No description available.

The purpose of the study was to determine the accuracy of 11 prediction equations in estimating the 1 repetition maximum (1RM) bench press from repetitions completed by collegiate football players (N = 69) using 225 lb. The demographic variables race, age, height, weight, fat-free weight, and percent body fat were measured to determine whether these variables increased the accuracy of the prediction equations; race was the most frequently selected variable in the regression analyses. The validity of the prediction equations was dependent upon the number of repetitions performed, i.e., validity was higher when fewer repetitions were completed. Explained variability of 1RM was slightly higher for all 11 equations when demographic variables were included. A new prediction equation was also developed using the number of repetitions performed and the demographic variables height and fat-free weight.

The objective of this study was to investigate the accuracy of eleven prediction equations and one prediction table when estimating isoinertial knee extension and leg press one repetition maximum (1-RM) performance in subjects with knee injuries and knee osteoarthritis. Study Design: A descriptive quantitative research study was undertaken utilizing a cross-sectional design. Background: Traumatic injuries and osteoarthritis are common musculoskeletal pathologies that can disrupt normal function of the knee joint. A frequent sequela of these pathologies is quadriceps femoris muscle weakness. Such weakness can contribute to disability and diminished levels of functional and recreational activity. Therefore, safe and accurate methods of measuring maximal strength are required to identify and quantify quadriceps strength deficits. One option proposed in the literature is the use of 1-RM prediction equations which estimate 1-RM performance from the number of repetitions completed with sub-maximal loads. These equations have been investigated previously using healthy populations and subjects with calf muscle injuries. However, to date, no known study has investigated their accuracy in individuals with joint pathologies. Method: Machine-weight seated knee extension and seated leg press exercises were investigated in this study. Twenty subjects with knee injuries and 12 subjects with knee OA completed the testing procedures for the knee extension exercise. Nineteen subjects with knee injuries and 18 subjects with knee OA completed the testing procedures for the leg press exercise. All subjects attended the testing venue on three occasions. At the first visit a familiarization session was carried out. At the second and third visits each subject was randomly assigned to perform either actual or predicted 1-RM testing for both of the exercises. Twelve different prediction methods were used to estimate 1-RM performance from the results. The estimates of 1-RM strength were then compared to actual 1-RM performance to assess the level of conformity between these measures. Statistical procedures including Bland and Altman analyses, intraclass correlation coefficients, typical error and total error of measurement were used in the analyses of the results. In addition, paired t-tests were performed to determine whether actual 1-RM values were significantly different across the control and affected limbs and whether there were any significant differences in predictive accuracy for each equation across the control and affected limbs. Finally, the number of subjects with predicted 1-RM values within 5% or less of their actual 1-RM values was determined for each equation. Results: When the knee injury group performed the knee extension exercise, the Brown, Brzycki, Epley, Lander, Mayhew et al., Poliquin and Wathen prediction methods demonstrated the greatest levels of predictive accuracy. When two atypical subjects were identified and excluded from the analyses, the accuracy of these equations improved further. Following the removal of these two subjects, no significant differences in predictive