Optimum Design of Cylindrical Walls Using Ensemble Learning Methods

Bekdaş, Gebrai̇l; Çakıroğlu, Celal; Islam, Kamrul; Kim, Sanghun; Geem, Zong Woo

doi:10.3390/app12042165

Cited by 11 publications

(8 citation statements)

References 22 publications

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“…Previous studies in the area of optimal structural dimensioning mostly attempted to minimize structural cost or weight for a single load case [ 8 , 9 ]. More recent studies in the area attempted to develop general-purpose predictive models based on a dataset of structural configurations with known structural behavior [ 21 , 22 ]. However, the availability of experimental or numerical data describing the structural behavior is a major limiting factor in the training of robust predictive models since the size of the database used in the training of these predictive models is a decisive factor that effects to what extent these models could be used reliably.…”

Section: Discussionmentioning

confidence: 99%

“…Ni et al [ 20 ] generated fragility curves for buried pipelines using Lasso Regression Analysis. Bekdaş et al [ 21 ] demonstrated the high accuracy of different Ensemble Learning Algorithms in predicting the optimal wall thickness of reinforced concrete cylindrical walls. Cakiroglu et al [ 22 ] developed predictive models using Ensemble Learning Algorithms to estimate the axial load-carrying capacity of FRP-reinforced concrete columns.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Dimensioning of Retaining Walls Using Explainable Ensemble Learning Algorithms

et al. 2022

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This paper develops predictive models for optimal dimensions that minimize the construction cost associated with reinforced concrete retaining walls. Random Forest, Extreme Gradient Boosting (XGBoost), Categorical Gradient Boosting (CatBoost), and Light Gradient Boosting Machine (LightGBM) algorithms were applied to obtain the predictive models. Predictive models were trained using a comprehensive dataset, which was generated using the Harmony Search (HS) algorithm. Each data sample in this database consists of a unique combination of the soil density, friction angle, ultimate bearing pressure, surcharge, the unit cost of concrete, and six different dimensions that describe an optimal retaining wall geometry. The influence of these design features on the optimal dimensioning and their interdependence are explained and visualized using the SHapley Additive exPlanations (SHAP) algorithm. The prediction accuracy of the used ensemble learning methods is evaluated with different metrics of accuracy such as the coefficient of determination, root mean square error, and mean absolute error. Comparing predicted and actual optimal dimensions on a test set showed that an R2 score of 0.99 could be achieved. In terms of computational speed, the LightGBM algorithm was found to be the fastest, with an average execution speed of 6.17 s for the training and testing of the model. On the other hand, the highest accuracy could be achieved by the CatBoost algorithm. The availability of open-source machine learning algorithms and high-quality datasets makes it possible for designers to supplement traditional design procedures with newly developed machine learning techniques. The novel methodology proposed in this paper aims at producing larger datasets, thereby increasing the applicability and accuracy of machine learning algorithms in relation to optimal dimensioning of structures.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Dimensioning of Retaining Walls Using Explainable Ensemble Learning Algorithms

et al. 2022

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“…EDT refers to a method of generating multiple decision trees and predicting them as the average of each decision tree result [38,39]. It is known to improve predictability and performance mainly when dealing with large regression models [40]. The EDT model can be subclassified into tree bagging, random forest, and hybrid ensemble decision tree models [41,42].…”

Section: Formulation Of Optimizationmentioning

confidence: 99%

Optimization of the Uniformity Index Performance in the Selective Catalytic Reduction System Using a Metamodel

Kim,

Park,

Yoo

et al. 2023

Sustainability

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The significance of the selective catalytic reduction system in vehicles increases in line with the high standards of emission control and enhanced selective catalytic reduction efficiency. This study aims to improve the performance of the selective catalytic reduction system through an optimization method using a metamodel. The objective function is defined as the ammonia uniformity index, and the design parameters are defined in relation to the pipe length and mixer related to the chemical reaction of the urea solution. The range of design parameters has been designated by a trial-and-error method in order to maintain the overall design drawings of the selective catalytic reduction system and prevent modeling errors. Three algorithms, namely, ensemble decision tree, Kriging, and radial basis function, are employed to develop the metamodel. The accuracy of the metamodel is verified based on three indicators: the normalized root mean square error, root mean square error, and maximum absolute error. The metamodel is generated using the Kriging model, which has the highest accuracy among the algorithms, and optimization is also performed. The predicted optimization results are confirmed by computational fluid dynamics numerical analysis with a 99.83% match. The ammonia uniformity index is improved by 1.38% compared to the base model, and it can be said that the NOx purification efficiency is improved by 30.95%. Consequently, optimizing the uniformity index performance through structural optimization is of utmost importance. Furthermore, this study reveals that the design variables related to the mixer play a crucial role in the performance. Therefore, using the metamodel to optimize the selectively catalytic reduction system’s structure should be considered significant. Finally, in the future, the analysis model can be validated using test equipment based on the findings of this study.

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“…In recent years, the architecture, engineering, and construction (AEC) industry has been benefiting much from artificial intelligence and machine learning. Several studies on machine learning in civil engineering exist in the recent literature, including a classification of failure modes [15,16], performance classifications and the prediction of reinforced masonry structures [17], a prediction of the optimum parameters of passive-tuned mass dampers [18,19], an estimation of the optimum design of structural systems [20], prediction models for optimum fiber-reinforced polymer beams [21,22], a prediction of the axial compression capacity of concrete-filled steel tubular columns [23], a prediction of the bearing strength of double shear bolted connections [24], and predictions of the shear stress and plastic viscosity of self-compacting concrete [25] and for the optimum design of cylindrical walls [26].…”

Section: Introductionmentioning

confidence: 99%

Use of Machine Learning Techniques in Soil Classification

et al. 2023

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In the design of reliable structures, the soil classification process is the first step, which involves costly and time-consuming work including laboratory tests. Machine learning (ML), which has wide use in many scientific fields, can be utilized for facilitating soil classification. This study aims to provide a concrete example of the use of ML for soil classification. The dataset of the study comprises 805 soil samples based on the soil drillings of the new Gayrettepe–Istanbul Airport metro line construction. The dataset has both missing data and class imbalance. In the data preprocessing stage, first, data imputation techniques were applied to deal with the missing data. Two different imputation techniques were tested, and finally, the data were imputed with the KNN imputer. Later, a balance was achieved with the synthetic minority oversampling technique (SMOTE). After the preprocessing, a series of ML algorithms were tested with 10-fold cross-validation. Unlike the studies conducted in previous research, new gradient-boosting methods such as XGBoost, LightGBM, and CatBoost were tested, high classification accuracy rates of up to +90% were observed, and a significant improvement in the accuracy of prediction (when compared with previous research) was achieved.

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Optimum Design of Cylindrical Walls Using Ensemble Learning Methods

Cited by 11 publications

References 22 publications

Optimal Dimensioning of Retaining Walls Using Explainable Ensemble Learning Algorithms

Optimal Dimensioning of Retaining Walls Using Explainable Ensemble Learning Algorithms

Optimization of the Uniformity Index Performance in the Selective Catalytic Reduction System Using a Metamodel

Use of Machine Learning Techniques in Soil Classification

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