Knowledge-enhanced data-driven models for quantifying the effectiveness of PP fibers in spalling prevention of ultra-high performance concrete

Liu, Jincheng; Huang, Le; Tian, Zushi; Ye, Hailong

doi:10.1016/j.conbuildmat.2021.123946

Cited by 17 publications

(4 citation statements)

References 55 publications

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“…A database containing 293 laboratory samples was collected from the open literature [ 32 – 34 , 44 – 65 ]. This 293 data point sample contains 11 independent variables and one dependent variable (output; classified as spalling/no spalling).…”

Section: Data Collection and Methodologymentioning

confidence: 99%

“…The authors would like the acknowledge the following works of Dr. Liu’s and Dr. Ye’s [ 32 – 34 , 89 , 90 ] group, who managed to compile and share the database used herein. We encourage our readers to visit such works.…”

Section: Acknowledgementsmentioning

confidence: 99%

“…For instance, Uysal and Tanyildizi [ 28 ] and McKinney and Ali [ 29 ] are perhaps some of the earliest works that tackled the spalling problem through AI. More recently, the use of modern algorithms and techniques has also been tried by our research group [ 5 , 12 , 29 – 31 ] and colleagues [ 32 – 34 ]. In hindsight, these recent studies examine the vulnerability of concrete elements or concrete mixes to spalling via investigating the influence of elemental features (such as geometrical, materials, and loading properties of load bearing members) or concrete mixtures (e.g., proportions of aggregates, sand, binders, etc.).…”

Section: Introductionmentioning

confidence: 99%

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Discovering Graphical Heuristics on Fire-Induced Spalling of Concrete Through Explainable Artificial Intelligence

Tapeh

Naser

2022

Fire Technol

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Fire-induced spalling of concrete continues to be an intriguing and intricate research problem. A deep dive into the open literature highlights the alarming discrepancy and inconsistency of existing theories, as well as the complexity of predicting spalling. This brings new challenges to creating fire-safe concretes and primes an opportunity to explore modern methods of investigation to tackle the spalling phenomenon. Thus, this paper leverages the latest advancements in explainable Artificial Intelligence (XAI) to vet existing theories on fire-induced spalling and to discover solutions/heuristics to predict spalling of concrete mixtures. The developed heuristics are in the form of graphs and nomograms . The proposed solutions allow interested researchers and engineers to graphically identify the propensity of a given concrete mixture to spalling directly and with ease. For example, we report that concrete mixtures with a combination of moderate water/binder ratio (of about 0.3), low heating rate (less than 2.5°C/min), moderate rise in temperature (less than 500°C), and have moisture content (less than 3%) are expected to be less prone to spalling. Further, findings from this research showcase the potential for developing simple (i.e., one-shot) and graphical (coding-free and formula-free) XAI-based solutions and web applications to address decades-long problems in the area of concrete research.

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Section: Data Collection and Methodologymentioning

confidence: 99%

Section: Acknowledgementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Discovering Graphical Heuristics on Fire-Induced Spalling of Concrete Through Explainable Artificial Intelligence

Tapeh

Naser

2022

Fire Technol

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“…Based on the decision tree (DT) method, the integrated random forests (RF) method was established according to the bagging strategy. The gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and categorical feature-supported gradient boosting (CatBoost) methods were established according to the boosting strategy [ 31 , 32 , 33 ].…”

Section: Introductionmentioning

confidence: 99%

Tensile Performance Mechanism for Bamboo Fiber-Reinforced, Palm Oil-Based Resin Bio-Composites Using Finite Element Simulation and Machine Learning

Wang

Liu

et al. 2023

Polymers

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Plant fiber-reinforced composites have the advantages of environmental friendliness, sustainability, and high specific strength and modulus. They are widely used as low-carbon emission materials in automobiles, construction, and buildings. The prediction of their mechanical performance is critical for material optimal design and application. However, the variation in the physical structure of plant fibers, the randomness of meso-structures, and the multiple material parameters of composites limit the optimal design of the composite mechanical properties. Based on tensile experiments on bamboo fiber-reinforced, palm oil-based resin composites, finite element simulations were carried out and the effect of material parameters on the tensile performances of the composites was investigated. In addition, machine learning methods were used to predict the tensile properties of the composites. The numerical results showed that the resin type, contact interface, fiber volume fraction, and multi-factor coupling significantly influenced the tensile performance of the composites. The results of the machine learning analysis showed that the gradient boosting decision tree method had the best prediction performance for the tensile strength of the composites (R2 was 0.786) based on numerical simulation data from a small sample size. Furthermore, the machine learning analysis demonstrated that the resin performance and fiber volume fraction were critical parameters for the tensile strength of composites. This study provides an insightful understanding and effective route for investigating the tensile performance of complex bio-composites.

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Effect of polymer fibers on pore pressure development and explosive spalling of ultra-high performance concrete at elevated temperature

Zhang

Chen

et al. 2022

Archiv.Civ.Mech.Eng

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Knowledge-enhanced data-driven models for quantifying the effectiveness of PP fibers in spalling prevention of ultra-high performance concrete

Cited by 17 publications

References 55 publications

Discovering Graphical Heuristics on Fire-Induced Spalling of Concrete Through Explainable Artificial Intelligence

Discovering Graphical Heuristics on Fire-Induced Spalling of Concrete Through Explainable Artificial Intelligence

Tensile Performance Mechanism for Bamboo Fiber-Reinforced, Palm Oil-Based Resin Bio-Composites Using Finite Element Simulation and Machine Learning

Effect of polymer fibers on pore pressure development and explosive spalling of ultra-high performance concrete at elevated temperature

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