Experimental data on the development of "Post-installed Screw Pullout test" for in-situ investigation of concrete strength.
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ABSTRACT:
SUBMITTER: Sourav SNA
PROVIDER: S-EPMC7475196 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:A biomechanical study of pedicle-screw pullout strength.To develop a decision tree based on pullout strength for evaluating pedicle-screw instrumentation.Clinically, a surgeon's understanding of the holding power of a pedicle screw is based on perioperative intuition (which is like insertion torque) while inserting the screw. This is a subjective feeling that depends on the skill and experience of the surgeon. With the advent of robotic surgery, there is an urgent need for the creation of a patient-specific surgical planning system. A learning-based predictive model is needed to understand the sensitivity of pedicle-screw holding power to various factors.Pullout studies were carried out on rigid polyurethane foam, representing extremely osteoporotic to normal bone for different insertion depths and angles of a pedicle screw. The results of these experimental studies were used to build a pullout-strength predictor and a decision tree using a machine-learning approach.Based on analysis of variance, it was found that all the factors under study had a significant effect (p <0.05) on the holding power of a pedicle screw. Of the various machine-learning techniques, the random forest regression model performed well in predicting the pullout strength and in creating a decision tree. Performance was evaluated, and a correlation coefficient of 0.99 was obtained between the observed and predicted values. The mean and standard deviation of the normalized predicted pullout strength for the confirmation experiment using the current model was 1.01±0.04.The random forest regression model was used to build a pullout-strength predictor and decision tree. The model was able to predict the holding power of a pedicle screw for any combination of density, insertion depth, and insertion angle for the chosen range. The decision-tree model can be applied in patient-specific surgical planning and a decision-support system for spine-fusion surgery.
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Project description:Masonry structures are widely used nowadays for their advantages like low-cost workmanship, efficiency and fast construction techniques. The compressive strength of the materials that compose masonry (block and mortar) is very important to the behavior of the system, but the tensile strength is even more significant for the standards and building codes. In this work, a dataset for indirect tensile tests of hollow concrete blocks is obtained. Splitting tests as described in ASTM C-1006-13 are applied. Two sets of blocks were tested, one with medium compressive strength and the other with high compressive strength. The first set was tested in three directions named A, B, and C; the second one was tested in two directions, A and B. The data was collected with a servo-hydraulic machine. The data is presented in tables and can be used by material researchers, as well as in numerical modeling.
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Project description:ObjectiveTo develop a novel semi-cannulated lateral mass screw (SC-LMS) for cervical posterior fixations and compare the fixation stability and safety of SC-LMS with regular solid lateral mass screw (S-LMS) in bone cement augmentation and pullout strength using fresh cadaveric cervical vertebrae.MethodsThe conventional multiaxial screw for cervical lateral mass fixation was modified to a cannulated screw with two lateral holes, used for bone cement injection in situ. Eight fresh human cervical vertebrae (C3, C4, and C5) were collected and used. μCT scan was performed to evaluate the bone quality of the lateral masses, including bone mineral density (BMD), trabecular thickness (Tb.Th), and trabecular separation (Tb.Sp). SCLMS or S-LMS were randomly inserted into the paired cervical vertebrae and pulled out as a screw loosening model. These screws were reinserted in with bone cement augmentation, scanned by μCT to obtain the bone cement distribution along the screws, and pulled out to test the screw purchase strength.ResultsFmax values exhibited strong positive correlations with the local BMD (𝑟 = 0.8640, p < 0.0001) and Tb.Th (𝑟 = 0.6795, p = 0.0038), whereas a negative correlation with Tb.Sp (𝑟 = -0.5567, p = 0.0251). A significant difference was observed between the Fmax before and after PMMA injection on the SC-LMS side (p = 0.019). The SC-LMS exhibited lower risk of cement leakage than S-LMS after PMMA injection, and a positive correlation was observed between 𝐹max and the distribution volumes on the SC-LMS side.ConclusionThe novel SC-LMS provides more robust fixation stability and is safer than the S-LMS for PMMA augmentation, which may be related to the cement-screw-cement-bone complex formation.
