scholarly journals Development of an Ensemble Intelligent Model for Assessing the Strength of Cemented Paste Backfill

2020 ◽  
Vol 2020 ◽  
pp. 1-6 ◽  
Author(s):  
Yuantian Sun ◽  
Guichen Li ◽  
Junfei Zhang ◽  
Junbo Sun ◽  
Jiahui Xu
Keyword(s):  
Mine Waste ◽  
Construction Materials ◽  
Sensitivity Study ◽  
Support Vector ◽  
Cemented Paste Backfill ◽  
Proposed Model ◽  
Highly Nonlinear ◽  
Paste Backfill ◽  
High Prediction ◽  
The Stability

Cemented paste backfill (CPB) is an eco-friendly composite containing mine waste or tailings and has been widely used as construction materials in underground stopes. In the field, the uniaxial compressive strength (UCS) of CPB is critical as it is closely related to the stability of stopes. Predicting the UCS of CPB using traditional mathematical models is far from being satisfactory due to the highly nonlinear relationships between the UCS and a large number of influencing variables. To solve this problem, this study uses a support vector machine (SVM) to predict the UCS of CPB. The hyperparameters of the SVM model are tuned using the beetle antennae search (BAS) algorithm; then, the model is called BSVM. The BSVM is then trained on a dataset collected from the experimental results. To explain the importance of each input variable on the UCS of CPB, the variable importance is obtained using a sensitivity study with the BSVM as the objective function. The results show that the proposed BSVM has high prediction accuracy on the test set with a high correlation coefficient (0.97) and low root-mean-square error (0.27 MPa). The proposed model can guide the design of CPB during mining.

scholarly journals Hybrid Failure of Cemented Paste Backfill

Minerals ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1141
Author(s):  
Andrew Pan ◽  
Mohammadamin Jafari ◽  
Lijie Guo ◽  
Murray Grabinsky
Keyword(s):  
Strength Properties ◽  
Shear Stresses ◽  
Cemented Paste Backfill ◽  
Test Procedures ◽  
Element Analysis ◽  
Laboratory Equipment ◽  
Proposed Model ◽  
Dimensional Finite Element ◽  
Paste Backfill ◽  
Coupled Stress

The hybrid failure is a coupled failure mechanism under the action of tensile and shear stresses. The failure is critical in cemented paste backfill (CPB) since there are no visible signs prior to the failure. Few studies have been conducted on the coupled stress response of CPB. This is most likely due to a lack of suitable laboratory equipment and test procedures. This paper presents a new punching shear apparatus to evaluate the hybrid failure of CPB. We harness two-dimensional finite element analysis (FEA) for supplementing experimental study in providing stress transformation, deformation, and possible failure mechanisms. Our study suggests that the coupled stress is a combination of tensile and shear strength in function of the angle of the frustum. The strengths measured by the coupled stress are comparable to those measured by direct shear and tensile strength tests, in which the strength properties of CPB are curing time and binder content dependent. The FEA results substantiate the effectiveness of proposed model for predicting the hybrid failure of CPB.

Exploratory and machine learning analysis of the stability constants of HgII- triazene ligands complexes

2021 ◽  
pp. 1-13
Author(s):  
Ahmadreza Hajihosseinloo ◽  
Maryam Salahinejad ◽  
Mohammad Kazem Rofouei ◽  
Jahan B. Ghasemi
Keyword(s):  
Stability Constants ◽  
Goodness Of Fit ◽  
Predictive Ability ◽  
Support Vector ◽  
Structure Property ◽  
Charge Effects ◽  
Proposed Model ◽  
The Stability ◽  
Leave One Out ◽  
External Test

Knowing stability constants for the complexes HgII with extracting ligands is very important from environmental and therapeutic standpoints. Since the selectivity of ligands can be stated by the stability constants of cation–ligand complexes, quantitative structure–property relationship (QSPR) investigations on binding constant of HgII complexes were done. Experimental data of the stability constants in ML2 complexation of HgII and synthesized triazene ligands were used to construct and develop QSPR models. Support vector machine (SVM) and multiple linear regression (MLR) have been employed to create the QSPR models. The final model showed squared correlation coefficient of 0.917 and the standard error of calibration (SEC) value of 0.141 log K units. The proposed model presented accurate prediction with the Leave-One-Out cross validation ( Q LOO 2  = 0.756) and validated using Y-randomization and external test set. Statistical results demonstrated that the proposed models had suitable goodness of fit, predictive ability, and robustness. The results revealed the importance of charge effects and topological properties of ligand in HgII - triazene complexation.

