Estimating dynamic compressive strength of rock subjected to freeze-thaw weathering by data-driven models and non-destructive rock properties DOI
Shengtao Zhou, Yu Lei, Zong‐Xian Zhang

et al.

Nondestructive Testing And Evaluation, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 24

Published: Feb. 5, 2024

The dynamic compressive strength (DCS) of frozen-thawed rock influences the stability mass in cold regions, especially when masses are possibly disturbed by loads. Laboratory freeze-thaw weathering treatment is usually time-consuming, and test destructive. Therefore, this paper attempts to quickly predict DCS sandstones using data-driven methods, non-destructive properties, basic environmental parameters. sparrow search algorithm (SSA), gorilla troops optimiser, dung beetle optimiser were chosen develop two hyperparameters random forest (RF). classic RF, back propagation neural network, support vector regression models taken as control group. These six developed DCS. Their prediction results compared. Finally, sensitivity analysis was carried out assess significance all input variables. indicate that SSA – RF model yields best result, three optimised have better performance than single machine-learning models. Strain rate, dry density, wave velocity found be most important parameters prediction, which further indicates there also a strong correlation between characteristic impedance

Language: Английский

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
Machine learning based computational approach for crack width detection of self-healing concrete DOI
Fadi Althoey, Muhammad Nasir Amin,

Kaffayatullah Khan

et al.

Case Studies in Construction Materials, Journal Year: 2022, Volume and Issue: 17, P. e01610 - e01610

Published: Oct. 25, 2022

Language: Английский

Citations

56
Compressive strength prediction of hollow concrete masonry blocks using artificial intelligence algorithms DOI
Pouyan Fakharian, Danial Rezazadeh Eidgahee, Mahdi Akbari

et al.

Structures, Journal Year: 2022, Volume and Issue: 47, P. 1790 - 1802

Published: Dec. 14, 2022

Language: Английский

Citations

55
Compressive strength evaluation of cement-based materials in sulphate environment using optimized deep learning technology DOI Creative Commons
Yang Yu, Chunwei Zhang,

Xingyang Xie

et al.

Developments in the Built Environment, Journal Year: 2023, Volume and Issue: 16, P. 100298 - 100298

Published: Dec. 1, 2023

Strength serves as a vital performance metric for assessing long-term durability of cement-based materials. Nevertheless, there is scarcity models available predicting residual strength in-situ structures made materials exposed to sulphate conditions. To address this challenge, study presents novel approach using deep learning predict the degradation compressive under marine environments. Specifically, convolutional neural network (DCNN) established, consisting two layers, one pooling layer, and fully connected layers. In innovative model, contents cement, water-to-cement ratio, sand, concentration exposure temperature are selected inputs, while output subjected deterioration. improve forecast capability, particle swarm optimization adopted optimizing hyperparameters DCNN, which can be implemented by reducing discrepancy between model prediction measured strength. Finally, experimental data used establish evaluate proposed method. The results show that learning-based predictive has best suffering from attack via comparison with other commonly models. outcome research offers potential solution remaining undergo practical attack.

Language: Английский

Citations

41
A novel approach to predicting the stability of the smart grid utilizing MLP-ELM technique DOI Creative Commons
Amjad Alsirhani,

Mohammed Mujib Alshahrani,

Abdulwahab Abukwaik

et al.

Alexandria Engineering Journal, Journal Year: 2023, Volume and Issue: 74, P. 495 - 508

Published: May 23, 2023

Evaluating and forecasting stability across different conditions is essential since smart grid stabilization among the most significant characteristics that could be employed to assess functionality of design. Some intelligent methods foresee are required mitigate unintended instability in a This due rise domestic commercial constructions incorporation green energy into grids. It currently hard forecast grid. In this framework, with reliable mechanisms being implemented meet fluctuating demands as well providing more availability. The involvement consumers producers one many factors influencing grid's stability. study suggested novel approach for locating statistics systems utilizing machine learning frameworks was presented. paper outlined multi-layer perceptron-Extreme Learning Machine (MLP-ELM) methodology predict sustainability Additionally, utilized principal component analysis (PCA) extracting features. addition an empirical assessment comparison various approaches, article presents implementation result Simulation findings demonstrate MLP-ELM outperforms traditional techniques, accuracy reaching up 95.8%, precision at 90%, recall 88%, F-measure 89%.

Language: Английский

Citations

40
Intelligent based decision-making strategy to predict fire intensity in subsurface engineering environments DOI
Muhammad Kamran, Ridho Kresna Wattimena, Danial Jahed Armaghani

et al.

Process Safety and Environmental Protection, Journal Year: 2023, Volume and Issue: 171, P. 374 - 384

Published: Jan. 2, 2023

Language: Английский

Citations

35
Prediction of shear strength of rock fractures using support vector regression and grid search optimization DOI

Shijie Xie,

Hang Lin, Yifan Chen

et al.

Materials Today Communications, Journal Year: 2023, Volume and Issue: 36, P. 106780 - 106780

Published: July 31, 2023

Language: Английский

Citations

32
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