scholarly journals A Novel Prediction Model of Strength of Paste Backfill Prepared from Waste-Unclassified Tailings

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Haiyong Cheng ◽  
Shunchuan Wu ◽  
Xiaoqiang Zhang ◽  
Junhong Li
Keyword(s):  
Prediction Model ◽  
Prediction Models ◽  
Orthogonal Design ◽  
Influential Factors ◽  
Waste Rock ◽  
Strength Prediction ◽  
Strength Development ◽  
Local Conditions ◽  
Unclassified Tailings ◽  
Paste Backfill

Paste backfilling is an important support for the development of green mines and deep mining. It can effectively reduce a series of risks of underground goaf and surface tailings ponds. Reasonable strength of backfill is an effective guarantee for controlling ground stress and realizing safe mining function. Under the combination of complex materials and local conditions, ensuring the optimal design and effective proportion for paste backfill strength is the bottleneck problem that restricts the safety, economy, and efficiency of filling mining. The strength developing trend of paste backfilling prepared from waste rock and unclassified tailings has been studied. Different levels of cement contents, tailings-waste ratios, and slurry concentrations were investigated through orthogonal design to obtain the relationship between the UCS and the multi-influential factors. Combined with the experimental results and the previous strength prediction models, the waste rock-unclassified tailings paste strength prediction model was proposed. Introducing the water-cement ratio, the cement-tailings ratio, the amount of cement, and the packing density that characterizing the overall gradation of unclassified tailings and waste rock, as well as the curing time, a strength prediction model of multifactors was developed. Moreover, the microscopic structure of the paste prepared from waste-unclassified tailings was analyzed with an Environment Scanning Electron Microscope (ESEM), and the influence mechanism was ascertained. The weight coefficient of strength development is carded in this paper, and the strength model of unclassified tailings-waste paste considering five factors is obtained, which is of great significance to guide the mining engineering.

Experiments and Optimization of Mix Proportions for Cement Paste Backfill Materials with Extra-Fine Unclassified Tailings

2017 ◽  
Vol 744 ◽  
pp. 146-151
Author(s):  
Ming Qing Huang ◽  
Xiao Hui Liu ◽  
Hai Yong Cheng
Keyword(s):  
Cement Paste ◽  
Mass Concentration ◽  
Influential Factors ◽  
Range Analysis ◽  
Bleeding Rate ◽  
Orthogonal Experiments ◽  
Optimal Mix ◽  
Backfill Materials ◽  
Unclassified Tailings ◽  
Paste Backfill

To produce effective cement paste backfill materials at iron mines technologically and economically, orthogonal experiments of mix proportions with extra-fine unclassified tailings were carried out. The results of the range analysis showed that the sensitivity of influential factors to the slurry slump is sequentially mass concentration, tailing/rock ratio, and cement/(tailing+rock) ratio. The sensitivity to bleeding rate, concretion time and 28-day uniaxial compressive strength is sequentially mass concentration, cement/(tailing+rock) ratio and tailing/rock ratio. Relationships of paste properties and influential factors can be demonstrated with regression analysis. Additionally, the optimal mix proportion for cement paste backfill was obtained with 78% mass concentration, 7:3 tailing/rock ratio and 1:25 cement/(tailing+rock) ratio. The slump, bleeding rate, concretion time and R28 of the optimal mixture are 25.2 cm, 8.77%, 20.9 h and 1.29 MPa, respectively. The experimental results show a feasible way to produce the industry standard backfilling materials.

scholarly journals Predicting the Insolvency of SMEs Using Technological Feasibility Assessment Information and Data Mining Techniques

2020 ◽  
Vol 12 (23) ◽  
pp. 9790
Author(s):  
Sanghoon Lee ◽  
Keunho Choi ◽  
Donghee Yoo
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Prediction Model ◽  
Internal Control ◽  
Prediction Models ◽  
Influential Factors ◽  
Financial Information ◽  
Data Mining Techniques ◽  
Feasibility Assessment ◽  
Using Data

