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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]

Due to the growing need to recycle plastics, new possibilities for their reuse are intensively sought. In the Asian market, waste polymers are increasingly used to modify road bitumen. This solution is beneficial in many aspects, especially in economic and ecological terms. In this work, recycled poly(ethylene terephthalate) (RPET), obtained from storage points located in Lesser Poland, was subjected to material recycling, and its properties were examined using three analyses: differential scanning calorimetry (DSC), thermogravimetric analysis (TG), and Fourier transform infrared spectroscopy (FTIR). The most important point of this research was the selection of conditions for obtaining modified asphalt mixtures through the addition of RPET. Subsequently, the effect of the polymer on the properties of road bitumens was assessed on the basis of penetration tests, softening point, elastic recovery, and structure. In the last stage of our research work, asphalt mixtures with the addition of modified waste PET (PMA) containing mineral filler in the form of basalt dust were obtained. The properties of the obtained mineral–polymer–asphalt mixtures were compared in terms of frost resistance, structure, and abrasion resistance with the properties of mineral–asphalt mixtures that were taken from damaged road surfaces in four points in the city of Tarnów (Lesser Poland) in the winter of 2022. It has been shown that the modification of road bitumen with the use of recyclate and mineral filler has a significant impact on its performance properties. [ABSTRACT FROM AUTHOR]

Unconfined compressive strength (UCS) can be used to assess the applicability of geopolymer binders as ecologically friendly materials for geotechnical projects. Furthermore, soft computing technologies are necessary since experimental research is often challenging, expensive, and time-consuming. This article discusses the feasibility and the performance required to predict UCS using a Random Forest (RF) algorithm. The alkali activator studied was sodium hydroxide solution, and the considered geopolymer source material was ground-granulated blast-furnace slag and fly ash. A database with 283 clayey soil samples stabilized with geopolymer was considered to determine the UCS. The database was split into two sections for the development of the RF model: the training data set (80%) and the testing data set (20%). Several measures, including coefficient of determination (R), mean absolute error (MAE), and root mean square error (RMSE), were used to assess the effectiveness of the RF model. The statistical findings of this study demonstrated that the RF is a reliable model for predicting the UCS value of geopolymer-stabilized clayey soil. Furthermore, based on the obtained values of RMSE = 0.9815 and R2 = 0.9757 for the testing set, respectively, the RF approach showed to provide excellent results for predicting unknown data within the ranges of examined parameters. Finally, the SHapley Additive exPlanations (SHAP) analysis was implemented to identify the most influential inputs and to quantify their behavior of input variables on the UCS. [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]

Large panel system (LPS) buildings, during the 1950s–1990s, were the most widespread among precast systems and are still a huge part of some national housing stocks. Disadvantages in characteristics, poor quality of all construction stages, sudden disasters and poor retrofit and repair techniques can result in demolishing some LPS buildings and thus the creation of huge amounts of waste. There is urgent need of evaluating the possibility of reusing the elements of LPS buildings. The novelty of the article is based on the formulation of research gaps in terms of recycling these buildings and the justification of such need. The authors reviewed the current state of knowledge regarding characteristics of LPS buildings, directions of retrofitting, durability and their locations within cities. The possibility of recycling elements of LPS buildings was analysed. The findings are that concrete load bearing panels, proved to be in satisfactory conditions in terms of strength and durability, have the possibility to be recycled into recycled coarse aggregate (RCA), so such waste could be beneficial for concrete. However, there are research gaps of such an application highlighted in the text. The authors suggest that conducting comprehensive basic research in this area could have significant benefits for the next generation of researchers and engineers. [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 study, nanofibers of poly (acrylic acid) (PAAc), polyacrylamide (PAAm) and poly (vinyl alcohol) (PVOH) were prepared using the electrospinning technique. Based on the Taguchi DOE (design of experiment) method, the effects of electrospinning parameters, i.e., needle tip to collector distance, polymer solution concentration, applied voltage, polymer solution feed rate and polymer type, on the diameter and morphology of polymer nanofibers were evaluated. Analyses of the experiments for the diameters of the polymer nanofibers showed that the type of polymer was the most significant factor. The optimal combination to obtain the smallest diameters with minimum deviations for electrospun polymer nanofibers was also determined. For this purpose, the appropriate factor levels were determined as follows: polymer PAAm, applied voltage 10 kV, delivery rate 0.1 mL/h, needle tip to collector distance 10 cm, and polymer solution concentration 8%, to obtain the thinnest nanofibers. This combination was further validated by conducting a confirmation experiment, and the average diameter of the polymer nanofibers was found to be close to the optimal conditions estimated by the Taguchi DOE method. [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]

