Firing Strength Research Articles

Innovative Materials in Architecture and Construction: Magnesite Panels

Magnesite panels studied herein, are characterized by high fire resistance, moisture resistance, strength, sound insulation and environmental properties. The relevance is due to the modern trends in sustainable development, imposing high demands on building materials and products. Preference is given to those products or technologies that can potentially be marked as eco-friendly.Purpose: The purpose of this work is to study magnesite panels, an innovative building material, to determine their performance characteristics, including fire resistance, moisture resistance, strength and environmental friendliness. The paper analyzes their effectiveness in comparison with other building materials.Methodology/approach: The paper analyzes the properties of panels made of magnesium oxide as the most environmentally friendly and safe innovative building material. Their advantages are assessed through a comparative analysis of the physical and mechanical properties of magnesium oxide panels with other available building panels (gypsum panels, oriented strand panels).Research findings: It is found that magnesite panels are an environmentally friendly and universal material in architecture and construction. It is shown that magnesite panels are superior to other building panels in the selected parameters, but are more expensive.

Fuzzy Logic Concepts, Developments and Implementation

Over the past few decades, the field of fuzzy logic has evolved significantly, leading to the development of diverse techniques and applications. Fuzzy logic has been successfully combined with other artificial intelligence techniques such as artificial neural networks, deep learning, robotics, and genetic algorithms, creating powerful tools for complex problem-solving applications. This article provides an informative description of some of the main concepts in the field of fuzzy logic. These include the types and roles of membership functions, fuzzy inference system (FIS), adaptive neuro-fuzzy inference system and fuzzy c-means clustering. The processes of fuzzification, defuzzification, implication, and determining fuzzy rules’ firing strengths are described. The article outlines some recent developments in the field of fuzzy logic, including its applications for decision support, industrial processes and control, data and telecommunication, and image and signal processing. Approaches to implementing fuzzy logic models are explained and, as an illustration, Matlab (version R2024b) is used to demonstrate implementation of a FIS. The prospects for future fuzzy logic developments are explored and example applications of hybrid fuzzy logic systems are provided. There remain extensive opportunities in further developing fuzzy logic-based techniques, including their further integration with various machine learning algorithms, and their adaptation into consumer products and industrial processes.

Soft sensor modeling method and application based on TSECIT2FNN-LSTM

To address the issue of low accuracy in soft sensor modeling of key variables caused by multi-variable coupling and parameter sensitivity in complex processes, this paper introduces a TSK-type-based self-evolving compensatory interval type-2 fuzzy Long short-term memory (LSTM) neural network (TSECIT2FNN-LSTM) soft sensor model. The proposed TSECIT2FNN-LSTM integrates the LSTM neural network with the interval type-2 fuzzy inference system to address long-term dependencies in sequence data by utilizing the gate mechanism of the LSTM neural network. The TSECIT2FNN-LSTM structure learning algorithm uses the firing strength of the network rule antecedent to decide whether to generate new rules to improve the rationality of the network structure. TSECIT2FNN-LSTM parameter learning utilizes the gradient descent method to optimize network parameters. However, unlike other interval type-2 fuzzy neural network gradient calculation processes, the error term in the LSTM node parameter gradient of TSECIT2FNN-LSTM is propagated backwards in the time dimension. Additionally, the error term is simultaneously transferred to the upper layer network to enhance network prediction accuracy and memory capabilities. The TSECIT2FNN-LSTM soft sensor model is utilized to predict the alcohol concentration in wine and the nitrogen oxide emission in gas turbines. Experimental results demonstrate that the proposed TSECIT2FNN-LSTM soft sensing model achieves higher prediction accuracy compared to other models.

The Potential Utility of Retinal Biomarkers to Index Central Nervous System Effects of Repetitive Blast Exposure in Military Personnel.

