Descriptive review on conversion of waste residues into valuable bionanocomposites for a circular bioeconomy

Nano-Structures & Nano-Objects, Год журнала: 2024, Номер 39, С. 101265 - 101265

Опубликована: Авг. 2, 2024

Язык: Английский

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Strength prediction of asphalt mixture under interactive conditions based on BPNN and SVM

Case Studies in Construction Materials, Год журнала: 2024, Номер 21, С. e03489 - e03489

Опубликована: Июль 2, 2024

Predicting the strength of asphalt mixtures with different specifications under various conditions was a highly challenging task. The standard test lacked consideration multiple factors, resulting in an inability to accurately characterize properties pavement. This paper proposed prediction approach based on influence factor analysis using back-propagation neural network (BPNN) and support vector machine (SVM). dataset processed realize physical factors influencing mixture strength. Stress state (Direct tensile with250mm×50mm×50mm, Uniaxial compression 100mm×Φ100mm, Indirect with63.5mm×Φ100mm,Four-point bending 380mm×63.5mm×50mm), temperatures (35 ̊C, 25 15 0 −15 −25 ̊C), load rates (0.02 MPa/s, 0.05 0.1 0.5 MPa/s) were selected as input features train BPNN SVM. model for complex established by optimizing parameters algorithms. performance SVM evaluated compared root mean square error, determination coefficient, absolute percentage deviation. results show that specimen stress states presents significant discrepancies. maximum compressive is followed strength, then comes indirect smallest direct difference role or aggregate main reason diversity increase temperature leads softening, which reduces mixture. increased loading rate meant time short cause increased. In addition, predictive value consistent experiments. hidden neurons set 9, achieving accuracy high (R2=0.99). penalty coefficient 500 kernel function parameter 300, error within 0.02 %. When comparing metrics SVM, it becomes evident outperforms terms accuracy. Specifically, exhibits 0.9983, 0.208, deviation 0.145, whereas demonstrates respective values 0.9979, 0.233, 0.067. study lays theoretical foundation digital intelligent road construction.

Язык: Английский

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Prediction on the freeze-thaw resistance of a one-part geopolymer stabilized soil by using deep learning method

Case Studies in Construction Materials, Год журнала: 2024, Номер 21, С. e03530 - e03530

Опубликована: Июль 20, 2024

The mechanical properties of soil located at cold areas may be deteriorated under freeze-thaw cycle condition. One-part geopolymer (OPG) is a kind alkaline-activated material by using industrial by-products and solid alkali. Obviously, OPG can replace ordinary portland cement (OPC) as stabilizer in ground improvement, which presents environmental low-carbon benefits. assessment unconfined compressive strength (UCS) vital for evaluating OPG-stabilized durability conditions, typically demanding extensive resources. Leveraging artificial intelligence, predictive model developed this purpose. This study collected small sample size 216 data points the soil's behaviour. Three deep learning (DL) models, Backpropagation Neural Network [BPNN], Convolutional [CNN], Gated Recurrent Unit [GRU], were trained on dataset to predict performance efficiently, offering promising approach streamline processes. In DL ratio fly ash (FA) granulated blast furnace slag (GGBFS), freezing temperature taken input variables, target output was UCS soil. Among all CNN achieved highest prediction accuracy with R2 0.9966, followed BPNN (R2=0.9893) GRU (R2=0.9872). After that, interpretable machine methods (i.e., Shapley Additive Explanation [SHAP] Partial Dependence Plot [PDP]) utilized further understand impact variables outcome predictions. addition, morphological analysis used verify mechanism derived from model. It revealed that inclusion FA crucially enhanced resistance However, beyond certain threshold, addition negatively impacted Freezing pinpointed key factor affecting stabilized

Язык: Английский

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Valorisation of coffee husk as replacement of sand in alkali-activated bricks

Case Studies in Construction Materials, Год журнала: 2024, Номер 21, С. e03440 - e03440

Опубликована: Июнь 19, 2024

The coffee industry is known to generate voluminous amount of waste during its production process. Different types such as hush ash and spent ground, name a few, have been extensively researched substitute in the construction industry. However, utilization husk for materials has seen limited exploration. In particular, there are no studies which investigate (WCH) alkali-activated bricks. Therefore, this research WCH was employed sand Alkali-activated bricks were synthesized with ground granulated blast furnace slag (GGBFS), fly (FA), sand, sodium silicate solution (SS). Sand replaced at replacement rates 0 %, 5 10 15 20 30 % by volume. developed evaluated strength, density, water absorption, porosity, efflorescence. Additionally, structural morphological characteristics assessed Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), Thermogravimetric analysis (TGA), Scanning electron microscopy (SEM) analysis. results indicate that improve compressive strength maximum value 15.7 MPa, reduce density minimum 1509 kg/m3 composites WCH, respectively. absorption porosity increased incorporation due porous structure WCH. physico-chemical shows effective geopolymerization composite system further depict thermal stability insignificant weight loss 575 ℃. Finally, classify good quality per IS 1077: 1992 specifications, will practical feasibility

Язык: Английский

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AI-Driven Prediction of Compressive Strength in Self-Compacting Concrete: Enhancing Sustainability through Ultrasonic Measurements

Sustainability, Год журнала: 2024, Номер 16(15), С. 6644 - 6644

Опубликована: Авг. 3, 2024

This study investigates the application of artificial intelligence (AI) to predict compressive strength self-compacting concrete (SCC) through ultrasonic measurements, thereby contributing sustainable construction practices. By leveraging advancements in computational techniques, specifically neural networks (ANNs), we developed highly accurate predictive models forecast SCC based on pulse velocity (UPV) measurements. Our findings demonstrate a clear correlation between higher UPV readings and improved quality, despite general trend decreased with increased air-entraining admixture (AEA) concentrations. The ANN show exceptional effectiveness predicting strength, coefficient (R2) 0.99 predicted actual values, providing robust tool for optimizing mix designs ensuring quality control. AI-driven approach enhances sustainability by improving material efficiency significantly reducing need traditional destructive testing methods, thus offering rapid, reliable, non-destructive alternative assessing properties.