Fibre-reinforced polymer materials (FRP) are increasingly used to reinforce structural elements. Due to this, it is possible to increase the load-bearing capacity of polymer, wooden, concrete, and metal structures. In this article, the authors collected all the crucial aspects that influence the behaviour of concrete elements reinforced with FRP. The main types of FRP, their characterization, and their impact on the load-carrying capacity of a composite structure are discussed. The most significant aspects, such as type, number of FRP layers including fibre orientation, type of matrix, reinforcement of concrete columns, preparation of a concrete surface, fire-resistance aspects, recommended conditions for the lamination process, FRP laying methods, and design aspects were considered. Attention and special emphasis were focused on the description of the current research results related to various types of concrete reinforced with FRP composites. To understand which aspects should be taken into account when designing concrete reinforcement with composite materials, the main guidelines are presented in tabular form. [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]

In this investigation, the potential of M5P, Random Tree (RT), Reduced Error Pruning Tree (REP Tree), Random Forest (RF), and Support Vector Regression (SVR) techniques have been evaluated and compared with the multiple linear regression-based model (MLR) to be used for prediction of the compressive strength of bacterial concrete. For this purpose, 128 experimental observations have been collected. The total data set has been divided into two segments such as training (87 observations) and testing (41 observations). The process of data set separation was arbitrary. Cement, Aggregate, Sand, Water to Cement Ratio, Curing time, Percentage of Bacteria, and type of sand were the input variables, whereas the compressive strength of bacterial concrete has been considered as the final target. Seven performance evaluation indices such as Correlation Coefficient (CC), Coefficient of determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Bias, Nash-Sutcliffe Efficiency (NSE), and Scatter Index (SI) have been used to evaluate the performance of the developed models. Outcomes of performance evaluation indices recommend that the Polynomial kernel function based SVR model works better than other developed models with CC values as 0.9919, 0.9901, R2 values as 0.9839, 0.9803, NSE values as 0.9832, 0.9800, and lower values of RMSE are 1.5680, 1.9384, MAE is 0.7854, 1.5155, Bias are 0.2353, 0.1350 and SI are 0.0347, 0.0414 for training and testing stages, respectively. The sensitivity investigation shows that the curing time (T) is the vital input variable affecting the prediction of the compressive strength of bacterial concrete, using this data set. [ABSTRACT FROM AUTHOR]

The effect of SiO2 nanoparticles on the formation of PAA (poly acrylic acid) gel structure was investigated with seeded emulsion polymerization method used to prepare SiO2/PAA nanoparticles. The morphologies of the nanocomposite nanoparticles were studied by transmission electron microscopy (TEM). Fourier-transform infrared (FTIR) spectroscopy results indicated that the PAA was chemically bonded to the surface of the SiO2 nanoparticles. Additionally, the resulting morphology of the nanocomposite nanoparticles confirmed the co-crosslinking role of the SiO2 nanoparticles in the formation of the 3D structure and hydrogel of PAA. SiO2/PAA nanocomposite hydrogels were synthesized by in situ solution polymerization with and without toluene. The morphology studies by field emission scanning electron microscopy (FESEM) showed that when the toluene was used as a pore forming agent in the polymerization process, a macroporous hydrogel structure was achieved. The pH-sensitive swelling behaviors of the nanocomposite hydrogels showed that the formation of pores in the gels structure was a dominant factor on the water absorption capacity. In the current research the absorption capacity was changed from about 500 to 4000 g water/g dry hydrogel. Finally, the macroporous nanocomposite hydrogel sample was tested as an amoxicillin release system in buffer solutions with pHs of 3, 7.2, and 9 at 37 °C. The results showed that the percentage cumulative release of amoxicillin from the hydrogels was higher in neutral and basic mediums than in the acidic medium and the amoxicillin release rate was decreased with increasing pH. Additionally, the release results were very similar to swelling results and hence amoxicillin release was a swelling controlled-release system. [ABSTRACT FROM AUTHOR]