Military personnel exposed to blasts receive repeated subconcussive head impacts. Although these events typically do not cause immediate symptoms and do not lead to medical evaluation, the cumulative effects of subconcussive impacts can be significant and can include postconcussive symptoms, changes in brain structure and function, long-term cognitive dysfunction, depression, and chronic traumatic encephalopathy. Retinal measures such as thickness of retinal neural layers, density of retinal microvasculature, and strength of retinal neuronal firing are associated with cognitive function and brain structure and function in healthy populations and in neurodegenerative disease cohorts, and changes over time in retinal indices predict cognitive decline and brain atrophy in longitudinal studies in a range of medical populations. This commentary highlights the potential benefits of using retinal biomarkers in the routine screening and monitoring of brain health in warfighters and veterans. Retinal measures can be rapidly acquired (often in a matter of seconds) using methods that are inexpensive and noninvasive, and they can be collected with movable and often portable equipment that uses automated scoring routines that can be used for prediction and decision-making at the individual level. To date, however, retinal biomarkers have not been included in studies of blast overpressure exposures in military personnel. Despite this, preclinical and human evidence suggests that they could be among the most effective methods for tracking central nervous system damage in people exposed to repeated blasts. Retinal biomarkers could also contribute to brief test batteries to determine who is most at risk for long-term negative effects of future exposures. In addition, the sensitivity of retinal indices to blast exposure and mild traumatic brain injury suggests that they should be incorporated into research on strategies to minimize or prevent blast-related short- and long-term central nervous system changes in blast-exposed military personnel.

Fire-resistant and thermal-insulating alginate aerogel with intelligent bionic armor for exceptional mechanical and fire early-warning performance

Intelligent bio-based aerogels have attracted increasing attention in several key areas due to their unique porous structure and sustainability, but they suffer from poor fire safety, strength, and durability under harsh conditions. Here, we prepared sodium alginate/hydroxyapatite/graphene oxide (SA@HAP@GO) aerogel by in-situ growth and directional assembly technology, and an intelligent “bionic armor” was put on the surface of SA aerogel. It not only endows aerogel with high strength (2.3 MPa) and long-time water resistance (30 days) but also maintains the lightweight (0.039 g cm−3) and thermal insulation properties (0.03537 W m-1K−1) of original aerogel. Under fire conditions, the compact physical barrier makes aerogel obtain the excellent fire-resistant stability and fire-safety performance. Besides, the directional assembly of GO and the chelating effect of Ca2+ on aerogel surface promote the formation of a three-dimensional electron transport network during reduction process, thus achieving a fast and long-lasting fire early-warning response (1.95 s). The corresponding mechanism was demonstrated by molecular theoretical calculation and experiments. This multifunctional aerogel provides a new way for sustainable and high-performance thermal-insulation materials and shows a wide application in energy security.

Evaluating pumice as a sustainable raw material in porcelain tile production: impact on technical properties

AbstractPumice, a porous rock resulting from the rapid cooling of tuff fragments during volcanic activity, exhibits a spongy texture and light color due to its low density. Found predominantly in Central Anatolia and Eastern Anatolia, it has drawn interest for industrial applications. This study delved into utilizing micronized pumice within the porcelain tile manufacturing process. Comparative analyses were conducted between formulations incorporating micronized pumice and the standard ceramic tile recipe. In place of feldspar, micronized pumice was introduced at concentrations of 3%, 5%, and 7%, while clay was substituted with micronized pumice at concentrations of 3%, 5%, 7%, and 10% by weight. The prepared bodies were fired in an industrial kiln at 1210 °C for 54 min, and various physical and mechanical properties were evaluated. These included viscosity, sieve residue, green strength pre-firing, firing shrinkage, water absorption, firing strength, and firing color after-firing. The results indicated that the samples incorporating micronized pumice closely matched the physical and mechanical properties of the standard porcelain tile. Phase and microstructural analyses revealed the presence of mullite and quartz phases. Notably, micronized pumice demonstrated promise as a substitute for clay or feldspar, with the optimal usage rate determined to be 7% in the porcelain tile recipe. This indicates that pumice has the potential to be an alternative raw material in the production of porcelain tiles.