The government makes great efforts to maintain the soundness of policy funds raised by the national budget and lent to corporate. In general, previous research on the prediction of company insolvency has dealt with large and listed companies using financial information with conventional statistical techniques. However, small- and medium-sized enterprises (SMEs) do not have to undergo mandatory external audits, and the quality of accounting information is low due to weak internal control. To overcome this problem, we developed an insolvency prediction model for SMEs using data mining techniques and technological feasibility assessment information as non-financial information. We divided the dataset into two types of data based on three years of corporate age. The synthetic minority over-sampling technique (SMOTE) was used to solve the data imbalance that occurred at this time. Six insolvency prediction models were created using logistic regression, a decision tree, an artificial neural network, and an ensemble (i.e., boosting) of each algorithm. By applying a boosted decision tree, the best accuracies of 69.1% and 82.7% were derived, and by applying a decision tree, nine and seven influential factors affected the insolvency of SMEs established for fewer than three years and more than three years, respectively. In addition, we derived several insolvency rules for the two types of SMEs from the decision tree-based prediction model and proposed ways to enhance the health of loans given to potentially insolvent companies using these derived rules. The results of this study show that it is possible to predict SMEs’ insolvency using data mining techniques with technological feasibility assessment information and find meaningful rules related to insolvency.

Application of the Prediction Model "SCHALL 03" for Railway Noise Calculation in Serbia

2013 ◽  
Vol 430 ◽  
pp. 237-243 ◽  
Author(s):  
Momir Praščevič ◽  
Aleksandar Gajicki ◽  
Darko Mihajlov ◽  
Nenad Živković ◽  
Ljiljana Zivkovic
Keyword(s):  
Prediction Model ◽  
Prediction Models ◽  
National Level ◽  
Rolling Stock ◽  
Railway Network ◽  
Local Conditions ◽  
Railway Noise ◽  
Technological Characteristics ◽  
Accuracy And Precision ◽  
Railway Infrastructure

Application of the prediction models for railway noise indicators calculation, which was already developed by other countries, represents a major challenge in Serbia. Prediction model "Schall 03" was developed in line with technical and technological characteristics of the rolling stock and infrastructure of German Railways. Prior to its application at the national level, due to different technical-technological characteristics of railway stock and railway infrastructure it is necessary to perform its validation and, depending on the needs, the calibration in accordance with local conditions. This will assure accuracy and precision of the calculations of noise indicators, as well as confidence in the results obtained by prediction model "Schall 03". This paper presents the analysis of the possibilities to apply German prediction model "Schall 03" on the Serbian railway network, more precisely railway section from Belgrade to Romanian border. Calculated values of noise indicators were compared with the results of measurements of noise indicators within measuring interval, which correspond to the referent time intervals for different day periods.

A building carbon emission prediction model by PSO-SVR method under multi-criteria evaluation

2021 ◽  
pp. 1-12
Author(s):  
Xiaolin Chu ◽  
Ruijuan Zhao
Keyword(s):  
Prediction Model ◽  
Environmental Sustainability ◽  
Carbon Emission ◽  
Prediction Models ◽  
Influential Factors ◽  
Support Vector ◽  
Model Parameters ◽  
Low Carbon ◽  
Practical Applications ◽  
Multi Criteria Evaluation

Building carbon emission prediction plays an irreplaceable role in low-carbon economy development, public health protection and environmental sustainability. It is significant to identify influential factors mainly contributed to building emission and predict emission accurately in order to harness the growth from the source. In this paper, 11 influencing factors of building carbon emission are identified and a support vector regression (SVR) prediction model is proposed to forecast building carbon emission considering improvement the prediction accuracy, generalization, and robustness. In the SVR model, parameters are optimized by particle swarm optimization (PSO) algorithm with the aim to improve performance. Cases in Shanghai’s building sector are adopted to demonstrate practical applications of the proposed PSO-SVR prediction model. The results indicate that the presented prediction system has an outstanding performance in forecasting building carbon emission under multi-criteria evaluation. Furthermore, compared to the results from other four prediction models (e.g., linear regression, decision tree), it is shown that PSO-SVR model can achieve higher accuracy (e.g., improvement average of 1.01% R2 under training subset), better generalization (e.g., improvement average of 19.89% R2 under testing subset), and better robustness (e.g., improvement average of 18.93% R2 under different levels of noise intensity).

scholarly journals Development of Roughness Prediction Models for Laos National Road Network

CivilEng ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 158-173
Author(s):  
Mohamed Gharieb ◽  
Takafumi Nishikawa
Keyword(s):  
Prediction Model ◽  
Road Network ◽  
Prediction Models ◽  
Parameter Analysis ◽  
Local Conditions ◽  
Pavement Condition ◽  
Analysis Technique ◽  
Roughness Prediction ◽  
National Road ◽  
Development And Management