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Project description:Study Design:A biomechanical study. Purpose:To develop a predictive model for pullout strength. Overview of Literature:Spine fusion surgeries are performed to correct joint deformities by restricting motion between two or more unstable vertebrae. The pedicle screw provides a corrective force to the unstable spinal segment and arrests motions at the unit that are being fused. To determine the hold of a screw, surgeons depend on a subjective perioperative feeling of insertion torque. The objective of the paper was to develop a machine learning based model using density of foam, insertion angle, insertion depth, and reinsertion to predict the pullout strength of pedicle screw. Methods:To predict the pullout strength of pedicle screw, an experimental dataset of 48 data points was used as training data to construct a model based on different machine learning algorithms. A total of five algorithms were tested in the Weka environment and the performance was evaluated based on correlation coefficient and error matrix. A sensitive study of various parameters for obtaining the best combination of parameters for predicting the pullout strength was also preformed using the L9 orthogonal array of Taguchi Design of Experiments. Results:Random forest performed the best with a correlation coefficient of 0.96, relative absolute error of 0.28, and root relative squared error of 0.29. The difference between the experimental and predicted value for the six test cases was not significant (p >0.05). Conclusions:This model can be used clinically for understanding the failure of pedicle screw pullout and pre-surgical planning for spine surgeon.
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Project description:An extensive simulation program is used in this study to discover the best ANN model for predicting the compressive strength of concrete containing Ground Granulated Blast Furnace Slag (GGBFS). To accomplish this purpose, an experimental database of 595 samples is compiled from the literature and utilized to find the best ANN architecture. The cement content, water content, coarse aggregate content, fine aggregate content, GGBFS content, carboxylic type hyper plasticizing content, superplasticizer content, and testing age are the eight inputs in this database. As a result, the optimal selection of the ANN design is carried out and evaluated using conventional statistical metrics. The results demonstrate that utilizing the best architecture [8-14-4-1] among the 240 investigated architectures, and the best ANN model, is a very efficient predictor of the compressive strength of concrete using GGBFS, with a maximum R2 value of 0.968 on the training part and 0.965 on the testing part. Furthermore, a sensitivity analysis is performed over 500 Monte Carlo simulations using the best ANN model to determine the reliability of ANN model in predicting the compressive strength of concrete. The findings of this research may make it easier and more efficient to apply the ANN model to many civil engineering challenges.
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Project description:Many practical aspects of processing fresh concrete depend on its rheology, such as the pumping of the material. It is known that a lubricating layer is formed in the process, which significantly reduces the pumping pressure. However, these phenomena can hardly be considered in the usual rheological measurements. A main problem is the optical inaccessibility of the material, which prevents estimations about, e.g., the thickness of the plug flow or particle migration. In this paper, the pneumatic pumping of a transparent model concrete is performed by means of a test plant. The flow profile over the entire pipe cross-section is resolved in time and space via Particle Image Velocimetry (PIV) measurements. This allows the comparison with the analytical flow profile from rheological measurements of the material using the Buckingham-Reiner equation. A reduction of the pressure loss to around 60% induced through segregation of the material is found. These measurements reflect the rheology of the material under realistic pumping conditions including particle migration. This makes it possible for the first time to observe a transparent material with concrete-like rheology under pulsating pumping conditions and to compare the true and calculated time-resolved pressure loss.
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Project description:This study aims to investigate the influence of all the mixture components of high-performance concrete (HPC) on its early compressive strength, ranging from 1 to 14 days. To this purpose, a Gaussian Process Regression (GPR) algorithm was first constructed using a database gathered from the available literature. The database included the contents of cement, blast furnace slag (BFS), fly ash (FA), water, superplasticizer, coarse, fine aggregates, and testing age as input variables to predict the output of the problem, which was the early compressive strength. Several standard statistical criteria, such as the Pearson correlation coefficient, root mean square error and mean absolute error, were used to quantify the performance of the GPR model. To analyze the sensitivity and influence of the HPC mixture components, partial dependence plots analysis was conducted with both one-dimensional and two-dimensional. Firstly, the results showed that the GPR performed well in predicting the early strength of HPC. Second, it was determined that the cement content and testing age of HPC were the most sensitive and significant elements affecting the early strength of HPC, followed by the BFS, water, superplasticizer, FA, fine aggregate, and coarse aggregate contents. To put it simply, this research might assist engineers select the appropriate amount of mixture components in the HPC production process to obtain the necessary early compressive strength.