Three-way decision-based Takagi–Sugeno–Kang fuzzy classifier for partially labeled data

The salient features of Takagi–Sugeno–Kang (TSK) fuzzy classifiers are their superior nonlinear fitting capability and better interpretability, rendering them widely applicable in the domains of data mining and machine learning. However, using TSK fuzzy classifiers for partially labeled data has received little attention. Based on the three-way decision (TWD) theory, this study proposed a TSK fuzzy model to learn from partially labeled data. First, an adaptive sample weight strategy is introduced for the fuzzy partition of the antecedent part and the cross-entropy loss of the consequent part, respectively. Subsequently, a prototype-based firing strength loss mechanism is proposed to constrain the optimization of the model in the fuzzy representation space. It employs a TWD strategy based on the entropy criterion to classify samples as useful, useless, and uncertain, where the proposed model enhances the performance by iteratively utilizing a certain number of useful pseudo-labeled samples. Finally, the validity of the model is theoretically analyzed based on the principle of noise learning. The experimental results on UCI datasets demonstrate that the proposed model exhibits superior performance compared with classical semi-supervised methods, even attaining a performance comparable to that of the model trained on all data with ground-truth labels.

Distinct Action Signals by Subregions in the Nucleus Accumbens during STOP-Change Performance.

The nucleus accumbens (NAc) is thought to contribute to motivated behavior by signaling the value of reward-predicting cues and the delivery of anticipated reward. The NAc is subdivided into core and shell, with each region containing different populations of neurons that increase or decrease firing to rewarding events. While there are numerous theories of functions pertaining to these subregions and cell types, most are in the context of reward processing, with fewer considering that the NAc might serve functions related to action selection more generally. We recorded from single neurons in the NAc as rats of both sexes performed a STOP-change task that is commonly used to study motor control and impulsivity. In this task, rats respond quickly to a spatial cue on 80% of trials (GO) and must stop and redirect planned movement on 20% of trials (STOP). We found that the activity of reward-excited neurons signaled accurate response direction on GO, but not STOP, trials and that these neurons exhibited higher precue firing after correct trials. In contrast, reward-inhibited neurons significantly represented response direction on STOP trials at the time of the instrumental response. Finally, the proportion of reward-excited to reward-inhibited neurons and the strength of precue firing decreased as the electrode traversed the NAc. We conclude that reward-excited cells (more common in core) promote proactive action selection, while reward-inhibited cells (more common in shell) contribute to accurate responding on STOP trials that require reactive suppression and redirection of behavior.

A Numerical Study of Smoke Concentration Distribution near Low-Intensity Fires: Sensitivity to Vertical Canopy Structure and Fire Heat Source Strength

Abstract Broadly speaking, prediction of the negative impacts of prescribed fire on air quality is limited by gaps in our understanding of the underlying fire, fuels, and atmospheric processes. These knowledge gaps hinder our ability to accurately predict smoke concentration distributions, leading to unintended smoke intrusions into nearby communities and subsequent threats to public health and safety. In this study, numerical simulations are performed using the Flexible Particle Weather Research and Forecasting (FLEXPART-WRF) Model, a Lagrangian particle dispersion model, with particle motion driven by output from a full-physics atmospheric model with a forest canopy submodel and 10-m horizontal grid spacing [Advanced Regional Prediction System (ARPS)-CANOPY], to address two research questions. First, what is the relationship between near-fire (within ∼50–150 m of fire) smoke concentration distribution and (i) vertical canopy structure and (ii) fire heat source strength? Second, what roles do mean transport (i.e., transport by the mean wind) and turbulent diffusion play in shaping the near-fire smoke concentration distribution? To address these questions, simulations are run with 25 combinations of plant area density profile and fire sensible heat flux magnitude, and smoke is represented by particles with diameters ≤ 2.5 μm (PM2.5). Results show that near-fire PM2.5 concentration distribution is primarily controlled by vertical canopy structure, with fire heat source strength primarily controlling the PM2.5 concentration magnitude. Analysis of the underlying ARPS-CANOPY variables driving the FLEXPART-WRF particle dispersion helps elucidate the roles of mean transport and turbulent diffusion. In total, the study findings suggest that the vertical distribution of canopy vegetation and fire heat source strength are important factors influencing PM2.5 dispersion and concentration distribution near low-intensity fires.

