Machine learning models for predicting concrete beams shear strength externally bonded with FRP

Rahman, Jesika; Arafin, Palisa; Billah, A. H. M. Muntasir

doi:10.1016/j.istruc.2023.04.069

Cited by 16 publications

(5 citation statements)

References 99 publications

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“…Machine learning predictive models are often considered "black boxes" due to their complex structures, which make interpreting and explaining their predictions difficult. However, a recent model-agnostic approach called SHAP has gained popularity for explaining feature importance in ML models [29,38,39].…”

Section: Shapley Additive Explanationsmentioning

confidence: 99%

See 1 more Smart Citation

Modelling nonlinear shear creep behaviour of a structural adhesive using deep neural networks (DNN)

Wang,

Shui,

Stratford

et al. 2024

Construction and Building Materials

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Section: Shapley Additive Explanationsmentioning

confidence: 99%

“…where M is the number of the input features, ϕ0 is a constant, and ϕi represents the attribution effect on each feature [39,40].…”

Section: Shapley Additive Explanationsmentioning

confidence: 99%

Modelling nonlinear shear creep behaviour of a structural adhesive using deep neural networks (DNN)

Wang,

Shui,

Stratford

et al. 2024

Construction and Building Materials

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“…The shear capacity of RC beams was predicted mathematically using a variety of ML approaches [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42]. The use of the well-known artificial neural network (ANN) technique was adopted to investigate the impact of various crucial variables on the shear strength of FRP-RC beams [25].…”

Section: Shear Strengthmentioning

confidence: 99%

“…Lately, Rahman et al [40] generated 10 ML models involving different ensemble techniques. With the use of an extensive database of rectangular and T-beams, the ensemble models RF, CatBoost, and XGBoost showed superior prediction performance in predicting the shear capacity when compared with existing design guidelines.…”

Section: Shear Strengthmentioning

confidence: 99%

The Efficiency of Using Machine Learning Techniques in Fiber-Reinforced-Polymer Applications in Structural Engineering

Alhusban,

Alkhawaldeh

2023

Sustainability

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Sustainable solutions in the building construction industry have emerged as a new method for retrofitting applications in the last two decades. Fiber-reinforced polymers (FRPs) have garnered much attention among researchers for improving reinforced concrete (RC) structures. The existing design guidelines for FRP-strengthened RC members were developed using empirical methods that are based on specific databases, limiting the accuracy of the predicted results. Therefore, the use of innovative and efficient prediction tools to predict the behavior of FRP-strengthened RC members has become essential. During the last few years, efforts have been progressively focused on the use of machine learning (ML) as a feasible and effective technique for solving various structural engineering problems. Its capability to predict the behavior of complex nonlinear structural systems while considering a wide range of parameters offers a distinctive opportunity to make the behavior of RC members more predictable and accurate. This paper aims to evaluate the current state of using various ML algorithms in RC members strengthened with FRP to enable researchers to determine the capabilities of current solutions as well as to find research gaps to carry out more research to bridge revealed knowledge and practice gaps. Scopus databases were searched using predefined standards. The search revealed ninety-six articles published between 2016 and 2023. Consequently, these articles were analyzed for ML applications in the field of FRP retrofitting, including flexural and shear strengthening of RC beams, flexural strengthening of slabs, confinement and compressive strength of columns, and FRP bond strength. The results reveal that 32% of the reviewed studies focused on the application of ML techniques to the flexural and shear strengthening of RC beams, 32% on the confinement and compressive strength of columns, 6.5% on the flexural strengthening of slabs, 22% on FRP bond strength, 6.5% on materials, and 1% on beam–column joints. This research also revealed that the application of various ML algorithms has shown a significant improvement in resistance prediction accuracy as compared with the existing empirical solutions. Supervised learning techniques were the most favorable learning method due to their good generalization, interpretability, adaptability, and predictive efficiency. In addition, the selection of suitable ML algorithms and optimization techniques is found to be mainly dictated by the nature of the problem and the characteristics of the dataset. Nonetheless, selecting the most appropriate ML model and optimization algorithm for each specific application remains a challenge, given that each algorithm is developed with different principles and methodologies.

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“…With the evolution of artificial intelligence (AI), machine learning (ML) algorithms are commonly used because ML can provide fast and economical solutions for civil engineering problems [ 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ]. Moreover, in the field of fiber-reinforced composites, ML algorithms have been increasingly applied [ 9 , 43 , 44 , 45 , 46 , 47 , 48 , 49 ].…”

Section: Introductionmentioning

confidence: 99%

Interpretable Predictive Modelling of Basalt Fiber Reinforced Concrete Splitting Tensile Strength Using Ensemble Machine Learning Methods and SHAP Approach

et al. 2023

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Basalt fibers are a type of reinforcing fiber that can be added to concrete to improve its strength, durability, resistance to cracking, and overall performance. The addition of basalt fibers with high tensile strength has a particularly favorable impact on the splitting tensile strength of concrete. The current study presents a data set of experimental results of splitting tests curated from the literature. Some of the best-performing ensemble learning techniques such as Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Random Forest, and Categorical Boosting (CatBoost) have been applied to the prediction of the splitting tensile strength of concrete reinforced with basalt fibers. State-of-the-art performance metrics such as the root mean squared error, mean absolute error and the coefficient of determination have been used for measuring the accuracy of the prediction. The impact of each input feature on the model prediction has been visualized using the Shapley Additive Explanations (SHAP) algorithm and individual conditional expectation (ICE) plots. A coefficient of determination greater than 0.9 could be achieved by the XGBoost algorithm in the prediction of the splitting tensile strength.

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Machine learning models for predicting concrete beams shear strength externally bonded with FRP

Cited by 16 publications

References 99 publications

Modelling nonlinear shear creep behaviour of a structural adhesive using deep neural networks (DNN)

Modelling nonlinear shear creep behaviour of a structural adhesive using deep neural networks (DNN)

The Efficiency of Using Machine Learning Techniques in Fiber-Reinforced-Polymer Applications in Structural Engineering

Interpretable Predictive Modelling of Basalt Fiber Reinforced Concrete Splitting Tensile Strength Using Ensemble Machine Learning Methods and SHAP Approach

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