Performance Evaluation of Pond Ash-Enhanced Flowable Fill for Plastic Concrete Cutoff Walls in Earthen Dams Using Advanced Machine Learning Models DOI

K. Lini Dev,

Divesh Ranjan Kumar,

Warit Wipulanusat

et al.

Arabian Journal for Science and Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: May 15, 2025

Language: Английский

Machine learning approach for predicting the compressive strength of biomedical waste ash in concrete: a sustainability approach DOI Creative Commons
Rakesh Kumar,

Shishir Karthik,

Abhishek Kumar

et al.

Discover Materials, Journal Year: 2025, Volume and Issue: 5(1)

Published: Feb. 21, 2025

Language: Английский

Citations

3
Estimation of the Compressive Strength of Ultrahigh Performance Concrete using Machine Learning Models DOI Creative Commons
Rakesh Kumar,

Divesh Ranjan Kumar,

Warit Wipulanusat

et al.

Intelligent Systems with Applications, Journal Year: 2024, Volume and Issue: 25, P. 200471 - 200471

Published: Dec. 25, 2024

Language: Английский

Citations

8
Machine learning prediction of the unconfined compressive strength of controlled low strength material using fly ash and pond ash DOI Creative Commons

K. Lini Dev,

Divesh Ranjan Kumar,

Warit Wipulanusat

et al.

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

Published: Nov. 11, 2024

The sustainable use of industrial byproducts in civil engineering is a global priority, especially reducing the environmental impact waste materials. Among these, coal ash from thermal power plants poses significant challenge due to its high production volume and potential for pollution. This study explores controlled low-strength material (CLSM), flowable fill made ash, cement, aggregates, water, admixtures, as solution large-scale utilization. CLSM suitable both structural geotechnical applications, balancing management with resource conservation. research focuses on two key properties: flowability unconfined compressive strength (UCS) at 28 days. Traditional testing methods are resource-intensive, empirical models often fail accurately predict UCS complex nonlinear relationships among variables. To address these limitations, four machine learning models-minimax probability regression (MPMR), multivariate adaptive splines (MARS), group method data handling (GMDH), functional networks (FN) were employed UCS. MARS model performed best, achieving R

Language: Английский

Citations

7
Mean Limiting Pressure Factors Determination in Contiguous Pile Walls using RAFELA and Nonlinear Regression Models in Spatially Random Soil DOI Creative Commons

Divesh Ranjan Kumar,

Sittha Kaorapapong,

Warit Wipulanusat

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104436 - 104436

Published: Feb. 1, 2025

Language: Английский

Citations

0
Predicting the uplift capacity of anchors in Bolton dense sand using LightGBM and FELA DOI
Wittaya Jitchaijaroen, Duy Tan Tran, Suraparb Keawsawasvong

et al.

Marine Georesources and Geotechnology, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 14

Published: March 13, 2025

Language: Английский

Citations

0
Hybrid machine learning models for predicting compressive strength of self-compacting concrete: an integration of ANFIS and Metaheuristic algorithm DOI

Somdutta,

Baboo Rai

Nondestructive Testing And Evaluation, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 33

Published: March 25, 2025

Self-compacting concrete (SCC) has become increasingly popular due to its superior workability, segregation resistance, and compressive strength. As the traditional methods for strength prediction are costly time-intensive, this study explores machine learning (ML) techniques as efficient alternatives SCC prediction. Three state-of-the-art hybrid Adaptive Neuro-Fuzzy Inference System (ANFIS) models, optimised using Firefly Algorithm (FA), Particle Swarm Optimization (PSO) Genetic (GA). For purpose, a robust dataset of 366 instances 7 input parameters is taken from literature. After data analysis pre-processing, hyperparameters models tuned best-fit model tested on unforeseen data. ANFIS-FF stands out best-performing (RTR2 = 0.945 RTS2 0.9395) in both training testing phases, closely followed by ANFIS-GA. All outperform ANFIS model, outlining significance hybridisation, however, ANFIS-PSO lags behind other two models. The highlights importance integrating with metaheuristic algorithms tackling complex engineering problems like design optimal mix design, minimising material waste ensuring cost-effectiveness. It serves benchmark future research comparing hybridisation starting point ANFIS.

Language: Английский

Citations

0
Bearing capacity prediction of open caissons in two-layered clays using five tree-based machine learning algorithms DOI Creative Commons

Rungroad Suppakul,

Kongtawan Sangjinda,

Wittaya Jitchaijaroen

et al.

Intelligent geoengineering., Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Language: Английский

Citations

0
An innovative approach to predict the bearing capacity of ring foundations in dense sand using Bolton failure criterion and hybrid XGBoost algorithms DOI

Nuchlee Boonjim,

Duy Tan Tran, Wittaya Jitchaijaroen

et al.

Ships and Offshore Structures, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 13

Published: May 8, 2025

Language: Английский

Citations

0
Performance Evaluation of Pond Ash-Enhanced Flowable Fill for Plastic Concrete Cutoff Walls in Earthen Dams Using Advanced Machine Learning Models DOI

K. Lini Dev,

Divesh Ranjan Kumar,

Warit Wipulanusat

et al.

Arabian Journal for Science and Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: May 15, 2025

Language: Английский

Citations

0