Hybrid data-driven approaches to predicting the compressive strength of ultra-high-performance concrete using SHAP and PDP analyses

Case Studies in Construction Materials, Год журнала: 2024, Номер 20, С. e02991 - e02991

Опубликована: Фев. 19, 2024

Ultra-high-performance concrete (UHPC) is a cutting-edge and advanced constructions material known for its exceptional mechanical properties durability. Recently, machine learning (ML) methods play pivotal role in predicting the compressive strength (CS) of UHPC evaluating dominant input parameters suitable mix design. This research, three hybrid models were utilized: Random Forest (RF), AdaBoost (AB), Gradient Boosting (GB) algorithms with particle swarm optimization (PSO), namely AB-PSO, RF-PSO, GB-PSO, to predict perform SHAP (Shapley additive explanation) analysis. To build predictive ML models, dataset 810 experimental data points was collected from published literature. Additionally, interaction plots generated visualize impact each feature on specific prediction made by model. Our results indicate that better than traditional GB-PSO model showed high accuracy among models. The had higher precision compared other two It achieved R2 values 0.9913 during training stage 0.9804 testing CS. analysis revealed age, fiber, cement, silica fume, superplasticizer significant influence strength, while comparatively lower. PDP (Partial Dependence Plots) amount individually variables can be calculated simply designed These findings are valuable construction applications offer essential insights design engineers builders, aiding their understanding significance component UHPC.

Язык: Английский

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Hybrid machine learning approach to prediction of the compressive and flexural strengths of UHPC and parametric analysis with shapley additive explanations

Case Studies in Construction Materials, Год журнала: 2023, Номер 20, С. e02723 - e02723

Опубликована: Ноя. 28, 2023

Ultra-high-performance concrete (UHPC) is a sustainable construction material; it can be applied as substitute for cement concrete. Artificial intelligence methods have been used to evaluate composites reduce time and money in the industries. So, this study machine learning (ML) hybrid ML approaches predict compressive flexural strength of UHPC. A dataset 626 317 data points was collected from published research articles, where fourteen important variables were selected input parameters analysis algorithms. This XGBoost, LightGBM, XGBoost- LightGBM algorithms UHPC materials. Grid search (GS) techniques adjust model hyper-parameters improved high accuracy efficiency. models train, test stage utilized statistical assessments such R-square, root mean square error (RMSE), absolute (MAE), coefficient efficiency (CE). The results presented algorithm superior XGBoost terms R-square RMSE values both prediction. two showed CS considerable above 0.94 at testing stages just over 0.97 training phase. Hybrid performance prediction value found that almost 0.996 0.963 phases. At same time, FS result traditional founded 0.95 phase around 0.81 But among them, XGB-LGB lowest trained its hyperparameters optimized. Additionally, SHAP investigation reveals impact relationship with output variables. outcome curing age steel fiber content parameter had highest positive on

Язык: Английский

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Explainable machine learning methods for predicting water treatment plant features under varying weather conditions

Results in Engineering, Год журнала: 2024, Номер 21, С. 101930 - 101930

Опубликована: Март 1, 2024

Accurately predicting key features in WWTPs is essential for optimizing plant performance and minimizing operational costs. This study assesses the potential of various machine learning models inflow to anoxic sludge reactors. Firstly, it conducts a comprehensive evaluation diverse models, including k-Nearest Neighbors (kNN), Random Forest (RF), XGBoost, CatBoost, LightGBM, Decision Tree Regression (DTR), flow into Anoxic section under weather conditions (dry, rainy, stormy). Secondly, introduces parsimonious guided by variable importance from XGBoost algorithm. Furthermore, employs SHAP (SHapley Additive exPlanations) elucidate model predictions, providing insights contribution each feature. Data COST Benchmark Simulation Model (BSM1) used verify investigated models' effectiveness. Each dataset consists 14 days influent data at 15-minute intervals, with 80% training. Results show that ensemble methods, particularly CatBoost demonstrate satisfactory predictive results presence increased variability rainy stormy conditions. Notably, achieve average Mean Absolute Percentage Error values 1.33% 1.59%, outperforming other methods.

