Effects of Anisotropic Mechanical Behavior on Nominal Moment Capability of 3D Printed Concrete Beam with Reinforcement DOI Creative Commons
Keunhyoung Park, Ali M. Memari, Maryam Hojati

и другие.

Buildings, Год журнала: 2024, Номер 14(10), С. 3175 - 3175

Опубликована: Окт. 5, 2024

In this study, 3D-printed reinforced concrete beams were tested for flexural performance and compared with the analytical model based on material test results. Two cementitious mixes (PSU GCT) designed printing mechanically compared. Anisotropies in compressive strength modulus of elasticity printed observed, applied to prediction bending behavior, validated by actual Significant differences between predictions experimental tests behaviors observed. Furthermore, higher strengths moduli observed when loading direction was perpendicular layers or PSU mix. The effect anisotropic mechanical properties a beam both mixes. results significant errors concrete’s structural performance, from 10% 50%, suggest that factors other than reduced may influence beams.

Язык: Английский

Establishing Benchmark Properties for 3D-Printed Concrete: A Study of Printability, Strength, and Durability DOI Open Access
Alise Sapata, Māris Šinka, Genādijs Šahmenko

и другие.

Journal of Composites Science, Год журнала: 2025, Номер 9(2), С. 74 - 74

Опубликована: Фев. 7, 2025

This study investigates the fresh state and hardened mechanical durability properties of 3D-printed concrete. The tests focused on its anisotropic behavior in response to different load orientations. Compressive, flexural, splitting tensile strengths were evaluated relative print layers orientation. Results showed that compressive strength varied significantly, achieving 85% cast sample when was applied parallel ([u] direction), 71% perpendicular object’s side plane ([v] while only reaching 59% top ([w] direction). Similar trends observed for flexural strength, with average values 75% ([v.u] [w.u] directions), but decreasing 53% ([u.w] underscoring weaknesses at interlayer interfaces. remained relatively consistent across orientations, 90% strength. Durability assessment revealed concrete exhibits reduced resistance environmental factors, particularly layer interfaces where cold joint formed, which are prone moisture penetration crack formation. These findings contribute valuable insights into concrete, emphasizing importance orientation bonding performance. understanding helps guide optimal use elements real-life applications by aligning or exposure factors material’s characteristics.

Язык: Английский

Процитировано

1
A critical analysis of compressive strength prediction of glass fiber and carbon fiber reinforced concrete over machine learning models DOI

K. K. Yaswanth,

V. S. Vani,

Krupasindhu Biswal

и другие.

Multiscale and Multidisciplinary Modeling Experiments and Design, Год журнала: 2025, Номер 8(3)

Опубликована: Фев. 14, 2025

Язык: Английский

Процитировано

1
Intelligent prediction of compressive strength of self-compacting concrete incorporating silica fume using hybrid IWOA-GPR model DOI
Yang Yu,

Guangyin Wang,

Ghasan Fahim Huseien

и другие.

Materials Today Communications, Год журнала: 2025, Номер unknown, С. 112282 - 112282

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0
Augmented Data-Driven Approach towards 3D Printed Concrete Mix Prediction DOI Creative Commons
Saif Rehman, Raja Dilawar Riaz, Muhammad Usman

и другие.

Applied Sciences, Год журнала: 2024, Номер 14(16), С. 7231 - 7231

Опубликована: Авг. 16, 2024

Formulating a mix design for 3D concrete printing (3DCP) is challenging, as it involves an iterative approach, wasting lot of resources, time, and effort to optimize the strength printability. A potential solution formulation through artificial intelligence (AI); however, being new emerging field, open-source availability datasets limited. Limited significantly restrict predictive performance machine learning (ML) models. This research explores data augmentation techniques like deep generative adversarial network (DGAN) bootstrap resampling (BR) increase available train three ML models, namely support vector (SVM), neural (ANN), extreme gradient boosting regression (XGBoost). Their was evaluated using R2, MSE, RMSE, MAE metrics. Models trained on BR-augmented showed higher accuracy than those DGAN-augmented data. The BR-trained XGBoost exhibited highest R2 scores 0.982, 0.970, 0.972, 0.971, 0.980 cast compressive strength, printed direction 1, 2, 3, slump flow respectively. proposed method predicting (mm), cast, anisotropic (MPa) can effectively predict printable concrete, unlocking its full application in construction industry.

Язык: Английский

Процитировано

2
Effects of Anisotropic Mechanical Behavior on Nominal Moment Capability of 3D Printed Concrete Beam with Reinforcement DOI Creative Commons
Keunhyoung Park, Ali M. Memari, Maryam Hojati

и другие.

Buildings, Год журнала: 2024, Номер 14(10), С. 3175 - 3175

Опубликована: Окт. 5, 2024

In this study, 3D-printed reinforced concrete beams were tested for flexural performance and compared with the analytical model based on material test results. Two cementitious mixes (PSU GCT) designed printing mechanically compared. Anisotropies in compressive strength modulus of elasticity printed observed, applied to prediction bending behavior, validated by actual Significant differences between predictions experimental tests behaviors observed. Furthermore, higher strengths moduli observed when loading direction was perpendicular layers or PSU mix. The effect anisotropic mechanical properties a beam both mixes. results significant errors concrete’s structural performance, from 10% 50%, suggest that factors other than reduced may influence beams.

Язык: Английский

Процитировано

1