accuracy were found for any of the equations across the affected and control limbs (p > 0.05). Typical errors and total errors were low for the more accurate prediction methods ranging from 2.4-2.8% and from 2.4-3.5%, respectively. Overall, the Poliquin table appeared to be the most accurate prediction method for this sample (affected limbs: bias 0.3 kg, 95% limits of agreement (LOA) -5.8 to 6.4 kg, typical error as a coefficient of variation (COV) 2.4%, total error of measurement (total error) 2.4%; control limbs: bias -1.3 kg, 95% LOA -9.0 to 6.3 kg, typical error as a COV 2.7%, total error 2.8%). When the knee OA group performed the knee extension exercise, the Brown, Brzycki, Epley, Lander, Mayhew et al., Poliquin and Wathen prediction methods demonstrated the greatest levels of predictive accuracy. No significant differences in predictive accuracy were found for any of the equations across the affected and control limbs (p > 0.05). When an atypical subject was identified and excluded from the analyses, the accuracy of the equations improved further. Typical errors as COVs and total errors for the more accurate prediction methods ranged from 2.5-2.7% and from 2.4-2.9%, respectively. Overall, the Poliquin table appeared to be the most accurate prediction method for this sample (affected limbs: bias 0.9 kg, 95% LOA -4.5 to 6.3 kg, typical error as a COV 2.5%, total error 2.5%; control limbs: bias -0.1 kg, 95% LOA -6.0 to 5.9 kg, typical error as a COV 2.5%, total error 2.4%). When the knee injury group performed the leg press, the Adams, Berger, Lombardi and O’Connor equations demonstrated the greatest levels of predictive accuracy. No significant differences in predictive accuracy were found for any of the equations across the affected and control limbs (p > 0.05). Typical errors as COVs and total errors for the more accurate equations ranged from 2.8-3.2% and from 2.9-3.3%, respectively. Overall, the Berger (affected limbs: bias -0.4 kg, 95% LOA -7.2 to 6.3 kg, typical error as a COV 3.2%, total error 3.2%; control limbs: bias 0.1 kg, 95% LOA -6.6 to 6.7 kg, typical error as a COV 3.1%, total error 3.0%) and O’Connor equations (affected limbs: bias -0.6 kg, 95% LOA-6.8 to 5.7 kg, typical error as a COV 2.9%, total error 3.0%; control limbs: bias -0.2 kg, 95% LOA -6.9 to 6.4 kg, typical error as a COV 2.9%, total error 2.9%) appeared to be the most accurate prediction methods for this sample. When the knee OA group performed the leg press, the Adams, Berger, KLW, Lombardi and O’Connor equations demonstrated the greatest levels of predictive accuracy. No significant differences in predictive accuracy were found for any of the equations across the affected and control limbs (p > 0.05). The typical errors as COVs and the total error values for the more accurate prediction methods were the highest observed in this study, ranging from 5.8-6.0% and from 5.7-6.2%, respectively. Overall, the Adams, Berger, KLW and O’Connor equations appeared to be the most accurate prediction methods for this sample. However, it is possible that the predicted leg press 1-RM values produced by the knee OA group might not have matched actual 1-RM values closely enough to be clinically acceptable for some purposes. Conclusion: The findings of the current study suggested that the Poliquin table produced the most accurate estimates of knee extension 1-RM performance for both the knee injury and knee OA groups. In contrast, the Berger and O’Connor equations produced the most accurate estimates of leg press 1-RM performance for the knee injury group, while the Adams, Berger, KLW and O’Connor equations produced the most accurate results for the knee OA group. However, the higher error values observed for the knee OA group suggested that predicted leg press 1-RM performance might not be accurate enough for some clinical purposes. Finally, it can be concluded that no single prediction equation was able to accurately estimate both knee extension and leg press 1-RM performance in subjects with knee injuries and knee OA.

