Shear strength of circular concrete-filled tube (CCFT) members using human-guided artificial intelligence approach

Alghossoon, Abdullah; Tarawneh, Ahmad; Almasabha, Ghassan; Murad, Yasmin; Saleh, Eman; yahia, Hamza Abu; yahya, Abdallah Abu; Sahawneh, Haitham

doi:10.1016/j.engstruct.2023.115820

Cited by 7 publications

(4 citation statements)

References 24 publications

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“…The ANN is a class of machine learning techniques implemented widely for estimating and solving various engineering problems with a high accuracy rate of predictions [17,25,[45][46][47][48]. A typical layout of an ANN model is a multi-layer feed-forward framework of input, hidden, and output layers that are successively connected by calibrated links called weights.…”

Section: Developing Ann Model and Generating New Datasetsmentioning

confidence: 99%

“…The GEP, a class of machine learning tools developed by Ferreira [61], is extensively used in the literature for developing robust predictive models in several engineering fields [16,62]. Also, many researchers have employed it as a hybrid predictive tool along with other machine learning approaches [45][46][47][48] due to its main feature of providing the model as an expression tree (ET), which can be converted into a simple mathematical equation. An ET of a GEP model consists of genes and chromosomes of constant length, where the genes are connected together by a predefined linking function, including addition, subtraction, multiplication, or division.…”

Section: Gep Predictive Modelmentioning

confidence: 99%

“…The model sensitivity in capturing the impact of the input parameters on the predicted RSI values was investigated through a parametric analysis. This type of analysis was found to be a valuable and reliable approach for validating the sensitivity of predictive models [16,17,25,[45][46][47]. It is based on utilizing the proposed model to predict a dependent variable that corresponds to interchangeable datasets of the input variables.…”

Section: Evaluation and Sensitivity Of The Proposed Modelmentioning

confidence: 99%

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Empirical Model for the Retained Stability Index of Asphalt Mixtures Using Hybrid Machine Learning Approach

Jweihan,

Al-Kheetan,

Rabi

2023

ASI

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Moisture susceptibility is a complex phenomenon that induces various distresses in asphalt pavements and can be assessed by the Retained Stability Index (RSI). This study proposes a robust model to predict the RSI using a hybrid machine learning technique, including Artificial Neural Network (ANN) and Gene Expression Programming. The model is expressed as a simple and direct mathematical function with input variables of mineral filler proportion (F%), water absorption rate of combined aggregate (Ab%), asphalt content (AC%), and air void content (Va%). A relative importance analysis ranked AC% as the most influential variable on RSI, followed by Va%, F%, and Ab%. The experimental RSI results of 150 testing samples of various mixes were utilized along with other data points generated by the ANN to train and validate the proposed model. The model promotes a high level of accuracy for predicting the RSI with a 96.6% coefficient of determination (R2) and very low errors. In addition, the sensitivity of the model has been verified by considering the effect of the variables, which is in line with the results of network connection weight and previous studies in the literature. F%, Ab%, and Va% have an inverse relationship with the RSI values, whereas AC% has the opposite. The model helps forecast the water susceptibility of asphalt mixes by which the experimental effort is minimized and the mixes’ performance can be improved.

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Section: Developing Ann Model and Generating New Datasetsmentioning

confidence: 99%

Section: Gep Predictive Modelmentioning

confidence: 99%

Section: Evaluation and Sensitivity Of The Proposed Modelmentioning

confidence: 99%

See 1 more Smart Citation

Empirical Model for the Retained Stability Index of Asphalt Mixtures Using Hybrid Machine Learning Approach

Jweihan,

Al-Kheetan,

Rabi

2023

ASI

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“…In several recent studies, this method has been used to interpret the results of ML models. 48,49,51,54 While several researchers have used ML methods, including regression tree, support vector regression, artificial neural network, gradient tree boosting, categorical gradient boosting, etc., to predict the compressive axial strength of CFSTs, [58][59][60][61][62][63][64][65][66][67][68] only one research work 69 is conducted on the use of ML models to estimate the shear strength of these members. Considering the high error and inconsistency of the shear strength estimates of empirical equations for CFSTs and the generally high accuracy of ML methods in estimating structural parameters, such data-driven models can be expected to provide better shear strength estimates for CFSTs.…”

mentioning

confidence: 99%

Interpretable machine learning model for shear strength estimation of circular concrete‐filled steel tubes

Mansouri,

Mangalathu

2024

Structural Design Tall Build

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SummaryPrecise estimation of the shear strength of concrete‐filled steel tubes (CFSTs) is a crucial requirement for the design of these members. The existing design codes and empirical equations are inconsistent in predicting the shear strength of these members. This paper provides a data‐driven approach for the shear strength estimation of circular CFSTs. For this purpose, the authors evaluated and compared the performance of nine machine learning (ML) methods, namely linear regression, decision tree (DT), k‐nearest neighbors (KNN), support vector regression (SVR), random forest (RF), bagging regression (BR), adaptive boosting (AdaBoost), gradient boosting regression tree (GBRT), and extreme gradient boosting (XGBoost) in estimating the shear strength of CFSTs on an experimental database compiled from the results of 230 shear tests on CFSTs in the literature. For each model, hyperparameter tuning was performed by conducting a grid search in combination with k‐fold cross‐validation (CV). Comparing the nine methods in terms of several performance measures showed that the XGBoost model was the most accurate in predicting the shear strength of CFSTs. This model also showed superior accuracy in predicting the shear strength of CFSTs when compared to the formulas provided in design codes and the existing empirical equations. The Shapley Additive exPlanations (SHAP) technique was also used to interpret the results of the XGBoost model. Using SHAP, the features with the greatest impact on the shear strength of CFSTs were found to be the cross‐sectional area of the steel tube, the axial load ratio, and the shear span ratio, in that order.

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Predictive models in machine learning for strength and life cycle assessment of concrete structures

Dinesh,

Rahul Prasad

2024

Automation in Construction

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Shear strength of circular concrete-filled tube (CCFT) members using human-guided artificial intelligence approach

Cited by 7 publications

References 24 publications

Empirical Model for the Retained Stability Index of Asphalt Mixtures Using Hybrid Machine Learning Approach

Empirical Model for the Retained Stability Index of Asphalt Mixtures Using Hybrid Machine Learning Approach

Interpretable machine learning model for shear strength estimation of circular concrete‐filled steel tubes

Predictive models in machine learning for strength and life cycle assessment of concrete structures

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