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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Predictive models in machine learning for strength and life cycle assessment of concrete structures

Automation in Construction, Год журнала: 2024, Номер 162, С. 105412 - 105412

Опубликована: Апрель 3, 2024

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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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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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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Advanced predictive machine and deep learning models for round-ended CFST column

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 20, 2025

Confined columns, such as round-ended concrete-filled steel tubular (CFST) are integral to modern infrastructure due their high load-bearing capacity and structural efficiency. The primary objective of this study is develop accurate, data-driven approaches for predicting the axial load-carrying (Pcc​) these columns benchmark performance against existing analytical solutions. Using an extensive dataset 200 CFST stub column tests, research evaluates three machine learning (ML) models - LightGBM, XGBoost, CatBoost deep (DL) Deep Neural Network (DNN), Convolutional (CNN), Long Short-Term Memory (LSTM). Key input features include concrete strength, length, cross-sectional dimensions, tube thickness, yield which were analysed uncover underlying relationships. results indicate that delivers highest predictive accuracy, achieving RMSE 396.50 kN R2 0.932, surpassing XGBoost (RMSE: 449.57 kN, R2: 0.906) LightGBM 0.916). less effective, with DNN attaining 496.19 0.958, while LSTM underperformed substantially 2010.46 0.891). SHapley Additive exPlanations (SHAP) identified width most critical feature, contributing positively capacity, length a significant negative influencer. A user-friendly, Python-based interface was also developed, enabling real-time predictions practical engineering applications. Comparison 10 demonstrates traditional methods, though deterministic, struggle capture nonlinear interactions inherent in thus yielding lower accuracy higher variability. In contrast, presented here offer robust, adaptable, interpretable solutions, underscoring potential transform design analysis practices ultimately fostering safer more efficient systems.

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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Soft computing approaches for forecasting discharge over symmetrical piano key weirs

AI in Civil Engineering, Год журнала: 2025, Номер 4(1)

Опубликована: Март 3, 2025

Abstract Piano Key Weir (PKW) is an advanced hydraulic structure that enhances water discharge efficiency and flood control through its innovative design, which allows for higher flow rates at lower upstream levels. Accurate prediction crucial PKW performance within various management systems. This study assesses the efficacy of Artificial-Neural-Network (ANN) Gene-Expression-Programming (GEP) models in improving symmetrical PKWs. A comprehensive dataset comprising 476 experimental records from previously published studies was utilized, considering a range geometric fluid parameters (PKW key widths, height, head). In training stage, ANN model demonstrated superior determination coefficient (R 2 ) 0.9997 alongside Mean Absolute Percentage Error (MAPE) 0.74%, whereas GEP yielded R 0.9971 MAPE 2.36%. subsequent testing both displayed high degree accuracy comparison to data, attaining value 0.9376. Furthermore, SHapley-Additive-exPlanations Partial-Dependence-Plot analyses were incorporated, revealing head exerted greatest influence on prediction, followed by height width. Therefore, these are recommended as reliable, robust, efficient tools forecasting Additionally, mathematical expressions associated script codes developed this made accessible, thus providing engineers researchers with means perform rapid accurate predictions.

Язык: Английский

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