RESUMEN
The mechanical properties of concrete are the important parameters in a design code. The amount of laboratory trial batches and experiments required to produce useful design data can be decreased by using robust prediction models for the mechanical properties of concrete, which can save time and money. Portland cement is frequently substituted with metakaolin (MK) because of its technical and environmental advantages. In this study, three mechanical properties of concrete with MK, i.e., compressive strength (f'c), splitting tensile strength (fst), and flexural strength (FS) were modelled by using four machine learning (ML) techniques: gene expression programming (GEP), artificial neural network (ANN), M5P model tree algorithm, and random forest (RF). For this purpose, a comprehensive database containing detail of concrete mixture proportions and values of f'c, fst, and FS at different ages was gathered from peer-reviewed published documents. Various statistical metrics were used to compare the predictive and generalization capability of the ML techniques. The comparative study of ML techniques revealed that RF has better predictive and generalization capability as compared with GEP, ANN, and M5P model tree algorithm. Moreover, the sensitivity and parametric analysis (PA) was carried out. The PA showed that the most suitable proportions of MK as partial cement replacement were 10% for FS and 15% for both f'c and fst.
RESUMEN
OBJECTIVES: To determine the relationship between health care use and the magnitude of change in patient-reported outcomes in individuals who received treatment for subacromial pain syndrome. The secondary objective was to determine the value of care, as measured by change in pain and disability per dollar spent. DESIGN: Secondary analysis of a randomized clinical trial that investigated the effects of nonsurgical care for subacromial pain syndrome. METHODS: Two groups of treatment responders were created, based on 1-year change in Shoulder Pain and Disability Index (SPADI) score (high, 46.83 points; low, 8.21 points). Regression analysis was performed to determine the association between health care use and 1-year change in SPADI score. Baseline SPADI score was used as a covariate in the regression analysis. Value was measured by comparing health care visits and costs expended per SPADI 1-point change between responder groups. RESULTS: Ninety-eight patients were included; 38 were classified as high responders (mean 1-year SPADI change score, 46.83 points) and 60 were classified as low responders (1-year SPADI change score, 8.21 points). Neither unadjusted medical visits (5.89; 95% confidence interval [CI]: 4.35, 7.44 versus 6.30; 95% CI: 5.14, 7.46) nor medical costs ($1404.86; 95% CI: $1109.34, $1779.09 versus $1679.26; 95% CI: $1391.54, $2026.48) were significantly different between high and low responders, respectively. CONCLUSION: Neither the number of visits nor the financial cost of nonsurgical shoulder- related care was associated with improvement in shoulder pain and disability at 1 year. J Orthop Sports Phys Ther 2020;50(11):642-648. doi:10.2519/jospt.2020.9440.
