scholarly journals Machine Learning-Based Seismic Fragility Analysis of Large-Scale Steel Buckling Restrained Brace Frames

2020 ◽  
Vol 125 (2) ◽  
pp. 755-776
Author(s):  
Baoyin Sun ◽  
Yantai Zhang ◽  
Caigui Huang
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Fragility Analysis ◽  
Seismic Fragility ◽  
Buckling Restrained Brace ◽  
Brace Frames

scholarly journals Efficient Seismic Fragility Analysis for Large-Scale Piping System Utilizing Bayesian Approach

2020 ◽  
Vol 10 (4) ◽  
pp. 1515 ◽  
Author(s):  
Shinyoung Kwag ◽  
YongHee Ryu ◽  
Bu-Seog Ju
Keyword(s):  
Large Scale ◽  
Complex Model ◽  
Fragility Analysis ◽  
Seismic Fragility ◽  
Computational Time ◽  
Piping System ◽  
Analysis Method ◽  
Structural System ◽  
Nonlinear Seismic Response ◽  
Similar Accuracy

In the event of an earthquake, it is essential to accurately assess the seismic fragility of piping systems to ensure the continued safety of society. When evaluating the seismic fragility of a piping system, which is generally a secondary structural system, this should mainly be an integrated model that includes both the primary structural frames and the secondary ones, unlike the primary structural system of a building. Hence, the piping seismic fragility evaluation has an issue in that it takes considerable computational time because numerical analyses must be performed on a relatively complex model. Given this background, the purpose of this study is to propose an efficient piping seismic fragility analysis method by utilizing the existing seismic fragility analysis method and the Bayesian updating concept. In order to verify the validity of the proposed method, it was applied to a building–piping coupled structural system example, and its results were analyzed and compared with the results of the existing method in terms of accuracy and efficiency. As a result, the proposed method showed a similar accuracy compared with the existing method while significantly reducing the numerical cost of nonlinear seismic response analyses necessary for these results.

scholarly journals Dynamic Damage Mechanism and Seismic Fragility Analysis of an Aqueduct Structure

2021 ◽  
Vol 11 (24) ◽  
pp. 11709
Author(s):  
Xinyong Xu ◽  
Xuhui Liu ◽  
Li Jiang ◽  
Mohd Yawar Ali Khan
Keyword(s):  
Ground Motion ◽  
Structural Damage ◽  
Large Scale ◽  
Seismic Analysis ◽  
Fragility Curves ◽  
Limit State ◽  
Fragility Analysis ◽  
Seismic Fragility ◽  
Exceedance Probability ◽  
Computation Efficiency

The Concrete Damaged Plasticity (CDP) constitutive is introduced to study the dynamic failure mechanism and the law of damage development to the aqueduct structure during the seismic duration using a large-scale aqueduct structure from the South-to-North Water Division Project (SNWDP) as a research object. Incremental dynamic analysis (IDA) and multiple stripe analysis (MSA) seismic fragility methods are introduced. The spectral acceleration is used as the scale of ground motion record intensity measure (IM), and the aqueduct pier top offset ratio quantifies the limit of structural damage measure (DM). The aqueduct structure’s seismic fragility evaluation curves are constructed with indicators of different seismic intensity measures to depict the damage characteristics of aqueduct structures under different seismic intensities through probability. The results show that penetrating damage is most likely to occur on both sides of the pier cap and around the pier shaft in the event of a rare earthquake, followed by the top of the aqueduct body, which requires the greatest care during an earthquake. The results of two fragility analysis methodologies reveal that the fragility curves are very similar. The aqueduct structure’s first limit state level (LS1) is quite steep and near the vertical line, indicating that maintaining the excellent condition without damage in the seismic analysis will be challenging. Except for individual results, the overall fragility results are in good agreement, and the curve change rule is the same. The exceedance probability in the case of any ground motion record IM may be estimated using only two factors when using the MSA approach, and the computation efficiency is higher. The study of seismic fragility analysis methods in this paper can provide a reference for the seismic safety evaluation of aqueducts and similar structures.

scholarly journals Seismic Fragility Analysis based on Artificial Ground Motions and Surrogate Modeling of Validated Structural Simulators

2020 ◽  
Author(s):  
Giuseppe Abbiati ◽  
Marco Broccardo ◽  
Imad Abdallah ◽  
Stefano Marelli ◽  
Fabrizio Paolacci
Keyword(s):  
Earthquake Engineering ◽  
Large Scale ◽  
Hazard Analysis ◽  
Computational Cost ◽  
Surrogate Modeling ◽  
Fragility Analysis ◽  
Seismic Fragility ◽  
Motion Modeling ◽  
Seismic Records ◽  
Performance Based Earthquake Engineering

This study introduces a computational framework for efficient and accurate seismic fragility analysis based on a combination of artificial ground motion modeling, polynomial-chaos-based global sensitivity analysis, and hierarchical kriging surrogate modeling. The framework follows the philosophy of the Performance-Based Earthquake Engineering PEER approach, where the fragility analysis is decoupled from hazard analysis. This study addresses three criticalities that are present in the current practice. Namely, reduced size of hazard-consistent size-specific ensembles of seismic records, validation of structural simulators against large-scale experiments, high computational cost for accurate fragility estimates. The effectiveness of the proposed framework is demonstrated for the Rio Torto Bridge, recently tested using hybrid simulation within the RETRO project.