The objective of this study was to investigate the accuracy of eleven prediction equations and one prediction table when estimating isoinertial knee extension and leg press one repetition maximum (1-RM) performance in subjects with knee injuries and knee osteoarthritis. Study Design: A descriptive quantitative research study was undertaken utilizing a cross-sectional design. Background: Traumatic injuries and osteoarthritis are common musculoskeletal pathologies that can disrupt normal function of the knee joint. A frequent sequela of these pathologies is quadriceps femoris muscle weakness. Such weakness can contribute to disability and diminished levels of functional and recreational activity. Therefore, safe and accurate methods of measuring maximal strength are required to identify and quantify quadriceps strength deficits. One option proposed in the literature is the use of 1-RM prediction equations which estimate 1-RM performance from the number of repetitions completed with sub-maximal loads. These equations have been investigated previously using healthy populations and subjects with calf muscle injuries. However, to date, no known study has investigated their accuracy in individuals with joint pathologies. Method: Machine-weight seated knee extension and seated leg press exercises were investigated in this study. Twenty subjects with knee injuries and 12 subjects with knee OA completed the testing procedures for the knee extension exercise. Nineteen subjects with knee injuries and 18 subjects with knee OA completed the testing procedures for the leg press exercise. All subjects attended the testing venue on three occasions. At the first visit a familiarization session was carried out. At the second and third visits each subject was randomly assigned to perform either actual or predicted 1-RM testing for both of the exercises. Twelve different prediction methods were used to estimate 1-RM performance from the results. The estimates of 1-RM strength were then compared to actual 1-RM performance to assess the level of conformity between these measures. Statistical procedures including Bland and Altman analyses, intraclass correlation coefficients, typical error and total error of measurement were used in the analyses of the results. In addition, paired t-tests were performed to determine whether actual 1-RM values were significantly different across the control and affected limbs and whether there were any significant differences in predictive accuracy for each equation across the control and affected limbs. Finally, the number of subjects with predicted 1-RM values within 5% or less of their actual 1-RM values was determined for each equation. Results: When the knee injury group performed the knee extension exercise, the Brown, Brzycki, Epley, Lander, Mayhew et al., Poliquin and Wathen prediction methods demonstrated the greatest levels of predictive accuracy. When two atypical subjects were identified and excluded from the analyses, the accuracy of these equations improved further. Following the removal of these two subjects, no significant differences in predictive accuracy were found for any of the equations across the affected and control limbs (p > 0.05). Typical errors and total errors were low for the more accurate prediction methods ranging from 2.4-2.8% and from 2.4-3.5%, respectively. Overall, the Poliquin table appeared to be the most accurate prediction method for this sample (affected limbs: bias 0.3 kg, 95% limits of agreement (LOA) -5.8 to 6.4 kg, typical error as a coefficient of variation (COV) 2.4%, total error of measurement (total error) 2.4%; control limbs: bias -1.3 kg, 95% LOA -9.0 to 6.3 kg, typical error as a COV 2.7%, total error 2.8%). When the knee OA group performed the knee extension exercise, the Brown, Brzycki, Epley, Lander, Mayhew et al., Poliquin and Wathen prediction methods demonstrated the greatest levels of predictive accuracy. No significant differences in predictive accuracy were found for any of the equations across the affected and control limbs (p > 0.05). When an atypical subject was identified and excluded from the analyses, the accuracy of the equations improved further. Typical errors as COVs and total errors for the more accurate prediction methods ranged from 2.5-2.7% and from 2.4-2.9%, respectively. Overall, the Poliquin table appeared to be the most accurate prediction method for this sample (affected limbs: bias 0.9 kg, 95% LOA -4.5 to 6.3 kg, typical error as a COV 2.5%, total error 2.5%; control limbs: bias -0.1 kg, 95% LOA -6.0 to 5.9 kg, typical error as a COV 2.5%, total error 2.4%). When the knee injury group performed the leg press, the Adams, Berger, Lombardi and O’Connor equations demonstrated the greatest levels of predictive accuracy. No significant differences in predictive accuracy were found for any of the equations across the affected and control limbs (p > 0.05). Typical errors as COVs and total errors for the more accurate equations ranged from 2.8-3.2% and from 2.9-3.3%, respectively. Overall, the Berger (affected limbs: bias -0.4 kg, 95% LOA -7.2 to 6.3 kg, typical error as a COV 3.2%, total error 3.2%; control limbs: bias 0.1 kg, 95% LOA -6.6 to 6.7 kg, typical error as a COV 3.1%, total error 3.0%) and O’Connor equations (affected limbs: bias -0.6 kg, 95% LOA-6.8 to 5.7 kg, typical error as a COV 2.9%, total error 3.0%; control limbs: bias -0.2 kg, 95% LOA -6.9 to 6.4 kg, typical error as a COV 2.9%, total error 2.9%) appeared to be the most accurate prediction methods for this sample. When the knee OA group performed the leg press, the Adams, Berger, KLW, Lombardi and O’Connor equations demonstrated the greatest levels of predictive accuracy. No significant differences in predictive accuracy were found for any of the equations across the affected and control limbs (p > 0.05). The typical errors as COVs and the total error values for the more accurate prediction methods were the highest observed in this study, ranging from 5.8-6.0% and from 5.7-6.2%, respectively. Overall, the Adams, Berger, KLW and O’Connor equations appeared to be the most accurate prediction methods for this sample. However, it is possible that the predicted leg press 1-RM values produced by the knee OA group might not have matched actual 1-RM values closely enough to be clinically acceptable for some purposes. Conclusion: The findings of the current study suggested that the Poliquin table produced the most accurate estimates of knee extension 1-RM performance for both the knee injury and knee OA groups. In contrast, the Berger and O’Connor equations produced the most accurate estimates of leg press 1-RM performance for the knee injury group, while the Adams, Berger, KLW and O’Connor equations produced the most accurate results for the knee OA group. However, the higher error values observed for the knee OA group suggested that predicted leg press 1-RM performance might not be accurate enough for some clinical purposes. Finally, it can be concluded that no single prediction equation was able to accurately estimate both knee extension and leg press 1-RM performance in subjects with knee injuries and knee OA.

