Non-Destructive Thermography Analysis of Impact Damage on Large-Scale CFRP Automotive Parts

Laminated composites are increasingly used in aeronautics and the wind energy industry, as well as in the automotive industry. In these applications, the construction and processing need to fulfill the highest requirements regarding weight and mechanical properties. Environmental issues, like fuel consumption and CO₂-footprint, set new challenges in producing lightweight parts that meet the highly monitored standards for these branches. In the automotive industry, one main aspect of construction is the impact behavior of structural parts. To verify the quality of parts made from composite materials with little effort, cost and time, non-destructive test methods are increasingly used. A highly recommended non-destructive testing method is thermography analysis. In this work, a prototype for a car’s base plate was produced by using vacuum infusion. For research work, testing specimens were produced with the same multi-layer build up as the prototypes. These specimens were charged with defined loads in impact tests to simulate the effect of stone chips. Afterwards, the impacted specimens were investigated with thermography analysis. The research results in that work will help to understand the possible fields of application and the usage of thermography analysis as the first quick and economic failure detection method for automotive parts.     

Influence of ns-Laser Cleaning Parameters on the Removal of the Painted Layer and Selected Properties of the Base Metal

The removal of the surface paint of Q345 (Gr·B) steel, as well as microstructure and hardness of the cleaned surface were investigated. The laser source used in this study is a nanosecond pulsed Gaussian light source. The surface morphology and microstructure were characterized by a scanning electron microscope and electron back-scattered diffraction. A hardness test was used for capturing variations of the parameter of the cleaned region in comparison to the base metal. The results show that when the X-scanning speed was 1500 mm/s and Y-moving speeds was 7 mm/s during ns-laser cleaning, respectively, the cleaned surface was relatively flat and there was only a few small residual paint. In addition, the contents of Fe and C elements on the cleaned surface reached to 89% and 9%, respectively. Moreover, the roughness was the lowest of 0.5 μm through the observation of the three-dimensional topography. In addition, a fine grain layer appeared on the cleaned surface after laser cleaning at the X-scanning speeds of 500 mm/s and 1000 mm/s. The maximum hardness of the fine grain layer was more than 400 HV, higher than the base metal.     

Chemical Cleaning of Magnetite Deposits on the Flow Mini-Channels of a Printed Circuit Heat Exchanger in an EDTA-Based Solution

The dissolution behavior of magnetite deposited on flow mini-channel surfaces within a printed circuit heat exchanger (PCHE) and the corrosion behavior of a STS 316L PCHE material were investigated in an ethylendiaminetetraacetic acid (EDTA)-based chemical cleaning solution at 93 °C. The fouling in the PCHE was simulated using a water-steam circulation loop system. Most of the magnetite deposits were rapidly dissolved in the early stage of the circulation chemical cleaning. An empirical equation for estimating the dissolution percentage was derived as a function of cleaning time. The PCHE material showed excellent corrosion resistance during the chemical cleaning tests. These results indicate the fouling layers in the PCHEs can be removed efficiently by the chemical cleaning process without concern about base metal corrosion.     

A DFT-Based Quantitative and Geometric Analysis of the Effect of Pressure on Boron Arsenate

Boron arsenate, BAsO₄, is a β-cristobalite-like crystal which has been reported to exhibit the rather unusual property of negative linear compressibility behaviour at elevated pressures, that is expanding rather than shrinking in a linear dimension when subjected to pressure. This work proposes a ‘geometry—deformation mechanism’-based mathematical model to aid the discernment of the manner how this anomalous pressure behaviour is achieved. The model makes use of data obtained from DFT simulations over an extended range of pressures, including extreme pressure conditions, and rigorously explains the macroscopic properties of this material in terms of the nanoscale deformations. More specifically, through this model, it was possible to decipher the different contributions to the deformation mechanism and compressibility properties of BAsO₄. Moreover, for the first time, it was shown that a rule related to the sum of angles of tetrahedrally coordinated atoms is so robust that it applies at the extreme pressures studied here.     

