Reduced‐order models for the seismic assessment of plan‐irregular low‐rise frame buildings

Ruggieri, Sergio; Chatzidaki, Akrivi; Vamvatsikos, Dimitrios; Uva, Giuseppina

doi:10.1002/eqe.3725

Cited by 27 publications

(5 citation statements)

References 49 publications

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“…The structural analysis computation time was optimized by running 16 models in parallel. However, it is possible to make equivalent models in order to optimize the number of parameters and decrease the consumed computation time [ 26 ].…”

Section: Structural Design and Input Ground Motionsmentioning

confidence: 99%

Machine Learning-Based Rapid Post-Earthquake Damage Detection of RC Resisting-Moment Frame Buildings

Alcantara

Saito

2023

Sensors

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This study proposes a methodology to predict the damage condition of Reinforced Concrete (RC) resisting-moment frame buildings using Machine Learning (ML) methods. Structural members of six hundred RC buildings with varying stories and spans in X and Y directions were designed using the virtual work method. Sixty thousand time-history analyses using ten spectrum-matched earthquake records and ten scaling factors were carried out to cover the structures’ elastic and inelastic behavior. The buildings and earthquake records were split randomly into training data and testing data to predict the damage condition of new ones. In order to reduce bias, the random selection of buildings and earthquake records was carried out several times, and the mean and standard deviation of the accuracy were obtained. Moreover, 27 Intensity Measures (IM) based on acceleration, velocity, or displacement from the ground and roof sensor responses were used to capture the building’s behavior features. The ML methods used IMs, the number of stories, and the number of spans in X and Y directions as input data and the maximum inter-story drift ratio as output data. Finally, seven Machine Learning (ML) methods were trained to predict the damage condition of buildings, finding the best set of training buildings, IMs, and ML methods for the highest prediction accuracy.

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Section: Structural Design and Input Ground Motionsmentioning

confidence: 99%

Machine Learning-Based Rapid Post-Earthquake Damage Detection of RC Resisting-Moment Frame Buildings

Alcantara

Saito

2023

Sensors

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“…For example, Kazantzi and Vamvatiskos showed that Sa avg can reduce bias in vulnerability assessment of building classes, particularly if calculated for class-average second and elongated first modes [82]. Similarly, Ruggieri et al showed that Sa avg can be used for fragility analysis of an RC building stock with different topological parameters or plan-irregular low-rise frames [83,84].…”

Section: Scalar Imsmentioning

confidence: 99%

A Holistic Review of GM/IM Selection Methods from a Structural Performance-Based Perspective

Esteghamati

2022

Sustainability

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Current fully probabilistic approaches to performance-based earthquake engineering describe structures’ behavior under a wide range of seismic hazard levels. These approaches require a detailed representation of ground motion (GM) uncertainty at all considered hazard levels, yet different GM selection methods lead to different estimations of structural performance. This paper presents a holistic review of the current practices in GM representation and selection for structural demand analysis through a performance-based lens. The multidisciplinary nature of GM selection, ranging from earth science to engineering seismology and statistics, has created a preponderance of literature to find the best practice for probabilistic assessment of structures in terms of computational efficiency and statistical accuracy. Many of these studies focus individually on GM selection or structural analysis, and the relatively scarce review papers either focus on code-based GM selection or do not specifically address risk-based evaluations by overlooking the interaction between GM selection and structural analysis. This paper aims to aid researchers in selecting appropriate GMs as part of a statistically valid and robust probabilistic demand analysis without performing an exhaustive literature review. Discussion on the available computational tools and their trade-offs for risk-based assessment of single structures is provided. While the problem-specific nature of GM selection means that no pre-selected set of GM/IM is applicable to all cases, the comprehensive narrative of this paper is expected to aid analysts in reaching a more informed decision.

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“…For example, Raheem et al(Abdel Raheem et al, 2018) analyzed the seismic response of L-shaped irregular buildings showing that buildings with irregular planar layouts have worse seismic performance compared to regular buildings; In the Wenchuan earthquake of 2008, the external-corridor type buildings widely used in classroom buildings, dormitories, and office buildings were severely damaged; In the 1985 Mexico earthquake, about 50% of buildings were damaged because of the torsional effect generated by the irregular arrangement of lateral load-resisting members directly or indirectly (Chandler, 1986;Meli and Rosenblueth, 1986) Therefore, considering the irregularity characteristic of buildings in regional seismic damage simulation has significant engineering values. Because of the high number of buildings in regions, a spatial simplified model is a feasible method to moderately consider the lateral-torsional coupling effect of irregular buildings (Ruggieri et al, 2022). Blasonea et al (Blasone et al, 2022) proposed a simplified spring model to simulate irregular RC frames, which use two perpendicular lateral shear springs to simulate the mechanical behavior of each column and the connected beams.…”

Section: Introductionmentioning

confidence: 99%

A moderately refined and efficient regional seismic damage simulation method considering building irregularity

Chen,

Yu,

2024

Preprint

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Irregular buildings that occupy a certain proportion on regional scales are prone to suffer serious damage from earthquakes due to the influence of lateral-torsional coupling effect. However, the current regional seismic damage simulation methods are mainly based on two-dimensional simplified models, which cannot take into account the spatial characteristics of irregular buildings. In addition, the refined finite-element method is unsuitable for the simulation of a host of buildings on a regional scale. This paper proposes a moderately refined seismic damage simulation method for building groups with high efficiency to consider the spatial irregularity characteristics of structures. The moderately refined layer element model is established to simulate the nonlinear behavior of each story of the building with irregular plan configurations. For the elevation-irregular structures, the proposed model can also be used by properly combining the elements representing various stories in series. Additionally, a simplified approach of considering P-∆ effect is incorporated by adding equivalent lateral forces to nodes and the method for determining the hysteretic parameter and backbone curve parameters of the proposed model is illustrated. Then, a two-level accelerated dynamic analysis method is proposed to reduce computation time for seismic damage simulation in region, which involves a perturbation nonlinear dynamic governing equation for a single structure and an OpenMP parallel solution strategy for structure groups. Finally, the two individual numerical examples and the seismic damage simulation for a region are performed to demonstrate the validity and efficiency of the proposed method.

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Reduced‐order models for the seismic assessment of plan‐irregular low‐rise frame buildings

Cited by 27 publications

References 49 publications

Machine Learning-Based Rapid Post-Earthquake Damage Detection of RC Resisting-Moment Frame Buildings

Machine Learning-Based Rapid Post-Earthquake Damage Detection of RC Resisting-Moment Frame Buildings

A Holistic Review of GM/IM Selection Methods from a Structural Performance-Based Perspective

A moderately refined and efficient regional seismic damage simulation method considering building irregularity

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