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- Alyami, Mana, et al.
(författare)
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Estimating compressive strength of concrete containing rice husk ash using interpretable machine learning-based models
- 2024
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Ingår i: Case Studies in Construction Materials. - : Elsevier. - 2214-5095. ; 20
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Tidskriftsartikel (refereegranskat)abstract
- The construction sector is a major contributor to global greenhouse gas emissions. Using recycled and waste materials in concrete is a practical solution to address environmental challenges. Currently, agricultural waste is widely used as a substitute for cement in the production of eco-friendly concrete. However, traditional methods for assessing the strength of such materials are both expensive and time-consuming. Therefore, this study uses machine learning techniques to develop prediction models for the compressive strength (CS) of rice husk ash (RHA) concrete. The ML techniques used in the present study include random forest (RF), light gradient boosting machine (LightGBM), ridge regression, and extreme gradient boosting (XGBoost). A total of 348 values of CS were collected from the experimental studies, and five characteristics of RHA concrete were taken as input variables. For the performance assessment of the models, multiple statistical metrics were used. During the training phase, the correlation coefficients (R) obtained for ridge regression, RF, XGBoost, and LightGBM were 0.943, 0.981, 0.985, and 0.996, respectively. In the testing set, the developed models demonstrated even higher performance, with correlation coefficients of 0.971, 0.993, 0.992, and 0.998 for ridge regression, RF, XGBoost, and LightGBM, respectively. The statistical analysis revealed that the LightGBM model outperformed other models, whereas the ridge regression model exhibited comparatively lower accuracy. SHapley Additive exPlanation (SHAP) method was employed for the interpretability of the developed model. The SHAP analysis revealed that water-to-cement is a controlling parameter in estimating the CS of RHA concrete. In conclusion, this study provides valuable guidance for builders and researchers to estimate the CS of RHA concrete. However, it is suggested that more input variables be incorporated and hybrid models utilized to further enhance the reliability and precision of the models.
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| 2. |
- Gielen, Marij, et al.
(författare)
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Body mass index is negatively associated with telomere length : A collaborative cross-sectional meta-analysis of 87 observational studies
- 2018
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Ingår i: American Journal of Clinical Nutrition. - : Elsevier BV. - 0002-9165. ; 108:3, s. 453-475
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Tidskriftsartikel (refereegranskat)abstract
- Background: Even before the onset of age-related diseases, obesity might be a contributing factor to the cumulative burden of oxidative stress and chronic inflammation throughout the life course. Obesity may therefore contribute to accelerated shortening of telomeres. Consequently, obese persons are more likely to have shorter telomeres, but the association between body mass index (BMI) and leukocyte telomere length (TL) might differ across the life span and between ethnicities and sexes. Objective: A collaborative cross-sectionalmeta-analysis of observational studies was conducted to investigate the associations between BMI and TL across the life span. Design: Eighty-seven distinct study samples were included in the meta-analysis capturing data from 146,114 individuals. Studyspecific age- and sex-adjusted regression coefficients were combined by using a random-effects model in which absolute [base pairs (bp)] and relative telomere to single-copy gene ratio (T/S ratio) TLs were regressed against BMI. Stratified analysis was performed by 3 age categories ("young": 18-60 y; "middle": 61-75 y; and "old": >75 y), sex, and ethnicity. Results: Each unit increase in BMI corresponded to a-3.99 bp (95% CI: -5.17, -2.81 bp) difference in TL in the total pooled sample; among young adults, each unit increase in BMI corresponded to a -7.67 bp (95% CI:-10.03,-5.31 bp) difference. Each unit increase in BMI corresponded to a -1.58 × 10-3 unit T/S ratio (0.16% decrease; 95% CI: -2.14 × 10-3, -1.01 × 10-3) difference in ageand sex-adjusted relative TL in the total pooled sample; among young adults, each unit increase in BMI corresponded to a -2.58 × 10-3 unit T/S ratio (0.26% decrease; 95% CI: -3.92 × 10-3, -1.25 × 10-3). The associations were predominantly for the white pooled population. No sex differences were observed. Conclusions: A higher BMI is associated with shorter telomeres, especially in younger individuals. The presently observed difference is not negligible. Meta-analyses of longitudinal studies evaluating change in body weight alongside change in TL arewarranted.
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