Physically explicable mathematical model for strength prediction of UHPFRC

Chu, Sharon Lynn; Kurumisawa, Kiyofumi; Kong, Y.K.

doi:10.1016/j.engstruct.2022.115191

Cited by 12 publications

(5 citation statements)

References 80 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fiber reinforcement is used in AAMs for enhancing the tensile strength, alleviating the inherent brittleness, increasing packing density and sometimes providing additional pozzolanic precursors (Chu 2021;Du et al 2021;Chu et al 2023). Based on the raw materials, commonly *:a, b, c and n may be varied with different cation ratios and water content.…”

Section: Fiber Reinforcementmentioning

confidence: 99%

Recent Advances in X-ray Computed Tomography for Alkali-Activated Materials: A Review

Kong

Kato

Kurumisawa

2023

ACT

View full text Add to dashboard Cite

Alkali-activated materials (AAMs) have been extensively studied due to their high performance and low carbon emissions. However, the long-term durability of AAMs remains a major concern, which is closely related to their microstructures and pore networks. X-ray computed tomography (CT) has emerged as a powerful non-destructive technique for threedimensional microstructural analysis in the field of civil engineering. While CT has been widely used to investigate cement-based materials, its application to AAMs is still limited. In this review, we provide a comprehensive summary of the background information and applications of CT in AAM research, focusing on pore structure analysis, phase identification, degradation analysis, fiber reinforcement, temporal evolution and simulations. Furthermore, the current challenges and future perspectives are also discussed.

show abstract

Section: Fiber Reinforcementmentioning

confidence: 99%

Recent Advances in X-ray Computed Tomography for Alkali-Activated Materials: A Review

Kong

Kato

Kurumisawa

2023

ACT

View full text Add to dashboard Cite

show abstract

“…This is particularly promising given the inherent complexity of concrete mixtures, which is often better captured by ML models than by traditional empirical and physics-based models that rely on assumptions and simplifications. [11][12][13][14] Nevertheless, the prediction performance of ML models is highly dependent on the availability of large datasets, which is one of the main challenges in concrete science. According to a recent literature survey, 4,15 datasets used in ML-based concrete research are often limited, with over 55% of publications having less than 200 samples and only about 11% containing more than 1 000 (Figure 1).…”

Section: Introductionmentioning

confidence: 99%

“…By leveraging large datasets compiled from experiments and/or computations, ML is capable of automatically learning intricate relationships from data without explicit instructions. This is particularly promising given the inherent complexity of concrete mixtures, which is often better captured by ML models than by traditional empirical and physics‐based models that rely on assumptions and simplifications 11–14 . Nevertheless, the prediction performance of ML models is highly dependent on the availability of large datasets, which is one of the main challenges in concrete science.…”

Section: Introductionmentioning

confidence: 99%

Can domain knowledge benefit machine learning for concrete property prediction?

Li,

Pei,

Ying

et al. 2023

J Am Ceram Soc.

View full text Add to dashboard Cite

Understanding and predicting process–structure–property–performance relationships for concrete materials is key to designing resilient and sustainable infrastructure. While machine learning has emerged as a powerful tool to supplement empirical analysis and physical modeling, its capabilities are yet to be fully realized due to the massive data requirements and generalizability challenges. To address these limitations, we propose a knowledge‐informed machine learning framework for concrete property prediction that aggregates the wealth of domain knowledge condensed in empirical formulas and physics‐based models. By integrating the knowledge through data augmentation, feature enhancement, and model pre‐training, we demonstrate that this framework has the potential to (i) accelerate model convergence, (ii) improve model performance with limited training data, and (iii) increase generalizability to real‐world scenarios (including extrapolation capability to other datasets and robustness against data outliers). The overall improvement of machine learning models by knowledge integration is particularly critical when these models are scaled up to tackle the increasing complexity of modern concrete and deployed in practical applications. While demonstrated for predicting concrete strength, this versatile framework is applicable to a wide range of properties of concrete and other composite materials, paving the way for accelerated materials design and discovery.

show abstract

“…UHPFRC is commonly defined as an advanced cement‐based composite material that possesses distinguished mechanical properties, including high strength, excellent energy absorption capacity, and outstanding durability. Compared with ordinary Portland cement concrete, UHPFRC has a lower water–cement ratio ( w / c ), which significantly improved its strength 4,5 . Besides, the optimum composition of raw materials is determined using the particle dense packing theory to augment the mechanical performances and enhance the durability of UHPFRC 6,7 .…”

Section: Introductionmentioning

confidence: 99%

“…Compared with ordinary Portland cement concrete, UHPFRC has a lower water-cement ratio (w/c), which significantly improved its strength. 4,5 Besides, the optimum composition of raw materials is determined using the particle dense packing theory to augment the mechanical performances and enhance the durability of UHPFRC. 6,7 It is noteworthy that in the composition of UHPFRC, coarse aggregate is typically absent or adopted in extremely limited quantities.…”

Section: Introductionmentioning

confidence: 99%

A novel damage evaluation method for ultra‐high‐performance fiber reinforced concrete under multiple impact loads

Shi,

Chen,

Ning

et al. 2023

Fatigue Fract Eng Mat Struct

View full text Add to dashboard Cite

In this study, repeated compression tests were conducted for ultra‐high‐performance fiber reinforced concrete (UHPFRC) under three different impact pressures. Results showed that as the impact number increased, the strain rate approximately showed a proportional increase, and the plateau region of the dynamic stress–strain curve became more prominent. Besides, when the repeated impacts increased, the maximum strain and dynamic toughness both exhibited upward trends, while the peak stress decreased. The average toughness was positively correlated with the strain rate. Additionally, under high repeated impact loads, a single crack propagated rapidly within the UHPFRC specimen after single impact. Conversely, cracks propagated from the center to the periphery on the surface of the specimen when repeated loads were low. Furthermore, a quantitative evaluation method for the damage of UHPFRC under repeated impact loads was proposed based on the characteristic parameters of the gray‐level co‐occurrence matrix (GLCM).

show abstract

Physically explicable mathematical model for strength prediction of UHPFRC

Cited by 12 publications

References 80 publications

Recent Advances in X-ray Computed Tomography for Alkali-Activated Materials: A Review

Recent Advances in X-ray Computed Tomography for Alkali-Activated Materials: A Review

Can domain knowledge benefit machine learning for concrete property prediction?

A novel damage evaluation method for ultra‐high‐performance fiber reinforced concrete under multiple impact loads

Contact Info

Product

Resources

About