Optimized Support Vector Machines Combined with Evolutionary Random Forest for Prediction of Back-Break Caused by Blasting Operation DOI Open Access

Qun Yu,

Masoud Monjezi, Ahmed Salih Mohammed

et al.

Sustainability, Journal Year: 2021, Volume and Issue: 13(22), P. 12797 - 12797

Published: Nov. 19, 2021

Back-break is an adverse event in blasting works that causes the instability of mine walls, equipment collapsing, and reduction effectiveness drilling. Therefore, it boosts total cost mining operations. This investigation intends to develop optimized support vector machine models forecast back-break caused by blasting. The Support Vector Machine (SVM) model was using two advanced metaheuristic algorithms, including whale optimization algorithm (WOA) moth–flame (MFO). Before models’ development, evolutionary random forest (ERF) technique used for input selection. selected five inputs out 10 candidate be predict back break. These SVM were evaluated various performance criteria. these also compared with other hybridized models. In addition, a sensitivity evaluation made find how influence magnitude. outcomes this study demonstrated both SVM–MFO SVM–WOA improved standard SVM. Additionally, showed better than research recommend can considered as powerful induced

Language: Английский

Clayey soil stabilization using alkali-activated volcanic ash and slag DOI Creative Commons

Hania Miraki,

Nader Shariatmadari, Pooria Ghadir

et al.

Journal of Rock Mechanics and Geotechnical Engineering, Journal Year: 2021, Volume and Issue: 14(2), P. 576 - 591

Published: Nov. 20, 2021

Lime and Portland cement are the most widely used binders in soil stabilization projects. However, due to high carbon emission production, research on by use of more environmentally-friendly with lower footprint has attracted much attention recent years. This investigated potential using alkali-activated ground granulated blast furnace slag (GGBS) volcanic ash (VA) as green clayey projects, which not been studied before. The effects different combinations VA GGBS, various liquid/solid ratios, curing conditions, periods (i.e. 7 d, 28 d 90 d) were investigated. Compressive strength durability specimens against wet-dry freeze-thaw cycles then through mechanical microstructural tests. results demonstrated that coexistence GGBS geopolymerization process was effective synergic formation N-A-S-H C-(A)-S-H gels. Moreover, although needs heat become activated develop strength, its partial replacement made binder suitable for application at ambient temperature resulted a remarkably superior resistance cycles. embodied mixtures also evaluated, confirmed low footprints mixtures. Finally, it concluded GGBS/VA could be promisingly projects instead conventional binders.

Language: Английский

Citations

156
Convolution-based ensemble learning algorithms to estimate the bond strength of the corroded reinforced concrete DOI
Liborio Cavaleri, Mohammad Sadegh Barkhordari, Constantinos C. Repapis

et al.

Construction and Building Materials, Journal Year: 2022, Volume and Issue: 359, P. 129504 - 129504

Published: Oct. 28, 2022

Language: Английский

Citations

108
Metamodel techniques to estimate the compressive strength of UHPFRC using various mix proportions and a high range of curing temperatures DOI

Wael Emad,

Ahmed Salih Mohammed, Ana Brás

et al.

Construction and Building Materials, Journal Year: 2022, Volume and Issue: 349, P. 128737 - 128737

Published: Aug. 18, 2022

Language: Английский

Citations

98
Slope Stability Classification under Seismic Conditions Using Several Tree-Based Intelligent Techniques DOI Creative Commons
Panagiotis G. Asteris,

Fariz Iskandar Mohd Rizal,

Mohammadreza Koopialipoor

et al.

Applied Sciences, Journal Year: 2022, Volume and Issue: 12(3), P. 1753 - 1753

Published: Feb. 8, 2022

Slope stability analysis allows engineers to pinpoint risky areas, study trigger mechanisms for slope failures, and design slopes with optimal safety reliability. Before the widespread usage of computers, was conducted through semi analytical methods, or charts. Presently, have developed many computational tools perform more efficiently. The challenge associated furthering methods is create a reliable solution estimations involving number geometric mechanical variables. objective this investigate application tree-based models, including decision tree (DT), random forest (RF), AdaBoost, in classification under seismic loading conditions. input variables used modelling were height, inclination, cohesion, friction angle, peak ground acceleration classify safe unsafe slopes. training data intelligence models resulted from series analyses performed using standard geotechnical engineering software commonly practice. Upon construction model assessment use calculation accuracy, F1-score, recall, precision indices. All could efficiently status, AdaBoost providing highest performance both development parts. proposed can be as screening tool during stage feasibility studies related infrastructure projects, according their expected status

Language: Английский

Citations

85
A case study of resilient modulus prediction leveraging an explainable metaheuristic-based XGBoost DOI
Biao He, Danial Jahed Armaghani, Markos Z. Tsoukalas

et al.

Transportation Geotechnics, Journal Year: 2024, Volume and Issue: 45, P. 101216 - 101216

Published: Feb. 18, 2024

Language: Английский

Citations

19
Mechanical Framework for Geopolymer Gels Construction: An Optimized LSTM Technique to Predict Compressive Strength of Fly Ash-Based Geopolymer Gels Concrete DOI Creative Commons
Xuyang Shi, Shuzhao Chen, Qiang Wang

et al.

