Data-driven shear strength predictions of recycled aggregate concrete beams with /without shear reinforcement by applying machine learning approaches

Jayasinghe, Thushara; Chen, B.; Zhang, Zhaorui; Meng, Xia; Li, Yajie; Gunawardena, Tharaka; Mangalathu, Sujith; Mendis, Priyan

doi:10.1016/j.conbuildmat.2023.131604

Cited by 16 publications

(3 citation statements)

References 51 publications

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“…To evaluate the performance of the fitted models, we used three commonly recommended quantitative methods [46,47]. We used the coefficient of determination (R 2 ) of the predicted (P i ) and observed (S i ) values to express the variation in the response variable over the variation in the predictor variables (Equation ( 2)) [48]:…”

Section: Choosing the Best Modelmentioning

confidence: 99%

Tactical Forwarder Planning: A Data-Driven Approach for Timber Forwarding

Munis,

Almeida,

Camargo

et al. 2023

Forests

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Tactical planning in timber harvesting involves aspects related to forest macro-planning and, particularly, the allocation of resources and sequencing of activities, all of which affect the allocation of timber in forest yards and roads and the productivity of forest machines. Data-driven approaches encourage the use of information obtained from data to enhance decision-making efficiency and support the development of short-term strategies. Therefore, our investigation was intended to determine whether a data-driven approach can generate sufficient input for modeling forwarder productivity in timber forwarding in Pinus and Eucalyptus planted forests, to support tactical planning. We utilized 3812 instances of raw data that were generated over a 36-month period. The data were collected from 23 loggers who operated in Pinus and Eucalyptus planted forests. We applied 22 regression algorithms that applied a supervised learning method from an experimental machine learning approach to the data instances. We evaluated the fitted models using three performance metrics. Out of the tested algorithms, the default mode of light gradient boosting produced a root mean squared error of 14.80 m3 h−1, a mean absolute error of 2.70, and a coefficient of determination of 0.77. Therefore, data-driven methods adequately support forwarder productivity modeling in timber forwarding in planted forests and help forest managers with tactical planning.

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Section: Choosing the Best Modelmentioning

confidence: 99%

Tactical Forwarder Planning: A Data-Driven Approach for Timber Forwarding

Munis,

Almeida,

Camargo

et al. 2023

Forests

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show abstract

“…A scholarly discussion centered on implementing a machine learning approach to calculate and optimize the modulus of elasticity of concrete containing recycled aggregates. A comparative analysis was conducted to assess the performance of the ensemble model against other algorithms, revealing that the ensemble model exhibited more precise predictions than the individual models [3]. The algorithms of machine learning were utilized to predict the shear strength of beams containing concrete with recycled aggregates, both with and without shear reinforcement.…”

Section: Introductionmentioning

confidence: 99%

Stacking Ensemble-Based Machine Learning Model for Predicting Deterioration Components of Steel W-Section Beams

Khoshkroodi,

Parvini Sani,

Aajami

2024

Buildings

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The collapse evaluation of the structural systems under seismic loading necessitates identifying and quantifying deterioration components (DCs). In the case of steel w-section beams (SWSB), three distinct types of DCs have been derived. These deterioration components for steel beams comprise the following: pre-capping plastic rotation (θp), post-capping plastic rotation (θpc), and cumulative rotation capacity (Λ). The primary objective of this research is to employ a machine learning (ML) model for accurate determination of these deterioration components. The stacking model is a powerful combination of meta-learners, which is used for better learning and performance of base learners. The base learners consist of AdaBoost, Random Forest (RF), and XGBoost. Among various machine learning algorithms, the stacking model exhibited superior functioning. The evaluation metrics of the stacking model were as follows: R2 = 0.9 and RMSE = 0.003 for θp, R2 = 0.97 and RMSE = 0.012 for θpc, and R2 = 0.98 and RMSE = 0.09 for Λ. The significance of input variables, specifically the web-depth-over-web-thickness ratio (h/tw) and the flange width-to-thickness ratio (bf/2tf), in determining the deterioration components was assessed using the Shapley Additive Explanations model. These parameters emerged as the most crucial factors in the evaluation.

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“…At the same time, the problem of small experimental datasets was solved by using an innovative method of data augmentation, facilitating the achievement of a higher prediction accuracy for the punching shear strength of steel fiber-reinforced concrete slabs 22 . Jayasinghe used eight machine-learning models to develop a framework for predicting the shear capacity of recycled aggregate concrete (RAC) beams, and the results revealed that the XGBoost model had the best prediction effect, with the coefficient of determination R 2 on the test set reaching 0.95 (slender beam) and 0.78 (deep beam) 23 . You utilized 48 datasets and three machine learning models to predict the bond strength between UHPC and deformed reinforcing bars.…”

Section: Introductionmentioning

confidence: 99%

Data-driven prediction on critical mechanical properties of engineered cementitious composites based on machine learning

Qing,

2024

Sci Rep

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The present study introduces a novel approach utilizing machine learning techniques to predict the crucial mechanical properties of engineered cementitious composites (ECCs), spanning from typical to exceptionally high strength levels. These properties, including compressive strength, flexural strength, tensile strength, and tensile strain capacity, can not only be predicted but also precisely estimated. The investigation encompassed a meticulous compilation and examination of 1532 datasets sourced from pertinent research. Four machine learning algorithms, linear regression (LR), K nearest neighbors (KNN), random forest (RF), and extreme gradient boosting (XGB), were used to establish the prediction model of ECC mechanical properties and determine the optimal model. The optimal model was utilized to employ SHapley Additive exPlanations (SHAP) for scrutinizing feature importance and conducting an in-depth parametric analysis. Subsequently, a comprehensive control strategy was devised for ECC mechanical properties. This strategy can provide actionable guidance for ECC design, equipping engineers and professionals in civil engineering and material science to make informed decisions throughout their design endeavors. The results show that the RF model demonstrated the highest prediction accuracy for compressive strength and flexural strength, with R2 values of 0.92 and 0.91 on the test set. The XGB model outperformed in predicting tensile strength and tensile strain capacity, with R2 values of 0.87 and 0.80 on the test set, respectively. The prediction of tensile strain capacity was the least accurate. Meanwhile, the MAE of the tensile strain capacity was a mere 0.84%, smaller than the variability (1.77%) of the test results in previous research. Compressive strength and tensile strength demonstrated high sensitivity to variations in both water-cement ratio (W) and water reducer (WR). In contrast, flexural strength exhibited high sensitivity solely to changes in W. Conversely, the sensitivity of tensile strain capacity to input features was moderate and consistent. The mechanical attributes of ECC emerged from the combined effects of multiple positive and negative features. Notably, WR exerted the most significant influence on compressive strength among all features, whereas polyethylene (PE) fiber emerged as the primary driver affecting flexural strength, tensile strength, and tensile strain capacity.

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Data-driven shear strength predictions of recycled aggregate concrete beams with /without shear reinforcement by applying machine learning approaches

Cited by 16 publications

References 51 publications

Tactical Forwarder Planning: A Data-Driven Approach for Timber Forwarding

Tactical Forwarder Planning: A Data-Driven Approach for Timber Forwarding

Stacking Ensemble-Based Machine Learning Model for Predicting Deterioration Components of Steel W-Section Beams

Data-driven prediction on critical mechanical properties of engineered cementitious composites based on machine learning

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