Prediction of the Unconfined Compressive Strength of a One-Part Geopolymer-Stabilized Soil Using Deep Learning Methods with Combined Real and Synthetic Data DOI Creative Commons
Qinyi Chen,

Guo Hu,

Jun Wu

et al.

Buildings, Journal Year: 2024, Volume and Issue: 14(9), P. 2894 - 2894

Published: Sept. 13, 2024

This study focused on exploring the utilization of a one-part geopolymer (OPG) as sustainable alternative binder to ordinary Portland cement (OPC) in soil stabilization, offering significant environmental advantages. The unconfined compressive strength (UCS) was key index for evaluating efficacy OPG traditionally demanding substantial resources terms cost and time. In this research, four distinct deep learning (DL) models (Artificial Neural Network [ANN], Backpropagation [BPNN], Convolutional [CNN], Long Short-Term Memory [LSTM]) were employed predict UCS OPG-stabilized soft clay, providing more efficient precise methodology. Among these models, CNN exhibited highest performance (MAE = 0.022, R2 0.9938), followed by LSTM 0.0274, 0.9924) BPNN 0.0272, 0.9921). Wasserstein Generative Adversarial (WGAN) further utilized generate additional synthetic samples expanding training dataset. incorporation generated WGAN into set DL led improved performance. When number achieved 200, WGAN-CNN model provided most accurate results, with an value 0.9978 MAE 0.9978. Furthermore, assess reliability gain insights influence input variables predicted outcomes, interpretable Machine Learning techniques, including sensitivity analysis, Shapley Additive Explanation (SHAP), 1D Partial Dependence Plot (PDP) analyzing interpreting models. research illuminates new aspects application real data properties soil, contributing saving time cost.

Language: Английский

Compressive strength prediction models for concrete containing nano materials and exposed to elevated temperatures DOI Creative Commons
Hany A. Dahish, Ahmed D. Almutairi

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 103975 - 103975

Published: Jan. 1, 2025

Language: Английский

Citations

5
Explainable Artificial Intelligence for Predicting the Compressive Strength of Soil and Ground Granulated Blast Furnace Slag Mixtures DOI Creative Commons
Ahmed Mohammed Awad Mohammed, Omayma Husain, Muyideen Abdulkareem

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: 25, P. 103637 - 103637

Published: Dec. 9, 2024

Language: Английский

Citations

3
Evaluation of the Changes in the Strength of Clay Reinforced with Basalt Fiber Using Artificial Neural Network Model DOI Creative Commons
Yasemin ASLAN TOPÇUOĞLU, Zeynep Bala Duranay, Zülfü Gürocak

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(22), P. 10362 - 10362

Published: Nov. 11, 2024

In this research, the impact of basalt fiber reinforcement on unconfined compressive strength clay soils was experimentally analyzed, and collected data were utilized in an artificial neural network (ANN) to predict based ratio length. For purpose, two different lengths (6 mm 12 mm) added unreinforced bentonite at ratios 0%, 1%, 2%, 3%, 4%, 5%, tests performed prepared reinforced samples determine (qu) values. The evaluation obtained experimental results carried out by creating ANN models. To validate prediction capabilities ANN, a comparative analysis using linear regression, support vector machines, Gaussian process regression Ultimately, five-fold cross-validation technique employed objectively evaluate overall performance model. evaluations revealed that model predictions from studies showed highest accuracy close agreement with results.

Language: Английский

Citations

2
Prediction of the Unconfined Compressive Strength of a One-Part Geopolymer-Stabilized Soil Using Deep Learning Methods with Combined Real and Synthetic Data DOI Creative Commons
Qinyi Chen,

Guo Hu,

Jun Wu

et al.

Buildings, Journal Year: 2024, Volume and Issue: 14(9), P. 2894 - 2894

Published: Sept. 13, 2024

This study focused on exploring the utilization of a one-part geopolymer (OPG) as sustainable alternative binder to ordinary Portland cement (OPC) in soil stabilization, offering significant environmental advantages. The unconfined compressive strength (UCS) was key index for evaluating efficacy OPG traditionally demanding substantial resources terms cost and time. In this research, four distinct deep learning (DL) models (Artificial Neural Network [ANN], Backpropagation [BPNN], Convolutional [CNN], Long Short-Term Memory [LSTM]) were employed predict UCS OPG-stabilized soft clay, providing more efficient precise methodology. Among these models, CNN exhibited highest performance (MAE = 0.022, R2 0.9938), followed by LSTM 0.0274, 0.9924) BPNN 0.0272, 0.9921). Wasserstein Generative Adversarial (WGAN) further utilized generate additional synthetic samples expanding training dataset. incorporation generated WGAN into set DL led improved performance. When number achieved 200, WGAN-CNN model provided most accurate results, with an value 0.9978 MAE 0.9978. Furthermore, assess reliability gain insights influence input variables predicted outcomes, interpretable Machine Learning techniques, including sensitivity analysis, Shapley Additive Explanation (SHAP), 1D Partial Dependence Plot (PDP) analyzing interpreting models. research illuminates new aspects application real data properties soil, contributing saving time cost.

Language: Английский

Citations

0