Concrete is the most widely used building material, but it is also a recognized pollutant, causing significant issues for sustainability in terms of resource depletion, energy use, and greenhouse gas emissions. As a result, efforts should be concentrated on reducing concrete's environmental consequences in order to increase its long-term viability. In order to design environmentally friendly concrete mixtures, this research intended to create a prediction model for the compressive strength of those mixtures. The concrete mixtures that were used in this study to build our proposed prediction model are concrete mixtures that contain both recycled aggregate concrete (RAC) and ground granulated blast-furnace slag (GGBFS). A white-box machine learning model known as multivariate polynomial regression (MPR) was developed to predict the compressive strength of eco-friendly concrete. The model was compared with the other two machine learning models, where one is also a white-box machine learning model, namely linear regression (LR), and the other is the black-box machine learning model, which is a support vector machine (SVM). The newly suggested model shows robust estimation capabilities and outperforms the other two models in terms of R2 (coefficient of determination) and RMSE (root mean absolute error) measurements. [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]

The effect of combining filler (carbon black) and fibrous materials (steel fiber and polypropylene fiber) with various sizes of coarse particles on the post-cracking behavior of conductive concrete was investigated in this study. Steel fibers (SF) and carbon black (CB) were added as monophasic, diphasic, and triphasic materials in the concrete to enhance the conductive properties of reinforced concrete. Polypropylene fiber (PP) was also added to steel fiber and carbon to improve the post-cracking behavior of concrete beams. This research mainly focused on the effects of macro fibers on toughness parameters and energy absorption capacity, as well as enhancing the self-sensing of multiple cracks and post-cracking behavior. Fractional changes in resistance and crack opening displacement (COD-FCR) and the relationship of load-deflection-FCR with different coarse aggregates of (5–10 mm and 15–20 mm) sizes were investigated, and the law of resistance signal changes with single and multiple cracking through load-time-FCR curves was explored. Results indicated that the smaller size coarse aggregates (5–10 mm) showed higher compressive strength: up to 8.3% and 14.83% with diphasic (SF + CB), respectively. The flexural strength of PC-10 increased 22.60 and 51.2%, respectively, with and without fibers, compared to PC-20. The diphasic and triphasic conductive material with the smaller size of aggregates (5–10 mm) increased the FCR values up to 38.95% and 42.21%, respectively, as compared to those of greater size coarse aggregates (15–20 mm). The hybrid uses of fibrous and filler materials improved post-cracking behavior as well as the self-sensing ability of reinforced concrete. [ABSTRACT FROM AUTHOR]

When understanding the influence of concrete surface preparation on the effectiveness of CFRP reinforcement, the load capacity of high-performance, self-compacting, fibre-reinforced concrete (HPSCFRC) is crucial for it efficiency. In this studies, few factors that have an influence on the stress–strain characteristic of concrete confined by CFRP laminates were examined. The method of surface preparation and the number of CFRP layers were considered as crucial parameters that have a significant impact on the characteristics of the load-bearing capacity of reinforced elements. Three different concrete surfaces were examined: grinded, sanded and unprepared. The morphology of each of the concrete surfaces was clearly, and the Abbott-Firestone profile material share curve was then extensively analyzed. During this research, 40 cylindrical concrete specimens were wrapped with one, two and three layers of CFRP, and then subjected to a uniaxial compressive test. The obtained results showed that the morphology of the concrete surface has a real impact on the load-bearing capacity of HPSCFRCreinforced with CFRP. Furthermore, the grinded concrete surface was characterized by the best cooperation with the composite reinforcement. [ABSTRACT FROM AUTHOR]

The casting and testing specimens for determining the mechanical properties of concrete is a time-consuming activity. This study employed supervised machine learning techniques, bagging, AdaBoost, gene expression programming, and decision tree to estimate the compressive strength of concrete containing supplementary cementitious materials (fly ash and blast furnace slag). The performance of the models was compared and assessed using the coefficient of determination (R2), mean absolute error, mean square error, and root mean square error. The performance of the model was further validated using the k-fold cross-validation approach. Compared to the other employed approaches, the bagging model was more effective in predicting results, with an R2 value of 0.92. A sensitivity analysis was also prepared to determine the level of contribution of each parameter utilized to run the models. The use of machine learning (ML) techniques to predict the mechanical properties of concrete will be beneficial to the field of civil engineering because it will save time, effort, and resources. The proposed techniques are efficient to forecast the strength properties of concrete containing supplementary cementitious materials (SCM) and pave the way towards the intelligent design of concrete elements and structures. [ABSTRACT FROM AUTHOR]