The Comparative Analysis of the Coatings Deposited on the Automotive Parts by the Cataphoresis Method

The paper presents preliminary research focused on the determination of the influence of surface preparation on the quality of the paint coating obtained by the cataphoresis method (KTL). The tests were carried out on steel parts used in the construction of trailers and truck bodies. The first research group consisted of cold-rolled and chemically cleaned parts, the second group were mechanically cleaned with abrasive blasting. In order to determine the influence of surface treatment on the corrosion resistance of the tested coatings, besides a corrosion test, roughness measurements were also carried out. Tests were performed on the crude surface and after coating deposition. Moreover, tests were supplemented by measuring the thickness of the coating using the magnetic induction method and the hardness with the use micro and nano hardness testers. Measurements of the tribological parameters under dry friction conditions were performed using a T11 tester. The corrosion resistance of the applied coatings was determined in a salt spray test. The obtained results were compared to those that were determined for different zinc coatings. It has been shown that the method of base steel surface preparation affects every measured parameter and property of tested paint coatings. The quality of the coating deposited on the steel base after chemical cleaning is much better than the one applied to the sandblasted surface. The measured corrosion resistance of the tested paint coatings is only greater than the corrosion resistance of the lamellar zinc coating. The other zinc coatings (galvanic, hot-dip, sherardized) show corrosion resistance by an order of magnitude higher.     

Preliminary Cleaning Approach with Alginate and Konjac Glucomannan Polysaccharide Gel for the Surfaces of East Asian and Western String Musical Instruments

The cleaning of string musical instruments is challenging due to the traditional finishing treatments used by the makers. Multilayered coating systems were applied to Western musical instruments, while the Nakdong technique was applied in East Asia. Furthermore, by restorations and performance, dust and grime were overlapped together with polishes, adhesives, and varnishes. Gel cleaning is important in the field of conservation because of the ability to selectively remove chemical and biological degradation products from the surface, minimizing the interactions with the inner layers. In this study, hydrogels based on sodium alginate (SA) and konjac glucomannan (KG) polysaccharides were applied on laboratory mock-ups of East Asian and Western instruments to test their ability to remove synthetic soiling and sweat from the surface. In particular, SA cross-linked with calcium cations and KG cross-linked with borate gels were used. To control the exposure of the cleaning solvent on the surface of mock-ups, the moisture content of the gels was determined. The effectiveness of removing synthetic contaminants was investigated by noninvasive analytical methods. Stereomicroscopy and colorimetry, together with Fourier Transform Infrared (FTIR) spectroscopy in reflection mode and X-Ray Fluorescence (XRF), were used to evaluate the cleaning efficacy. Overall, polysaccharide hydrogels resulted in promising cleaning systems on both smooth and rough surfaces of wood.     

Experimental Study on the Influence of Rubber Content on Chloride Salt Corrosion Resistance Performance of Concrete

In order to enhance the corrosion resistance of concrete to chloride salt, 5% NaCl solution was used to corrode ordinary concrete (OC) and rubber concrete (RC) with 5%, 10%, and 15% rubber content, respectively. By testing the compressive strength, mass, chloride ion concentration at different depths and relative dynamic elastic modulus, the erosion mechanism was analyzed by means of SEM scanning and EDS patterns, and the mechanical properties and deterioration degree of ordinary concrete (OC) and rubber concrete (RC) under the corrosion environment of chloride salt were studied. The results show that: the quality of rubber mixed into concrete increases first and then decreases, and rubber can increase the compressive strength of concrete, improve its internal structure. At the same time, the mechanical properties of concrete in the corrosion environment of chloride salt are improved to a certain extent, and the deterioration degree is reduced. Considering the comprehensive performance of OC and RC in the dry–wet alternation mechanism under chloride salt corrosion, the best content of rubber is 10%.     

Microstructure Analysis of Ti-xPt Alloys and the Effect of Pt Content on the Mechanical Properties and Corrosion Behavior of Ti Alloys

The microstructure, mechanical properties, and corrosion behavior of binary Ti-xPt alloys containing 5, 10, 15 and 20 wt% Pt were investigated in order to develop new Ti-based dental materials possessing superior properties than those of commercially pure titanium (cp-Ti). All of the Ti-xPt (x = 5, 10, 15, 20) alloys showed hexagonal α-Ti structure with cubic Ti₃Pt intermetallic phase. The mechanical properties and corrosion behavior of Ti-xPt alloys were sensitive to the Pt content. The addition of Pt contributed to hardening of cp-Ti and to improving its oxidation resistance. Electrochemical results showed that the Ti-xPt alloys exhibited superior corrosion resistance than that of cp-Ti.     

