Adaptive Harmony Search for Tuned Liquid Damper Optimization under Seismic Excitation

Ocak, Ayla; Niğdeli, Sinan Melih; Bekdaş, Gebrai̇l; Kim, Sanghun; Geem, Zong Woo

doi:10.3390/app12052645

Cited by 15 publications

(9 citation statements)

References 38 publications

(39 reference statements)

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“…One of the most widely used and wellestablished algorithms in this category is the harmony search algorithm. The algorithm was invented by Geem et al [39] and has been applied to a wide range of engineering problems such as the optimum design of retaining walls [40,41], laminated composite plates [42,43], highway bridge plate girders [44], concrete-filled steel tubular columns [45], reinforced concrete cylindrical walls [46][47][48], and tuned liquid dampers [49].…”

Section: Harmony Search Optimizationmentioning

confidence: 99%

Interpretable Machine Learning Algorithms to Predict the Axial Capacity of FRP-Reinforced Concrete Columns

et al. 2022

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Fiber-reinforced polymer (FRP) rebars are increasingly being used as an alternative to steel rebars in reinforced concrete (RC) members due to their excellent corrosion resistance capability and enhanced mechanical properties. Extensive research works have been performed in the last two decades to develop predictive models, codes, and guidelines to estimate the axial load-carrying capacity of FRP-RC columns. This study utilizes the power of artificial intelligence and develops an alternative approach to predict the axial capacity of FRP-RC columns more accurately using data-driven machine learning (ML) algorithms. A database of 117 tests of axially loaded FRP-RC columns is collected from the literature. The geometric and material properties, column shape and slenderness ratio, reinforcement details, and FRP types are used as the input variables, while the load-carrying capacity is used as the output response to develop the ML models. Furthermore, the input-output relationship of the ML model is explained through feature importance analysis and the SHapely Additive exPlanations (SHAP) approach. Eight ML models, namely, Kernel Ridge Regression, Lasso Regression, Support Vector Machine, Gradient Boosting Machine, Adaptive Boosting, Random Forest, Categorical Gradient Boosting, and Extreme Gradient Boosting, are used in this study for capacity prediction, and their relative performances are compared to identify the best-performing ML model. Finally, predictive equations are proposed using the harmony search optimization and the model interpretations obtained through the SHAP algorithm.

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Section: Harmony Search Optimizationmentioning

confidence: 99%

Interpretable Machine Learning Algorithms to Predict the Axial Capacity of FRP-Reinforced Concrete Columns

et al. 2022

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“…Jeong et al [31] proposed an advanced parameter-less version of HS to solve the parameter setting problem by using an improved Parameter-Setting-Free (PSF) scheme, reducing memory consumption, and improving efficiency. Valdez et al [32] applied fuzzy logic during the algorithm execution and dynamically adjusted the main parameter HMCR using triangular membership functions. Ocak et al [33] designed a version of HS with adaptive parameter variation for optimizing the Tuned Liquid Damper (TLD).…”

Section: Introductionmentioning

confidence: 99%

Harmony Search Algorithm Based on Dual-Memory Dynamic Search and Its Application on Data Clustering

Wang,

Ouyang,

Zhou

et al. 2023

Complex Syst. Model. Simul.

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Harmony Search (HS) algorithm is highly effective in solving a wide range of real-world engineering optimization problems. However, it still has the problems such as being prone to local optima, low optimization accuracy, and low search efficiency. To address the limitations of the HS algorithm, a novel approach called the Dual-Memory Dynamic Search Harmony Search (DMDS-HS) algorithm is introduced. The main innovations of this algorithm are as follows: Firstly, a dual-memory structure is introduced to rank and hierarchically organize the harmonies in the harmony memory, creating an effective and selectable trust region to reduce approach blind searching. Furthermore, the trust region is dynamically adjusted to improve the convergence of the algorithm while maintaining its global search capability. Secondly, to boost the algorithm's convergence speed, a phased dynamic convergence domain concept is introduced to strategically devise a global random search strategy. Lastly, the algorithm constructs an adaptive parameter adjustment strategy to adjust the usage probability of the algorithm's search strategies, which aim to rationalize the abilities of exploration and exploitation of the algorithm. The results tested on the Computational Experiment Competition on 2017 (CEC2017) test function set show that DMDS-HS outperforms the other nine HS algorithms and the other four state-of-the-art algorithms in terms of diversity, freedom from local optima, and solution accuracy. In addition, applying DMDS-HS to data clustering problems, the results show that it exhibits clustering performance that exceeds the other seven classical clustering algorithms, which verifies the effectiveness and reliability of DMDS-HS in solving complex data clustering problems.

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“…Bekdaş [ 4 ] applied the applications of harmony search, flower pollination, and teaching–learning-based optimization algorithms to minimizing the total construction cost associated with axially symmetric cylindrical reinforced concrete walls. Ocak et al [ 5 , 6 ] optimized a tuned liquid damper device, which was used for lateral displacement control of structures using the adaptive harmony search algorithm. Ulusoy [ 7 ] applied the teaching–learning-based optimization algorithm to the problem of the fire-resistant design of timber-based roof structures.…”

Section: Introductionmentioning

confidence: 99%

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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Adaptive Harmony Search for Tuned Liquid Damper Optimization under Seismic Excitation

Cited by 15 publications

References 38 publications

Interpretable Machine Learning Algorithms to Predict the Axial Capacity of FRP-Reinforced Concrete Columns

Interpretable Machine Learning Algorithms to Predict the Axial Capacity of FRP-Reinforced Concrete Columns

Harmony Search Algorithm Based on Dual-Memory Dynamic Search and Its Application on Data Clustering

Optimal Dimensioning of Retaining Walls Using Explainable Ensemble Learning Algorithms

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