An Ensemble Random Forest Model to Predict Bead Geometry in GMAW Process

2021 ◽  
pp. 1-18
Author(s):  
Vahide Babaiyan ◽  
Nader Mollayi ◽  
Morteza Taheri ◽  
Majid Azargoman
Keyword(s):  
Random Forest ◽  
Gas Metal Arc Welding ◽  
Machine Learning Algorithms ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Bead Geometry ◽  
Weld Bead Geometry ◽  
Global Database ◽  
Proposed Model ◽  
Highly Nonlinear

A prevalent method for rapid prototyping of metallic parts is gas metal arc welding (GMAW). As the input parameters impose a highly nonlinear impact on the weld bead geometry, precise estimation of the geometry is a complex problem. Therefore, in this study, a novel combination of the most powerful machine learning algorithms is selected to overcome the complexity of the problem and also reach an acceptable degree of precision. To this end, the hybrid combination of the support vector machine (SVM) and relevance vector machine (RVM) is developed based on the random forest (RF) ensemble learning approach. The models are established based on a global database of welding geometry, and the corresponding process parameters obtained are based on a set of experiments. Performance evaluation between RVM, SVM, and the proposed model was performed based on the coefficient of determination ([Formula: see text]) and the ratio of root means square error (RMSE) to the maximum measured outputs ([Formula: see text]/[Formula: see text]). The RF-based RVM-SVM model obtained 0.9725 and 0.8850 for [Formula: see text] and 0.0257 and 0.0447 for [Formula: see text]/[Formula: see text] in predicting the height and width of the bead, respectively. The result clearly showed the effectiveness of the proposed model in predicting the GMAW trend.

scholarly journals Fluidization Analysis of Thickening in the Deep Cone for Cemented Paste Backfill

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Haiyong Cheng ◽  
Jin Liu ◽  
Shunchuan Wu ◽  
Xiaoqiang Zhang
Keyword(s):  
Critical Velocity ◽  
Failure Time ◽  
Cemented Paste Backfill ◽  
Tailings Pond ◽  
Paste Backfill ◽  
Efficient Machine ◽  
The Stability ◽  
Deep Cone Thickener ◽  
The Relationship ◽  
The Ideal

Cemented paste backfill (CPB) can effectively eliminate the risk of dam break in goaf and tailings pond which used tailings waste. Deep cone thickener (DCT) is an efficient machine for the system of paste preparation, and the concentration of slurry at the bottom is high and distributed unevenly, which will cause too much partial resistance and failure of thickener. Focusing on the above problems, fluidization design was conducted by using the fluidization theory. The delivery law of flocs was analyzed, and the isobaric surface was obtained. The equation of pressure and critical velocity of the ideal fluidized bed was acquired by analyzing the relationship between pressure and critical velocity. Based on the characteristics of tailings and distribution of the bonding zone, the arrangement, number, and working mode of spray nozzles were reformed. It is verified that the failure time of thickener decreased from 14 hours to 1 hour and the range of concentration increased from 74%∼78% to 78%∼80%, which improved the stability and reliability of DCT. The depth thickening mechanism is obtained, and the thickening method has been improved which provides a theoretical basis for the effective preparation of paste.

scholarly journals Analytical Assessment of Internal Stress in Cemented Paste Backfill

