Data-Driven Modeling of Mechanical Properties of Fiber-Reinforced Concrete: A Critical Review

Kazemi, Farzin; Shafighfard, Torkan; Yoo, Doo-Yeol

doi:10.1007/s11831-023-10043-w

Cited by 30 publications

(2 citation statements)

References 183 publications

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“…The fundamental objective of feature engineering is to identify a minimal subset of features that exhibit the highest degree of mutual orthogonality while capturing the most pivotal patterns intrinsic to the problem [18]. Feature dimensionality increase and reduction are two concepts commonly used in feature engineering [45]. Feature dimensionality increase is the process of exploring feature combinations, which involves data-driven methods with feature creation, the validation of physical quantities, and physics-driven methods with domain knowledge of the problem.…”

Section: Methodsmentioning

confidence: 99%

Machine Learning Prediction Model for Boundary Transverse Reinforcement of Shear Walls

Ding,

Li,

Xiao

et al. 2024

Buildings

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Due to their roles as efficient lateral force-resisting systems, reinforced concrete shear walls exert a tremendous degree of influence on the overall seismic performance of buildings. The ability to predict the boundary transverse reinforcement of shear walls is critical to the seismic design process, as well as in the overall evaluation and retrofitting of existing buildings. Contemporary empirical models attain low predictive accuracy, with an inability to capture nonlinearity between boundary transverse reinforcement and different influencing variables. This study proposes a boundary transverse reinforcement prediction model for shear walls with boundary elements based on the demand of ductility. Using the extreme gradient boosting machine learning algorithm and 501 samples, some 52 input variables are considered, and a subset with six features is selected, monitored, and analyzed using both internal methods (gain and cover) and external methods. The results (R2=0.884) display superior predictive capacity compared with existing models. Interpretation and error analysis are performed. Safety analysis is conducted to obtain references for use in practical engineering. Overall, this study presents a more accurate tool for use in seismic design and provides references for the evaluation and retrofitting of existing buildings. Our contributions hold significant implications for enhancing the safety and resilience of reinforced concrete structures.

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Section: Methodsmentioning

confidence: 99%

Machine Learning Prediction Model for Boundary Transverse Reinforcement of Shear Walls

Ding,

Li,

Xiao

et al. 2024

Buildings

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show abstract

“…Data-driven modeling techniques, such as machine learning, can analyze large sets of experimental data and develop predictive models that can accurately estimate the thermal properties of bio-sourced materials [18,19]. These models can handle complex relationships between input variables and output properties, as well as incorporate data from various sources [19]. Several studies have demonstrated the potential of machine learning in predicting thermal properties of materials BOUASRIA, as shown by Manal et al [20].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Approach for Bio-Based Materials’ Characterization and Explanation

Alami,

Rajaoarisoa,

Dujardin

et al. 2024

Buildings

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This paper introduces a numerical methodology for classifying and identifying types of bio-based materials through experimental thermal characterization. In contrast to prevailing approaches that primarily focus on thermal conductivity, our characterization methodology encompasses several thermal parameters. In this paper, the physical characteristics of seven types of bio-based concrete were analyzed, focusing on the thermal properties of palm- and esparto-fiber-reinforced concrete. The proposed method uses artificial intelligence techniques, specifically the k-means clustering approach, to segregate data into homogeneous groups with shared thermal characteristics. This enables the elucidation of insights and recommendations regarding the utilization of bio-based insulation in building applications. The results show that the k-means algorithm is able to efficiently classify the reference concrete (RC) with a performance of up to 71%. Additionally, the technique is more accurate when retaining only six centroids, which, among other things, allows all the characteristics associated with each type of concrete to be grouped and identified. Indeed, whether for k clusters k = 7 or k = 5, the technique was not able to predict the typical characteristics of 2% or 3% esparto concrete (EC).

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Active Learning on Ensemble Machine-Learning Model to Retrofit Buildings Under Seismic Mainshock-Aftershock Sequence

Asgarkhani,

Kazemi,

Jankowski

2024

Lecture Notes in Computer Science

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Data-Driven Modeling of Mechanical Properties of Fiber-Reinforced Concrete: A Critical Review

Cited by 30 publications

References 183 publications

Machine Learning Prediction Model for Boundary Transverse Reinforcement of Shear Walls

Machine Learning Prediction Model for Boundary Transverse Reinforcement of Shear Walls

Artificial Intelligence Approach for Bio-Based Materials’ Characterization and Explanation

Active Learning on Ensemble Machine-Learning Model to Retrofit Buildings Under Seismic Mainshock-Aftershock Sequence

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