Application of self-learning interval type-2 fuzzy neural network in PM2.5 concentration prediction

Abstract The change of PM2.5 concentration in air quality is nonlinear and difficult to predict. Therefore, a self-learning interval type-2 fuzzy neural network (SLIT2FNN) is proposed. SLIT2FNN has two parts: online structure learning and parameter learning. In structure learning, to improve the training accuracy and speed of the model, the Possibilistic Fuzzy C-Means (PFCM) algorithm is used to process the input data and obtain the number of initial rules before model training. The PFCM algorithm introduces the concept of possibility P to Fuzzy C-Means (FCM), allowing PFCM to overcome the shortcomings of FCM that cannot accurately cluster a large number of nonlinear problems. SLIT2FNN can establish an appropriate number of rules in the preparation stage, and then use the firing strength of the antecedents of the rules to judge whether to generate fuzzy rules for online self-learning, thereby optimizing its network structure. Then, the improved Levenberg–Marquardt (ILM) algorithm is used to modify the relevant parameters of SLIT2FNN. The ILM algorithm can address the challenge of numerous parameters in the Jacobian matrix and complex calculations and improve the calculation speed and adaptive ability of SLIT2FNN parameter learning. Finally, SLIT2FNN is applied to the prediction of air quality PM2.5 concentration, and the performance is compared with other models. The experiment proves that SLIT2FNN has a high prediction accuracy and fast convergence.

Robust Fuzzy Neural Network With an Adaptive Inference Engine.

Fuzzy neural networks (FNNs) have been very successful at handling uncertainty in data using fuzzy mappings and if-then rules. However, they suffer from generalization and dimensionality issues. Although deep neural networks (DNNs) represent a step toward processing high-dimensional data, their capacity to address data uncertainty is limited. Furthermore, deep learning algorithms designed to improve robustness are either time consuming or yield unsatisfactory performance. In this article, we propose a robust fuzzy neural network (RFNN) to overcome these problems. The network contains an adaptive inference engine that is capable of handling samples with high-level uncertainty and high dimensions. Unlike traditional FNNs that use a fuzzy AND operation to calculate the firing strength for each rule, our inference engine is able to learn the firing strength adaptively. It also further processes the uncertainty in membership function values. Taking advantage of the learning ability of neural networks, the acquired fuzzy sets can be learned from training inputs automatically to cover the input space well. Furthermore, the consequent layer uses neural network structures to enhance the reasoning ability of the fuzzy rules when dealing with complex inputs. Experiments on a range of datasets show that RFNN delivers state-of-the-art accuracy even at very high levels of uncertainty. Our code is available online. https://github.com/leijiezhang/RFNN.

Embedded feature selection approach based on TSK fuzzy system with sparse rule base for high-dimensional classification problems

In high-dimensional problems of fuzzy rule-based embedded feature selection, the challenges include loss of interpretability, curse of dimensionality, and arithmetic underflow, among others. The primary reason for these problems is the exponential increase in the number of fuzzy rules with an increase in input dimension. In this study, an embedded feature selection approach, the Takagi–Sugeno–Kang (TSK) fuzzy system with sparse rule base (TSK-SRB), is proposed for high-dimensional data. Based on clustering, a broader initial rule base is designed with a suitable number of rules. In the rule layer, refined softmin (Ref-softmin) is introduced to calculate the firing strength, which can approximate the minimum T-norm while avoiding arithmetic underflow. Two Group Lasso regularization terms are used to realize feature and rule selection. In addition, an automatic threshold segmentation is introduced to determine the appropriate number of selected features/rules. Extensive experiments on 17 classification datasets showed that TSK-SRB is effective and competitive in high-dimensional feature selection.