A Systematic Review of the Research Development on the Application of Machine Learning for Concrete

Research on the applications of new techniques such as machine learning is advancing rapidly. Machine learning methods are being employed to predict the characteristics of various kinds of concrete such as conventional concrete, recycled aggregate concrete, geopolymer concrete, fiber-reinforced concrete, etc. In this study, a scientometric-based review on machine learning applications for concrete was performed in order to evaluate the crucial characteristics of the literature. Typical review studies are limited in their capacity to link divergent portions of the literature systematically and precisely. Knowledge mapping, co-citation, and co-occurrence are among the most challenging aspects of innovative studies. The Scopus database was chosen for searching for and retrieving the data required to achieve the study’s aims. During the data analysis, the relevant sources of publications, relevant keywords, productive writers based on publications and citations, top articles based on citations received, and regions actively engaged in research into machine learning applications for concrete were identified. The citation, bibliographic, abstract, keyword, funding, and other data from 1367 relevant documents were retrieved and analyzed using the VOSviewer software tool. The application of machine learning in the construction sector will be advantageous in terms of economy, time-saving, and reduced requirement for effort. This study can aid researchers in building joint endeavors and exchanging innovative ideas and methods, due to the statistical and graphical portrayal of participating authors and countries.     

Influence of Molybdenum on the Microstructure,Mechanical Properties and Corrosion Resistance of Ti20Ta20Nb20(ZrHf)20−xMox (Where: x = 0, 5, 10, 15, 20) High Entropy Alloys

The presented work was focused on investigating the influence of the (hafnium and zirconium)/molybdenum ratio on the microstructure and properties of Ti₂₀Ta₂₀Nb₂₀(ZrHf)_20−xMo_x (where: x = 0, 5, 10, 15, 20 at.%) high entropy alloys in an as-cast state. The designed chemical composition was chosen due to possible future biomedical applications. Materials were obtained from elemental powders by vacuum arc melting technique. Phase analysis revealed the presence of dual body-centered cubic phases. X-ray diffraction showed the decrease of lattice parameters of both phases with increasing molybdenum concentration up to 10% of molybdenum and further increase of lattice parameters. The presence of two-phase matrix microstructure and hafnium and zirconium precipitates was proved by scanning and transmission electron microscopy observation. Mechanical property measurements revealed decreased micro- and nanohardness and reduced Young’s modulus up to 10% of Mo content, and further increased up to 20% of molybdenum addition. Additionally, corrosion resistance measurements in Ringers’ solution confirmed the high biomedical ability of studied alloys due to the presence of stable oxide layers.     

Machine learning-based screening of the diagnostic genes and their relationship with immune-cell infiltration in patients with lung adenocarcinoma

BackgroundLung adenocarcinoma (LUAD) is the most common type of lung cancer, and has a dismal mortality rate of 80%, mainly due to diagnosis at an advanced stage. Biomarkers with high specificity and sensitivity for the early diagnosis of LUAD are sparse. This study aimed to identify markers for the early diagnosis of LUAD.MethodsThe {"type":"entrez-geo","attrs":{"text":"GSE32863","term_id":"32863"}}GSE32863 and {"type":"entrez-geo","attrs":{"text":"GSE75037","term_id":"75037"}}GSE75037 data sets were standardized and merged to screen for differentially expressed genes (DEGs). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted. The intersected DEGs from the least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM) regression analyses were considered the hub genes. Then the diagnostic ability and expression of hub genes was tested in {"type":"entrez-geo","attrs":{"text":"GSE63459","term_id":"63459"}}GSE63459 data set, Finally, CIBERSORT was used to analyze the correlation between the immune-infiltrating cells and hub genes.ResultsThe following 7 DEGs were intersected by the LASSO and SVM regression analyses: Locus 401286 (LOC401286), flavin-containing monooxygenase 2 (FMO2), XLKD1, Ras homolog family member J (RHOJ), scavenger receptor Class A member 5 (SCARA5), heat shock protein beta-2 (HSPB2), and serine incorporator 2 (SERINC2). The area under the receiver operating characteristic curve (AUC) of LOC401286, FMO2, XLKD1, RHOJ, SCARA5, HSPB2, and SERINC2 was 0.99, 1.00, 0.99, 1.00, 0.99, 0.99, and 0.98, respectively in the training groups. The AUC of LOC401286, FMO2, XLKD1, RHOJ, SCARA5, HSPB2, and SERINC2 was 0.97, 0.96, 0.94, 0.88, 0.85, 0.94 and 0.89, respectively in the validation group. The immune-cell infiltrations of naive B cells, memory B cells, plasma cells, naive cluster of differentiation (CD) 4 T cells, T follicular helper cells, regulatory T cells, gamma delta T cells, monocytes, M0 macrophages, M1 macrophages, resting mast cells, activated mast cells, and neutrophils were different between the normal and tumor tissues. Notably, these immune cells were correlated with the above-mentioned 7 diagnostic genes.ConclusionsWe identified 7 DEGs in LUAD tissue that can be considered diagnostic genes based on 2 machine-learning regression methods, which could be very helpful for the early diagnosis of LUAD in clinical practice.     

