Effect of modified nano‐titanium and fly ash on ultra‐high‐performance concrete properties

Structural Concrete, Год журнала: 2023, Номер 24(5), С. 6815 - 6832

Опубликована: Апрель 5, 2023

Abstract This paper aims to study the effect of using modified nano‐TiO 2 with fly ash (FA) on ultra‐high performance concrete's (UHPC) mechanical, transport, and microstructure properties (UHPC). A ball mill was used disband distribute it uniformly within FA powder. In this research, 20% cement weight replaced by FA, added 0.4%, 0.8%, 1.2%, 1.6%, 2% weight. To investigate period UHPC properties, periods 10, 20, 30, 40 min were applied a binder 6% . addition, 30‐min also investigated. Tests compressive strength after 1, 7, 28, 91 days curing in tap water, splitting tensile strength, flexural modulus elasticity performed 28 water. chloride permeability, sorptivity coefficient, water The results showed that addition higher percentages led decrease workability. improved mechanical properties. highest 208.9 MPa achieved for mixture 1.2% at age days. application best compared other periods. ball‐mill re‐mix between impressive comparative results.

Язык: Английский

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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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Application of optimization‐based regression analysis for evaluation of frost durability of recycled aggregate concrete

Structural Concrete, Год журнала: 2024, Номер 25(1), С. 716 - 737

Опубликована: Янв. 7, 2024

Abstract Concrete constructed using recycled aggregates in place of natural is an efficient approach to increase the construction sector's sustainability. To improve aggregate concrete () technologies permafrost, it essential certify stability frost‐induced conditions. The main goal this study was use support vector regression methods forecast frost durability on basis agent value cold climates. Herein, three optimization called Ant lion (), Grey wolf and Henry Gas Solubility Optimization were employed for indicating optimal values key parameters. results depicted that all developed models have capability predicting regions. as a comprehensive index model has higher at 0.0571 weakest model, then 0.0312 recognized second‐highest finally system 0.0224 mentioned outperformed model. approaches likewise capable accurately forecasting regions, but created method them when taking into account explanations justifications.

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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Nonlinear finite element and analytical modelling of reinforced concrete filled steel tube columns under axial compression loading

Results in Engineering, Год журнала: 2023, Номер 19, С. 101341 - 101341

Опубликована: Авг. 3, 2023

Local buckling of steel and excessive spalling concrete have necessitated the need for evaluation reinforced columns subjected to axial compression loading. Thus, this study investigates behaviour filled tube (CFST) (RCFST) RCFST under using finite element modelling (FEM) machine learning (ML) techniques. To achieve aim, a total 85 from existing studies were analysed FEM simulation. The ultimate load generated datasets was predicted various ML findings showed that columns' compressive strength, ductility, toughness improved by reducing transverse reinforcement spacing, increasing number reinforcing bars, thickness yield strength outer tube. Under loading, analysis provided an accurate assessment structural performance columns. Compared other approaches, gradient boosting exhibited best metrics with R2 RMSE 99.925% 0.00708 99.863% 0.00717 in training testing phases, respectively predict column's load. Overall, can be applied prediction CFST compression, conserving resources, time, cost investigate through laboratory testing.

Язык: Английский

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Integrated behavioural analysis of FRP-confined circular columns using FEM and machine learning

Composites Part C Open Access, Год журнала: 2024, Номер 13, С. 100444 - 100444

Опубликована: Фев. 12, 2024

This study investigates the structural behaviour of double-skin columns, introducing novel – double filled tubular (DSDFT) which utilize dual steel tubes and concrete to enhance load-carrying capacity ductility beyond conventional hollow (DSHT) employing a combination finite element model (FEM) machine learning (ML) techniques. A total 48 columns (DSHT+DSDFT) were created examine impact various parameters, such as tube configurations, thickness fibre-reinforced polymer (FRP) layer, type FRP material, diameter, on columns. The results validated against experimental findings ensure their accuracy. Key highlight advantages DSDFT configuration. Compared DSHT exhibited remarkable 19.54% 101.21% increase in capacity, demonstrating improved load-bearing capabilities. Thicker layers enhanced up 15%, however at expense reduced axial strain. It is also observed that glass wrapping displayed 25% superior ultimate strain than aramid wrapping. Four different ML models examined predict with long short-term memory bidirectional LSTM emerging choices exhibiting exceptional predictive interdisciplinary approach offers valuable insights into designing optimizing confined column systems. sheds light both double-tube single-tube propelling advancements engineering practices for new constructions retrofitting. Further, it lays out blueprint maximizing performance under compression.

Язык: Английский

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Development of hybrid gradient boosting models for predicting the compressive strength of high-volume fly ash self-compacting concrete with silica fume

Structures, Год журнала: 2024, Номер 66, С. 106850 - 106850

Опубликована: Июль 8, 2024

Язык: Английский

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Nonlinear finite element and machine learning modeling of tubed reinforced concrete columns under eccentric axial compression loading

Alexandria Engineering Journal, Год журнала: 2024, Номер 92, С. 380 - 416

Опубликована: Март 2, 2024

There is still insufficient data on the behavior of tubed-reinforced concrete columns (TRCCs) under eccentric compression. Thus, this research work comprehensively examines compression TRCCs using nonlinear finite element modeling and machine learning (ML). To do this, numerical simulation parametric analysis based existing investigations were conducted. In addition to 22 specimens with limited test variables, additional 188 developed cover a wide range parameters, including load eccentricity, transverse reinforcement spacing, columns' slenderness ratio, yield strength steel, outer steel tube diameter. Additionally, six ML models created estimate ultimate results. The results indicated that increasing diameter, reducing spacing enhanced load-carrying capacity columns. Gaussian process regression model demonstrated superior performance metrics in comparison other models, highest R2 values (0.998613 training 0.99823 testing stages) lowest root mean square error (0.007213 0.008471 stages). save money, time, resources compared laboratory testing, an online-based prediction program finally presented predict load.

Язык: Английский

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Development of ANN-based metaheuristic models for the study of the durability characteristics of high-volume fly ash self-compacting concrete with silica fume

Journal of Building Engineering, Год журнала: 2024, Номер 94, С. 109844 - 109844

Опубликована: Июнь 5, 2024

Язык: Английский

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Prediction of compressive strength of high-volume fly ash self-compacting concrete with silica fume using machine learning techniques

Construction and Building Materials, Год журнала: 2024, Номер 438, С. 136933 - 136933

Опубликована: Июнь 15, 2024

Язык: Английский

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