byproducts, on the other hand, are naturally occurring radioactive elements that could expose people and the environment to radiation dangers. As a result, this research assesses the radiological characteristics of agricultural byproducts utilized as building and construction materials with special attention to their activity concentrations (226Ra series, 232Th series, and 40K isotopes). The levels of alpha and gamma radiation were measured via the activity concentrations. Alpha and gamma radiation (output data) and activity concentrations (input data) were trained using artificial intelligence techniques, and the model's effectiveness was evaluated. In terms of the metrics of the model, the linear regression algorithm outperformed other algorithms. Finally, none of the agricultural byproducts studied are at risk from alpha and gamma radiation. Thus, the findings provide the reference information needed to build a framework for radiation monitoring of surveyed agricultural byproducts

Influence of Nano Silica Particles on Durability of Flax Fabric Reinforced Geopolymer Composites

The durability of natural fibres as reinforcement in geopolymer composites continues to be a matter of concern due to the alkalinity of activators of geopolymer matrices. The alkaline environment is the main reason for natural fibres degradation in cementitious matrices. This paper presents the influence of nano silica (NS) on the durability and mechanical performance of geopolymer composites that are reinforced with flax fabric (FF). The durability investigations were conducted after the storage of samples at ambient temperature for 32 weeks. The study revealed that the addition of nano silica has a positive influence on the physical and mechanical properties of these composites. The presence of NS accelerated the geopolymeric reaction and lowered the alkalinity of the system, thus reducing the degradation of flax fibres

Facile synthesis of novel CoWO₄/FeWO₄ hetrocomposite with efficient visible light photocatalytic degradation and hydrogen evolution aspects

Tungstate-based nanomaterials exhibit efficient photocatalytic performance and offer several advantages owing to their electrical and superior optical features, charge transport potentials, and superb corrosion resistance. The objective of the present study is to fabricate cobalt tungstate (CoWO4), Ferric tungstate (FeWO4) and CoWO4/FeWO4 heterojunction composite photocatalysts using a hydrothermal route with various molar concentrations (2:1, 1:1, 1:2, 1:5). The model pollutant Methyl Orange (MO) and Congo Red (CR) azo dyes were degraded 98.26% and 99.61% in 150 min by the as-synthesized CoWO4/FeWO4 at a molar concentration ratio of 1:2. A feasible photodegradation mechanism is purposed and the optimum values for different parameters are also evaluated by considering two different dyes as model organic pollutants. Hydrogen production efficiency reaches up to 36 μmolg−1 h−1 under visible light over 1:2 CoWO4/FeWO4. This work may open new possibilities for the use of CoWO4/FeWO4 composite for potential applications such as the hydrothermal synthesis of composites and their photocatalytic wastewater remedy and as hydrogen evolution applications.</p

Investigating the Effects of Polypropylene Fibers on the Mechanical Strength, Permeability, and Erosion Resistance of Freshwater and Seawater Mixed Concretes

Seawater mixed (SW) concrete lessens the freshwater (FW) demand and eases the stress on the already depleting FW resources. The use of SW concrete is a sustainable solution that mitigates the environmental impact of concrete production, especially in coastal regions and islands vulnerable to FW scarcity. This study investigated the influence of polypropylene (PP) fiber incorporation on high-performance-SW concrete’s long-term mechanical and durability performance. The findings indicate that the incorporation of seawater in the production of concrete containing ground granulated blast furnace slag (GGBFS) has a beneficial effect on its early strength. This is due to the fact that SW accelerates the hardening process. SW concrete mixes showed an improvement in strength with aging. The difference between the strength of SW and FW concretes reduced with aging. The PP fiber showed phenomenal improvements in the tensile properties of SW and FW concretes. At the addition of 0.3% PP fiber, SW yielded 56% and 48% higher splitting tensile and flexural strength than plain FW concrete at 28 days, respectively. The use of 0.15% of PP fiber caused notable reductions of around 20% in the water absorption (WA) capacity and a 12–20% reduction in chloride ion permeability (CIP) of SW concrete. The incorporation of PP fiber increases the number of drying–wetting cycles to initiate the erosion of SW and FW concretes in a simulated environment. The use of 0.15% PP fiber is beneficial, as compared to 0.3% PP fiber to control the tidal erosion of SW and FW concretes. After exposure to 126 drying–wetting cycles (stimulated tidal erosion), the mass loss of SW concrete was reduced from 0.56% to 0.22%