• The Journal of Advanced Prosthodontics
• /
• v.16 no.3
• /
• pp.174-188
• /
• 2024

PURPOSE. The purpose of this study was to investigate the effects of surface treatments, liquids, and aging on color, translucency, and surface properties of monolithic ceramics. MATERIALS AND METHODS. Lithium disilicate (LDS) and zirconia-reinforced lithium silicate (ZLS) ceramics (n = 135 each) were cut and divided into three groups [crystallization+glaze (single stage), crystallization-glaze (two stages), and crystallization-polish (two stages)]. One sample from each group was examined using scanning electron microscopy (SEM). Remaining samples were divided into four subgroups (distilled water, coffee, grape juice, and smoothie) (n = 11 each), stored for 12 d in the respective liquids, and thermally aged. One sample from each subgroup was analyzed using SEM. The color, gloss, and roughness values of the samples were analyzed after surface treatment (initial) and storage under different liquids+aging conditions. The initial data and both the aged data and data change values were analyzed using robust two- and three-way analyses of variance. RESULTS. The glazed groups exhibited smoother surfaces. Ceramic type and ceramic-surface treatment interactions affected the initial translucency parameter (TP) (P < .001) and the initial and aged roughness values (P ≤ .001). Surface treatment type affected the color change (P < .001), and ceramic type affected the aged TP values (P < .001). Type of ceramic, surface treatment, and their interactions affected both the initial and aged gloss (P ≤ .001) and TP change values (P ≤ .015). Surface treatment type and ceramic-surface treatment interactions affected the gloss change values (P ≤ .001). CONCLUSION. Although both ceramics and all surface treatments are clinically applicable, crystallization-glaze is recommended. When gloss and smoothness are important or when translucency is important, ZLS or LDS may be preferred, respectively.

• Computers and Concrete
• /
• v.15 no.5
• /
• pp.759-770
• /
• 2015

In this study, a multi-layer perceptron neural network (MLPNN) prediction model for compressive strength of the cement mortars has been developed. For purpose of constructing this model, 8 different mixes with 240 specimens of the 2, 7, 28, 56 and 90 days compressive strength experimental results of cement mortars containing fly ash (FA), silica fume (SF) and FA+SF used in training and testing for MLPNN system was gathered from the standard cement tests. The data used in the MLPNN model are arranged in a format of four input parameters that cover the FA, SF, FA+SF and age of samples and an output parameter which is compressive strength of cement mortars. In the model, the training and testing results have shown that MLPNN system has strong potential as a feasible tool for predicting 2, 7, 28, 56 and 90 days compressive strength of cement mortars.

• Computers and Concrete
• /
• v.19 no.2
• /
• pp.161-169
• /
• 2017

Chloride induced reinforcement corrosion is widely accepted to be the most frequent mechanism causing premature degradation of reinforced concrete structures. The electrochemical impedance of reinforcing steel in diatomite- and zeolite-containing concrete exposed to sodium chloride was assessed. Chemical, physical and mineralogical properties of three concrete samples (20% diatomite, 20% zeolite, and a reference containing neither) were correlated with corrosion investigations. The steel-reinforced samples were exposed to 3.5% NaCl solution for 500 days, and measured every 15 days via EIS method. Results indicated that porosity and capillary spaces increase the diffusion rate of water and electrolytes throughout the concrete, making it more susceptible to cracking. Reinforcement in the reference concrete was the most corroded compare to the zeolite and the diatomite samples.

• Computers and Concrete
• /
• v.17 no.6
• /
• pp.815-829
• /
• 2016

This study has been carried out to investigate the effect of the surface properties of Portland cement, diatomite and zeolite on the performance of concrete composites. In this context, to describe the materials used in this study and determine the properties of them, chemical, physical, mineralogical, molecular, thermal, and zeta potential analysis have been applied. In the study, reference (Portland cement), 10%-20% diatomite, 10%-20% zeolite, 5+5%-10+10% diatomite and zeolite were substituted for Portland cement, a total of 7 different cements were obtained. Ultrasonic pulse velocity, capillary water absorption and compressive strength tests were performed on the hardened concrete specimens. Hardened concrete tests have been done on seven different types of concrete, for 28, 56 and 90 days. As a result of experiments it has been identified that both the zeolite and diatomite substitution has a positive effect on the performance of concrete.