No description available.

This study is an investigation into methods of developing ergonomic guidelines for automotive assembly tasks involving insertion of small parts. The study was conducted in four major parts: 1) a method of determining and evaluating subjective exertion limits was modified and tested, 2) a large dataset was collected from an industrial population in 10 simulated assembly line tasks, 3) a smaller dataset was collected from a student population to assess hand dominance effects, and 4) strength data obtained was compared with a strength prediction model to determine if the model could predict manual insertion forces. The traditional method of psychophysical data collection requires participants to extrapolate sensations from a relativity short session to judge if the task could be done for a much longer period. Maximum acceptable limits (MALs) are typically derived from having participants adjust a weight, resistance, or frequency to an acceptable level. The present study evaluated a relatively new method of collecting MAL data for simple, single-digit exertions where participants were asked to determine an MAL by self-adjusting and then regulating to maintain the exertion level. Results showed that MAL values obtained from a series of self-regulated exertions were independent of both analysis method and duration (5 minutes vs. 25 minutes) used for evaluation, and that the method was repeatable both within and between sessions. Ergonomic guidelines are often obtained from the strength capacity for a certain task, as it is important to ensure that workers possess sufficient strength to accomplish a task. As task demands increase, however, a larger percentage of a worker's strength capability in required, and other factors, such as performance and worker comfort, tend to be compromised. In this work, both strength capacity and subjective limits were obtained for a variety of simulated tasks to facilitate development of guidelines for the specific tasks. The relationship between these two measures (maximum force, acceptable force) was determined, and acceptable limits were found to be approximately 55% of population strength capacity, with correlations (R2) ranging from 0.40 to 0.60 depending on the task, suggesting the subjective limits and strength capacity are related in these tasks. Hand dominance was found to have a small (5%), but significant (p = 0.006) effect on strength capability, and no significant effect on subjective limit. Biomechanical strength prediction models can be used to assess loads placed on the human performing various tasks. One of the more popular models, Three-Dimensional Static Strength Prediction Program, is often used for heavy material handling tasks, such as lifting or pushing. The tasks studied presently, however, are manual insertions, requiring localized force application rather than whole body exertion. The prediction capabilities of this strength prediction model were compared with strength values obtained from the simulated assembly tasks. Results indicated that the model was not successful when predicting localized force, accounting for only 40% of the observed variance in strength (R2 = 0.4) / Master of Science

yes / A 9 × 18 × 1 feed-forward neural network (NN) model trained using a resilient back-propagation algorithm and early stopping technique is constructed to predict the shear strength of deep reinforced concrete beams. The input layer covering geometrical and material properties of deep beams has nine neurons, and the corresponding output is the shear strength. Training, validation and testing of the developed neural network have been achieved using a comprehensive database compiled from 362 simple and 71 continuous deep beam specimens. The shear strength predictions of deep beams obtained from the developed NN are in better agreement with test results than those determined from strut-and-tie models. The mean and standard deviation of the ratio between predicted capacities using the NN and measured shear capacities are 1·028 and 0·154, respectively, for simple deep beams, and 1·0 and 0·122, respectively, for continuous deep beams. In addition, the trends ascertained from parametric study using the developed NN have a consistent agreement with those observed in other experimental and analytical investigations.