Язык: Английский

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ENHANCING LOAN APPROVAL DECISION-MAKING: AN INTERPRETABLE MACHINE LEARNING APPROACH USING LIGHTGBM FOR DIGITAL ECONOMY DEVELOPMENT

Опубликована: Апрель 1, 2024

This study aims to enhance loan approval decision-making in the digital economy using an interpretable machine learning approach. The primary research question investigates how integrating approach can improve accuracy and transparency of processes. We employed LightGBM, a gradient-boosting framework for classification, optimized via Random Search hyperparameter tuning validated 10-fold cross-validation. incorporated Shapley Additive exPlanations (SHAP) address challenge interpretability learning. LightGBM model outperformed conventional algorithms (Decision Tree, Forest, AdaBoost, Extra Trees) (98.13%), precision (97.78%), recall (97.17%), F1-score (97.48%). demonstrates that with SHAP significantly decisions. method offers promising avenue financial institutions their mechanisms, ensuring more reliable, efficient, transparent economy. also underscores importance deploying solutions sectors significant socio-economic impacts.

Язык: Английский

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A comparative study of machine learning models for construction costs prediction with natural gradient boosting algorithm and SHAP analysis

Asian Journal of Civil Engineering, Год журнала: 2024, Номер 25(4), С. 3301 - 3316

Опубликована: Фев. 9, 2024

Язык: Английский

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Feature fusion method for rock mass classification prediction and interpretable analysis based on TBM operating and cutter wear data

Tunnelling and Underground Space Technology, Год журнала: 2025, Номер 157, С. 106351 - 106351

Опубликована: Янв. 5, 2025

Язык: Английский

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Explainable AI-Enhanced Human Activity Recognition for Human–Robot Collaboration in Agriculture

Applied Sciences, Год журнала: 2025, Номер 15(2), С. 650 - 650

Опубликована: Янв. 10, 2025

This study addresses a critical gap in human activity recognition (HAR) research by enhancing both the explainability and efficiency of classification collaborative human–robot systems, particularly agricultural environments. While traditional HAR models often prioritize improving overall accuracy, they typically lack transparency how sensor data contribute to decision-making. To fill this gap, integrates explainable artificial intelligence, specifically SHapley Additive exPlanations (SHAP), thus interpretability model. Data were collected from 20 participants who wore five inertial measurement units (IMUs) at various body positions while performing material handling tasks involving an unmanned ground vehicle field harvesting scenario. The results highlight central role torso-mounted sensors, lumbar region, cervix, chest, capturing core movements, wrist sensors provided useful complementary information, especially for load-related activities. XGBoost-based model, selected mainly allowing in-depth analysis feature contributions considerably reducing complexity calculations, demonstrated strong performance HAR. findings indicate that future should focus on enlarging dataset, investigating use additional placements, real-world trials enhance model’s generalizability adaptability practical applications.

Язык: Английский

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Interpretable predictive modelling of outlet temperatures in Central Alberta’s hydrothermal system using boosting-based ensemble learning incorporating Shapley Additive exPlanations (SHAP) approach

Energy, Год журнала: 2025, Номер unknown, С. 134738 - 134738

Опубликована: Янв. 1, 2025

Язык: Английский

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Advancing forest fire prediction: A multi-layer stacking ensemble model approach

Earth Science Informatics, Год журнала: 2025, Номер 18(3)

Опубликована: Фев. 19, 2025

Язык: Английский

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Prediction of microalgae harvesting efficiency and identification of important parameters for ballasted flotation using an optimized machine learning model

Algal Research, Год журнала: 2025, Номер unknown, С. 103985 - 103985

Опубликована: Фев. 1, 2025

Язык: Английский

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