• Physical Therapy Korea
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• v.18 no.3
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• pp.8-15
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• 2011

Abdominal muscle plays a crucial role in postural control and respiration control. However, thickness of abdominal muscle in the paretic side of a hemiplegic patient has not been reported in previous studies. The purpose of this research was to compare lateral abdominal muscle thickness between the nonparetic and paretic side in patients with chronic stroke using rehabilitative ultrasound imaging. Twenty two patients with chronic stroke participated in this study. Absolute thickness of transversus abdominis (TrA), internal oblique (IO) and external oblique (EO) was measured at the end of inspiration and expiration during quiet breathing, and relative thickness was calculated (thickness of each muscle as a percentage of total muscle thickness). Ultrasound imaging was recorded three times and the average value was determined for statistical analysis. Differences in absolute and relative lateral abdominal muscle thickness between the nonparetic and paretic side were assessed with paired t-tests. Absolute muscle thickness of the paretic side TrA was thinner than that of the nonparetic side at the end of inspiration and expiration during quiet breathing. Relative muscle thickness of the paretic side TrA was thinner than the paretic side only at the end of expiration during quiet breathing (p>.05). Therefore, it is necessary to strength TrA in patients with chronic stroke during physical therapy intervention. Further study is needed whether physical therapy intervension will induce TrA thickness in patients with chronic stroke in prospective study design.

• Earthquakes and Structures
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• v.5 no.4
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• pp.439-459
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• 2013

Main objective of the present study is to determine the statistical properties and suitable probability distribution functions of spectral displacements from nonlinear static and nonlinear dynamic analysis within the frame work of Monte Carlo simulation for typical low rise and high rise RC framed buildings located in zone III and zone V and designed as per Indian seismic codes. Probabilistic analysis of spectral displacement is useful for strength assessment and loss estimation. To the author's knowledge, no study is reported in literature on comparison of spectral displacement including the uncertainties in capacity and demand in Indian context. In the present study, uncertainties in capacity of the building is modeled by choosing cross sectional dimensions of beams and columns, density and compressive strength of concrete, yield strength and elastic modulus of steel and, live load as random variables. Uncertainty in demand is modeled by choosing peak ground acceleration (PGA) as a random variable. Nonlinear static analysis (NSA) and nonlinear dynamic analysis (NDA) are carried out for typical low rise and high rise reinforced concrete framed buildings using IDARC 2D computer program with the random sample input parameters. Statistical properties are obtained for spectral displacements corresponding to performance point from NSA and maximum absolute roof displacement from NDA and suitable probability distribution functions viz., normal, Weibull, lognormal are examined for goodness-of-fit. From the hypothesis test for goodness-of-fit, lognormal function is found to be suitable to represent the statistical variation of spectral displacement obtained from NSA and NDA.

• The korean journal of orthodontics
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• v.42 no.1
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• pp.4-10
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• 2012

Objective: To determine the effect of surface anodization on the interfacial strength between an orthodontic microimplant (MI) and the rabbit tibial bone, particularly in the initial phase aft er placement. Methods: A total of 36 MIs were driven into the tibias of 3 mature rabbits by using the self-drilling method and then removed aft er 6 weeks. Half the MIs were as-machined (n = 18; machined group), while the remaining had anodized surfaces (n = 18; anodized group). The peak insertion torque (PIT) and the peak removal torque (PRT) values were measured for the 2 groups of MIs. These values were then used to calculate the interfacial shear strength between the MI and cortical bone. Results: There were no statistical differences in terms of PIT between the 2 groups. However, mean PRT was significantly greater for the anodized implants ($3.79{\pm}1.39$ Ncm) than for the machined ones ($2.05{\pm}1.07$ Ncm) (p < 0.01). The interfacial strengths, converted from PRT, were calculated at 10.6 MPa and 5.74 MPa for the anodized and machined group implants, respectively. Conclusions: Anodization of orthodontic MIs may enhance their early-phase retention capability, thereby ensuring a more reliable source of absolute anchorage.