<p>This paper presents an experimental and numerical study of the behavior of steel wide flange sections subjected to loads causing compression buckling in the web. This research includes experimental investigation of the effects of load width and duration on web compression buckling. This data is then used to calibrate numerical models. Experimental investigations were conducted on specimens with load widths of approximately 2.5, 1.75, and 1.5 times their section depth. Loads sustained on the specimens had a magnitude of about 85% of the expected buckling strength to investigate creep effects near failure. Results of these experiments were used to calibrate numerical models for a parametric study.</p><p>The numerical parametric study examined 60 specimens of four wide flange sections, investigating the effects of loaded width and angle of load application on web compression buckling. The numerical models accounted for initial imperfections in the specimens by applying imperfections with a magnitude of 0.13*<i>t_w</i> to the first mode shape obtained from a linear perturbation analysis. This value of imperfection was chosen because it is the average imperfection measured in the experimental specimens and is likely a good representation of a typical wide flange section.</p><p>A prediction method is provided based on the data obtained from the numerical parametric study. This prediction method is derived from rectangular plate buckling solutions and considers the cases where the width of the concentrated load is a function of the section depth, and when the applied load is not orthogonal to the specimen. The current AISC 360-16 provisions do not directly address the influence of load width on the calculation of web compression buckling strength and refer to the design of compression members when the loaded width is greater than or equal to the section depth. The AISC approach was also evaluated and deemed conservative for design.</p><br>

Ergonomics software programs often use an independent axis approach (IAA) to calculate resultant shoulder strength to predict manual arm strength (MAS). The IAA treats strength about each joint axis (joint axis strengths: JAS) in the arm as independent motors, which all combine to complete an exertion. However, this form of modeling is not a true physiological representation of how the shoulder/arm function. The weighted average approach (WAA) was proposed, which combines the axes by weighting each strength based on its relative contribution to the resultant moment vector. The primary purpose of this thesis was to test the IAA using participant-specific JAS values, such that it afforded the IAA the best opportunity to predict MAS accurately. The secondary purpose was to test the WAA, to determine if it was a viable replacement for the IAA. Fifteen university age females completed two data collections. One tested the eight different JASs for the shoulder and elbow, and the other tested participant’s MASs in four hand locations and six exertion directions. The JAS force data, and postural kinematic data (from the MAS collection), were inputs into two models, which completed the MAS predictions. A 4 x 6 x 3 repeated measures ANOVA revealed a significant three-way interaction between hand location, exertion direction, and method of MAS estimation (p<0.0001) on MAS. The most important finding of the thesis was that both the IAA and WAA predictions were significantly different than the MAS values. The IAA and WAA explained only 17.9% & 19.1% of the variance with RMS errors of 74.5 N & 73.4 N, respectively. This indicated that ergonomics software programs, using the IAA, should not be used to make arm strength predictions by ergonomists, and that WAA was not a viable replacement for the IAA. / Thesis / Master of Science in Kinesiology

The research in this dissertation is divided between three different approaches for predicting the shear strength of reinforcement masonry shear walls. Each approach provides increasing accuracy and precision in predicting the shear strength of masonry walls. The three approaches were developed or validated using data from 353 wall tests that have been conducted over the past half century. The data were collected, scrutinized, and synthesized using principles of meta-analysis. Predictions made with current Masonry Standards Joint Committee (MSJC) shear strength equation are unconservative and show a higher degree of variation for partially-grouted walls. The first approach modifies the existing MSJC equation to account for the differences in nominal strength and uncertainty between fully- and partially-grouted walls. The second approach develops a new shear strength equation developed to perform equally well for both fully- and partially-grouted walls to replace and improve upon the current MSJC equation. The third approach develops a methodology for creating strut-and-tie models to analyze or design masonry shear walls. It was discovered that strut-and-tie modeling theory provides the best description of masonry shear wall strength and performance. The masonry strength itself provides the greatest contribution to the overall shear capacity of the wall and can be represented as diagonal compression struts traveling from the top of the wall to the compression toe. The shear strength of masonry wall is inversely related to the shear span ratio of the wall. Axial load contributes to shear strength, but to a lesser degree than what has been previously believed. The prevailing theory about the contribution of horizontal shear reinforcement was shown to not be correct and the contribution is much smaller than was originally assumed by researchers. Horizontal shear reinforcement principally acts by resisting diagonal tensile forces in the masonry and by helping to redistribute stresses in a cracked masonry panel. Vertical reinforcement was shown to have an effect on shear strength by precluding overturning of the masonry panel and by providing vertical anchorages to the diagonal struts.