INVESTIGATION OF THE USE OF KÜTAHYA REGION KAOLIN IN WALL TILES

The main raw materials in the ceramic wall tiles are clay, kaolin, calcite, and feldspar. The technical properties of the tiles (water absorption, total shrinkage, dry firing strength, fracture strength) and the desired physical properties (L, a, b, surface smoothness) vary depending on the raw material content in the recipe. Rheological parameters are crucial in the formulation development stages to avoid production problems and to ensure the final desired product parameters. Density, viscosity, and sieve analysis have therefore been included in the developed formulations. The use of raw materials from the region close to the production facility facilitates easy access to these materials. Thanks to this advantage, the use of domestic raw materials is becoming more widespread. In addition to the use of alternative raw materials, it contributes to the reduction of greenhouse gas emissions. The aim of this study, is to formulate a recipe for wall tiles using kaolins from the Kütahya region instead of clay from the Afyon region. Recipes containing 2%, 4%, 6%, and 8% of Kütahya region kaolin, with a corresponding reduction in clay from the Afyon region, were developed and sintered at 1130-1150°C with a 29-minute firing cycle. By analyzing the technical properties of the developed formulations, it was found that a maximum of 2% of Kütahya region kaolin could be used in the wall tile formulation. It was found that exceeding this percentage was not feasible due to the associated increase in water absorption, expansion of product size, and decrease in dry strength.

Z-number based neural network structured inference system

Z-number based Neural Network structured Inference System (ZNIS) with rule-base consisting of linguistic Z-terms trainable with Differential Evolution with Constraints (DEC) optimization algorithm is suggested. The inference mechanism of the multi-layered ZNIS consists of a fuzzifier, fuzzy rule base, inference engine, and output processor. Due to the use of extended fuzzy terms, each processing layer implements appropriate extended fuzzy operations, including computation of fuzzy valued rule firing strengths, fuzzy Level-2 aggregate outputs, and two consecutive Center of Gravity (COG) defuzzification procedures. The experiments with different versions of ZNIS have demonstrated that it is a universal modeling tool suitable for dealing with both approximate reasoning and functional mapping tasks. Random experiments on benchmark examples (among which are simple functional mapping, Parkinson disease, and non-linear system identification) have shown that ZNIS performance is equivalent to or better than FLS Type 2 and far superior to FLS Type 1, showing on average 2–3 times lower MSE. Along with this, the main advantages of ZNIS over other inference systems are better semantic expressing power, higher degree of perception and interpretability of the linguistic rules by humans, and a higher confidence in the reliability of achieved decision due to the transparency of the underlying decision-making mechanism.

Dynamic Network Connectivity: from monkeys to humans.

Human brain imaging research using functional MRI (fMRI) has uncovered flexible variations in the functional connectivity between brain regions. While some of this variability likely arises from the pattern of information flow through circuits, it may also be influenced by rapid changes in effective synaptic strength at the molecular level, a phenomenon called Dynamic Network Connectivity (DNC) discovered in non-human primate circuits. These neuromodulatory molecular mechanisms are found in layer III of the macaque dorsolateral prefrontal cortex (dlPFC), the site of the microcircuits shown by Goldman-Rakic to be critical for working memory. This research has shown that the neuromodulators acetylcholine, norepinephrine, and dopamine can rapidly change the strength of synaptic connections in layer III dlPFC by (1) modifying the depolarization state of the post-synaptic density needed for NMDA receptor neurotransmission and (2) altering the open state of nearby potassium channels to rapidly weaken or strengthen synaptic efficacy and the strength of persistent neuronal firing. Many of these actions involve increased cAMP-calcium signaling in dendritic spines, where varying levels can coordinate the arousal state with the cognitive state. The current review examines the hypothesis that some of the dynamic changes in correlative strength between cortical regions observed in human fMRI studies may arise from these molecular underpinnings, as has been seen when pharmacological agents or genetic alterations alter the functional connectivity of the dlPFC consistent with the macaque physiology. These DNC mechanisms provide essential flexibility but may also confer vulnerability to malfunction when dysregulated in cognitive disorders.