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Naifei Liu ◽  
Liang Cui ◽  
Yan Wang
Keyword(s):  
Internal Stress ◽  
Effective Stress ◽  
Water Retention ◽  
Operation Time ◽  
Model Parameters ◽  
Cemented Paste Backfill ◽  
Arching Effect ◽  
Proposed Model ◽  
Paste Backfill ◽  
Analytical Assessment

To analytically describe the internal stress in a fill mass made of granular man-made material (cemented paste backfill, CPB), a new 3D effective stress model is developed. The developed model integrates Bishop effective stress principle, water retention relationship, and arching effect. All model parameters are determined from measurable experimental data. The uncertainties of the model parameters are examined by sensitivity analysis. A series of model application is conducted to investigate the effects of field conditions on the internal stress in CPB. The obtained results show that the proposed model is able to capture the influence of operation time, stope geometry, and rock/CPB interface properties on the effective stress in CPB. Hence, the developed model can be used as a useful tool for the optimal design of CPB structure.

scholarly journals Identification of heat-integrated distillation column using hybrid support vector regression and particle swarm optimization

2018 ◽  
Vol 24 (2) ◽  
pp. 101-115 ◽  
Author(s):  
Abdul Jaleel ◽  
K. Aparna
Keyword(s):  
Particle Swarm Optimization ◽  
Support Vector Regression ◽  
Distillation Column ◽  
Particle Swarm ◽  
High Energy ◽  
Simulation Software ◽  
Support Vector ◽  
Swarm Optimization ◽  
Proposed Model ◽  
Highly Nonlinear

Distillation is the most commonly used method for separating fluid mixtures in oil and gas industries. It is a process that requires high energy usage. One of the efficient ways to save energy in a distillation column is by heat integration. One such type of distillation column is called a heat-integrated distillation column (HIDC). In HIDC, the prediction of mole fractions of the component in the product can be made using proper identification, or modeling, of the HIDC. However, nonlinear modeling of HIDC is a highly challenging task. Methods based on first principles are not sufficient for a highly nonlinear HIDC. Hence, a novel method for identification of HIDC using a non-parametric ?support vector regression (SVR)? method for predicting benzene composition in benzene-toluene HIDC is proposed in this work. The data used for identification is generated using process simulation software HYSYS. 100 samples of data were used for training and 50 samples of data were employed for validating the model. Particle swarm optimization (PSO) was also incorporated with SVR for obtaining optimized parameters of SVR. The proposed model is compared with other SVR models optimized with optimization methods other than PSO. The proposed model showed better performance over others.

scholarly journals Prediction Models for Evaluating the Strength of Cemented Paste Backfill: A Comparative Study

Minerals ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 1041
Author(s):  
Jiandong Liu ◽  
Guichen Li ◽  
Sen Yang ◽  
Jiandong Huang
Keyword(s):  
Prediction Models ◽  
Underground Mining ◽  
Back Propagation ◽  
Fine Sand ◽  
Back Propagation Neural Network ◽  
Support Vector ◽  
Cemented Paste Backfill ◽  
Cement Ratio ◽  
Paste Backfill ◽  
Future Work

Cemented paste backfill (CPB) is widely used in underground mining, and attracts more attention these years as it can reduce mining waste and avoid environmental pollution. Normally, to evaluate the functionality of CPB, the compressive strength (UCS) is necessary work, which is also time and money consuming. To address this issue, seven machine learning models were applied and evaluated in this study, in order to predict the UCS of CPB. In the laboratory, a series of tests were performed, and the dataset was constructed considering five key influencing variables, such as the tailings to cement ratio, curing time, solids to cement ratio, fine sand percentage and cement types. The results show that different variables have various effects on the strength of CPB. The optimum models for predicting the UCS of CPB are a support vector machine (SVM), decision tree (DT), random forest (RF) and back-propagation neural network (BPNN), which means that these models can be directly applied for UCS prediction in future work. Furthermore, the intelligent model reveals that the tailings to cement ratio has the most important influence on the strength of CPB. This research can boost CPB application in the field, and guide the artificial intelligence application in future mining.

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