Machine learning and interactive GUI for concrete compressive strength prediction

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Июль 19, 2024

Concrete compressive strength (CS) is a crucial performance parameter in concrete structure design. Reliable prediction reduces costs and time design prevents material waste from extensive mixture trials. Machine learning techniques solve structural engineering challenges such as CS prediction. This study used Learning (ML) models to enhance the of CS, analyzing 1030 experimental data ranging 2.33 82.60 MPa previous research databases. The ML included both non-ensemble ensemble types. were regression-based, evolutionary, neural network, fuzzy-inference-system. Meanwhile, consisted adaptive boosting, random forest, gradient boosting. There eight input parameters: cement, blast-furnace-slag, aggregates (coarse fine), fly ash, water, superplasticizer, curing days, with output. Comprehensive evaluations include visual quantitative methods k-fold cross-validation assess study's reliability accuracy. A sensitivity analysis using Shapley-Additive-exPlanations (SHAP) was conducted understand better how each variable affects CS. findings showed that Categorical-Gradient-Boosting (CatBoost) model most accurate during testing stage. It had highest determination-coefficient (R

Язык: Английский

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Compressive strength prediction of sustainable concrete incorporating rice husk ash (RHA) using hybrid machine learning algorithms and parametric analyses

Case Studies in Construction Materials, Год журнала: 2024, Номер 20, С. e03030 - e03030

Опубликована: Март 5, 2024

The construction industry is making efforts to reduce the environmental impact of cement production in concrete by incorporating alternative and supplementary cementitious materials, as well lowering carbon emissions. One such material that has gained popularity this context rice husk ash (RHA) due its pozzolanic reactions. This study aims forecast compressive strength (CS) RHA-based (RBC) examining effects several factors cement, RHA content, curing age, water usage, aggregate amount, superplasticizer content. To accomplish this, collected analyzed data from literature, resulting a dataset 1404 observations. Several machine learning (ML) models, light gradient boosting (LGB), extreme (XGB), random forest (RF), hybrid (HML) approaches like XGB-LGB XGB-RF were employed thoroughly analyze these parameters assess their on strength. was split into training testing groups, statistical analyses performed determine relationships between input CS. Moreover, performance all models evaluated using various evaluation criteria, including mean absolute percentage error (MAPE), coefficient efficiency (CE), root square (RMSE), determination (R2). model found have higher precision (R2 = 0.95, RMSE 5.255 MPa) compared other models. SHAP (SHapley Additive exPlanations) analysis revealed RHA, had positive effect Overall, study's findings suggest with identified can be used accurately predict CS RBC. application technologies sector facilitate rapid low-cost identification qualities parameters.

Язык: Английский

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Determining Seepage Loss Predictions in Lined Canals Through Optimizing Advanced Gradient Boosting Techniques

Water Conservation Science and Engineering, Год журнала: 2024, Номер 9(2)

Опубликована: Окт. 17, 2024

Язык: Английский

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Enhancing Discharge Prediction over Type-A Piano Key Weirs: An Innovative Machine Learning Approach

Flow Measurement and Instrumentation, Год журнала: 2024, Номер 100, С. 102732 - 102732

Опубликована: Ноя. 4, 2024

Язык: Английский

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Improved forecasting of the compressive strength of ultra‐high‐performance concrete (UHPC) via the CatBoost model optimized with different algorithms

Structural Concrete, Год журнала: 2024, Номер unknown

Опубликована: Май 19, 2024

Abstract This paper focuses on the applicability of CatBoost models constructed using various optimization techniques for improved forecasting compressive strength ultra‐high‐performance concrete (UHPC). Phasor particle swarm (PPSO), dwarf mongoose (DMO), and atom search (ASO), which have been very popular recently, are preferred as algorithms. A comprehensive reliable data set is used to develop models, include 785 test results with 15 input features. The performance (PPSO‐CatBoost, DMO‐CatBoost, ASO‐CatBoost) optimized different algorithms thoroughly assessed by means statistical metrics error analysis determine model best capability, this compared obtained from previous studies. In addition, Shapley additive exPlanations (SHAP) ensure interpretability overcome “black box” problem machine learning (ML) models. demonstrate that all outstandingly forecast UHPC. Among these DMO‐CatBoost stands out other in metrics, such high coefficient determination ( R 2 ) values, low root mean squared (RMSE), absolute percentage (MAPE), (MAE) along a smaller ratio. words, RMSE, , MAPE, MAE values training 3.67, 0.993, 0.019, 2.35, respectively, whereas those 6.15, 0.978, 0.038, 4.51. Additionally, ranking optimize hyperparameters follows: DMO > PPSO ASO. On hand, SHAP showed age, fiber dosage, cement dosage significantly influence These findings can guide structural engineers design UHPC, thus assisting them developing strategies improve properties material. Finally, based developed work, graphical user interface has easily UHPC practical applications without additional tools or software.

Язык: Английский

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Stacked-based machine learning to predict the uniaxial compressive strength of concrete materials

Computers & Structures, Год журнала: 2025, Номер 308, С. 107644 - 107644

Опубликована: Янв. 6, 2025

Язык: Английский

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Predicting Penetration Depth in Ultra-High-Performance Concrete Targets under Ballistic Impact: An Interpretable Machine Learning Approach Augmented by Deep Generative Adversarial Network

Results in Engineering, Год журнала: 2025, Номер unknown, С. 103909 - 103909

Опубликована: Янв. 1, 2025

Язык: Английский

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A predictive model for the freeze-thaw concrete durability index utilizing the deeplabv3+ model with machine learning

Construction and Building Materials, Год журнала: 2025, Номер 459, С. 139788 - 139788

Опубликована: Янв. 1, 2025

Язык: Английский

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Estimating the compressive and tensile strength of basalt fibre reinforced concrete using advanced hybrid machine learning models

Structures, Год журнала: 2025, Номер 71, С. 108138 - 108138

Опубликована: Янв. 1, 2025

Язык: Английский

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Prediction of mechanical properties of eco-friendly concrete using machine learning algorithms and partial dependence plot analysis

Smart Construction and Sustainable Cities, Год журнала: 2025, Номер 3(1)

Опубликована: Янв. 26, 2025

Язык: Английский

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