Effect of an Adaptive-Density Filling Structure on the Mechanical Properties of FDM Parts with a Variable Cross-Section

Fused deposition modeling (FDM) technique is one of the most popular additive manufacturing techniques. Infill density is a critical factor influencing the mechanical properties of 3D-printed components using the FDM technique. For irregular components with variable cross-sections, to increase their overall mechanical properties while maintaining a lightweight, it is necessary to enhance the local infill density of the thin part while decreasing the infill density of the thick part. However, most current slicing software can only generate a uniform infill throughout one model to be printed and cannot adaptively create a filling structure with a varying infill density according to the dimensional variation of the cross-section. In the present study, to improve the mechanical properties of irregular components with variable cross-sections, an adaptive-density filling structure was proposed, in which Hilbert curve with the same order was used to fill each slice, i.e., the level of the Hilbert curves in each slice is the same, but the side length of the Hilbert curve decreases with the decreasing size of each slice; hence, the infill density of the smaller cross-section is greater than that of the larger cross-section. The ultimate bearing capacity of printed specimens with the adaptive-density filling structure was evaluated by quasi-static compression, three-point bending, and dynamic compression tests, and the printed specimens with uniform filling structure and the same overall infill density were tested for comparison. The results show that the maximum flexural load, the ultimate compression load, and the maximum impact resistance of the printed specimens with the adaptive-density filling structure were increased by 140%, 47%, and 82%, respectively, compared with their counterparts using the uniform filling structure.     

Effect of Temperature on the Complex Modulus of Mg-Based Unidirectionally Aligned Carbon Fiber Composites

Composite materials based on magnesium–lithium (MgLi) and magnesium–yttrium (MgY) matrices reinforced with unidirectional carbon fibers were prepared using the gas pressure infiltration method. Two types of carbon fibers were used, high-strength PAN-based T300 fibers and high-modulus pitch-based Granoc fibers. The PAN-based carbon fibers have an internal turbostratic structure composed of crystallites. The pitch-based carbon fibers have a longitudinally aligned graphite crystal structure. The internal carbon fiber structure is crucial in the context of the interfacial reaction with the alloying element. There are various mechanisms of bonding to carbon fibers in the case of magnesium–lithium and magnesium–yttrium alloys. This paper presents the use of the DMA method for the characterization of the role of alloying elements in the quality of interfacial bonding and the influence on the complex modulus at increasingly elevated temperatures (50–250 °C). The complex modulus values of the composites with T300 fibers were in the range of 118–136 GPa. The complex modulus values of the composites with Granoc fibers were in the range of 198–236 GPa. The damping capacity of magnesium-based unidirectionally aligned carbon fiber composites is related to the quality of the interfacial bonding.     

