Strength Model of Cemented Filling Body Based on a Neural Network Algorithm DOI Open Access
Daiqiang Deng, Yihua Liang, Guodong Cao

et al.

Mathematical Problems in Engineering, Journal Year: 2022, Volume and Issue: 2022, P. 1 - 10

Published: April 29, 2022

As one of the key measures for comprehensive management goaf in various mines, filling mining has been recognized by practitioners recent years due to its functions (e.g., resource utilization solid waste and thorough treatment). The performance material is core challenge mining, it influenced settling speed, conveying characteristics, body strength. To understand strength characteristics a cemented composed medium-fine tailings, this study, ratio tests under different content cement, water were conducted. A backpropagation (BP) neural network topology structure was established study. after curing times used as output variable analyze impact on body. 3-Hn-3 structural model employed. When number hidden layers Hn 7, achieved best learning training effect. results show that predicted value, which close measured value (fitting accuracy 92.43–99.92%; average error 0.0792–7.5682%), satisfies engineering requirements. can be employed predict body’s provide good reference change law

Language: Английский

Assessment of tunnel blasting-induced overbreak: A novel metaheuristic-based random forest approach DOI
Biao He, Danial Jahed Armaghani, Sai Hin Lai

et al.

Tunnelling and Underground Space Technology, Journal Year: 2023, Volume and Issue: 133, P. 104979 - 104979

Published: Jan. 5, 2023

Language: Английский

Citations

76
Intelligent prediction of rock mass deformation modulus through three optimized cascaded forward neural network models DOI
Mahdi Hasanipanah, Mehdi Jamei, Ahmed Salih Mohammed

et al.

Earth Science Informatics, Journal Year: 2022, Volume and Issue: 15(3), P. 1659 - 1669

Published: May 31, 2022

Language: Английский

Citations

39
Study on permeability performance of cemented tailings backfill based on fractal characteristics of pore structure DOI
Yao Liu, Hongwei Deng

Construction and Building Materials, Journal Year: 2022, Volume and Issue: 365, P. 130035 - 130035

Published: Dec. 20, 2022

Language: Английский

Citations

33
Rheological behavior with time dependence and fresh slurry liquidity of cemented aeolian sand backfill based on response surface method DOI
Shushuai Wang, Yongliang Li, Renshu Yang

et al.

Construction and Building Materials, Journal Year: 2023, Volume and Issue: 371, P. 130768 - 130768

Published: Feb. 22, 2023

Language: Английский

Citations

19
Prediction of Flyrock Distance in Surface Mining Using a Novel Hybrid Model of Harris Hawks Optimization with Multi-strategies-based Support Vector Regression DOI
Chuanqi Li, Jian Zhou, Kun Du

et al.

Natural Resources Research, Journal Year: 2023, Volume and Issue: 32(6), P. 2995 - 3023

Published: Sept. 4, 2023

Language: Английский

Citations

19
Synthesis of cemented paste backfill by reutilizing multiple industrial waste residues and ultrafine tailings: Strength, microstructure, and GA-GPR prediction modeling DOI
Qianlong Li,

Bingwen Wang,

Lei Yang

et al.

Powder Technology, Journal Year: 2023, Volume and Issue: 434, P. 119337 - 119337

Published: Dec. 27, 2023

Language: Английский

Citations

12
Rheological properties of cemented paste backfill and the construction of a prediction model DOI Creative Commons

Yonghui Niu,

Haiyong Cheng,

Shunchuan Wu

et al.

Case Studies in Construction Materials, Journal Year: 2022, Volume and Issue: 16, P. e01140 - e01140

Published: May 11, 2022

The Cemented Paste Backfill (CPB) yield stress is a key rheological parameter for paste filling technology, which has significant practical value pipeline optimization and equipment selection of conveying systems. However, the slurry affected by many factors. In order to accurately analyze predict CPB stress, this study uses sparrow search algorithm optimize relevance vector machine (SSA-RVM) proposes prediction model SSA-RVM regression. Based on 136 sets tests copper mine, different waste rock/tailing sand ratios, mass concentrations, water-cement ratios select at training set (78%, 85%, 92%). Compared with traditional (RVM) regression model, higher accuracy. addition, coefficient determination R2 predicted true values obtained from increased 0.0407, 0.0438, 0.0500 78%, 92%, respectively. results suggested that can efficiently be reference design paste-filled pipe

Language: Английский

Citations

18
Machine Learning Algorithm-Based Prediction Model and Software Implementation for Strength Efficiency of Cemented Tailings Fills DOI Open Access
Hui Cao, Aiai Wang, Erol Yilmaz

et al.

Minerals, Journal Year: 2025, Volume and Issue: 15(4), P. 405 - 405

Published: April 11, 2025

A novel artificial intelligence (AI) application was proposed in the current study to predict CTF’s compressive strength (CS). The database contained six input parameters: age of curing for specimens (AS), cement–sand ratio (C/S), maintenance temperature (T), additives (EA), additive type (AT), concentration (AC), and one output parameter: CS. Then, adaptive boosting (AdaBoost) applied existing AI soft computing techniques, using AdaBoost, random forest (RF), SVM, ANN. Data were arbitrarily separated into training (70%) test (30%) sets. Results confirm that AdaBoost RF have best prediction accuracy, with a correlation coefficient (R2) 0.957 between these sets AdaBoost. Using Python 3.9 (64-bit), IDLE (Python 64-bit), PyQt5 achieve machine learning model computation software function interface development, this can quickly property CTF specimens, which saves time costs efficiently backfill researchers developing new eco-efficient components.

Language: Английский

Citations

0
Stated preference survey for predicting eco-friendly transportation choices among Mansoura University students DOI Creative Commons
Usama Elrawy Shahdah,

Marwa Elharoun,

Eman K. Ali

et al.

Innovative Infrastructure Solutions, Journal Year: 2025, Volume and Issue: 10(5)

Published: April 19, 2025

Language: Английский

Citations

0
The Optimization Study of Karst-Filling Clay-Cement Grout Based on Orthogonal Experiment and Regression Analysis DOI Open Access

Wenqin Yan,

Chao Deng,

Yuehui Cai

et al.

Materials, Journal Year: 2025, Volume and Issue: 18(9), P. 1943 - 1943

Published: April 24, 2025

During shield tunnel construction, karst development along the axis and in surrounding area frequently poses a significant threat to engineering safety. To address this challenge, study proposes multiple grouting systems systematically analyzes key mechanical properties of five grout formulations through orthogonal experiments, identifying optimal for applications. A predictive model was established using linear regression, its accuracy validated independent single-factor experiments. The results indicate following: (1) Water content is primary factor influencing fluidity, with significance varying by system composition. lake mud-cement exhibits highest compressive pstrength. Moderate sand addition enhances strength, but excessive amounts significantly reduce fluidity. Additives demonstrate dependency: HY-4 effectively improves while sodium silicate increases strength reduces (2) developed demonstrates good goodness fit, coefficients determination (R2) ranging from 0.74 0.95, without autocorrelation or multicollinearity. Validation experiments confirm model’s high accuracy, overall trends consistent measured data. (3) (A3B1C3) recommended reinforcement projects prioritizing stability, achieving 28-day 4.74 MPa. on-site wet clay-cement (A2B3C1) suitable high-permeability formations, 1.1 MPa fluidity 292.5 mm, both exceeding standard requirements. findings provide optimized theoretical references projects.

Language: Английский

Citations

0