Simplification of ANFIS based on importance-confidence-similarity measures

Adaptive-network-based fuzzy inference system (ANFIS) is a well-known neuro-fuzzy system, which is widely implemented in nonlinear system approximation, prediction, control, and pattern classification. However, either too many fuzzy sets or fuzzy rules are indispensable to establish an accurate ANFIS for a high-dimensional system, which increases the complexity and limits the widespread application of the ANFIS. In this paper, an approach to simplify the ANFIS is developed based on importance-confidence-similarity (ICS) measures. The simplification process consists of selection of fuzzy rules with large importance-confidence (IC) values and reduction of similar fuzzy rules. The importance is defined as the average normalized firing strength of the rule. The confidence which reflects the approximation performance of a T-S fuzzy rule is estimated by the weighted orthogonal distance from the output data to the rule hyperplane (the consequent of a linear T-S fuzzy rule). The IC measures provide a comprehensive basis for the selection of the appropriate fuzzy rules. Furthermore, to the best of our knowledge, an approach to measure similarities of the T-S fuzzy rules including the rule premises and consequents is first proposed in this paper. We also develop a direct and simple method to remove the similar fuzzy rules based on a hierarchical tree and the IC measures. The proposed simplification approach is applied to Mackey-Glass chaotic time series prediction problem and the strip temperature increment prediction problem of the indirect-fired furnace of the annealing heating line. The results show that our proposed method achieves a balance between simplicity and accuracy of the ANFIS for the high-dimensional system.

Pengaplikasian Prosedur Perhitungan untuk Menentukan Produksi Batik Tulis Madura Menggunakan Metode Fuzzy Database Model Tahani

One of the subdivisions of fuzzy logic is the study of the fuzzy database with Tahani model which is described as a model used to process data searches based on operations in fuzzy set theory to obtain appropriate information so that the data search process becomes accurate. The research aims to implement the Fuzzy Database with Tahani Model in determining the appropriate variant of batik tulis with the specified criteria. The initial step of the research is fuzzification, namely determining the domain of each complete variables with intervals of numerical variables and linguistic variables. In this research some variables were used: the price of the cloth, the number of colors and the work durations. Each variable is divided based on the interval between the lowest data and the highest data so that the variable price of the cloth is divided into 4, the number of colors is 3, and the work durations is 5. Then determine the membership function, the variable price of cloth, the number of colors, and the work duration using a linear model and triangle in determining the degree of its membership. Data tabulation from strict sets to fuzzy sets with numerical variables and linguistic variables. In preparing the criteria, 60 rule bases are based on the theory of consumer needs for batik tulis. Not all rule bases occur, because the data collects only a few criteria. Determining the value of fire strength, using the AND rule means selecting the minimum degree of membership from the available. The recommendation results determine to sort the washing machine data that has the highest fire strength value to the smallest fire strength value from each criterion.

Influence of External Factors on the Strength of Firefighting Hoses Used in Fire Protection Units

