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The production of municipal solid waste (MSW) has led to an unprecedented level of environmental pollution, worsening the global challenges posed by climate change. Researchers and policymakers have recently made significant strides in the field of sustainable and renewable energy sources, which are viable from technological, environmental, and economic perspectives. Consequently, the waste-to-energy programs enhance nations' socioeconomic status while positively impacting the environment. To predict the higher heating value (HHV) of MSW fuel based on carbon, hydrogen, oxygen, nitrogen, and sulfur content, the current study introduces a Gradient Boosting Regression Tree (GBRT) model optimized with the Slime Mold Algorithm (SMA). This model was evaluated using an additional 50 data points after being trained with 202 MSW biomass data points. The performance of the model was assessed using three metrics: root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2). The results indicated that our model outperformed previously developed models in terms of accuracy and reliability. Additionally, a graphical user interface (GUI) was developed to facilitate the practical application of the model, allowing users to easily input data and receive predictions on the enthalpy of the combustion of MSW fuel. [ABSTRACT FROM AUTHOR]

This study focuses on examining the influence of bast fibers on the flammability and thermal properties of the polylactide matrix (PLA). For this purpose, Urtica dioica and Vitis vinifera fibers were subjected to two types of modifications: mercerization in NaOH solution (M1 route) and encapsulation in an organic PLA solution (M2 route). In a further step, PLA composites containing 5, 10, and 15 wt% of unmodified and chemically treated fibers were obtained. The results of the tests show that only biocomposites containing mercerized fibers had a nearly 20% reduced flammability compared to that of PLA. Moreover, the biofiller obtained in this way belongs to the group of flame retardants that generate char residue during combustion, which was also confirmed by TGA tests. The M2 modification route allowed to achieve higher mass viscosity than the addition of unmodified and M1-modified fibers. The reason is that fibers additionally encapsulated in a polymer layer impede the mobility of the chain segments. The inferior homogenization of the M2-modified fibers in the PLA matrix translated into a longer combustion time and only a 15% reduction in flammability. [ABSTRACT FROM AUTHOR]

The effectiveness of concrete confinement by fiber-reinforced polymer (FRP) materials is highly influenced by the orientation of fibers in the FRP laminates. In general, acceptable deviation limit from the intended direction is given as 5° in most design guidelines, without solid bases and reasoning. In this paper, a numerical study using finite element modeling was conducted to assess the effects of small deviations in fiber orientation from the hoop direction on compressive behavior of concrete cylinders confined with FRP. Different fiber angles of 0°, 2°, 5°, 8°, 10° and 15° with respect to hoop direction, unconfined concrete compressive strengths of 20, 35 and 50 MPa, FRP thicknesses of 0.2, 0.5 and 1.0 mm and FRP moduli of elasticity of 50 and 200 GPa were considered. The results showed that total dissipated energy ( E t ), ultimate axial strain ( ε c u ′ ) and compressive strength ( f c u ′ ) exhibited the most reduction with deviation angle. For 5° deviation in fiber orientation, the average reduction in f c u ′ , ε c u ′ and E t were 2.4%, 2.8% and 4.5%, respectively. Furthermore, the calculated allowable limit of deviation in fiber orientation for a 2.5% reduction in f c u ′ , ε c u ′ and E t were 6°, 3° and 2°, respectively, with a 95% confidence. [ABSTRACT FROM AUTHOR]

The purpose of this review was to present clay–cement suspensions that are mainly used in the construction and renovation of hydrotechnical facilities and flood protection. We present the characteristics of clay–cement slurries that are used in waterproofing barriers. One of their significant components are clays of different types and origins. Examples of kaolin-type and smectite-type clays, which are associated minerals, as balanced components of suspensions are presented. As mentioned, before the hydration process, barriers are suspended, and the main test methods are rheometric measurements. Their rheological parameters, extremely important from the point of view of injection mechanics, are shown. The higher the flow limit and faster the reconstruction of thixotropic structures, the higher the energy input of the injection processes. After the hydration process, it is important to form agglomerates and seal the barrier; therefore, we present a summary of the strength results, filtration coefficients and microscopic images of the resulting structures. Additional properties of such barriers in terms of limiting contaminant migration are indicated. One of the aspects affecting the use of liquid slurries is their application methods; thus, a summary of low-energy slurry injection methods is also presented. [ABSTRACT FROM AUTHOR]