The innovation of geopolymer concrete (GPC) plays a vital role not only in reducing the environmental threat but also as an exceptional material for sustainable development. The application of supervised machine learning (ML) algorithms to forecast the mechanical properties of concrete also has a significant role in developing the innovative environment in the field of civil engineering. This study was based on the use of the artificial neural network (ANN), boosting, and AdaBoost ML approaches, based on the python coding to predict the compressive strength (CS) of high calcium fly-ash-based GPC. The performance comparison of both the employed techniques in terms of prediction reveals that the ensemble ML approaches, AdaBoost, and boosting were more effective than the individual ML technique (ANN). The boosting indicates the highest value of R2 equals 0.96, and AdaBoost gives 0.93, while the ANN model was less accurate, indicating the coefficient of determination value equals 0.87. The lesser values of the errors, MAE, MSE, and RMSE of the boosting technique give 1.69 MPa, 4.16 MPa, and 2.04 MPa, respectively, indicating the high accuracy of the boosting algorithm. However, the statistical check of the errors (MAE, MSE, RMSE) and k-fold cross-validation method confirms the high precision of the boosting technique. In addition, the sensitivity analysis was also introduced to evaluate the contribution level of the input parameters towards the prediction of CS of GPC. The better accuracy can be achieved by incorporating other ensemble ML techniques such as AdaBoost, bagging, and gradient boosting. [ABSTRACT FROM AUTHOR]

Artificial intelligence and machine learning are employed in creating functions for the prediction of self-compacting concrete (SCC) strength based on input variables proportion as cement replacement. SCC incorporating waste material has been used in learning approaches. Artificial neural network (ANN) support vector machine (SVM) and gene expression programming (GEP) consisting of 300 datasets have been utilized in the model to foresee the mechanical property of SCC. Data used in modeling consist of several input parameters such as cement, water–binder ratio, coarse aggregate, fine aggregate, and fly ash (FA) in combination with the superplasticizer. The best predictive models were selected based on the coefficient of determination (R2) results and model validation. Empirical relation with mathematical expression has been proposed using ANN, SVM, and GEP. The efficiency of the models is assessed by permutation features importance, statistical analysis, and comparison between regression models. The results reveal that the proposed machine learning models achieved adamant accuracy and has elucidated performance in the prediction aspect. [ABSTRACT FROM AUTHOR]

In a fast-growing population of the world and regarding meeting consumer's requirements, solid waste landfills will continue receiving a substantial amount of waste. The utilization of solid waste materials in concrete has gained the attention of the researchers. Ceramic waste powder (CWP) is considered to be one of the most harmful wastes for the environment, which may cause water, soil, and air pollution. The aim of this study was comprised of two phases. Phase one was based on the characterization of CWP with respect to its composition, material testing (coarse aggregate, fine aggregate, cement,) and evaluation of concrete properties both in fresh and hardened states (slump, 28 days compressive strength, and dry density). Concrete mixes were prepared in order to evaluate the compressive strength (CS) of the control mix, with partial replacement of the cement with CWP of 10 and 20% by mass of cement and 60 prepared mixes. However, phase two was based on the application of the artificial neural network (ANN) and decision tree (DT) approaches, which were used to predict the CS of concrete. The linear coefficient correlation (R2) value from the ANN model indicates better performance of the model. Moreover, the statistical check and k-fold cross validation methods were also applied for the performance confirmation of the model. The mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) were evaluated to confirm the model's precision. [ABSTRACT FROM AUTHOR]

