A neural network-based multivariate seismic classifier for simultaneous post-earthquake fragility estimation and damage classification

Yuan, Xinzhe; Chen, Genda; Jiao, Pu; Li, Liujun; Han, Jun Hyun; Zhang, Haibin

doi:10.1016/j.engstruct.2022.113918

Cited by 27 publications

(13 citation statements)

References 54 publications

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“…Over the last few decades, this approach has been applied extensively in the engineering field to solve numerical problems by constructing a model that maps the input and output of a given dataset [8]. Previously, ANN was used in various civil engineering problems, such as concrete mixtures' mechanical properties prediction [9,10,11,12], damage detection [13,14,15], structural response estimation [16,17,18], soil behavior modeling [19,20,21]. On the other hand, multiple models using the feed-forward back-propagation neural network technique were developed for slope stability evaluation [22,23,24,25,26].…”

Section: Introductionmentioning

confidence: 99%

Comparative Assessment of Various Artificial Neural Network Techniques for Estimating the Safety Factor of Road Embankments

Alqawasmeh¹

2023

GEOMATE

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The slope-stability analysis is one of the most important parameters for ensuring a safe design of road embankments. Currently, various traditional approaches to computing this variable can be seen in the literature. Among them, the finite element method is considered an accurate way to define the safety factor of road embankments. Previous research has investigated the capability of artificial neural networks for rapid safety-factor estimation to overcome the long process of modeling and calculations required in the aforementioned approach. However, most of these studies have focused on a single type of neural network and did not investigate the capabilities of other approaches. Therefore, this study is intended to evaluate the performance of various artificial neural network techniques in predicting the safety factor of road embankments. Within this context, the feed-forward back-propagation, cascade forward neural networks, and general regression neural network results will be compared and benchmarked against various methods used to predict this parameter. Moreover, it is intended to report the influence of neural network architecture on the accuracy of the estimation. Generally, the study results have shown that an artificial neural network provides a rapid and accurate method for calculating road embankments' safety factors. Besides, the best neural network model achieved a coefficient of determination of about 0.91 and a root mean square error of 0.236, which proves the efficiency of this technique. Moreover, the reliability assessment by comparing the neural network models against the traditional methods has shown that they provide better agreement with the finite element technique.

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Section: Introductionmentioning

confidence: 99%

Comparative Assessment of Various Artificial Neural Network Techniques for Estimating the Safety Factor of Road Embankments

Alqawasmeh¹

2023

GEOMATE

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“…, 2010; Nguyen et al. , 2021; Yuan et al. , 2022) and the development of earth resource maps (Pourghasemi et al.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the accuracy and effectiveness of machine learning methods for rapidly determining the safety factor of road embankments

Habib¹,

Bashir

Alsalman

et al. 2023

MMMS

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PurposeSlope stability analysis is essential for ensuring the safe design of road embankments. While various conventional methods, such as the finite element approach, are used to determine the safety factor of road embankments, there is ongoing interest in exploring the potential of machine learning techniques for this purpose.Design/methodology/approachWithin the study context, the outcomes of the ensemble machine learning models will be compared and benchmarked against the conventional techniques used to predict this parameter.FindingsGenerally, the study results have shown that the proposed machine learning models provide rapid and accurate estimates of the safety factor of road embankments and are, therefore, promising alternatives to traditional methods.Originality/valueAlthough machine learning algorithms hold promise for rapidly and accurately estimating the safety factor of road embankments, few studies have systematically compared their performance with traditional methods. To address this gap, this study introduces a novel approach using advanced ensemble machine learning techniques for efficient and precise estimation of the road embankment safety factor. Besides, the study comprehensively assesses the performance of these ensemble techniques, in contrast with established methods such as the finite element approach and empirical models, demonstrating their potential as robust and reliable alternatives in the realm of slope stability assessment.

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“…The vulnerability calculation method has been improved by the emerging technologies, such as artificial neural networks and so on (Morfidis and Kostinakis, 2019; Pang et al. , 2021; Yuan et al. , 2022; Zhao et al.…”

Section: Introductionmentioning

confidence: 99%

“…The study of the performance of structures under ground motions has been a hot issue in recent years, and much work has been carried out by scholars on this subject. The vulnerability calculation method has been improved by the emerging technologies, such as artificial neural networks and so on (Morfidis and Kostinakis, 2019;Pang et al, 2021;Yuan et al, 2022;Zhao et al, 2021). Some researchers have conducted a series of explorations on the seismic characteristics and performance of frame results in environments such as far-field earthquakes, near-field earthquakes and strong earthquakes (Bhandari et al, 2019;Sharma et al, 2020a, b, c;2021a, b, c).…”

Section: Introductionmentioning

confidence: 99%

Analysis of time-varying seismic fragility of rigid frame pier of offshore bridge

Liang

Wei

et al. 2022

IJSI

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PurposeAlthough mechanical behavior of rigid frame pier has been clearly recognized, their time-varying seismic performance are yet to be well characterized due to some offshore piers that are eroded by chloride ion and located in earthquake-prone area. In this study, the time-variant seismic fragility analysis was conducted to evaluate seismic performance of rigid frame pier under four damage states with considering the time-varying characteristics of the material.Design/methodology/approachThis paper establishes the nonlinear finite element model for the investigated offshore reinforcement concrete (RC) pier with considering the time-varying durability damage of the materials and defines the damage state, damage position and damaged index of the offshore RC pier. It also analyzes the time-varying seismic fragility of the offshore RC pier by using the capacity demand ratio method in the whole life cycle.FindingsThe results show that chloride induced corrosion has a significant effect on the rigid frame pier and bending capacity of top section is less than that of bottom section. The rate of decline accelerates after the service life reaching 30 years under the coupling of the earthquake and the environmental erosion. In the early years of service, the seismic fragility of the structure changed slowly.Originality/valueThis paper analyzes the influencing factors of seismic performance of rigid structure pier, and analyzes the seismic capacity and seismic performance of rigid structure pier under different service periods.

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A neural network-based multivariate seismic classifier for simultaneous post-earthquake fragility estimation and damage classification

Cited by 27 publications

References 54 publications

Comparative Assessment of Various Artificial Neural Network Techniques for Estimating the Safety Factor of Road Embankments

Comparative Assessment of Various Artificial Neural Network Techniques for Estimating the Safety Factor of Road Embankments

Evaluating the accuracy and effectiveness of machine learning methods for rapidly determining the safety factor of road embankments

Analysis of time-varying seismic fragility of rigid frame pier of offshore bridge

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