• Computers and Concrete
• /
• v.18 no.1
• /
• pp.1-16
• /
• 2016

In this study, an Artificial Neural Network (ANN) and Adaptive Network-based Fuzzy Inference Systems (ANFIS) prediction models for flexural strength of the cement mortars have been developed. For purpose of constructing this models, 12 different mixes with 144 specimens of the 2, 7, 28 and 90 days flexural strength experimental results of cement mortars containing pure Portland cement (PC), blast furnace slag (BFS), waste tire rubber powder (WTRP) and BFS+WTRP used in training and testing for ANN and ANFIS were gathered from the standard cement tests. The data used in the ANN and ANFIS models are arranged in a format of four input parameters that cover the Portland cement, BFS, WTRP and age of samples and an output parameter which is flexural strength of cement mortars. The ANN and ANFIS models have produced notable excellent outputs with higher coefficients of determination of $R^2$, RMS and MAPE. For the testing of dataset, the $R^2$, RMS and MAPE values for the ANN model were 0.9892, 0.1715 and 0.0212, respectively. Furthermore, the $R^2$, RMS and MAPE values for the ANFIS model were 0.9831, 0.1947 and 0.0270, respectively. As a result, in the models, the training and testing results indicated that experimental data can be estimated to a superior close extent by the ANN and ANFIS models.

• Computers and Concrete
• /
• v.21 no.1
• /
• pp.21-30
• /
• 2018

In this study, we analyze the behavior of concrete which contains zeolite and diatomite. In order to achieve the goal, we utilize expert system methods. The utilized methods are artificial neural network and adaptive network-based fuzzy inference systems. In this respect, we exploit seven different mixes of concrete. The concrete mixes contain zeolite, diatomite, mixture of zeolite and diatomite. All seven concrete mixes are exposed to 28, 56 and 90 days' compressive strength experiments with 63 specimens. The results of the compressive strength experiments are used as input data during the training and testing of expert system methods. In terms of artificial neural network and adaptive network-based fuzzy models, data format comprises seven input parameters, which are; the age of samples (days), amount of Portland cement, zeolite, diatomite, aggregate, water and hyper plasticizer. On the other hand, the output parameter is defined as the compressive strength of concrete. In the models, training and testing results have concluded that both expert system model yield thrilling medium to predict the compressive strength of concrete containing zeolite and diatomite.

• Advances in environmental research
• /
• v.11 no.1
• /
• pp.17-30
• /
• 2022

Road transport allows all forms of land conditions to be met at less cost. Because of this function, despite numerous disadvantages, it becomes the most frequently used method of transport, especially in underdeveloped or developing countries. One of the most significant factors used in evaluating the atmosphere's air quality is the amount of CO₂, increasing people's density in indoor spaces. The amount of CO₂ indoors is, therefore, vital to determine. In this study, CO₂ and temperature measurements made on nine different bus journey was made in Turkey. The minimum and maximum values were recorded as 555 ppm and 3000 ppm CO₂, respectively, in the measurements. On all journeys, the average concentration is 1088.72 ppm. The minimum and maximum values were measured as 17.4℃ and 32.7℃ in the temperature measurements, and the average of all trips was calculated to be 25.76℃. In this study conducted before the Covid-19 pandemic, it was determined that the amount of CO₂ increased with the density and insufficient ventilation in the buses. The risk of infection increases in places with high human density and low clean air. For situations such as pandemics, CO₂ measurement is a rapid indicator of determining human density.

