An uncertainty-aware dynamic shape optimization framework: Gravity dam design

Abdollahi, Azam; Amini, Ali; Hariri-Ardebili, Mohammad Amin

doi:10.1016/j.ress.2022.108402

Cited by 39 publications

(11 citation statements)

References 52 publications

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“…It is well known that there are many uncertainties in the design of engineering structures, such as material randomness, manufacturing anomalies, and external loading, which play an important role in reliability-based design optimization (RBDO) [45]. In addition, the particle swarm optimization algorithm (PSO) is a very classic intelligent optimization algorithm, which has good results in parameter optimization and is widely used in various fields [46].…”

Section: Discussionmentioning

confidence: 99%

Importance Analysis of Structural Seismic Demand Based on Support Vector Machine

Wang¹,

Xu²,

Jiang³

et al. 2022

Mathematical Problems in Engineering

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Seismic demand analysis of structures plays an important role in the structural seismic calculation; however, studies on the importance analysis of seismic demand are limited. A new method based on a support vector machine (SVM) is proposed to analyze the importance of structural seismic demand and study the influence of random variables on structural seismic demand in this study, where the linear kernel function, Gauss kernel function, and polynomial kernel function are used in SVM. The time history analysis of the steel-reinforced concrete (SRC) frame structures has been carried out by the finite element software OpenSees under the action of different seismic records. Four kinds of seismic demand of the SRC frame structure are analyzed in this study, which are top displacement, maximum floor acceleration, base shear, and maximum interstory drift angle, respectively. Importance indexes of the four kinds of structural seismic demand are in good agreement with those of the Monte Carlo (MC) numerical simulation method and Tornado graphic method, which verify the accuracy of the proposed method. Moreover, the sample size of the proposed method is greatly smaller than that of the MC method. Therefore, the computation efficiency has been improved significantly by the proposed method.

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Section: Discussionmentioning

confidence: 99%

Importance Analysis of Structural Seismic Demand Based on Support Vector Machine

Wang¹,

Xu²,

Jiang³

et al. 2022

Mathematical Problems in Engineering

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“…Four types of ML models, namely Extreme Gradient Boosting (XGBoost), RF, Categorical features-support gradient Boosting (CatBoost), and GP, are developed in this study to forecast the PHL of RCSWs. For XGBoost, RF, and CatBoost models, Bayesian optimization is employed to efficiently tune their hypermeters (Abdollahi et al, 2022).…”

Section: Description Of ML Modelsmentioning

confidence: 99%

Machine learning based prediction model for plastic hinge length calculation of reinforced concrete structural walls

Barkhordari

Jawdhari

2023

Advances in Structural Engineering

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Reinforced concrete structural walls (RCSWs) are integral part of buildings and other structures and are used to carry in-plane and out-plane loads. For assessment purposes and to ensure safety and resilience of the structure, the curvature, capacity, and strain demands of RCSWs need to be estimated. Nonlinear numerical models are increasingly used in earthquake engineering design and assessment, where it is critical to develop high fidelity simulation tools to precisely forecast the global and local behavior of RCSWs. Plastic deformations concentrated in small regions, i.e. plastic hinges, and characterized through the concept of plastic hinge length (PHL), are conventionally used to define the inelastic response of RCSWs. In this study machine learning (ML) algorithms were leveraged in evaluating the PHL of RCSWs. A database containing 721 planar and nonplanar RCSW samples were utilized for training and testing ML models. Four different algorithms were employed, namely: XGBoost, CatBoost, Random Forest (RF), and genetic programming. The RF model outperformed other ML counterparts. The results of the best ML model were also compared against empirical equations available in literature and nonlinear regression analysis. The proposed ML model provided much better predictions than the existing relationships. Predictions of most empirical models were found to be extremely over (or under) conservative. The proposed ML model was also presented via an online tool where users can obtain the PHL of any RCSW simply by inputting wall parameters and clicking a run button.

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“…The damage type has a direct impact on the mechanical properties of the RC members [36]. Failures of RC elements can be broadly categorized into three major groups (flexural, shear, and flexural-shear).…”

Section: Dataset Of Damaged and Undamaged Rc Elementsmentioning

confidence: 99%

Hybrid Wavelet Scattering Network-Based Model for Failure Identification of Reinforced Concrete Members

Barkhordari

Armaghani

et al. 2022

Sustainability

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After earthquakes, qualified inspectors typically conduct a semisystematic information gathering, physical inspection, and visual examination of the nation’s public facilities, buildings, and structures. Manual examinations, however, take a lot of time and frequently demand too much work. In addition, there are not enough professionals qualified to assess such structural damage. As a result, in this paper, the efficiency of computer-vision hybrid models was investigated for automatically detecting damage to reinforced concrete elements. Data-driven hybrid models are generated by combining wavelet scattering network (WSN) with bagged trees (BT), random subspace ensembles (RSE), artificial neural networks (ANN), and quadratic support vector machines (SVM), named “BT-WSN”, “RSE-WSN”, “ANN-WSN”, and “SVM-WSN”. The hybrid models were trained on an image database containing 4585 images. In total, 15% of images with different sorts of damage were used to test the trained models’ robustness and adaptability; these images were not utilized in the training or validation phase. The WSN-SVM algorithm performed best in classifying the damage. It had the highest accuracy of the hybrid models, with a value of 99.1% in the testing phase.

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An uncertainty-aware dynamic shape optimization framework: Gravity dam design

Cited by 39 publications

References 52 publications

Importance Analysis of Structural Seismic Demand Based on Support Vector Machine

Importance Analysis of Structural Seismic Demand Based on Support Vector Machine

Machine learning based prediction model for plastic hinge length calculation of reinforced concrete structural walls

Hybrid Wavelet Scattering Network-Based Model for Failure Identification of Reinforced Concrete Members

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