Concrete Strength Prediction Using Different Machine Learning Processes: Effect of Slag, Fly Ash and Superplasticizer

Blast furnace slag (BFS) and fly ash (FA), as mining-associated solid wastes with good pozzolanic effects, can be combined with superplasticizer to prepare concrete with less cement utilization. Considering the important influence of strength on concrete design, random forest (RF) and particle swarm optimization (PSO) methods were combined to construct a prediction model and carry out hyper-parameter tuning in this study. Principal component analysis (PCA) was used to reduce the dimension of input features. The correlation coefficient (R), the explanatory variance score (EVS), the mean absolute error (MAE) and the mean square error (MSE) were used to evaluate the performance of the model. R = 0.954, EVS = 0.901, MAE = 3.746, and MSE = 27.535 of the optimal RF-PSO model on the testing set indicated the high generalization ability. After PCA dimensionality reduction, the R value decreased from 0.954 to 0.88, which was not necessary for the current dataset. Sensitivity analysis showed that cement was the most important feature, followed by water, superplasticizer, fine aggregate, BFS, coarse aggregate and FA, which was beneficial to the design of concrete schemes in practical projects. The method proposed in this study for estimation of the compressive strength of BFS-FA-superplasticizer concrete fills the research gap and has potential engineering application value

Role of Mg Impurity in the Water Adsorption over Low-Index Surfaces of Calcium Silicates: A DFT-D Study

Calcium silicates are the most predominant phases in ordinary Portland cement, inside which magnesium is one of the momentous impurities. In this work, using the first-principles density functional theory (DFT), the impurity formation energy (Efor) of Mg substituting Ca was calculated. The adsorption energy (Ead) and configuration of the single water molecule over Mg-doped β-dicalcium silicate (β-C2S) and M3-tricalcium silicate (M3-C3S) surfaces were investigated. The obtained Mg-doped results were compared with the pristine results to reveal the impact of Mg doping. The results show that the Efor was positive for all but one of the calcium silicates surfaces (ranged from −0.02 eV to 1.58 eV), indicating the Mg substituting for Ca was not energetically favorable. The Ead of a water molecule on Mg-doped β-C2S surfaces ranged from –0.598 eV to −1.249 eV with the molecular adsorption being the energetically favorable form. In contrast, the Ead on M3-C3S surfaces ranged from −0.699 eV to −4.008 eV and the more energetically favorable adsorption on M3-C3S surfaces was dissociative adsorption. The influence of Mg doping was important since it affected the reactivity of surface Ca/Mg sites, the Ead of the single water adsorption, as well as the adsorption configuration compared with the water adsorption on pristine surfaces

Effect of Scutellarin on Promoting the Sensitivity of Breast Cancer 4T1 Cells to Cisplatin in Vitro and in Vivo

To detect the effect of scutellarin (SCU) on promoting the sensitivity of breast cancer 4T1 cells to cisplatin (CDDP), and the molecular mechanism as well. CCK-8, scratch assay, Transwell assay and flow cytometry were employed to investigate the effects of SCU combined with CDDP on the proliferation, migration, invasion and apoptosis of 4T1 cells in vitro. Then, the 4T1 tumor-bearing mouse model was established to explore the effect of SCU combined with CDDP on tumor growth in vivo. The morphology, necrotic area and microvascular density of tumor tissues were observed by H&E staining. Real-time fluorescence quantitative PCR and Western Blot were used to detect the mRNA and proteins expression of apoptosis factors in tumor tissues. The results showed that SCU (200 μmol/L) combined with CDDP (80 μmol/L) significantly inhibited the in vitro proliferation of 4T1 cells (P<0.01). Moreover, the migration and invasion capacity of 4T1 cell were apparently reduced and the apoptosis of tumor cells were significantly promoted when treated with SCU (200 μmol/L) combined with CDDP (80 μmol/L) (P<0.01). Besides, the growth of 4T1 cells in vivo was remarkably slower after administration of 60 mg/kg SCU combined with 3.0 mg/kg CDDP (P<0.01). Further H&E semi-quantitative results revealed the microvascular density of 4T1 tumor tissues was significantly decreased (P<0.01). What’s more, the combination of SCU and CDDP significantly promoted the expression of pro-apoptotic factors Caspase-3, Bax, Caspase-9, Cleaved-Caspase-3 and Cleaved-Caspase-9 (P<0.01) and inhibited the expression of anti-apoptotic factor Bcl-2 in tumor tissues (P<0.05). In conclusion, SCU combined with CDDP could inhibit the function of tumor cells by enhancing the sensitivity of 4T1 cells to CDDP, and also regulate the expression of apoptosis factors, thus inhibiting the growth of tumor

Efficient Machine Learning Model for Predicting the Stiffness of Circular Footings on Clay Overlying Sand