High temperature severely affects the nature of the ingredients used to produce concrete, which in turn reduces the strength properties of the concrete. It is a difficult and time-consuming task to achieve the desired compressive strength of concrete. However, the application of supervised machine learning (ML) approaches makes it possible to initially predict the targeted result with high accuracy. This study presents the use of a decision tree (DT), an artificial neural network (ANN), bagging, and gradient boosting (GB) to forecast the compressive strength of concrete at high temperatures on the basis of 207 data points. Python coding in Anaconda navigator software was used to run the selected models. The software requires information regarding both the input variables and the output parameter. A total of nine input parameters (water, cement, coarse aggregate, fine aggregate, fly ash, superplasticizers, silica fume, nano silica, and temperature) were incorporated as the input, while one variable (compressive strength) was selected as the output. The performance of the employed ML algorithms was evaluated with regards to statistical indicators, including the coefficient correlation (R2), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). Individual models using DT and ANN gave R2 equal to 0.83 and 0.82, respectively, while the use of the ensemble algorithm and gradient boosting gave R2 of 0.90 and 0.88, respectively. This indicates a strong correlation between the actual and predicted outcomes. The k-fold cross-validation, coefficient correlation (R2), and lesser errors (MAE, MSE, and RMSE) showed better performance than the ensemble algorithms. Sensitivity analyses were also conducted in order to check the contribution of each input variable. It has been shown that the use of the ensemble machine learning algorithm would enhance the performance level of the model. [ABSTRACT FROM AUTHOR]

Composite materials are increasingly used to strengthen existing structures or new load-bearing elements, also made of timber. In this paper, the effect of the number of layers of Carbon Fiber Reinforced Polymer (CFRP) on the load-bearing capacity and stiffness of Glued Laminated Timber beams was determined. Experimental research was performed on 32 elements—a series of eight unreinforced beams, and three series of eight reinforced beams: with one, three and five layers of laminate each. The beams with a cross-section of 38 mm × 80 mm and a length of 750 mm were subjected to the four-point bending test according to standard procedure. For each series, destructive force, deflection, mode of failure, and equivalent stiffness were determined. In addition, for the selected samples, X-ray computed tomography was performed before and after their destruction to define the quality of the interface between wood and composite. The results of the conducted tests and analyses showed that there was no clear relationship between the number of reinforcement layers and the load-bearing capacity of the beams and their stiffness. Unreinforced beams failed due to tension, while reinforced CFRP beams failed due to shear. Despite this, a higher energy of failure of composite-reinforced elements was demonstrated in relation to the reference beams. [ABSTRACT FROM AUTHOR]

Marble is currently a commonly used material in the building industry, and environmental degradation is an inevitable consequence of its use. Marble waste occurs during the exploitation of deposits using shooting technologies. The obtained elements most mainly often have an irregular geometry and small dimensions, which excludes their use in the stone industry. There is no systematic way of disposing of these massive mounds of waste, which results in the occurrence of landfills and environmental pollution. To mitigate this problem, an effort was made to incorporate waste marble powder into clay bricks. Different percentage proportions of marble powder were considered as a partial substitute for clay, i.e., 5–30%. A total of 105 samples were prepared in order to assess the performance of the prepared marble clay bricks, i.e., their water absorption, bulk density, apparent porosity, salt resistance, and compressive strength. The obtained bricks were 1.3–19.9% lighter than conventional bricks. The bricks with the addition of 5–20% of marble powder had an adequate compressive strength with regards to the values required by international standards. Their compressive strength and bulk density decreased, while their water absorption capacity and porosity improved with an increased content of marble powder. The obtained empirical equations showed good agreement with the experimental results. The use of waste marble powder in the construction industry not only lowers project costs, but also reduces the likelihood of soil erosion and water contamination. This can be seen to be a crucial factor for economic growth in agricultural production. [ABSTRACT FROM AUTHOR]

Structures located on the coast are subjected to the long-term influence of chloride ions, which cause the corrosion of steel reinforcements in concrete elements. This corrosion severely affects the performance of the elements and may shorten the lifespan of an entire structure. Even though experimental activities in laboratories might be a solution, they may also be problematic due to time and costs. Thus, the application of individual machine learning (ML) techniques has been investigated to predict surface chloride concentrations (Cc) in marine structures. For this purpose, the values of Cc in tidal, splash, and submerged zones were collected from an extensive literature survey and incorporated into the article. Gene expression programming (GEP), the decision tree (DT), and an artificial neural network (ANN) were used to predict the surface chloride concentrations, and the most accurate algorithm was then selected. The GEP model was the most accurate when compared to ANN and DT, which was confirmed by the high accuracy level of the K-fold cross-validation and linear correlation coefficient (R2), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) parameters. As is shown in the article, the proposed method is an effective and accurate way to predict the surface chloride concentration without the inconveniences of laboratory tests. [ABSTRACT FROM AUTHOR]