• Steel and Composite Structures
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• v.39 no.1
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• pp.81-93
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• 2021

Splitting tensile strength (STS) is an important mechanical parameter of concrete. This study offers novel methodologies for the early prediction of this parameter. Artificial neural network (ANN), which is a leading predictive method, is synthesized with two metaheuristic algorithms, namely atom search optimization (ASO) and equilibrium optimizer (EO) to achieve an optimal tuning of the weights and biases. The models are applied to data collected from the published literature. The sensitivity of the ASO and EO to the population size is first investigated, and then, proper configurations of the ASO-NN and EO-NN are compared to the conventional ANN. Evaluating the prediction results revealed the excellent efficiency of EO in optimizing the ANN. Accuracy improvements attained by this algorithm were 13.26 and 11.41% in terms of root mean square error and mean absolute error, respectively. Moreover, it raised the correlation from 0.89958 to 0.92722. This is while the results of the conventional ANN were slightly better than ASO-NN. The EO was also a faster optimizer than ASO. Based on these findings, the combination of the ANN and EO can be an efficient non-destructive tool for predicting the STS.

• Computers and Concrete
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• v.28 no.6
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• pp.635-642
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• 2021

In this paper, soft learning techniques are used to predict the ultimate torsional capacity of Reinforced Concrete beams strengthened with Fiber Reinforced Polymer. Soft computing techniques, namely Artificial Neural Network, trained by various back propagation algorithms, and Particle Swarm Optimization (PSO) algorithm, have been used to model and predict the torsional strength of Reinforced Concrete beams strengthened with Fiber Reinforced Polymer. The performance of each model has been evaluated by using statistical parameters such as coefficient of determination (R²), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The hybrid PSO NN model resulted in an R² of 0.9292 with an RMSE of 5.35 for training and an R² of 0.9328 with an RMSE of 4.57 for testing. Another model, ANN BP, produced an R² of 0.9125 with an RMSE of 6.17 for training and an R² of 0.8951 with an RMSE of 5.79 for testing. The results of the PSO NN model were in close agreement with the experimental values. Thus, the PSO NN model can be used to predict the ultimate torsional capacity of RC beams strengthened with FRP with greater acceptable accuracy.

• Journal of Korean Association for Spatial Structures
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• v.23 no.4
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• pp.81-88
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• 2023

Numerous factors contribute to the deterioration of reinforced concrete structures. Elevated temperatures significantly alter the composition of the concrete ingredients, consequently diminishing the concrete's strength properties. With the escalation of global CO2 levels, the carbonation of concrete structures has emerged as a critical challenge, substantially affecting concrete durability research. Assessing and predicting concrete degradation due to thermal effects and carbonation are crucial yet intricate tasks. To address this, multiple prediction models for concrete carbonation and compressive strength under thermal impact have been developed. This study employs seven machine learning algorithms-specifically, multiple linear regression, decision trees, random forest, support vector machines, k-nearest neighbors, artificial neural networks, and extreme gradient boosting algorithms-to formulate predictive models for concrete carbonation and thermal impact. Two distinct datasets, derived from reported experimental studies, were utilized for training these predictive models. Performance evaluation relied on metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analytical outcomes demonstrate that neural networks and extreme gradient boosting algorithms outshine the remaining five machine learning approaches, showcasing outstanding predictive performance for concrete carbonation and thermal effect modeling.

• Computers and Concrete
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• v.27 no.4
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• pp.333-341
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• 2021

Steel slag, an industrial reject from the steel rolling process, has been identified as one of the suitable, environmentally friendly materials for concrete production. Given that the coarse aggregate portion represents about 70% of concrete constituents, other economic approaches have been found in the use of alternative materials such as steel slag in concrete. Unfortunately, a standard framework for its application is still lacking. Therefore, this study proposed functional model equations for the determination of strength properties (compression and splitting tensile) of steel slag aggregate concrete (SSAC), using gene expression programming (GEP). The study, in the experimental phase, utilized steel slag as a partial replacement of crushed rock, in steps 20%, 40%, 60%, 80%, and 100%, respectively. The predictor variables included in the analysis were cement, sand, granite, steel slag, water/cement ratio, and curing regime (age). For the model development, 60-75% of the dataset was used as the training set, while the remaining data was used for testing the model. Empirical results illustrate that steel aggregate could be used up to 100% replacement of conventional aggregate, while also yielding comparable results as the latter. The GEP-based functional relations were tested statistically. The minimum absolute percentage error (MAPE), and root mean square error (RMSE) for compressive strength are 6.9 and 1.4, and 12.52 and 0.91 for the train and test datasets, respectively. With the consistency of both the training and testing datasets, the model has shown a strong capacity to predict the strength properties of SSAC. The results showed that the proposed model equations are reliably suitable for estimating SSAC strength properties. The GEP-based formula is relatively simple and useful for pre-design applications.