Assessing the stiffness of circular foundations is the key to evaluating their deformation; thus, it is important for foundation design. The current determination methods for the stiffness coefficient are either time-consuming or inaccurate. In this paper, a novel stiffness prediction model has been proposed, using the decision tree (DT) algorithm optimized by particle size optimization (PSO). The condition of the embedded foundation, the embedded depth (ZD/2R), the thickness of the clay layer beneath the foundation base (T/2R), and the ratio of shear stiffness between clay and sand (Gsand/Gclay) were used as input variables, while the elastic stiffness coefficients (Kc, Kh, Km, and Kv) were used as output variables. The optimum DT model has undergone comprehensive validation, and independent model verification using extra simulations. The results illustrate that PSO could promote further increases in the capability of DT modeling in predicting stiffness coefficients. The optimum DT model achieved a good level of performance on stiffness coefficient modeling. (The R for the training set was greater than 0.98 for all of the stiffness coefficients.) The variable importance analysis showed that the T/2R was the most significant variable for all stiffness coefficients, followed by Gsand/Gclay. The optimum DT model achieved good predictive performance upon independent verification, with the R being 0.97, 0.99, 0.99, and 0.95 for Kv, Kh, Km, and Kc, respectively. The proposed reliable and efficient DT-PSO model for stiffness coefficients in layered soil could further promote the safe and efficient utilization of circular foundations

Comparison and Determination of Optimal Machine Learning Model for Predicting Generation of Coal Fly Ash

The rapid development of industry keeps increasing the demand for energy. Coal, as the main energy source, has a huge level of consumption, resulting in the continuous generation of its combustion byproduct coal fly ash (CFA). The accumulated CFA will occupy a large amount of land, but also cause serious environmental pollution and personal injury, which makes the resource utilization of CFA gradually to be attached importance. However, given the variability of the amount of CFA generation, predicting it in advance is the basis to ensure effective disposal and rational utilization. In this study, CFA generation was taken as the target variable, three machine learning (ML) algorithms were used to construct the model, and four evaluation indices were used to evaluate its performance. The results showed that the DNN model with the R = 0.89, R2 = 0.77 on the testing set performed better than the traditional multiple linear regression equation and other ML algorithms, and the feasibility of DNN as the optimal model framework was demonstrated. Applying this model framework to the engineering field enables managers to identify the next step of the disposal method in advance, so as to rationally allocate ways of recycling and utilization to maximize the use and sales benefits of CFA while minimizing its disposal costs. In addition, sensitivity analysis further explains ML’s internal decisions and verifies that coal consumption is more important than installed capacity, which provides a certain reference for ensuring the rational utilization of CFA

Stability Evaluation of Layered Backfill Considering Filling Interval, Backfill Strength and Creep Behavior

Cemented paste backfill (CPB) is the primary solution to improving the safety of continuous mining. The interaction between rock mass and backfill is an important indicator of backfill stability. The creep behavior of weak rock mass is an essential factor, which causes the evolution of stresses and displacements in the backfill stope. In this paper, numerical models were constructed to analyze the interactions between rock mass and backfill by considering the creep behavior of the rock mass, filling interval, and backfill strength. The numerical simulation results showed the effects of different parameters, including the number of backfilling layers, filling interval time (FIT), and backfill strength under creep behavior on stress, displacements, and plastic deformation. The horizontal displacement near the mid-height and vertical displacement at the top of the backfilled stope is the largest compared to layered backfilling. The stress within the backfilled stope is smallest when the stope is filled in a single layer. With increasing FIT, stress in the backfilled stope decreases. FIT mainly affected the horizontal displacement of the stope. The stresses on the stope bottom decrease when the strength of the middle-backfilled stope decreases. Overall, this study provides important insights for understanding the creep behavior of rock mass in underground backfilling practices

Comparison and Determination of Optimal Machine Learning Model for Predicting Generation of Coal Fly Ash

The rapid development of industry keeps increasing the demand for energy. Coal, as the main energy source, has a huge level of consumption, resulting in the continuous generation of its combustion byproduct coal fly ash (CFA). The accumulated CFA will occupy a large amount of land, but also cause serious environmental pollution and personal injury, which makes the resource utilization of CFA gradually to be attached importance. However, given the variability of the amount of CFA generation, predicting it in advance is the basis to ensure effective disposal and rational utilization. In this study, CFA generation was taken as the target variable, three machine learning (ML) algorithms were used to construct the model, and four evaluation indices were used to evaluate its performance. The results showed that the DNN model with the R = 0.89, R2 = 0.77 on the testing set performed better than the traditional multiple linear regression equation and other ML algorithms, and the feasibility of DNN as the optimal model framework was demonstrated. Applying this model framework to the engineering field enables managers to identify the next step of the disposal method in advance, so as to rationally allocate ways of recycling and utilization to maximize the use and sales benefits of CFA while minimizing its disposal costs. In addition, sensitivity analysis further explains ML’s internal decisions and verifies that coal consumption is more important than installed capacity, which provides a certain reference for ensuring the rational utilization of CFA

