Evaluation of the mechanical behaviour of restoration dental materials is essential to understand their performance under different load conditions and to estimate their durability under clinical oral function. Restorative materials and dental tissues like other materials by having specific mechanical properties, such as static strength (i.e. compressive strength, tensile strength, flexural strength) and dynamic strength (i.e. fatigue strength). The selection of proper mechanical test type depends on the goals that the study claims to define. On such basis, the mechanical test can be chosen correctly. Laboratory studies should be designed as replications of the clinical oral circumstances to measure the mechanical and physical properties of a material and any arbitrary choices in the design of the study may result in large variations of data.
In the last decade, there has been an increase in research on ecologically benign, cost-effective, and socially useful cement alternative materials for concrete. Alternatives involve industrial and agriculture waste, the potential advantages of which may be recognized by recycling, repurposing, and recreating techniques. Important energy reserves and a decrease in Portland cement (PC) consumption may be attained by using these wastes as supplementary and substitute ingredients, contributing to a reduction in carbon dioxide (CO2) production. Consequently, the incorporation of marble dust powder (MDP) and calcined clay (CC) as supplementary cementitious material (SCM) in high strength concrete may lower the environmental effect and reduce the amount of PC in mixes. This study is conducted on concrete containing 0%, 5%, 10%, 15%, and 20% of MDP and CC as cementitious materials alone and in combination. The main objectives of this investigations are to examine the effect of MDP and CC as cementitious materials on the flowability and mechanical characteristics of high strength concrete. In order to examine the ecological effect of CC and MDP, the eco-strength efficiency and embodied carbon were considered. In this context, there are so many trial mixes were performed on cubical specimens for achieving targeted compressive strength about 60 MPa after 28 days. After getting it, a total of 273 concrete specimens (cubes, cylinders, and prisms) were used to test the compressive, splitting tensile, and flexural strength of high strength concrete correspondingly. Moreover, when the amount of MDP and CC as SCM in the mixture grows, the workability of green concrete decreases. In addition, the compressive strength, flexural strength, and splitting tensile strength are increased by 6.38 MPa, 67.66 MPa, and 4.88 MPa, respectively, at 10% SCM (5% MDP and 5% CC) over a period of 28 days. In addition, using ANOVA, response prediction models were generated and confirmed at a 95% level of significance. The R2 values of the models varied from 96 to 99%. Furthermore, increasing the amount of CC and MDP as SCM in concrete also reduces the amount of carbon embedded in the material. It is recommended that the utilization of 10% SCM (5% MDP and 5% CC) in high strength concrete is providing optimum outcomes for construction industry in the field of Civil Engineering.
A new logistic model tree (LMT) model is developed to predict slope stability status based on an updated database including 627 slope stability cases with input parameters of unit weight, cohesion, angle of internal friction, slope angle, slope height and pore pressure ratio. The performance of the LMT model was assessed using statistical metrics, including accuracy (Acc), Matthews correlation coefficient (Mcc), area under the receiver operating characteristic curve (AUC) and F-score. The analysis of the Acc together with Mcc, AUC and F-score values for the slope stability suggests that the proposed LMT achieved better prediction results (Acc = 85.6%, Mcc = 0.713, AUC = 0.907, F-score for stable state = 0.967 and F-score for failed state = 0.923) as compared to other methods previously employed in the literature. Two case studies with ten slope stability events were used to verify the proposed LMT. It was found that the prediction results are completely consistent with the actual situation at the site. Finally, risk analysis was carried out, and the result also agrees with the actual conditions. Such probability results can be incorporated into risk analysis with the corresponding failure cost assessment later.
The use of supplementary cementitious materials has been widely accepted due to increasing global carbon emissions resulting from demand and the consequent production of Portland cement. Moreover, researchers are also working on complementing the strength deficiencies of concrete; fiber reinforcement is one of those techniques. This study aims to assess the influence of recycling wheat straw ash (WSA) as cement replacement material and coir/coconut fibers (CF) as reinforcement ingredients together on the mechanical properties, permeability and embodied carbon of concrete. A total of 255 concrete samples were prepared with 1:1.5:3 mix proportions at 0.52 water-cement ratio and these all-concrete specimens were cured for 28 days. It was revealed that the addition of 10 % WSA and 2 % CF in concrete were recorded the compressive, splitting tensile and flexural strengths by 33 MPa, 3.55 MPa and 5.16 MPa which is greater than control mix concrete at 28 days respectively. Moreover, it was also observed that the permeability of concrete incorporating 4 % of coir fiber and 20 % of WSA was reduced by 63.40 % than that of the control mix after 28 days which can prevent the propagation of major and minor cracks. In addition, the embodied carbon of concrete is getting reduced when the replacement level of cement with WSA along with CF increases in concrete. Furthermore, based on the results obtained, the optimum amount of WSA was suggested to be 10 % and that of coir fiber reinforcement was suggested to be 2 % for improved results.