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Project description:In this paper, an extensive simulation program is conducted to find out the optimal ANN model to predict the shear strength of fiber-reinforced polymer (FRP) concrete beams containing both flexural and shear reinforcements. For acquiring this purpose, an experimental database containing 125 samples is collected from the literature and used to find the best architecture of ANN. In this database, the input variables consist of 9 inputs, such as the ratio of the beam width, the effective depth, the shear span to the effective depth, the compressive strength of concrete, the longitudinal FRP reinforcement ratio, the modulus of elasticity of longitudinal FRP reinforcement, the FRP shear reinforcement ratio, the tensile strength of FRP shear reinforcement, the modulus of elasticity of FRP shear reinforcement. Thereafter, the selection of the appropriate architecture of ANN model is performed and evaluated by common statistical measurements. The results show that the optimal ANN model is a highly efficient predictor of the shear strength of FRP concrete beams with a maximum R2 value of 0.9634 on the training part and an R2 of 0.9577 on the testing part, using the best architecture. In addition, a sensitivity analysis using the optimal ANN model over 500 Monte Carlo simulations is performed to interpret the influence of reinforcement type on the stability and accuracy of ANN model in predicting shear strength. The results of this investigation could facilitate and enhance the use of ANN model in different real-world problems in the field of civil engineering.
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Project description:A biomechanical study.A new biomechanical model of the vertebra has been developed that accounts for the inhomogeneity of bone and the contribution of the pedicle toward the holding strength of a pedicle screw.Pullout strength studies are typically carried out on rigid polyurethane foams that represent the homogeneous vertebral framework of the spine. However, the contribution of the pedicle region, which contributes to the inhomogeneity in this framework, has not been considered in previous investigations. Therefore, we propose a new biomechanical model that can account for the vertebral inhomogeneity, especially the contribution of the pedicles toward the pullout strength of the pedicle screw.A bilayer foam model was developed by joining two foams representing the pedicle and the vertebra. The results of the pullout strength tests performed on the foam models were compared with those from the tests performed on the cadaver lumbar vertebra.Significant differences (p <0.05) were observed between the pullout strength of the pedicle screw in extremely osteoporotic (0.18±0.11 kN), osteoporotic (0.37±0.14 kN), and normal (0.97±0.4 kN) cadaver vertebra. In the monolayer model, significant differences (p <0.05) were observed in pullout strength between extremely osteoporotic (0.3±0.02 kN), osteoporotic (0.65±0.12 kN), and normal (0.99±0.04 kN) bone model. However, the bilayer foam model exhibited no significant differences (p >0.05) in the pullout strength of pedicle screws between osteoporotic (0.85±0.08 kN) and extremely osteoporotic bone models (0.94±0.08 kN), but there was a significant difference (p <0.05) between osteoporotic (0.94±0.08 kN) and normal bone models (1.19±0.05 kN). There were no significant differences (p >0.05) in pullout strength between cadaver and bilayer foam model in normal bones.The new synthetic bone model that reflects the contribution of the pedicles to the pullout strength of the pedicle screws could provide a more efficacious means of testing pedicle-screw pullout strength. The bilayer model can match the pullout strength value of normal lumbar vertebra bone whereas the monolayer foam model was able to match that of the extremely osteoporotic lumbar vertebra.
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Project description:Owing to its importance in the assessment of reinforced concrete structures, it is essential to determine the chloride diffusivity of fly ash concrete. This paper presents an investigation into the diffusion characteristics of chloride ions in fly ash concrete. Through experiment, the relationship between chloride diffusivity and curing age up to 1800 days is measured and the effects of curing age, water/binder ratio, aggregate volume fraction, and fly ash content (i.e., percentage of total cementitious material by mass) on chloride diffusivity are evaluated. It is found that the chloride diffusivity decreases with the increase of curing age, aggregate volume fraction, and fly ash content, but increases with the increase of water/binder ratio. In analytical modeling, an equivalent aggregate model is constructed and the equivalent interfacial transition zone (ITZ) thickness is derived analytically. With the equivalent aggregate model, three-phase fly ash concrete reduces to a two-phase composite material. By extending the Maxwell method, the chloride diffusivity of fly ash concrete is formulated. Finally, the validity of the analytical method is verified by experimental results.
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