Utilisation of Water-Washing Pre-Treated Phosphogypsum for Cemented Paste Backfill

Recycling phosphogypsum (PG) for cemented paste backfill (CPB) has been widely used at phosphate mines in China. However, the impurities in PG prolong the setting time and reduce the uniaxial compressive strength (UCS), limiting the engineering application of PG. This paper aims to investigate the feasibility of treated PG (TPG) washed repeatedly using deionised water (DW) for CPB. A water-washing pre-experiment was first conducted to find the proportion with the least DW demand and the effects of water-washing on ordinary PG (OPG). Then, based on the PG:DW ratio obtained from the pre-experiment, the properties of the OPG-based CPB (OCPB) and TPG-based CPB (TCPB) were tested using slump tests, UCS tests, and microstructural analysis. The results show that (1) after 11 water-washings at the PG:DW ratio of 1:1.75, the pH of the supernatant (pH = 6.328) meets the requirements of Chinese standard GB 8978-1996. (2) Water-washing improves the particle gradation quality of PG and removes the soluble impurities adsorbed at the surface of PG crystals. (3) The initial slump values of TCPB are 0.19&ndash;1.15 cm higher than that of OCPB, furthermore, the diffusivity values of TCPB are better than the performance of OCPB, with 0.61&ndash;1.68 cm of superiority. (4) The UCS values of TCPB are up to 0.838 MPa, 1.953 MPa, and 2.531 MPa, after curing for 7, 14, and 28 days. These are 0.283 MPa, 0.823 MPa, and 0.881 MPa higher than that of OCPB, respectively. It can be concluded that water-washing pre-treatment greatly improves the workability and mechanical property of PG-based CPB. These results are of great value for creating a reliable and environmentally superior alternative for the recycling of PG and for safer mining production

Efficient Machine Learning Models for the Uplift Behavior of Helical Anchors in Dense Sand for Wind Energy Harvesting

Helical anchors are widely used in engineering to resist tension, especially during offshore wind energy harvesting, and their uplift behavior in sand is influenced by many factors. Experimental studies are often used to investigate these anchors; however, scale effects are inevitable in 1&times; g model tests, soil conditions for in situ tests are challenging to control, and centrifuge tests are expensive and rare. To make full use of the limited valid data and to gain more knowledge about the uplift behaviors of helical anchors in sand, a prediction model integrating gradient-boosting decision trees (GBDT) and particle swarm optimization (PSO) was proposed in this study. Data obtained from a series of centrifuge tests formed the dataset of the prediction model. The relative density of soil, embedment ratio, helix spacing ratio, and the number of helices were used as input parameters, while the anchor mobilization distance and the ultimate monotonic uplift resistance were set as output parameters. A GBDT algorithm was used to construct the model, and a PSO algorithm was used for hyperparameter tuning. The results show that the optimal GBDT model accurately predicted the anchor mobilization distance and the ultimate monotonic uplift resistance of helical anchors in dense fine silica sand. By analyzing the relative importance of influencing variables, the embedment ratio was found to be the most significant variable in the model, while the relative density of the fine silica sand soil, the helix spacing ratio, and the number of helices had relatively minor influence. In particular, the helix spacing ratio was found to have no influence on the capacity of adjacent helices when S/D &gt; 6

Efficient Machine Learning Models for the Uplift Behavior of Helical Anchors in Dense Sand for Wind Energy Harvesting

Helical anchors are widely used in engineering to resist tension, especially during offshore wind energy harvesting, and their uplift behavior in sand is influenced by many factors. Experimental studies are often used to investigate these anchors; however, scale effects are inevitable in 1× g model tests, soil conditions for in situ tests are challenging to control, and centrifuge tests are expensive and rare. To make full use of the limited valid data and to gain more knowledge about the uplift behaviors of helical anchors in sand, a prediction model integrating gradient-boosting decision trees (GBDT) and particle swarm optimization (PSO) was proposed in this study. Data obtained from a series of centrifuge tests formed the dataset of the prediction model. The relative density of soil, embedment ratio, helix spacing ratio, and the number of helices were used as input parameters, while the anchor mobilization distance and the ultimate monotonic uplift resistance were set as output parameters. A GBDT algorithm was used to construct the model, and a PSO algorithm was used for hyperparameter tuning. The results show that the optimal GBDT model accurately predicted the anchor mobilization distance and the ultimate monotonic uplift resistance of helical anchors in dense fine silica sand. By analyzing the relative importance of influencing variables, the embedment ratio was found to be the most significant variable in the model, while the relative density of the fine silica sand soil, the helix spacing ratio, and the number of helices had relatively minor influence. In particular, the helix spacing ratio was found to have no influence on the capacity of adjacent helices when S/D > 6