Explainable machine learning-based model for failure mode identification of RC flat slabs without transverse reinforcement

Shen, Yuanxie; Wu, Liangliang; Liang, Shixue

doi:10.1016/j.engfailanal.2022.106647

Cited by 33 publications

(17 citation statements)

References 56 publications

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“…Table 1 lists the major hyperparameter potential values used by each ML model in grid search. In order to determine the final prediction model, its prediction performance needs to be tested by five-fold cross validation [ 20 ]. Table 2 presents the values of the major hyperparameters of the ML model, as well as the mean scores of five-fold cross-validation.…”

Section: Model Results and Discussionmentioning

confidence: 99%

“…In addition, ML can be applied to predict the mechanical properties and durability of reinforced concrete structures and to evaluate their service life. At present, some scholars have used ML models to analyze reinforced concrete beams [ 18 ], squat reinforced concrete walls [ 19 ], reinforced concrete slabs and other engineering problems [ 20 , 21 ]. Whatever the accuracy, however, the black-box nature of the predictions makes ML models unexplainable.…”

Section: Introductionmentioning

confidence: 99%

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Ensemble Machine-Learning-Based Prediction Models for the Compressive Strength of Recycled Powder Mortar

et al. 2023

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Recycled powder (RP) serves as a potential and prospective substitute for cementitious materials in concrete. The compressive strength of RP mortar is a pivotal factor affecting the mechanical properties of RP concrete. The application of machine learning (ML) approaches in the engineering problems, particularly for predicting the mechanical properties of construction materials, leads to high prediction accuracy and low experimental costs. In this study, 204 groups of RP mortar compression experimental data are collected from the literature to establish a dataset for ML, including 163 groups in the training set and 41 groups in the test set. Four ensemble ML models, namely eXtreme Gradient-Boosting (XGBoost), Random Forest (RF), Light Gradient-Boosting Machine (LightGBM) and Adaptive Boosting (AdaBoost), were selected to predict the compressive strength of RP mortar. The comparative results demonstrate that XGBoost has the highest prediction accuracy when the a10-index, MAE, RMSE and R2 of the training set are 0.926, 1.596, 2.155 and 0.950 and the a10-index, MAE, RMSE and R2 of the test set are 0.659, 3.182, 4.285 and 0.842, respectively. SHapley Additive exPlanation (SHAP) is adopted to interpret the prediction process of XGBoost and explain the influence of influencing factors on the compressive strength of RP mortar. According to the importance of influencing factors, the order is the mass replacement rate of RP, the size of RP, the kind of RP and the water binder ratio of RP. The compressive strength of RP mortar decreases with the increase in the RP mass replacement rate. The compressive strength of RBP mortar is slightly higher than that of RCP mortar. Machine learning technologies will benefit the construction industry by facilitating the rapid and cost-effective evaluation of RP material properties.

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Section: Model Results and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ensemble Machine-Learning-Based Prediction Models for the Compressive Strength of Recycled Powder Mortar

et al. 2023

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“…In recent years, the application of black-box machine learning (ML) and artificial intelligence techniques has shown great promise in predicting complex structural behaviors [1,[10][11][12][13][14][15][16][17]. These data-driven methods can have the potential to improve the accuracy and reliability of punching shear capacity predictions by capturing intricate relationships among the influencing factors.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The SHapley Additive exPlanation (SHAP) approach was used to elucidate the factors influencing the predictions [1]. In another study [16], an effective prediction model was developed, chosen from eight ML-based models, to determine the failure mode of flat slabs based on 610 experimental data points. XGBoost surpasses other models, achieving a 99.02% accuracy rate.…”

Section: Literature Reviewmentioning

confidence: 99%

The Machine-Learning-Based Prediction of the Punching Shear Capacity of Reinforced Concrete Flat Slabs: An Advanced M5P Model Tree Approach

Abdallah,

Thoeny,

Henedy

et al. 2023

Applied Sciences

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Reinforced concrete (RC) flat slabs are widely employed in modern construction, and accurately predicting their load-carrying capacity is crucial for ensuring safety and reliability. Existing design methods and empirical equations still exhibit discrepancies in determining the ultimate load capacity of flat slabs. This study aims to develop a robust machine learning model, specifically the M5P model tree, for predicting the punching shear capacity of a RC flat slab without shear reinforcement. A comprehensive dataset of 482 experimentally tested flat slabs without shear reinforcement was gathered through an extensive literature review and utilized for the development of the M5P model. The model takes into account influential parameters, such as slab thickness, longitudinal reinforcement ratios, and concrete strength. The performance of the proposed M5P model was compared with existing design codes and other empirical models. The comparison highlights that the developed M5P model tree provides a more accurate and reliable prediction of the punching shear capacity of RC flat slabs. This study contributes to the advancement of structural engineering knowledge and has the potential to improve the design and safety assessment of concrete flat slab structures.

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“…Four performance indicators are used to compare the performance of the final prediction models: average, coefficient of variation COV, root mean square error (RMSE), and lower 95 % value. To display the advantages of the ML models, two design provisions (CSA 23.3-14 [18], and ACI 318-19 [7]), as well as two prediction models proposed by Tian et al [4], and Wu et al [19], are utilized to compare the prediction performance of ML models. Based on the existing ML model, MCS is used to analyze the dependability of a slab-column structure in a realworld engineering application.…”

Section: Introductionmentioning

confidence: 99%

Predicting of Punching Shear Capacity of Corroded Reinforced Concrete Slab-column Joints Using Artificial Intelligence Techniques

Lotfy

Gomaa

Hosny

et al. 2023

MSA Engineering Journal

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Rebars in reinforced concrete (RC) slab-column structures may corrode under unfavourable conditions, making slab-column joints (SCJs) more susceptible to punching shear (PS) failure. Moreover, PS failure is a common brittle failure, which makes it more difficult to evaluate slab column systems' functioning and failure probability. Thus, the prediction of PS resistance and the related reliability analysis are key factors for building RC slab-column systems. In this study, a highfidelity finite-element model was created using Abaqus. A comprehensive experimental record is compiled for corroded RC slab-column joints subjected to punching shear loading. Then, effective parameters are established by applying

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Explainable machine learning-based model for failure mode identification of RC flat slabs without transverse reinforcement

Cited by 33 publications

References 56 publications

Ensemble Machine-Learning-Based Prediction Models for the Compressive Strength of Recycled Powder Mortar

Ensemble Machine-Learning-Based Prediction Models for the Compressive Strength of Recycled Powder Mortar

The Machine-Learning-Based Prediction of the Punching Shear Capacity of Reinforced Concrete Flat Slabs: An Advanced M5P Model Tree Approach

Predicting of Punching Shear Capacity of Corroded Reinforced Concrete Slab-column Joints Using Artificial Intelligence Techniques

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