Influence of Different Thermal Aging Conditions on Soot Combustion with Catalyst by Thermogravimetric Analysis

Diesel particulates are deposited in the diesel particulate filter and removed by the regeneration process. The Printex-U (PU) particles are simulated as the diesel soot to investigate the influence of thermal aging conditions on soot combustion performance with the addition of catalysts. The comprehensive combustion index S, combustion stability index R_w and peak temperature T_p are obtained to evaluate the combustion performance. Compared with the PU/Pt mixtures of different Pt contents (2 g/ft³, 3.5 g/ft³, and 5 g/ft³), the 10 g/ft³ Pt contents improve soot combustion with the outstanding oxygen absorption ability. When the weight ratio of PU/Pt mixture is 1:1, the promoted effect achieves the maximum degree. The S and R_w increase to 8.90 × 10⁻⁸ %²min⁻²°C⁻³ and 39.11 × 10⁵, respectively, compared with pure PU. After the thermal aging process, the PU/Pt mixture with a 350 °C aging temperature for 10 h promotes the soot combustion the best when compared to pure PU particles. It is not good as the PU/Pt mixture without aging, because the inner properties of soot and Pt/Al₂O₃ catalyst may have been changed. The S and R_w are 9.07 × 10⁻⁸ %²min⁻²°C⁻³ and 38.39 × 10⁵, respectively, which are close to the no aging mixture. This work plays a crucial role in understanding the mechanism of the comprehensive effect of soot and catalyst on soot combustion after the thermal aging process.     

The Influence of Heat Treatment on the Mechanical Properties and Corrosion Resistance of the Ultrafine-Grained AA7075 Obtained by Hydrostatic Extrusion

In this paper, the corrosion resistance and mechanical properties of the 7075 aluminum alloy are studied. The alloy was deformed by hydrostatic extrusion and then aged both naturally and artificially. Results are compared with those of coarse-grained material subjected to T6 heat treatment. The aim of the research is to find the optimal correlation between the mechanical properties and the corrosion resistance of the alloy. To this end, static tensile tests with subsequent fractography, open circuit potential, and potentiodynamic polarization tests in 0.05 M NaCl were conducted. Obtained results show that a combination of precipitate hardening and a deformed microstructure leads to increased mechanical strength with high anisotropy due to the presence of fibrous grains. Plastic deformation increases susceptibility to corrosion due to the increased number of grain boundaries, which act as paths along that corrosion propagates. However, further artificial aging incurs a positive effect on corrosion resistance due to changes in the chemical composition of the matrix as a result of the precipitation process.     

Model of Production System Evaluation with the Influence of FDM Machine Reliability and Process-Dependent Product Quality

This paper investigates the Job Shop Scheduling Problem (JSSP) with FDM (Fused Deposition Modeling) machine unavailability constraints due to Predictive Maintenance (PdM) tasks, under the objective of minimizing the makespan, total tardiness and machine idle time. The Ant-Colony Optimization (ACO) algorithm is elaborated to deal with the JSSP. The reliability characteristics of the critical machine (FDM) influence the product as well as the production system quality. PdM periods are estimated based on historical data on failure-free times of the FDM machine components and deviations from the standards established for the key process parameters: infill density, layer thickness and extruder temperature. The standards for the key process parameters are identified based on investigation of the mechanical properties of printed elements. The impact of failure time and the number of nonstandard measurements of parameters on the quality of the Job Shop System (JSS) are observed. Failure rate of the FDM machine is corrected with the probability of a stoppage in the future period due to the “outlier” in measurements of any key parameters of the additive process. The quality robustness of production schedules increases with the disturbance-free operation of the FDM up to the peak value. After reaching the peak value the quality robustness decreases. The original issue of this paper is a model of scheduling production and maintenance tasks in a job shop system with an FDM machine as a bottleneck using ACO. Additionally, an original FDM-reliability model is also proposed. The model is based on weighted p-moving averages of the observed number of deviations from the norms, established for key process parameters such as fill density, layer thickness and extruder temperature.     

Machine Learning-Based Void Percentage Analysis of Components Fabricated with the Low-Cost Metal Material Extrusion Process

Additive manufacturing (AM) is a widely used layer-by-layer manufacturing process. Material extrusion (ME) is one of the most popular AM techniques. Lately, low-cost metal material extrusion (LCMME) technology is developed to perform metal ME to produce metallic parts with the ME technology. This technique is used to fabricate metallic parts after sintering the metal infused additively manufactured parts. Both AM and sintering process parameters will affect the quality of the final parts. It is evident that the sintered parts do not have the same mechanical properties as the pure metal parts fabricated by the traditional manufacturing processes. In this research, several machine learning algorithms are used to predict the size of the internal voids of the final parts based on the collected data. Additionally, the results show that the neural network (NN) is more accurate than the support vector regression (SVR) on prediction.     