• The Journal of Advanced Prosthodontics
• /
• v.16 no.1
• /
• pp.25-37
• /
• 2024

PURPOSE. To investigate the effect of sintering programs and surface treatments on surface properties, phase transformation and flexural strength of monolithic zirconia. MATERIALS AND METHODS. Zirconia specimens were sintered using three distinct sintering programs [classic (C), speed (S), and superspeed (SS)] (n = 56, each). One sample from each group underwent scanning electron microscopy (SEM) and grain size analysis following sintering. Remaining samples were divided into five subgroups (n = 11) based on the surface treatments: control (CL), polish (P), glaze (G), grind + polish (GP), and grind + glaze (GG). One sample from each subgroup underwent SEM analysis. Remaining samples were thermally aged. Monoclinic phase volume, surface roughness, and three-point flexural strength were measured. Monoclinic phase volume and surface roughness were analyzed by Kruskal-Wallis and Dunn tests. Flexural strength was analyzed by two-way ANOVA and Weibull analysis. The relationships among the groups were analyzed using Spearman's correlation analysis. RESULTS. Sintering program, surface treatment, and sintering × surface treatment (P ≤ .010) affected the monoclinic phase volume, whereas the type of surface treatment and sintering × surface treatment affected the surface roughness (P < .001). Type of sintering program or surface treatment did not affect the flexural strength. Weibull analysis revealed no significant differences between the m and σ_o values. Monoclinic phase volume was positively correlated with surface roughness in the SGG and SSP groups. CONCLUSION. After sintering monolithic zirconia in each of the three sintering programs, each of the surface treatments can be used. However, for surface quality and aging resistance, G or GG can be recommended as a surface finishing method.

• Computers and Concrete
• /
• v.19 no.3
• /
• pp.275-282
• /
• 2017

The aim of this study is to build Machine Learning models to evaluate the effect of blast furnace slag (BFS) and waste tire rubber powder (WTRP) on the compressive strength of cement mortars. In order to develop these models, 12 different mixes with 288 specimens of the 2, 7, 28, and 90 days compressive strength experimental results of cement mortars containing BFS, WTRP and BFS+WTRP were used in training and testing by Random Forest, Ada Boost, SVM and Bayes classifier machine learning models, which implement standard cement tests. The machine learning models were trained with 288 data that acquired from experimental results. The models had four input parameters that cover the amount of Portland cement, BFS, WTRP and sample ages. Furthermore, it had one output parameter which is compressive strength of cement mortars. Experimental observations from compressive strength tests were compared with predictions of machine learning methods. In order to do predictive experimentation, we exploit R programming language and corresponding packages. During experimentation on the dataset, Random Forest, Ada Boost and SVM models have produced notable good outputs with higher coefficients of determination of R2, RMS and MAPE. Among the machine learning algorithms, Ada Boost presented the best R2, RMS and MAPE values, which are 0.9831, 5.2425 and 0.1105, respectively. As a result, in the model, the testing results indicated that experimental data can be estimated to a notable close extent by the model.

• Restorative Dentistry and Endodontics
• /
• v.48 no.4
• /
• pp.36.1-36.15
• /
• 2023

Objectives: This study aimed to compare the effectiveness of a single-file reciprocating system (WaveOne Gold, WOG) and a multi-file rotary system (ProTaper Universal Retreatment, PTUR) in removing canal filling from severely curved canals and to evaluate the possible adjunctive effects of XP-Endo Finisher (XPF), the Self-Adjusting File (SAF), and an erbium, chromium: yttrium, scandium, gallium garnet (Er,Cr:YSGG) laser using microcomputed tomography (µCT). Materials and Methods: Sixty-six curved mandibular molars were divided into 2 groups based on the retreatment technique and then into 3 based on the supplementary method. The residual filling volumes and root canals were evaluated with µCT before and after retreatment, and after the supplementary steps. The data were statistically analyzed with the t-test, Mann-Whitney U test, analysis of covariance, and factorial analysis of variance (p < 0.05). Results: PTUR and WOG showed no significant difference in removing filling materials (p > 0.05). The supplementary techniques were significantly more effective than reciprocating or rotary systems only (p < 0.01). The supplementary steps showed no significant differences in canal filling removal effectiveness (p > 0.05), but XPF showed less dentin reduction than the SAF and Er,Cr:YSGG laser (p < 0.01). Conclusions: The supplementary methods significantly decreased the volume of residual filling materials. XPF caused minimal changes in root canal volume and might be preferred for retreatment in curved root canals. Supplementary approaches after retreatment procedures may improve root canal cleanliness.