Differences between 3D printed concrete and 3D printing reinforced concrete technologies: a review

Frontiers in Built Environment, Год журнала: 2025, Номер 10

Опубликована: Янв. 6, 2025

This review aims to provide a comprehensive analysis of the difference between 3D printed concrete (3DPC) and printing reinforced (3DPRC) technologies, as well potential future paths for these technologies based on current consolidated approaches. Although technology attempts strengthen using with polymer ingredients, concentrates building structures. In recent years, both have advanced rapidly become global research innovation hotspot due their advantages over traditional construction technology, such high efficiency, low labor costs, less waste. Unfortunately, there are several issues 3DPC 3DPRC including competing rheological requirements, integrating hurdles, inadequate interlayer bonding, anisotropic properties material that result in lacking structural performance. The findings investigation discuss gaps theoretical possibilities development which can advance safeguard structures under various loads. present study, two distinct analyzed, along respective uses engineering. Additionally, advantages, methods, materials utilized types described, difficulties solutions associated real-world projects demonstrated. None earlier investigations examined differences technologies. aim by incorporating forms has been studied its mechanical qualities rheology. Meanwhile, engineers try improve large-scale printers concrete, while design new patterns reinforcing improved concrete. study examines

Язык: Английский

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Chopped and minibars reinforced high-performance concrete: machine learning prediction of mechanical properties

Frontiers in Built Environment, Год журнала: 2025, Номер 11

Опубликована: Апрель 8, 2025

A novel form of high-tech concrete known basalt fiber-reinforced high-performance (BFHPC) has been developed using traditional materials that require extra admixtures to improve its mechanical properties. Machine learning (ML) techniques provide a more flexible and economical way predict the property chopped minibar based on material properties processing parameters, enabling durable environmentally friendly construction. Predicting BFHPC precisely is crucial since it reduces design costs time, also minimizes waste from several mixing experiments. In this study, compressive strength flexural are predicted via different types machine models. Experiments carried out in laboratory under standard controlled settings at 7, 14, 28-day curing periods yielded sample data for analysis model development. The characteristics have combination decision tree, partial least squares, lasso, rigid, random forest regressor, K Neighbours, linear regressions. According results, all regression best results except KNeighbors Regressor, Random Forest Lasso Regression, with correlation coefficient R 2 93%. Each model’s performance application examined thoroughly, leading identification useful suggestions, existing knowledge gaps, areas need further study.

Язык: Английский

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Comparative Analysis of Machine Learning Models for Predicting the Compressive Strength of Ultra-High-Performance Steel Fiber Reinforced Concrete

Journal of Engineering Research, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

Язык: Английский

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Investigating on thermal insulation in concrete partition wall with insulation-material-infilled Voronoi sections: Feature impact analysis and efficient assessment approach

Journal of Building Engineering, Год журнала: 2024, Номер 96, С. 110437 - 110437

Опубликована: Авг. 12, 2024

Язык: Английский

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A reliable approach for classifying metakaolin content of high-Performance concrete via machine learning algorithm

International Journal of Pavement Engineering, Год журнала: 2025, Номер 26(1)

Опубликована: Фев. 26, 2025

Язык: Английский

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Performance Evaluation of Current Design Models in Predicting Shear Resistance of UHPC Girders

Journal of Composites Science, Год журнала: 2025, Номер 9(5), С. 211 - 211

Опубликована: Апрель 27, 2025

This manuscript delivers a comprehensive evaluation of five different ultra-high-performance concrete (UHPC) shear resistance models: FHWA-HRT-23-077 (2023), ePCI report (2021), French Standard NF-P-18-710 (2016), Canadian Standards A23.3-04 (2004), and Modified Eurocode2/German DAfStb (2023). The models differ in accounting for the steel fiber reinforcement contribution determining angle inclination diagonal compression strut. was carried out using an experimental database 198 UHPC specimens focused on accuracy, conservatism, ease use each considered model. included beams with prestressed reinforcement, ratios, wide range geometrical material properties. In order to apply FHWA method, utilization tensile stress (ft,loc) prediction equation developed. Generally, method showed superior performance other terms statistical measures consistent conservatism across variable ranges. Although methods yielded highest it can be said that ePCI, AFGC, CSA similar behavior degrees conservatism. lowest accuracy greatest scatter data. Except all reduction at high transverse ratio.

Язык: Английский

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Multidimensional Numbers: A Framework for Multi-Objective Optimization in Concrete Engineering Using Machine Learning Approaches

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Май 11, 2025

This study presents a novel framework for multi-objective optimization in concrete engineering using multidimensional numbers and machine learning approaches. The of mixture proportions requires the simultaneous numerous objectives variables (such as components) under highly nonlinear constraints. Multi-objective is not compatible with existing single-objective models. In order to optimize mixture, this suggests technique based on (ML) different algorithmic study, we introduce concept numbers, which incorporate both quantitative values contextual dimensions (e.g., units, time, or error), enabling unified representation complex parameters. By applying design, simultaneously conflicting criteria such strength. Seven regression models—Linear, Ridge, Lasso, Support Vector, Random Forest, Gradient Boosting, Neural Network—were evaluated. Results demonstrate that linear models outperformed counterparts, neural networks exhibiting poor performance, highlighting dominance relationships dataset. Visualization tools, including Taylor diagrams 3D surface plots, further validated these findings. underscores efficacy capturing real-world complexities while emphasizing model selection should prioritize data structure over complexity. approach offers practical insights civil applications, cost-effective, sustainable design balanced performance metrics.

Язык: Английский

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Behavior of Damaged Reinforced Concrete Columns Retrofitted with Ultra-High Performance Fiber Reinforced Concrete Jackets under Uniaxial Loading

Journal of Building Engineering, Год журнала: 2025, Номер unknown, С. 112837 - 112837

Опубликована: Май 1, 2025

Язык: Английский

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Building Automated Computational Models for Predicting Energy Consumption in High-Performance Concrete Production

Computational Economics, Год журнала: 2025, Номер unknown

Опубликована: Май 16, 2025

Язык: Английский

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Defects Identification and Crack Depth Determination in Porous Media on the Brick Masonry Example Using Ultrasonic Methods: Numerical Analysis and Machine Learning

Journal of Composites Science, Год журнала: 2025, Номер 9(6), С. 267 - 267

Опубликована: Май 28, 2025

Automation of the structural health monitoring process involves use successful methods for detecting defects and determining their critical characteristics. An efficient means crack detection in composite materials is ultrasonic method, but its application to determine parameters, such as depth construction practice, difficult or leads large errors. This article focuses on machine learning usage detect cracks like brickwork. Ceramic bricks with various mechanical properties pre-grown from 2 60 mm are considered. To understand processes occurring during pulse transmission, modeling was performed ANSYS environment. The brick considered a porous medium weakened by crack. Numerical allows identification main features signal response determination amplitude-time range different porosity values. Using made it possible solve two related problems. first, binary classification, i.e., presence absence crack, solved 100% accuracy. second depth. A neural network built using an ensemble decision trees. accuracy prediction R2 = 0.983, error predicted values within 8%.

Язык: Английский

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