The present research employs new boosting-based ensemble machine learning models i.e., gradient boosting (GB) and adaptive boosting (AdaBoost) to predict the unconfined compressive strength (UCS) of geopolymer stabilized clayey soil. The GB and AdaBoost models were developed and validated using 270 clayey soil samples stabilized with geopolymer, with ground-granulated blast-furnace slag and fly ash as source materials and sodium hydroxide solution as alkali activator. The database was randomly divided into training (80%) and testing (20%) sets for model development and validation. Several performance metrics, including coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and mean squared error (MSE), were utilized to assess the accuracy and reliability of the developed models. The statistical results of this research showed that the GB and AdaBoost are reliable models based on the obtained values of R2 (= 0.980, 0.975), MAE (= 0.585, 0.655), RMSE (= 0.969, 1.088), and MSE (= 0.940, 1.185) for the testing dataset, respectively compared to the widely used artificial neural network, random forest, extreme gradient boosting, multivariable regression, and multi-gen genetic programming based models. Furthermore, the sensitivity analysis result shows that ground-granulated blast-furnace slag content was the key parameter affecting the UCS.
Oral drug delivery is natural, most acceptable and desirable route for nearly all drugs, but many drugs like NSAIDs when delivered by this route cause gastrointestinal irritation, gastric bleeding, ulcers, and many undesirable effects which limits their usage by oral delivery. Moreover, it is almost impossible to control the release of a drug in a targeted location in body. We developed thermo-responsive chitosan-co-poly(N-isopropyl-acrylamide) injectable hydrogel as an alternative for the gastro-protective and controlled delivery of loxoprofen sodium as a model drug. A free radical polymerization technique was used to synthesize thermo-responsive hydrogel by cross-linking chitosan HCl with NIPAAM using glutaraldehyde as cross-linker. Confirmation of crosslinked hydrogel structure was done by Fourier transform infrared spectra (FTIR). The thermal stability of hydrogel was confirmed through thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC). The scanning electron microscopy (SEM) was performed to evaluate the structural morphology of cross-linked hydrogel. To evaluate the rheological behavior of hydrogel with increasing temperature, rheological study was performed. Swelling and in vitro drug release studies were carried out under various temperature and pH conditions. The swelling study revealed that maximum swelling was observed at low pH (pH 1.2) and low temperature (25 °C) compared to the high range of pH and temperature and it resulted in quick release of the drug. The high range of pH (7.4) and temperature (37 °C) however caused controlled release of the drug. The in vivo evaluation of the developed hydrogel in rabbits demonstrated the controlled release behavior of fabricated system.
The California bearing ratio (CBR) is one of the basic subgrade strength characterization properties in road pavement design for evaluating the bearing capacity of pavement subgrade materials. In this research, a new model based on the Gaussian process regression (GPR) computing technique was trained and developed to predict CBR value of hydrated lime-activated rice husk ash (HARHA) treated soil. An experimental database containing 121 data points have been used. The dataset contains input parameters namely HARHA-a hybrid geometrical binder, liquid limit, plastic limit, plastic index, optimum moisture content, activity and maximum dry density while the output parameter for the model is CBR. The performance of the GPR model is assessed using statistical parameters, including the coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), Relative Root Mean Square Error (RRMSE), and performance indicator (ρ). The obtained results through GPR model yield higher accuracy as compare to recently establish artificial neural network (ANN) and gene expression programming (GEP) models in the literature. The analysis of the R2 together with MAE, RMSE, RRMSE, and ρ values for the CBR demonstrates that the GPR achieved a better prediction performance in training phase with (R2 = 0.9999, MAE = 0.0920, RMSE = 0.13907, RRMSE = 0.0078 and ρ = 0.00391) succeeded by the ANN model with (R2 = 0.9998, MAE = 0.0962, RMSE = 4.98, RRMSE = 0.20, and ρ = 0.100) and GEP model with (R2 = 0.9972, MAE = 0.5, RMSE = 4.94, RRMSE = 0.202, and ρ = 0.101). Furthermore, the sensitivity analysis result shows that HARHA was the key parameter affecting the CBR.
The resilient modulus (MR) of different pavement materials is one of the most important input parameters for the mechanistic-empirical pavement design approach. The dynamic triaxial test is the most often used method for evaluating the MR, although it is expensive, time-consuming, and requires specialized lab facilities. The purpose of this study is to establish a new model based on Long Short-Term Memory (LSTM) networks for predicting the MR of stabilized base materials with various additives during wet-dry cycles (WDC). A laboratory dataset of 704 records has been used using input parameters, including WDC, ratio of calcium oxide to silica, alumina, and ferric oxide compound, Maximum dry density to the optimal moisture content ratio (DMR), deviator stress (σd), and confining stress (σ3). The results demonstrate that the LSTM technique is very accurate, with coefficients of determination of 0.995 and 0.980 for the training and testing datasets, respectively. The LSTM model outperforms other developed models, such as support vector regression and least squares approaches, in the literature. A sensitivity analysis study has determined that the DMR parameter is the most significant factor, while the σd parameter is the least significant factor in predicting the MR of the stabilized base material under WDC. Furthermore, the SHapley Additive exPlanations approach is employed to elucidate the optimal model and examine the impact of its features on the final result.