Gels, Journal Year: 2024, Volume and Issue: 10(2), P. 148 - 148

Published: Feb. 16, 2024

As an environmentally responsible alternative to conventional concrete, geopolymer concrete recycles previously used resources prepare the cementitious component of product. The challenging issue with employing in building business is absence a standard mix design. According chemical composition its components, this work proposes thorough system or framework for estimating compressive strength fly ash-based (FAGC). It could be possible construct predicting FAGC using soft computing methods, thereby avoiding requirement time-consuming and expensive experimental tests. A complete database 162 datasets was gathered from research papers that were published between years 2000 2020 prepared develop proposed models. To address relationships inputs output variables, long short-term memory networks deployed. Notably, model examined several methods. modeling process incorporated 17 variables affect CSFAG, such as percentage SiO

Language: Английский

Citations

17
Dimensionless Machine Learning: Dimensional Analysis to Improve LSSVM and ANN models and predict bearing capacity of circular foundations DOI Creative Commons
Hongchao Li, Shahab Hosseini, Behrouz Gordan

et al.

Artificial Intelligence Review, Journal Year: 2025, Volume and Issue: 58(4)

Published: Jan. 30, 2025

Language: Английский

Citations

2
Prediction of resilient modulus of ballast under cyclic loading using machine learning techniques DOI
Buddhima Indraratna, Danial Jahed Armaghani, A. Gomes Correia

et al.

Transportation Geotechnics, Journal Year: 2022, Volume and Issue: 38, P. 100895 - 100895

Published: Nov. 5, 2022

Language: Английский

Citations

51
Several Tree-Based Solutions for Predicting Flyrock Distance Due to Mine Blasting DOI Creative Commons
Mojtaba Yari, Danial Jahed Armaghani, Chrysanthos Maraveas

et al.

Applied Sciences, Journal Year: 2023, Volume and Issue: 13(3), P. 1345 - 1345

Published: Jan. 19, 2023

Blasting operations involve some undesirable environmental issues that may cause damage to equipment and surrounding areas. One of them, probably the most important one, is flyrock induced by blasting, where its accurate estimation before operation essential identify blasting zone’s safety zone. This study introduces several tree-based solutions for an prediction flyrock. has been done using four techniques, i.e., decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), adaptive (AdaBoost). The modelling techniques was conducted with in-depth knowledge understanding their influential factors. mentioned factors were designed through use parametric investigations, which can also be utilized in other engineering fields. As a result, all models are capable enough blasting-induced prediction. However, predicted values obtained AdaBoost technique. Observed forecasted training testing phases received coefficients determination (R2) 0.99 0.99, respectively, confirm power this technique estimating Additionally, according results input parameters, powder factor had highest influence on flyrock, whereas burden spacing lowest impact

Language: Английский

Citations

31
Decision tree models for the estimation of geo-polymer concrete compressive strength DOI Creative Commons
Ji Zhou,

Zhanlin Su,

Shahab Hosseini

et al.

Mathematical Biosciences & Engineering, Journal Year: 2023, Volume and Issue: 21(1), P. 1413 - 1444

Published: Jan. 1, 2023

<abstract> <p>The green concretes industry benefits from utilizing gel to replace parts of the cement in concretes. However, measuring compressive strength geo-polymer (CSGPoC) needs a significant amount work and expenditure. Therefore, best idea is predicting CSGPoC with high level accuracy. To do this, base learner super machine learning models were proposed this study anticipate CSGPoC. The decision tree (DT) applied as learner, random forest extreme gradient boosting (XGBoost) techniques are used system. In regard, database was provided involving 259 data samples, which four-fifths considered for training model one-fifth selected testing models. values fly ash, ground-granulated blast-furnace slag (GGBS), Na2SiO3, NaOH, fine aggregate, gravel 4/10 mm, 10/20 water/solids ratio, NaOH molarity input estimate evaluate reliability performance (DT), XGBoost, (RF) models, 12 evaluation metrics determined. Based on obtained results, highest degree accuracy achieved by XGBoost mean absolute error (MAE) 2.073, percentage (MAPE) 5.547, Nash–Sutcliffe (NS) 0.981, correlation coefficient (R) 0.991, R<sup>2</sup> 0.982, root square (RMSE) 2.458, Willmott's index (WI) 0.795, weighted (WMAPE) 0.046, Bias (SI) 0.054, p 0.027, relative (MRE) -0.014, a<sup>20</sup> 0.983 MAE 2.06, MAPE 6.553, NS 0.985, R 0.993, 0.986, RMSE 2.307, WI 0.818, WMAPE 0.05, SI 0.056, 0.028, MRE -0.015, 0.949 model. By importing set into trained 0.8969, 0.9857, 0.9424 DT, RF, respectively, show superiority estimation. conclusion, capable more accurately than DT RF models.</p> </abstract>

Language: Английский

Citations

28