On the Use of Machine Learning Models for Prediction of Compressive Strength of Concrete: Influence of Dimensionality Reduction on the Model Performance

Compressive strength is the most significant metric to evaluate the mechanical properties of concrete. Machine Learning (ML) methods have shown promising results for predicting compressive strength of concrete. However, at present, no in-depth studies have been devoted to the influence of dimensionality reduction on the performance of different ML models for this application. In this work, four representative ML models, i.e., Linear Regression (LR), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN), are trained and used to predict the compressive strength of concrete based on its mixture composition and curing age. For each ML model, three kinds of features are used as input: the eight original features, six Principal Component Analysis (PCA)-selected features, and six manually selected features. The performance as well as the training speed of those four ML models with three different kinds of features is assessed and compared. Based on the obtained results, it is possible to make a relatively accurate prediction of concrete compressive strength using SVR, XGBoost, and ANN with an R-square of over 0.9. When using different features, the highest R-square of the test set occurs in the XGBoost model with manually selected features as inputs (R-square = 0.9339). The prediction accuracy of the SVR model with manually selected features (R-square = 0.9080) or PCA-selected features (R-square = 0.9134) is better than the model with original features (R-square = 0.9003) without dramatic running time change, indicating that dimensionality reduction has a positive influence on SVR model. For XGBoost, the model with PCA-selected features shows poorer performance (R-square = 0.8787) than XGBoost model with original features or manually selected features. A possible reason for this is that the PCA-selected features are not as distinguishable as the manually selected features in this study. In addition, the running time of XGBoost model with PCA-selected features is longer than XGBoost model with original features or manually selected features. In other words, dimensionality reduction by PCA seems to have an adverse effect both on the performance and the running time of XGBoost model. Dimensionality reduction has an adverse effect on the performance of LR model and ANN model because the R-squares on test set of those two models with manually selected features or PCA-selected features are lower than models with original features. Although the running time of ANN is much longer than the other three ML models (less than 1s) in three scenarios, dimensionality reduction has an obviously positive influence on running time without losing much prediction accuracy for ANN model.     

Influence of the Type of Cement and the Addition of an Air-Entraining Agent on the Effectiveness of Concrete Cover in the Protection of Reinforcement against Corrosion

The concrete cover is the basic protection of the reinforcement against the influence of external factors that may lead to its corrosion. Its effectiveness depends mainly on the composition of the concrete mix, including the cement used. Depending on external environmental factors that may aggressively affect the structure, various types of cements and concrete admixtures are recommended. The paper presents the results of tests that allow us to assess the effect of the type of cement used and the air-entraining agent on the effectiveness of the concrete cover as a layer protecting the reinforcement against corrosion. In order to initiate the corrosion process, the reinforced concrete specimens were subjected to cycles of freezing and thawing in a sodium chloride solution. The degree of advancement of the corrosion process was investigated using the electrochemical galvanostatic pulse technique. Additionally, the microstructure of specimens taken from the cover was observed under a scanning electron microscope. The research has shown that in the situation of simultaneous action of chloride ions and freezing cycles, in order to effectively protect the reinforcement against corrosion, the application of both blast-furnace slag cement and an air-entraining agent performed the best.     

Prediction of the Compressive Strength for Cement-Based Materials with Metakaolin Based on the Hybrid Machine Learning Method

Cement-based materials are widely used in construction engineering because of their excellent properties. With the continuous improvement of the functional requirements of building infrastructure, the performance requirements of cement-based materials are becoming higher and higher. As an important property of cement-based materials, compressive strength is of great significance to its research. In this study, a Random Forests (RF) and Firefly Algorithm (FA) hybrid machine learning model was proposed to predict the compressive strength of metakaolin cement-based materials. The database containing five input parameters (cement grade, water to binder ratio, cement-sand ratio, metakaolin to binder ratio, and superplasticizer) based on 361 samples was employed for the prediction. In this model, FA was used to optimize the hyperparameters, and RF was used to predict the compressive strength of metakaolin cement-based materials. The reliability of the hybrid model was verified by comparing the predicted and actual values of the dataset. The importance of five variables was also evaluated, and the results showed the cement grade has the greatest influence on the compressive strength of metakaolin cement-based materials, followed by the water-binder ratio.