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Concrete compressive strength prediction using the imperialist competitive algorithm
Łukasz Sadowski,Mehdi Nikoo,Mohammad Nikoo 사단법인 한국계산역학회 2018 Computers and Concrete, An International Journal Vol.22 No.4
In the following paper, a socio-political heuristic search approach, named the imperialist competitive algorithm (ICA) has been used to improve the efficiency of the multi-layer perceptron artificial neural network (ANN) for predicting the compressive strength of concrete. 173 concrete samples have been investigated. For this purpose the values of slump flow, the weight of aggregate and cement, the maximum size of aggregate and the water-cement ratio have been used as the inputs. The compressive strength of concrete has been used as the output in the hybrid ICA-ANN model. Results have been compared with the multiple-linear regression model (MLR), the genetic algorithm (GA) and particle swarm optimization (PSO). The results indicate the superiority and high accuracy of the hybrid ICA-ANN model in predicting the compressive strength of concrete when compared to the other methods.
Sedigheh-Sadat Mirbagheri,Amir Rahmani-Rasa,Farzad Farmani,Payam Amini,Mohammad-Reza Nikoo 대한척추외과학회 2015 Asian Spine Journal Vol.9 No.3
Study Design: A cross-sectional, descriptive study. Purpose: This study aimed to investigate the relationship between kyphosis and lordosis measured by using a flexible ruler and musculoskeletal pain in students of Hamadan University of Medical Sciences. Overview of Literature: The spine supports the body during different activities by maintaining appropriate body alignment and posture. Normal alignment of the spine depends on its structural, muscular, bony, and articular performance. Methods: Two hundred forty-one students participated in this study. A single examiner evaluated the angles of lumbar lordosis and thoracic kyphosis by using a flexible ruler. To determine the severity and frequency of pain in low-back and inter-scapular regions, a tailor-made questionnaire with visual analog scale was used. Finally, using the Kendall correlation coefficient, the data were statistically analyzed. Results: The mean value of lumbar lordosis was 34.46°±12.61° in female students and 22.46°±9.9° in male students. The mean value of lumbar lordosis significantly differed between female and male students (p <0.001). However, there was no difference in the level of the thoracic curve (p =0.288). Relationship between kyphosis measured by using a flexible ruler and inter-scapular pain in male and female students was not significant (p =0.946). However, the relationship between lumbar lordosis and low back pain was statistically significant (p =0.006). Also, no significant relationship was observed between abnormal kyphosis and frequency of inter-scapular pain, and between lumbar lordosis and low back pain. Conclusions: Lumbar lordosis contributes to low back pain. The causes of musculoskeletal pain could be muscle imbalance and muscle and ligament strain.