Today, endless discourses about Large Panel System (LPS) buildings take place to ask whether the residents should be concerned about their flats being demolished. Taking into account: turning from the "machine for housing" created by Le Corbusier to modern construction, sudden disasters like gas explosion, progressive collapse, planned demolitions, the trend can result in the creation of huge amounts of waste. LPS buildings are usually located in areas of dense urban development. Recycling of these wastes is very problematic. Because concrete load bearing panels seem to be in good condition, there is urgent need to evaluate the possibility of recycling them, e.g. as coarse aggregate in a new concrete mix. Self-Compacting Concrete (SCC) seems promising with its practical feature that it does not have to be vibrated during concreting, which in turn leads to a reduction in noise, making it a perfect material for investments in areas of dense urban development. Such solution would allow to eliminate the transport of waste, and at the same time to reduce it. The aim of the research is to preliminary evaluate the properties of Recycled Coarse Aggregate (RCA), acquired from the Construction and Demolition Waste (C&DW) of LPS residential building. The results of the sieve analysis in the products of 4-8 mm showed little irregular grains content, amounted to 5.9 % of the selected representative sample. Because no study before analysed RCA from LPS, there is a need of further research focusing on properties of acquired aggregate and concrete. The novelty of the manuscript lies in conducting preliminary research towards the recycling LPS for new SCC. [ABSTRACT FROM AUTHOR]

In this paper, novel microgels containing nano-SiO2 were prepared by in situ copolymerization using nano-SiO2 particles as a reinforcing agent, nanosilica functional monomer (silane-modified nano-SiO2) as a structure and morphology director, acrylamide (AAm) as a monomer, acrylic acid (AAc) as a comonomer, potassium persulfate (KPS) as a polymerization initiator, and N,N′-methylene bis (acrylamide) (MBA) as a crosslinker. In addition, a conventional copolymeric hydrogel based on poly (acrylamide/acrylic acid) was synthesized by solution polymerization. The microgel samples, hydrogel and nanoparticles were characterized by transmission electron microscopy (TEM), field emission scanning electron microscopy (FESEM), Fourier transform infrared (FTIR) spectroscopy, thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC). A FESEM micrograph of copolymeric hydrogel showed the high porosity and 3D interconnected microstructure. Furthermore, FESEM results demonstrated that when nano-SiO2 particles were used in the AAm/AAc copolymerization process, the microstructure and morphology of product changed from porous hydrogel to a nanocomposite microgel with cauliflower-like morphology. According to FESEM images, the copolymerization of AAm and AAc monomers with a nanosilica functional monomer or polymerizable nanosilica particle as a seed led to a microgel with core–shell structure and morphology. These results demonstrated that the polymerizable vinyl group on nano-SiO2 particles have controlled the copolymerization and the product morphology. FTIR analysis showed that the copolymeric chains of polyacrylamide (PAAm) and poly (acrylic acid) (PAAc) were chemically bonded to the surfaces of the nano-SiO2 particles and silane-modified nano-SiO2. The particulate character of microgel samples and the existence of long distance among aggregations of particles led to rapid swelling and increasing of porosity and therefore increasing of degree of swelling. [ABSTRACT FROM AUTHOR]

The utilization of waste material, such as fly ash, in the concrete industry will provide a valuable alternative solution for creating an eco-friendly environment. However, experimental work is time-consuming; employing soft machine learning techniques can accelerate the process of forecasting the strength properties of concrete. Ensemble machine learning modeling using Python Jupyter Notebook was employed in the forecasting of compressive strength (CS) of high-performance concrete. Multilayer perceptron neuron network (MLPNN) and decision tree (DT) were used as individual learning which then ensembled with bagging and boosting to provide strong correlations. Random forest (RF) and gradient boosting regression (GBR) were also used for prediction. A total of 471 data points with input parameters (e.g., cement, fine aggregate, coarse aggregate, superplasticizer, water, days, and fly ash), and an output parameter of compressive strength (CS), were retrieved to train and test the individual learners. Cross-validation with K-fold and statistical error (i.e., MAE, MSE, RMSE, and RMSLE) analysis was applied to check the accuracy of all models. All models showed the best correlation with an ensemble model rather than an individual one. DT with AdaBoost and random forest gave a strong correlation of R2 = 0.89 with fewer errors. Cross-validation results revealed a good response with an error of less than 10 MPa. Thus, ensemble modeling not only trains the data by employing several weak learners but also produces a robust correlation that can then be used to model and predict the mechanical performance of concrete. [ABSTRACT FROM AUTHOR]

To avoid time-consuming, costly, and laborious experimental tests that require skilled personnel, an effort has been made to formulate the depth of wear of fly-ash concrete using a comparative study of machine learning techniques, namely random forest regression (RFR) and gene expression programming (GEP). A widespread database comprising 216 experimental records was constructed from available research. The database includes depth of wear as a response parameter and nine different explanatory variables, i.e., cement content, fly ash, water content, fine and coarse aggregate, plasticizer, air-entraining agent, age of concrete, and time of testing. The performance of the models was judged via statistical metrics. The GEP model gives better performance with R2 and ρ equals 0.9667 and 0.0501 respectively and meet with the external validation criterion suggested in the previous literature. The k-fold cross-validation also verifies the accurateness of the model by evaluating R2, RSE, MAE, and RMSE. The sensitivity analysis of GEP equation indicated that the time of testing is the influential parameter. The results of this research can help the designers, practitioners, and researchers to quickly estimate the depth of wear of fly-ash concrete thus shortening its ecological susceptibilities that push to sustainable and faster construction from the viewpoint of environmentally friendly waste management. [ABSTRACT FROM AUTHOR]