The International Roughness Index (IRI) has been accepted globally as an essential indicator for assessing pavement condition. The Laos Road Management System (RMS) utilizes a default Highway Development and Management (HDM-4) IRI prediction model. However, developed IRI values have shown the need to calibrate the IRI prediction model. Data records are not fully available for Laos yet, making it difficult to calibrate IRI for the local conditions. This paper aims to develop an IRI prediction model for the National Road Network (NRN) based on the available Laos RMS database. The Multiple Linear Regression (MLR) analysis technique was applied to develop two new IRI prediction models for Double Bituminous Surface Treatment (DBST) and Asphalt Concrete (AC) pavement sections. The final database consisted of 83 sections with 269 observations over a 1850 km length of DBST NRN and 29 sections with 122 observations over a 718 km length of AC NRN. The proposed models predict IRI as a function of pavement age and Cumulative Equivalent Single-Axle Load (CESAL). The model’s parameter analysis confirmed their significance, and R2 values were 0.89 and 0.84 for DBST and AC models, respectively. It can be concluded that the developed models can serve as a useful tool for engineers maintaining paved NRN.

Strength prediction model considering material, ultrasonic and stress of cemented waste rock backfill for recycling gangue

2020 ◽  
Vol 276 ◽  
pp. 123189 ◽  
Author(s):  
Jiangyu Wu ◽  
Hongwen Jing ◽  
Qian Yin ◽  
Liyuan Yu ◽  
Bo Meng ◽  
...  
Keyword(s):  
Prediction Model ◽  
Waste Rock ◽  
Strength Prediction

Bioactivity Prediction Based on Matched Molecular Pair and Matched Molecular Series Methods

2020 ◽  
Vol 26 (33) ◽  
pp. 4195-4205
Author(s):  
Xiaoyu Ding ◽  
Chen Cui ◽  
Dingyan Wang ◽  
Jihui Zhao ◽  
Mingyue Zheng ◽  
...  
Keyword(s):  
Prediction Model ◽  
Large Scale ◽  
Prediction Models ◽  
Predictive Accuracy ◽  
Lead Optimization ◽  
Consensus Method ◽  
Molecular Pair ◽  
Bioactivity Prediction ◽  
Compound Synthesis ◽  
Consensus Modeling

Background: Enhancing a compound’s biological activity is the central task for lead optimization in small molecules drug discovery. However, it is laborious to perform many iterative rounds of compound synthesis and bioactivity tests. To address the issue, it is highly demanding to develop high quality in silico bioactivity prediction approaches, to prioritize such more active compound derivatives and reduce the trial-and-error process. Methods: Two kinds of bioactivity prediction models based on a large-scale structure-activity relationship (SAR) database were constructed. The first one is based on the similarity of substituents and realized by matched molecular pair analysis, including SA, SA_BR, SR, and SR_BR. The second one is based on SAR transferability and realized by matched molecular series analysis, including Single MMS pair, Full MMS series, and Multi single MMS pairs. Moreover, we also defined the application domain of models by using the distance-based threshold. Results: Among seven individual models, Multi single MMS pairs bioactivity prediction model showed the best performance (R2 = 0.828, MAE = 0.406, RMSE = 0.591), and the baseline model (SA) produced the most lower prediction accuracy (R2 = 0.798, MAE = 0.446, RMSE = 0.637). The predictive accuracy could further be improved by consensus modeling (R2 = 0.842, MAE = 0.397 and RMSE = 0.563). Conclusion: An accurate prediction model for bioactivity was built with a consensus method, which was superior to all individual models. Our model should be a valuable tool for lead optimization.

Fire modelling in Tasmanian buttongrass moorlands. III. Dead fuel moisture

2001 ◽  
Vol 10 (2) ◽  
pp. 241 ◽  
Author(s):  
Jon B. Marsden-Smedley ◽  
Wendy R. Catchpole
Keyword(s):  
Prediction Model ◽  
Regression Model ◽  
Fire Management ◽  
Prediction Models ◽  
Dew Point ◽  
Seasonal Effects ◽  
Experimental Program ◽  
Fuel Moisture ◽  
Fire Behaviour ◽  
Fire Modelling

An experimental program was carried out in Tasmanian buttongrass moorlands to develop fire behaviour prediction models for improving fire management. This paper describes the results of the fuel moisture modelling section of this project. A range of previously developed fuel moisture prediction models are examined and three empirical dead fuel moisture prediction models are developed. McArthur’s grassland fuel moisture model gave equally good predictions as a linear regression model using humidity and dew-point temperature. The regression model was preferred as a prediction model as it is inherently more robust. A prediction model based on hazard sticks was found to have strong seasonal effects which need further investigation before hazard sticks can be used operationally.

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