With the development of concrete technology, the tendency to combine different materials with each other to achieve a greater efficiency and durability of structures can be observed. In the modern construction industry, various materials and techniques are increasingly being combined in order to achieve e.g., an increased resistance to dynamic impacts of a structure, or an increased scope of work of a selected constructional element, which translates into a significant increase in the energy of destruction. Thus, hybrid elements, known as composite ones, are created, which consist of concrete and reinforcements. This study examined the influence of the preparation of the concrete surface on the behavior of high-performance, self-compacting, fiber-reinforced concrete (HPSCFRC), reinforced with carbon fibers (CF) using a cement matrix. In the general lamination processes, this is preformed using epoxy resin. However, epoxy resin is sensitive to relatively low temperatures, and therefore the authors attempted to use a cement matrix in the lamination process. When connecting hardened concrete with a fresh concrete matrix or mixture, the type of the concrete surface is significant. In this research, three types of concrete surfaces e.g., unprepared, sanded and grinded were considered. All of the surfaces were examined using a 3D laser scanner, to determine the Abbott-Firestone profile material share curve. In this research, cylindrical concrete specimens were reinforced with one, two and three layers of laminates. They were then subjected to a uniaxial compressive test. The results of tests showed that the use of cement matrix in the lamination process, due to its low efficiency, should not be applied when reinforcing concrete elements with a high compressive strength. Moreover, the grinded surface of concrete showed the best cooperation with CF reinforcement. [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 (Et), ultimate axial strain (εcu') and compressive strength (fcu') exhibited the most reduction with deviation angle. For 5° deviation in fiber orientation, the average reduction in fcu', εcu' and Et 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 fcu', εcu' and Et were 6°, 3° and 2°, respectively, with a 95% confidence.

Environment-friendly concrete is gaining popularity these days because it consumes less energy and causes less damage to the environment. Rapid increases in the population and demand for construction throughout the world lead to a significant deterioration or reduction in natural resources. Meanwhile, construction waste continues to grow at a high rate as older buildings are destroyed and demolished. As a result, the use of recycled materials may contribute to improving the quality of life and preventing environmental damage. Additionally, the application of recycled coarse aggregate (RCA) in concrete is essential for minimizing environmental issues. The compressive strength (CS) and splitting tensile strength (STS) of concrete containing RCA are predicted in this article using decision tree (DT) and AdaBoost machine learning (ML) techniques. A total of 344 data points with nine input variables (water, cement, fine aggregate, natural coarse aggregate, RCA, superplasticizers, water absorption of RCA and maximum size of RCA, density of RCA) were used to run the models. The data was validated using k-fold cross-validation and the coefficient correlation coefficient (R 2 ), mean square error (MSE), mean absolute error (MAE), and root mean square error values (RMSE). However, the model's performance was assessed using statistical checks. Additionally, sensitivity analysis was used to determine the impact of each variable on the forecasting of mechanical properties.

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 R 2 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 R 2 , 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.

The current trend in modern research revolves around novel techniques that can predict the characteristics of materials without consuming time, effort, and experimental costs. The adaptation of machine learning techniques to compute the various properties of materials is gaining more attention. This study aims to use both standalone and ensemble machine learning techniques to forecast the 28-day compressive strength of high-performance concrete. One standalone technique (support vector regression (SVR)) and two ensemble techniques (AdaBoost and random forest) were applied for this purpose. To validate the performance of each technique, coefficient of determination (R 2 ), statistical, and k-fold cross-validation checks were used. Additionally, the contribution of input parameters towards the prediction of results was determined by applying sensitivity analysis. It was proven that all the techniques employed showed improved performance in predicting the outcomes. The random forest model was the most accurate, with an R 2 value of 0.93, compared to the support vector regression and AdaBoost models, with R 2 values of 0.83 and 0.90, respectively. In addition, statistical and k-fold cross-validation checks validated the random forest model as the best performer based on lower error values. However, the prediction performance of the support vector regression and AdaBoost models was also within an acceptable range. This shows that novel machine learning techniques can be used to predict the mechanical properties of high-performance concrete.