Accurate prediction of the compressive strength of concrete is of great significance to construction quality and progress. In order to understand the current research status in the concrete compressive strength prediction field, a bibliometric analysis of the relevant literature published in this field in the last decade was conducted first. The 3135 journal articles published from 2012 to 2021 in the Web of Science core database were used as the database, and the knowledge map was drawn with the help of the visualisation software CiteSpace 6.1R2 to analyse the field at the macro level in terms of spatial and temporal distribution, hotspot distribution and evolutionary trends, respectively. Afterwards, we go into the detail and divide concrete compressive strength prediction methods into two categories: traditional and machine-learning methods, and introduce the typical methods of each. In addition, a boosting-based ensemble machine-learning algorithm, namely the gradient boosting regression tree (GBRT) algorithm, is proposed for predicting the compressive strength of concrete. 1030 sets of concrete compressive strength test data were collected as the dataset, of which 60% were used to train the model, 20% to validate the model and 20% to test the trained model. The coefficient of determination (R2) of the GBRT model was 0.92, the mean square error (MSE) was 22.09 MPa, and the root mean square error (RMSE) was 4.7 MPa, which is an excellent prediction accuracy compared to prediction models constructed by other machine-learning algorithms. In addition, a five-fold cross-validation analysis was carried out, and the eight input variables were analyzed for their characteristic importance.
Measurement of viscosity of crude oil is critical for reservoir simulators. Computational modeling is a useful tool for correlation of crude oil viscosity to reservoir conditions such as pressure, temperature, and fluid compositions. In this work, multiple distinct models are applied to the available dataset to predict heavy-oil viscosity as function of a variety of process parameters and oil properties. The computational techniques utilized in this work are Decision Tree (DT), MLP, and GRNN which were utilized in estimation of heavy crude oil samples collected from middle eastern oil fields. For the estimation of viscosity, the firefly algorithm (FA) was employed to optimize the hyper-parameters of the machine learning models. The RMSE error rates for the final models of DT, MLP, and GRNN are 40.52, 25.08, and 30.83, respectively. Also, the R2-scores are 0.921, 0. 978, and 0.933, respectively. Based on this and other criteria, MLP is chosen as the best model for this study in estimating the values of crude oil viscosity.
Hydraulic fracture morphology and propagation mode are difficult to predict in layers of the various lithological strata, which seriously affects exploitation efficiency. This paper studies the fundamental mechanical and microscopic properties of the two main interfaces in inter-salt shale reservoirs. On this basis, cement-salt combination samples with composite interfaces are prepared, and hydraulic fracturing tests are carried out under different fluid velocities, viscosity, and stress conditions. The result shows that the shale bedding and salt-shale interface are the main geological interfaces of the inter-salt shale reservoir. The former is filled with salt, and the average tensile strength is 0.42 MPa, c = 1.473 MPa, and φ = 19.00°. The latter is well cemented, and the interface strength is greater than that of shale bedding, with c = 2.373MPa and φ = 26.15°. There are three basic fracture modes for the samples with compound interfaces. Low-viscosity fracturing fluid and high-viscosity fracturing fluid tend to open the internal bedding interface and produce a single longitudinal crack, respectively, so properly selecting the viscosity and displacement is necessary. Excessive geostress differences will aggravate the strain incompatibility of the interface between different rock properties, which makes the interfaces open easily. The pump pressure curves' morphological characters are different with different failure modes.
Background The presence or absence of human leukocyte antigen (HLA) antibodies, especially the strength of donor‐specific HLA antibodies (DSAs), has important roles in clinical evaluation and diagnostic decision‐making for solid‐organ transplantation. Dilution patterns help to give a new sight of HLA epitopes. “Epitope matching” is likely to lower the risk of developing DSA and increase the likelihood of matching a compatible donor. Methods We collected data evaluating HLA antibodies with a titration study using mean fluorescence intensity. Results Diluting the serum of recipients can reduce potential inhibitory effects, accurately evaluate the intensity of donor‐specific HLA antibodies, and guide surgeons to take or not take intervention measures. Dilution patterns also help to give a new sight of HLA epitopes. Conclusion We believe that from the viewpoint of HLA antibodies, the dilution model can provide new tools and insights for the study of HLA epitopes.
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