Prediction of seismic collapse risk of steel moment frame mid-rise structures by meta-heuristic algorithms

Jough, Fooad Karimi Ghaleh; Şensoy, Serhan

doi:10.1007/s11803-016-0362-9

Cited by 22 publications

(11 citation statements)

References 25 publications

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“…Furthermore, in several studies Arias intensity, characteristic intensity and cumulative absolute velocity (CAV) have also been found useful. 29,61,62 Based on those studies, we chose to consider input parameters from the set of quantities listed in Table 1. The choice of the best parameters depends F I G U R E 6 Neural network training prediction workflow: (A) generated earthquake accelerogram; (B) three-story two-bay system, submitted to ground excitation; (C) single degree of freedom system (SDOF); (D) acceleration response spectrum: the intensity is highlighted blue, the response of the SDOF with natural period 1 is marked red; (E) velocity response spectrum: the intensity is highlighted orange, the response of the SDOF with natural period 1 is marked red; (F) deformation response spectrum: the response of the SDOF with natural period on the structure and the data available.…”

Section: Machine Learning Enhanced Evaluation Of Tail-end Probabilitiesmentioning

confidence: 99%

“…60 Notably, Arias intensity, characteristic intensity, and CAV are chosen more often as intensity measures as input for the neural network prediction. 61,62 As mentioned in Section 3.4, earthquake intensity measures are used to characterize the generated accelerations. These measures can be derived from the generated accelerations and from response spectra.…”

Section: Input Parameters and Hyperparameter Searchmentioning

confidence: 99%

“…Due to the correlations shown in Figure 8 and their performance in combination with the spectral displacement at the natural period ( 1 ), further parameters are chosen. Stepwise increasing of the number of input parameters and taking into account previous studies, 29,[60][61][62] the F I G U R E 9 Input parameter distributions visualized as violin plots; 67 distributions of the intensity measures from the generated accelerations without additional intensity variation (A) and with additional intensity variation (B) following intensity measures are chosen: spectral acceleration at the natural period ( 1 ), velocity spectrum intensity ∫ , spectral displacement at the natural period ( 1 ), CAV, peak ground acceleration (PGA) and EPA.…”

Section: Input Parameters and Hyperparameter Searchmentioning

confidence: 99%

“…In that regard, Housner intensity, peak ground acceleration, and effective peak acceleration (EPA) have been shown to be promising. Furthermore, in several studies Arias intensity, characteristic intensity and cumulative absolute velocity (CAV) have also been found useful 29, 61, 62 . Based on those studies, we chose to consider input parameters from the set of quantities listed in Table 1.…”

Section: Machine Learning Enhanced Monte Carlo Simulationmentioning

confidence: 99%

“…Investigations based on different parameters for the inputs using a classical neural network approach can be found in the existing literature 60 . Notably, Arias intensity, characteristic intensity, and CAV are chosen more often as intensity measures as input for the neural network prediction 61, 62 …”

Section: Numerical Examplementioning

confidence: 99%

See 4 more Smart Citations

Machine‐learning‐enhanced tail end prediction of structural response statistics in earthquake engineering

Thaler

Stoffel

Markert

et al. 2021

Earthq Engng Struct Dyn

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Evaluating the response statistics of nonlinear structures constitutes a key issue in engineering design. Hereby, the Monte Carlo method has proven useful, although the computational cost turns out to be considerably high. In particular, around the design point of the system near structural failure, a reliable estimation of the statistics is unfeasible for complex high-dimensional systems. Thus, in this paper, we develop a machine-learning-enhanced Monte Carlo simulation strategy for nonlinear behaving engineering structures. A neural network learns the response behavior of the structure subjected to an initial nonstationary ground excitation subset, which is generated based on the spectral properties of a chosen ground acceleration record. Then using the superior computational efficiency of the neural network, it is possible to predict the response statistics of the full sample set, which is considerably larger than the initial training sample set. To K E Y W O R D Searthquake generation, elastoplastic structures, Kanai-Tajimi filter, machine learning, Monte Carlo method, neural networks This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Machine Learning Enhanced Evaluation Of Tail-end Probabilitiesmentioning

confidence: 99%

Section: Input Parameters and Hyperparameter Searchmentioning

confidence: 99%

Section: Input Parameters and Hyperparameter Searchmentioning

confidence: 99%

Section: Machine Learning Enhanced Monte Carlo Simulationmentioning

confidence: 99%

Section: Numerical Examplementioning

confidence: 99%

See 3 more Smart Citations

Machine‐learning‐enhanced tail end prediction of structural response statistics in earthquake engineering

Thaler

Stoffel

Markert

et al. 2021

Earthq Engng Struct Dyn

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Machine learning‐based prediction of the seismic response of fault‐crossing natural gas pipelines

Zhang¹,

Ayello²,

Honegger

et al. 2023

Earthq Engng Struct Dyn

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Herein, we utilized machine‐learning (ML) and data‐driven (regression) techniques to tackle a critical infrastructure engineering problem—namely, predicting the seismic response of natural gas pipelines crossing earthquake faults. Such a 3D nonlinear problem can take up to 10 h to solve by performing finite element analysis (FEA), considering the length of the pipeline and a large number of pipe and soil elements. However, the ML and data‐driven techniques can learn the projection rule of input‐output and predict the pipeline response instantaneously given a set of input features. In addition, the well‐trained ML model can be implemented for regional‐scale risk and rapid post‐event damage assessments. In this study, the input for ML comprised approximately 217K nonlinear FEAs, which covered a wide range of combinations of soil, structural and fault properties and yielded critical pipe strain responses under fault‐rupture displacements. We adopted various regression models and physics‐constrained neural networks, which can accurately and rapidly predict the tensile and compressive strains for a broad range of probable fault‐rupture displacements. Performances of various ML and conventional statistical models were systematically examined. Not surprisingly, neural networks exhibited the best performance for this multi‐output regression problem, in which R2 > 0.95 was achieved for a wide range of fault displacement (FD) levels. Further, we used the trained neural network with 14.5 million Monte‐Carlo‐generated input samples to predict the maximum tensile and compressive strain curves of pipelines. This new dataset aimed at filling the missing input‐output points from the 217K FEAs, and improved the accuracy of the prediction of probability of failure for natural gas pipelines under FD hazards.

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Optimal seismic retrofit model for steel moment resisting frames with brittle connections

Park

Choi

2018

Earthq. Eng. Eng. Vib.

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Prediction of seismic collapse risk of steel moment frame mid-rise structures by meta-heuristic algorithms

Cited by 22 publications

References 25 publications

Machine‐learning‐enhanced tail end prediction of structural response statistics in earthquake engineering

Machine‐learning‐enhanced tail end prediction of structural response statistics in earthquake engineering

Machine learning‐based prediction of the seismic response of fault‐crossing natural gas pipelines

Optimal seismic retrofit model for steel moment resisting frames with brittle connections

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