Modeling of the effect of gradation and compaction characteristics on the california bearing ratio of granular materials for subbase and landfill liner construction DOI Creative Commons
Majed Alzara, Kennedy C. Onyelowe, Ahmed M. Ebid

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Oct. 9, 2024

The California bearing ratio (CBR) of a granular materials are influence by the soil particle distribution indices such as D10, D30, D50, and D60 also compaction properties maximum dry density (MDD) optimum moisture content (OMC). For this reason, packing compactibility play big role in design construction subbases landfills. In research paper, experimental data entries have been collected reflecting CBR behavior used to construct landfill subbase. database was utilized 78-22% predict considering artificial neural network (ANN), evolutionary polynomial regression (EPR), genetic programming (GP), Extreme Gradient Boosting (XGBoost), Random Forest (RF) response surface methodology (RSM) intelligent learning symbolic abilities. relative importance values for each input parameter were carried out, which indicated that value depends mainly on average size (D30, 50 & 60). They showed combined index 66% considered parameters model exercise. This further shows structural particles within D50 range material consistency purposes. Performance study ability models. ANN best performance with accuracy 88%, then GP, EPR RF almost same accuracies 85% lastly XGBoost 81%. Also, RSM produced an R2 0.9464 p-value less than 0.0001. These show decisive superior forecast subbase waste compacted earth liner material. results optimal depended subgrade, subbase, purposes during monitoring phase constructed flexible pavement foundations liners.

Language: Английский

Applying Optimized Machine Learning Models for Predicting Unconfined Compressive Strength in Fine-Grained Soil DOI
Ishwor Thapa, Sufyan Ghani

Transportation Infrastructure Geotechnology, Journal Year: 2024, Volume and Issue: 11(4), P. 2235 - 2269

Published: Feb. 8, 2024

Language: Английский

Citations

11
Uncertainty quantification in multiaxial fatigue life prediction using Bayesian neural networks DOI
GaoYuan He, Yongxiang Zhao,

ChuLiang Yan

et al.

Engineering Fracture Mechanics, Journal Year: 2024, Volume and Issue: 298, P. 109961 - 109961

Published: Feb. 15, 2024

Language: Английский

Citations

11
Efficient prediction of California bearing ratio in solid waste-cement-stabilized soil using improved hybrid extreme gradient boosting model DOI
Yiliang Tu, Qianglong Yao, Shuitao Gu

et al.

Materials Today Communications, Journal Year: 2025, Volume and Issue: 43, P. 111627 - 111627

Published: Jan. 15, 2025

Language: Английский

Citations

1
Dimensionality analysis in assessing the unconfined strength of lime-treated soil using machine learning approaches DOI
Jitendra Khatti, Asma Muhmed, Kamaldeep Singh Grover

et al.

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(2)

Published: Feb. 1, 2025

Language: Английский

Citations

1
Geotechnical Characterization and Stability Prediction of Nano-Silica-Stabilized Slopes: A Machine Learning Approach to Mitigating Geological Hazards DOI Creative Commons
Ishwor Thapa, Sufyan Ghani, Sunita Kumari

et al.

Transportation Infrastructure Geotechnology, Journal Year: 2025, Volume and Issue: 12(2)

Published: Feb. 1, 2025

Language: Английский

Citations

1
A Review of the Data-Driven Prediction Method of Vehicle Fuel Consumption DOI Creative Commons

Dengfeng Zhao,

Haiyang Li, Junjian Hou

et al.

Energies, Journal Year: 2023, Volume and Issue: 16(14), P. 5258 - 5258

Published: July 9, 2023

Accurately and efficiently predicting the fuel consumption of vehicles is key to improving their economy. This paper provides a comprehensive review data-driven prediction models. Firstly, by classifying summarizing relevant data that affect consumption, it was pointed out commonly used currently involve three aspects: vehicle performance, driving behavior, environment. Then, from model structure, predictive energy characteristics traditional machine learning (support vector machine, random forest), neural network (artificial deep network), this point that: (1) based on networks has higher processing ability, training speed, stable ability; (2) combining advantages different models build hybrid for prediction, accuracy can be greatly improved; (3) when comparing indicts, both method consistently exhibit coefficient determination above 0.90 root mean square error below 0.40. Finally, summary prospect analysis are given various models’ performance application status.

Language: Английский

Citations

18
Assessment of Unconfined Compressive Strength of Stabilized Soil Using Artificial Intelligence Tools: A Scientometrics Review DOI
Billal Sari-Ahmed, Mohamed Ghrici, Ali Benzaamia

et al.

Studies in systems, decision and control, Journal Year: 2024, Volume and Issue: unknown, P. 271 - 288

Published: Jan. 1, 2024

Language: Английский

Citations

5
Comparative study on the prediction of the unconfined compressive strength of the one-part geopolymer stabilized soil by using different hybrid machine learning models DOI Creative Commons
Qinyi Chen,

Guo Hu,

Jun Wu

et al.

Case Studies in Construction Materials, Journal Year: 2024, Volume and Issue: 21, P. e03439 - e03439

Published: July 26, 2024

With the development of green, low-carbon, and sustainable economic systems, issues high pollution energy consumption in construction materials have become increasingly prominent. This study focuses on adopting one-part geopolymer (OPG) soil stabilization for underground engineering, which exhibits environmental low-carbon advantages. The unconfined compressive strength (UCS) serves as a crucial parameter assessing stabilized soil's performance. However, it is necessary to conduct large number experiments, inducing costs time consumption. In this study, one multiple linear regression model, Decision Tree (DT) five ensemble machine learning (ML) models (i.e. Random Forest [RF], Extra [ET], Gradient Boosting [GB], [GBDT], Extreme [XGBoost]), hybrid those single with Particle Swarm Optimization (PSO) PSO-RF, PSO-ET, PSO-GB, PSO-GBDT, PSO-XGBoost) were adopted compared achieve better prediction UCS OPG-stabilized soil. Furthermore, interpretable method including SHAP PDP (1D 2D), was employed investigate precise mechanisms by input parameters influenced output label. results revealed that model delivered lowest accuracy, PSO-XGBoost PSO-ET exhibited best performance R2 value 0.9964 0.9928, respectively. addition, Curing exerted most significant impact UCS, followed FA/GGBFS, Molarity, Water/Binder, NaOH/Precursor. Compared method, offered more intuitive approach reveal relationship between inputs output. outcome shed new light application ML engineering.

Language: Английский

Citations

4
Advancing earth science in geotechnical engineering: A data-driven soft computing technique for unconfined compressive strength prediction in soft soil DOI
Ishwor Thapa, Sufyan Ghani

Journal of Earth System Science, Journal Year: 2024, Volume and Issue: 133(3)

Published: Aug. 17, 2024

Language: Английский

Citations

4
Submarine pipeline corrosion rate prediction model based on high-dimensional mapping augmentation and residual update gradient forest DOI Creative Commons
Hongbing Liu,

Zhenhao Zhu,

Jingyang Zhang

et al.

Applied Ocean Research, Journal Year: 2025, Volume and Issue: 155, P. 104432 - 104432

Published: Jan. 21, 2025

Language: Английский

Citations

0