Aim: The purpose of this article is to review information on firefighting hoses in terms of their division and the materials used in their production, and to determine if and how external factors affect their strength and durability. Introduction: Firefighting hoses are one of the most important products of technical equipment of Fire Protection Units. Since they are an indispensable element during rescue and firefighting operations, conducting research on the strength of fire hoses is needed and extremely important from the point of view of fire protection. Project and methods: Technological advances in the area of using various types of materials for fire hoses over time have had (and continue to have) a significant impact on the undertaking of rescue and firefighting operations, the speed with which these operations are carried out, as well as their effectiveness. Therefore, at the beginning of the article, a historical outline of the fire hoses used for firefighting in various countries around the world over the years was made. In the following part of the article, the influence of external factors on the adhesive strength of firefighting hoses used in fire protection units in Poland was analysed. The effect of exposure to a 3% foam solution of a fire extinguishing agent on hose tape samples placed in this solution was tested, depending on the time of direct contact of the samples with this agent. The effect of UV exposure (under wetting and drying conditions) on the hose tape samples was also studied, with time intervals of different lengths. Results: When testing the exposure of the hose tape samples to UV light (for 90 minutes, at 38 ± 3°C, without water spray, followed by water exposure for 30 minutes, at 50 ± 10% humidity), decreasing trends were observed for the average values of delamination strengths. Tests were also made on a series of samples exposed to a 3% solution of aqueous foam extinguishing agent on the hose tape, both externally and internally. Declining trends were observed for the mean values of adhesive strengths (after 3 and 6 weeks). The impact of factors such as exposure to UV radiation, as well as the impact of the foam extinguishing agent solution negatively affects the hose tape, reducing its strength. The article was prepared on the basis of the authors’ own research results, as well as available sources, both national and foreign, based on the available literature on the raised issues. Keywords: firefighting hose, fire hose strength, adhesion, UV, foam extinguishing agent

Research on the Concrete-Filled Double Skin steel Tubular (CFDST) columns subjected to axial force after fire

This article investigates the structural performance of Concrete-Filled Double Skin Steel Tubular (CFDST) columns after fire under the axial compress. 13 experiments were conducted on the residual bearing capacity of CFDST columns under axial compression after different fire duration time. The variation parameters included the hollow ratio, fire duration time and strength of concrete. The effect of different parameters on the axial load and displacement changes after fire was revealed. The experimental and finite element simulation results were compared and analyzed, and they were in good agreement. The results indicate that CFDST columns have good fire resistance, and the strength of concrete has little effect on the residual bearing capacity of CFDST columns after the fire. With the increase of hollow ratio from 0 to 0.72, the axial residual bearing capacity of CFDST columns increases first and then decreases after the same fire duration time. Compared with the column without initial load, the residual bearing capacity of CFDST columns will increase with an initial load before fire, but different initial loads have no significant impact on the ultimate bearing capacity after fire.

Use of Post-Consumer Plastics in the Production of Wood-Plastic Composites for Building Components: A Systematic Review

This systematic review study adopted the PRISMA methodology to investigate recent research on wood-plastic composites (WPC) utilizing post-consumer plastics in the construction industry. Initially, 3111 articles were selected from academic databases using keywords such as “wood and plastic composites”, “WPC”, “polymer”, “recycled”, “waste”, “construction”, and “sustainability.” After stringent exclusion criteria, 15 relevant studies on plastic waste composites were identified. These studies often employ post-consumer plastics like polypropylene and high-density polyethylene, along with plant-based fillers, aiming to enhance mechanical properties and reduce reliance on virgin materials. Analysis of these studies revealed that the optimal plastic composition in the composites ranged from 40% to 45% wood and from 50% to 60% plastic, with the extrusion process being the most employed for shaping. Specific factors, such as the use of compatibilizers and the particle size of raw materials, were identified as significant influencers on composite strength. These materials exhibited high thermal stability, rendering them suitable for construction applications exposed to high temperatures. The diversity of plastic waste explored in the studies underscores the potential to tailor thermal properties to specific application demands. These composites facilitate closed-loop plastic recycling, enabling their reintegration into the production chain and offering opportunities for lightweight, durable, and high-performance products in the construction industry. However, beyond the factors examined in the studies, a meticulous assessment of the fire resistance, weather resistance, ultraviolet resistance, moisture absorption, dimensional stability, degradation, long-term durability, impact strength, recyclability, and cost-effectiveness of the material is crucial. Thoughtful consideration of these factors is essential to achieving a comprehensive understanding of the potential and limitations of recycled plastic composites in promoting energy efficiency and sustainability in the construction industry.