scholarly journals Seismic Fragility Analysis of the Reinforced Concrete Continuous Bridge Piers Based on Machine Learning and Symbolic Regression Fusion Algorithms

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Hanbo Zhu ◽  
Changqing Miao
Keyword(s):  
Machine Learning ◽  
Reinforced Concrete ◽  
Damage Index ◽  
Regression Function ◽  
Element Model ◽  
Seismic Intensity ◽  
Symbolic Regression ◽  
Fragility Analysis ◽  
Seismic Fragility ◽  
Intensity Measures

In the fragility analysis, researchers mostly chose and constructed seismic intensity measures (IMs) according to past experience and personal preference, resulting in large dispersion between the sample of engineering demand parameter (EDP) and the regression function with IM as the independent variable. This problem needs to be solved urgently. Firstly, the existing 46 types of ground motion intensity measures were taken as a candidate set, and the composite intensity measures (IMs) based on machine learning methods were selected and constructed. Secondly, the modified Park–Ang damage index was taken as EDP, and the symbolic regression method was used to fit the functional relationship between the composite intensity measures (CIMs) and EDP. Finally, the probabilistic seismic demand analysis (PSDA) and seismic fragility analysis were performed by the cloud-stripe method. Taking the pier of a three-span continuous reinforced concrete hollow slab bridge as an example, a nonlinear finite element model was established for vulnerability analysis. And the composite IM was compared with the linear composite IM constructed by Kiani, Lu Dagang, and Liu Tingting. The functions of them were compared. The analysis results indicated that the standard deviation of the composite IM fragility curve proposed in this paper is 60% to 70% smaller than the other composite indicators which verified the efficiency, practicality, proficiency, and sufficiency of the proposed machine learning and symbolic regression fusion algorithms in constructing composite IMs.

Evolution of Metastable Structures in Bimetallic Catalysts from Microscopy and Machine-Learning Molecular Dynamics

2020 ◽  
Author(s):  
Jin Soo Lim ◽  
Jonathan Vandermause ◽  
Matthijs A. van Spronsen ◽  
Albert Musaelian ◽  
Christopher R. O’Connor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Large Scale ◽  
Materials Science ◽  
Complete Characterization ◽  
Layer By Layer ◽  
Surface Restructuring ◽  
Metastable Structures ◽  
Mechanistic Investigation ◽  
Underlying Mechanisms

Restructuring of interface plays a crucial role in materials science and heterogeneous catalysis. Bimetallic systems, in particular, often adopt very different composition and morphology at surfaces compared to the bulk. For the first time, we reveal a detailed atomistic picture of the long-timescale restructuring of Pd deposited on Ag, using microscopy, spectroscopy, and novel simulation methods. Encapsulation of Pd by Ag always precedes layer-by-layer dissolution of Pd, resulting in significant Ag migration out of the surface and extensive vacancy pits. These metastable structures are of vital catalytic importance, as Ag-encapsulated Pd remains much more accessible to reactants than bulk-dissolved Pd. The underlying mechanisms are uncovered by performing fast and large-scale machine-learning molecular dynamics, followed by our newly developed method for complete characterization of atomic surface restructuring events. Our approach is broadly applicable to other multimetallic systems of interest and enables the previously impractical mechanistic investigation of restructuring dynamics.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

Adsorption Isotherm Predictions for Multiple Molecules in MOFs Using the Same Deep Learning Model

2019 ◽  
Author(s):  
Ryther Anderson ◽  
Achay Biong ◽  
Diego Gómez-Gualdrón
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Molecular Simulation ◽  
Large Scale ◽  
Learning Model ◽  
Operating Conditions ◽  
Small Subset ◽  
Screening Methods ◽  
Large Set ◽  
Metal Organic

<div>Tailoring the structure and chemistry of metal-organic frameworks (MOFs) enables the manipulation of their adsorption properties to suit specific energy and environmental applications. As there are millions of possible MOFs (with tens of thousands already synthesized), molecular simulation, such as grand canonical Monte Carlo (GCMC), has frequently been used to rapidly evaluate the adsorption performance of a large set of MOFs. This allows subsequent experiments to focus only on a small subset of the most promising MOFs. In many instances, however, even molecular simulation becomes prohibitively time consuming, underscoring the need for alternative screening methods, such as machine learning, to precede molecular simulation efforts. In this study, as a proof of concept, we trained a neural network as the first example of a machine learning model capable of predicting full adsorption isotherms of different molecules not included in the training of the model. To achieve this, we trained our neural network only on alchemical species, represented only by their geometry and force field parameters, and used this neural network to predict the loadings of real adsorbates. We focused on predicting room temperature adsorption of small (one- and two-atom) molecules relevant to chemical separations. Namely, argon, krypton, xenon, methane, ethane, and nitrogen. However, we also observed surprisingly promising predictions for more complex molecules, whose properties are outside the range spanned by the alchemical adsorbates. Prediction accuracies suitable for large-scale screening were achieved using simple MOF (e.g. geometric properties and chemical moieties), and adsorbate (e.g. forcefield parameters and geometry) descriptors. Our results illustrate a new philosophy of training that opens the path towards development of machine learning models that can predict the adsorption loading of any new adsorbate at any new operating conditions in any new MOF.</div>

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