• Computers and Concrete
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• v.31 no.2
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• pp.111-121
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• 2023

The influence of material composition such as aggregate types, addition of supplementary cementitious materials as well as exposed temperature levels have significant impacts on concrete residual mechanical strength properties when exposed to elevated temperature. This study is based on data obtained from literature for fly ash blended concrete produced with natural and recycled concrete aggregates to efficiently develop prediction models for estimating its residual compressive strength after exposure to high temperatures. To achieve this, an extensive database that contains different mix proportions of fly ash blended concrete was gathered from published articles. The specific design variables considered were percentage replacement level of Recycled Concrete Aggregate (RCA) in the mix, fly ash content (FA), Water to Binder Ratio (W/B), and exposed Temperature level. Thereafter, a simplified mathematical equation for the prediction of concrete's residual compressive strength using Gene Expression Programming (GEP) was developed. The relative importance of each variable on the model outputs was also determined through global sensitivity analysis. The GEP model performance was validated using different statistical fitness formulas including R², MSE, RMSE, RAE, and MAE in which high R² values above 0.9 are obtained in both the training and validation phase. The low measured errors (e.g., mean square error and mean absolute error are in the range of 0.0160 - 0.0327 and 0.0912 - 0.1281 MPa, respectively) in the developed model also indicate high efficiency and accuracy of the model in predicting the residual compressive strength of fly ash blended concrete exposed to elevated temperatures.

• Smart Structures and Systems
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• v.25 no.2
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• pp.183-195
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• 2020

Mineral admixtures have been widely used to produce concrete. Pozzolans have been utilized as partially replacement for Portland cement or blended cement in concrete based on the materials' properties and the concrete's desired effects. Several environmental problems associated with producing cement have led to partial replacement of cement with other pozzolans. Furnace slag and fly ash are two of the pozzolans which can be appropriately used as partial replacements for cement in concrete. However, replacing cement with these materials results in significant changes in the mechanical properties of concrete, more specifically, compressive strength. This paper aims to intelligently predict the compressive strength of concretes incorporating furnace slag and fly ash as partial replacements for cement. For this purpose, a database containing 1030 data sets with nine inputs (concrete mix design and age of concrete) and one output (the compressive strength) was collected. Instead of absolute values of inputs, their proportions were used. A hybrid artificial neural network-genetic algorithm (ANN-GA) was employed as a novel approach to conducting the study. The performance of the ANN-GA model is evaluated by another artificial neural network (ANN), which was developed and tuned via a conventional backpropagation (BP) algorithm. Results showed that not only an ANN-GA model can be developed and appropriately used for the compressive strength prediction of concrete but also it can lead to superior results in comparison with an ANN-BP model.

• The Journal of Korean Academy of Prosthodontics
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• v.39 no.6
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• pp.641-658
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• 2001

All-ceramic restorations have become an attractive alternative to porcelain-fused-to-metal crowns. In-Ceram, and more recently IPS Empress 2 were introduced as a new all-ceramic system for single crowns and 3-unit fixed partial dentures. But their strength and marginal fit are still an important issue. This study evaluated the fracture resistance and marginal fit of three systems of 3 unit all-ceramic bridge fabricated on prepared maxillary anterior resin teeth in vitro. The 3 all-ceramic bridge systems were: (1) a glass-infiltrated, sintered alumina system (In-Ceram) fabricated conventionally, (2) the same system with copy-milled alumina cores (copy-milled In-Ceram), (3) a heat pressed, lithium disilicate reinforced glass-ceramic system (IPS Empress 2). Ten bridges of each system with standardized design of framework were fabricated. All specimens of each system were compressed at $55^{\circ}$ at the palatal surface of pontic until catastrophic fracture occurred. Another seven bridges of each system were fabricated with standard method. All of the bridge-die complexes were embedded in epoxy resin and sectioned buccolingually and mesiodistally. The absolute marginal discrepancy was measured with stereomicroscope at ${\times}50$ power. The following results were obtained: 1. There was no significant difference in the fracture strength among the 3 systems studied. 2. The Weibull modulus of copy-milled In-Ceram was higher than that of In-Ceram and IPS Empress 2 bridges. 3. Copy-milled In-Ceram($112{\mu}m$) exhibited significantly greater marginal discrepancy than In Ceram ($97{\mu}m$), and IPS Empress 2 ($94{\mu}m$) at P=0.05. 4. The lingual surfaces of the ceramic crowns showed smaller marginal discrepancies than mesial and distal points. There was no significant difference between teeth (incisor, canine) at P=0.05. 5. All-ceramic bridges of three systems appeared to exhibit sufficient initial strength and accept able marginal fit values to allow clinical application.