| 1. |
- Alatalo, J. M., et al.
(författare)
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Impact of ambient temperature, precipitation and seven years of experimental warming and nutrient addition on fruit production in an alpine heath and meadow community
- 2022
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Ingår i: Science of the Total Environment. - Amsterdam : Elsevier BV. - 0048-9697 .- 1879-1026. ; 836
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Tidskriftsartikel (refereegranskat)abstract
- Alpine and polar regions are predicted to be among the most vulnerable to changes in temperature, precipitation, and nutrient availability. We carried out a seven-year factorial experiment with warming and nutrient addition in two alpine vegetation communities. We analyzed the relationship between fruit production and monthly mean, maximum, and min temperatures during the fall of the pre-fruiting year, the fruiting summer, and the whole fruit production period, and measured the effects of precipitation and growing and thawing degree days (GDD & TDD) on fruit production. Nutrient addition (heath: 27.88 +/- 3.19 fold change at the end of the experiment; meadow: 18.02 +/- 4.07) and combined nutrient addition and warming (heath: 20.63 +/- 29.34 fold change at the end of the experiment; meadow: 18.21 +/- 16.28) increased total fruit production and fruit production of graminoids. Fruit production of evergreen and deciduous shrubs fluctuated among the treatments and years in both the heath and meadow. Pre-maximum temperatures had a negative effect on fruit production in both communities, while current year maximum temperatures had a positive impact on fruit production in the meadow. Pre-minimum, pre-mean, current mean, total minimum, and total mean temperatures were all positively correlated with fruit production in the meadow. The current year and total precipitation had a negative effect on the fruit production of deciduous shrubs in the heath. GDD had a positive effect on fruit production in both communities, while TDD only impacted fruit production in the meadow. Increased nutrient availability increased fruit production over time in the high alpine plant communities, while experimental warming had either no effect or a negative effect. Deciduous shrubs were the most sensitive to climate parameters in both communities, and the meadow was more sensitive than the heath. The difference in importance of TDD for fruit production may be due to differences in snow cover in the two communities.
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| 2. |
- Amin, Fayiz, et al.
(författare)
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Influence of arch action on load-carrying capacity of double-sized industrial precast slabs: A combined numerical and experimental study
- 2024
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Ingår i: Results in Materials. - : Elsevier. - 2590-048X. ; 23
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Tidskriftsartikel (refereegranskat)abstract
- The precast industry offers slab panels of different geometries according to the field conditions. These slab panels are popular in temporary constructions and beneficial in sustainability but have some financial limitations and local constraints. For a long time, the construction industry has used the arch action method, which restricts the stresses to the compression zone in concrete members and develops the required load-carrying capacity. For the same motives, industrial buildings have preferred semi-circular precast roofs, but the morphology was not suitable. For the proposed slab in this research, firstly, the typical industrial precast slab panel was doubled in width to minimize the time and efforts required for its casting, curing, and placement. Secondly, that doubled-in-width slab was provided with the arch action to confine the stresses to compression and benefit from the section entirely. Lastly, the top of the slab was kept flat to take advantage of the roof space. All these changes aimed for structural stability, reduced material’s weight, improved load-carrying capacity, appropriate mobilization, and financial viability. A numerical approach and practical testing were adopted using the finite element modeling software, ABAQUS, to analyze load–deflection responses of both slabs through the concrete damage plasticity model. The proposed slab exhibited better performance as its capacity enhanced by about 1.5 times that of a typical slab. Although the volume of the material in the proposed slab increased slightly from 0.040 m3 to 0.045 m3, the reductions in joint filler materials, reinforcements, and efforts required for mixing and lifting machinery compensated for this increase significantly. Hence, the slab can be recommended for the industry to save the costs while taking heavier loads efficiently.
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| 3. |
- Garcia, Cesar, et al.
(författare)
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Predicting the impact of adding metakaolin on the splitting strength of concrete using ensemble ML classification and symbolic regression techniques –a comparative study
- 2024
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Ingår i: Frontiers in Built Environment. - : Frontiers. - 2297-3362. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- The mechanical characteristics of concrete are crucial factors in structural design standards especially in concrete technology. Employing reliable prediction models for concrete’s mechanical properties can reduce the number of necessary laboratory trials, checks and experiments to obtain valuable representative design data, thus saving both time and resources. Metakaolin (MK) is commonly utilized as a supplementary replacement for Portland cement in sustainable concrete production due to its technical and environmental benefits towards net-zero goals of the United Nations Sustainable Development Goals (UNSDGs). In this research work, 204 data entries from concrete mixes produced with the addition of metakaolin (MK) were collected and analyzed using eight (8) ensemble machine learning tools and one (1) symbolic regression technique. The application of multiple machine learning protocols such as the ensemble group and the symbolic regression techniques have not been presented in any previous research work on the modeling of splitting tensile strength of MK mixed concrete. The data was partitioned and applied according to standard conditions. Lastly, some selected performance evaluation indices were used to test the models’ accuracy in predicting the splitting strength (Fsp) of the studied MK-mixed concrete. At the end, results show that the k-nearest neighbor (KNN) outperformed the other techniques in the ensemble group with the following indices; SSE of 4% and 1%, MAE of 0.1 and 0.2 MPa, MSE of 0, RMSE of 0.1 and 0.2 MPa, Error of 0.04% and 0.04%, Accuracy of 0.96 and 0.96 and R2 of 0.98 and 0.98 for the training and validation models, respectively. This is followed closely by the support vector machine (SVM) with the following indices; SSE of 7% and 3%, MAE of 0.2 and 0.2 MPa, MSE of 0.0 and 0.1 MPa, RMSE of 0.2 and 0.3 MPa, Error of 0.05% and 0.06%, Accuracy of 0.95 and 0.94, and R2 of 0.96 and 0.95, for the training and validation models, respectively. The third model in the superiority rank is the CN2 with the following performance indices; SSE of 15% and 4%, MAE of 0.2 and 0.2 MPa, MSE of 0.1 and 0.1 MPa, RMSE of 0.3 and 0.3 MPa, Error of 0.08% and 0.07%, Accuracy of 0.92 and 0.93 and R2 of 0.92 and 0.93, for the training and validation models, respectively. These models outperformed the models utilized on the MK-mixed concrete found in the literature, therefore are the better decisive modes for the prediction of the splitting strength (Fsp) of the studied MK-mixed concrete with 204 mix data entries. Conversely, the NB and SGD produced unacceptable model performances, however, this is true for the modeled database collected for the MK-mixed Fsp. The RSM model also produced superior performance with an accuracy of over 95% and adequate precision of more than 27. Overall, the KNN, SVM, CN2 and RSM have shown to possess the potential to predict the MK-mixed Fsp for structural concrete designs and production.
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