Язык: Английский

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Integrating AI in Food Contaminant Analysis: Enhancing Quality and Environmental Protection

Journal of Hazardous Materials Advances, Год журнала: 2024, Номер unknown, С. 100509 - 100509

Опубликована: Окт. 1, 2024

Язык: Английский

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Investigation of the Mechanical Properties and Durability of Fiber-Reinforced Geopolymer Mortars Containing Metakaolin and Glass Powder

Infrastructures, Год журнала: 2025, Номер 10(2), С. 25 - 25

Опубликована: Янв. 21, 2025

The increasing global emphasis on sustainable construction practices has spurred significant international research into developing durable and eco-friendly concrete materials. This study investigates the potential of metakaolin glass powder as supplementary aluminosilicate materials in slag- based geopolymer mortars, aiming to enhance their mechanical properties durability. To further improve performance, polypropylene fibers were incorporated at various dosages. Therefore, 13 mixtures mortar blast furnace slag have been developed. control mix does not contain or replacement materials, whereas other formulations, employed substitutes for weight percentages (relative slag) 5% 10%, separately combination. Additionally, fiber-containing samples are divided two groups volume percentage fibers, comprising 0.2% 0.4%. results investigation show that use powder, particularly a leads an improvement 28-day compressive strength. Furthermore, mixes containing demonstrated higher flexural strength compared those metakaolin, irrespective fibers. best performance rapid chloride permeability test is associated with combination 10%. Satisfactory obtained when using this utilized fuzzy inference system predict indicate that, by considering uncertainties, can be predicted error less than 1% without need complex mathematical calculations.

Язык: Английский

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A systematic review of metakaolin-based alkali-activated and geopolymer concrete: A step toward green concrete

REVIEWS ON ADVANCED MATERIALS SCIENCE, Год журнала: 2025, Номер 64(1)

Опубликована: Янв. 1, 2025

Abstract Expanding the world’s infrastructure drives up demand for building materials, particularly ordinary Portland cement (OPC) concrete, whose high carbon dioxide (CO 2 ) emissions have a detrimental effect on environment. To address this issue, researchers looked into employing alternative supplementary cementitious materials (SCMs), including metakaolin (MK), which is derived from calcined kaolin clay with pozzolanic properties, to partially or completely replace OPC in concrete. This review article examines MK’s application alkali-activated (AAMs) and OPC-based By interacting calcium hydroxide, MK functions as additive enhancing its mechanical qualities durability. The use of source material AAMs, newly developed class sustainable binders, also covered article. effects different combinations additional SCMs, fly ash (FA), ground granulated blast furnace slag (GGBFS), silica fume, rice husk ash, characteristics concrete both fresh hardened states, are compiled. majority articles considered study past decade, while some relevant 2014 earlier taken account. results showed that adding combination FA GGBFS has excellent synergistic microstructural development, activity, strength increases. In particular, MK–FA mix demonstrated most encouraging performance gains. Because large surface area, nano-MK helped achieve denser geopolymer structure improve properties. best curing temperatures MK-based geopolymers gain were found be between 40 80°C total 28 days. pointed out compressive geopolymerization process enhanced by increasing mass ratio Na SiO 3 NaOH concentration. Nevertheless, was hampered unnecessarily alkali concentrations. Moreover, increased replacing TiO GGBFS. combining other SCMs highlight potential solutions lowering environmental footprint buildings.

Язык: Английский

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Geopolymer Concrete Optimization: A Critical Review of Supplementary Materials and Performance Enhancement

Journal of Mines Metals and Fuels, Год журнала: 2025, Номер unknown, С. 401 - 417

Опубликована: Фев. 24, 2025

In the realm of environmentally friendly building materials, geopolymer concrete stands out as a viable substitute for traditional made cement. This literature review explores recent developments and innovations in concrete, with an emphasis on optimizing its properties through use various supplementary materials. The aim is to enhance both mechanical strength concrete's microstructural properties, while also investigating cost efficiency these enhancements. systematically examines different combinations cementitious analyzing their impact stability long-term performance. Additionally, study delves into creation precise Artificial Neural Network models using mixed synthetic data accurately predict properties. Microstructural are evaluated XRD SEM analyses, providing valuable insights structural integrity. Furthermore, comparative analysis parameters between conducted, shedding light economic viability GPC construction projects. thorough evaluation aims support ongoing research development field offering engineers, researchers, industry professionals seeking sustainable efficient Incorporating such fly ash, slag, metakaolin, significantly enhances durability (GPC), superior resistance environmental degradation, sulfate attack, compared Portland cement concrete. application artificial intelligence, specifically ANN modeling datasets, demonstrates high degree accuracy predicting compressive performance GPC. facilitates optimized reliable mix designs tailored diverse needs. A detailed highlights While initial production costs may be higher due specialized cost-effectiveness evident enhanced reduced maintenance. Its lower carbon footprint makes alternative future projects industrial by-products promotes circular economy. Major Findings: Geopolymer Concrete (GPC) can Advanced techniques like Networks (ANN) analyses (XRD, SEM) provide deeper GPC's benefits

Язык: Английский

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Analyzing the Behavior of Geopolymer Concrete with Different Novel Machine-Learning Algorithms

Journal of structural design and construction practice., Год журнала: 2025, Номер 30(3)

Опубликована: Март 19, 2025

Язык: Английский

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