Seismic damage state predictions of reinforced concrete structures using stacked long short-term memory neural networks

Ahmed, Bilal; Mangalathu, Sujith; Jeon, Jong‐Su

doi:10.1016/j.jobe.2021.103737

Cited by 23 publications

(12 citation statements)

References 48 publications

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“…[10] machine learning techniques with boosting algorithms is used for response prediction of reinforced concrete frame buildings. In the aforementioned studies and many other similar works [11,12,13,14,15,16], it has been clearly demonstrated that once the machine learning model is properly trained, it can replicate a non-linear finite element model output accurately. It must be noted that a considerable computational effort is expended in the initial training of the machine models to replicate the non-linear behavior of the structure, however, the value in these methods is drawn from the fact once trained subsequent estimation of non-linear prediction are computationally trivial.…”

Section: Introductionmentioning

confidence: 86%

“…Machine learning models have been widely used in literature of earthquake engineering to tackle different problems, including surrogate modeling [6,11,12,13,14,15,16] . Depending on the available data, objective and the complexity of the problem, available computational expenditure and desired level of accuracy, various machine learning models, like the simplistic multiple linear regression, to the more complex artificial neural networks are available to choose from.…”

Section: Selection Of Suitable Machine Learning Modelmentioning

confidence: 99%

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Machine learning based surrogate modeling with SVD enabled training for nonlinear civil structures subject to dynamic loading

Parida¹,

Bose²,

Butcher³

et al. 2022

Preprint

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The computationally expensive estimation of engineering demand parameters (EDPs) via finite element (FE) models, while considering earthquake and parameter uncertainty limits the use of the Performance Based Earthquake Engineering framework. Attempts have been made to substitute FE models with surrogate models, however, most of these models are a function of building parameters only. This necessitates re-training for earthquakes not previously seen by the surrogate. In this paper, the authors propose a machine learning based surrogate model framework, which considers both these uncertainties in order to predict for unseen earthquakes. Accordingly,

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Section: Introductionmentioning

confidence: 86%

Section: Selection Of Suitable Machine Learning Modelmentioning

confidence: 99%

Machine learning based surrogate modeling with SVD enabled training for nonlinear civil structures subject to dynamic loading

Parida¹,

Bose²,

Butcher³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Somala et al [ 18 ] showed that in the fundamental period estimation of masonry infilled reinforced concrete frames, Ensemble Learning Techniques such as Random Forest and XGBoost could outperform the existing empirical predictive models available in the literature. Ahmed et al [ 19 ] developed a novel long short-term memory network with overlapping data for the accurate prediction of earthquake-induced damage in ductile and non-ductile frame structures. Ni et al [ 20 ] generated fragility curves for buried pipelines using Lasso Regression Analysis.…”

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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“…Consequently, they constitute a general and flexible modeling tool for prediction. It comes as no surprise that many engineering disciplines are witnessing an intense engagement with neural network models to solve a broad range of challenging problems [18][19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Fast Seismic Assessment of Built Urban Areas with the Accuracy of Mechanical Methods Using a Feedforward Neural Network

et al. 2022

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Capacity curves obtained from nonlinear static analyses are widely used to perform seismic assessments of structures as an alternative to dynamic analysis. This paper presents a novel ‘en masse’ method to assess the seismic vulnerability of urban areas swiftly and with the accuracy of mechanical methods. At the core of this methodology is the calculation of the capacity curves of low-rise reinforced concrete buildings using neural networks, where no modeling of the building is required. The curves are predicted with minimal error, needing only basic geometric and material parameters of the structures to be specified. As a first implementation, a typology of prismatic buildings is defined and a training set of more than 7000 structures generated. The capacity curves are calculated through push-over analysis using SAP2000. The results feature the prediction of 100-point curves in a single run of the network while maintaining a very low mean absolute error. This paper proposes a method that improves current seismic assessment tools by providing a fast and accurate calculation of the vulnerability of large sets of buildings in urban environments.

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Seismic damage state predictions of reinforced concrete structures using stacked long short-term memory neural networks

Cited by 23 publications

References 48 publications

Machine learning based surrogate modeling with SVD enabled training for nonlinear civil structures subject to dynamic loading

Machine learning based surrogate modeling with SVD enabled training for nonlinear civil structures subject to dynamic loading

Optimal Dimensioning of Retaining Walls Using Explainable Ensemble Learning Algorithms

Fast Seismic Assessment of Built Urban Areas with the Accuracy of Mechanical Methods Using a Feedforward Neural Network

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