Measurement, Characterization, and Modeling of Initial Geometric Imperfections in Wide-Flange Steel Members Subjected to Combined Axial and Cyclic Lateral Loading

Sediek, Omar A.; Wu, Tung-Yu; McCormick, Jason; El‐Tawil, Sherif

doi:10.1061/(asce)st.1943-541x.0003086

Cited by 6 publications

(5 citation statements)

References 16 publications

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“…Input variables (Tables 1 and 2) are considered including web slenderness ratio (h/t w ), flange slenderness ratio (b f /2t f ), global slenderness ratio (L/ r y ), torsional slenderness ratio (J/(S × h 0 )), axial load ratio (AxLoad), axial loading protocol (AxPro), boundary conditions (BouCon), and lateral loading protocol (LatPro). These variables are selected based on previous research (experimental and parametric studies) [9,19]. The column's bottom is presumed to be completely fixed.…”

Section: Experimental Databasementioning

confidence: 99%

“…Scholars have investigated the behavior of the DSWCs experimentally or computationally. The use of extensive finite element analysis through parametric studies is a common method for discovering failure modes of DSWCs [8,9]. Such a deep examination is valuable, but it necessitates a significant amount of computing cost and effort, as well as time and resources to complete [4].…”

Section: Introductionmentioning

confidence: 99%

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Data-driven Dynamic-classifiers-based Seismic Failure Mode Detection of Deep Steel W-shape Columns

Barkhordari

Tehranizadeh

2023

Period. Polytech. Civil Eng.

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It is vital to assess the health of buildings following a major earthquake. New technologies such as deep learning algorithms have grown increasingly tempting in such rapid applications because of their increased reliabilities and simplicity to traditional methods. Due to the kinematics of steel moment frames, inelastic deformations tend to concentrate within the steel column during an earthquake, resulting in local or global buckling. Rapid failure mode detection of the existing deep steel W-shape columns (DSWCs) cannot be quickly identified due to a lack of comprehensive empirical and mechanics-based models. This research proposed a machine learning (ML) algorithm based on the state-of-the-art techniques of dynamic classifiers for failure mode forecasting of the DSWCs using an experimental database and illustrated why the ML model suggests a specific failure mode for a particular sample. The database was created by combining 939 instances from various studies that have been published. A total of six machine learning models based on Dynamic Selection strategy were implemented. Three metrics, i.e., accuracy, precision, and recall, were used to evaluate the performance of models. As a result of the extensive examination, a machine learning model based on the META-DES model was proposed. In the training stage, Overall Local Accuracy, A-Priori, and META-DES algorithms, received the highest score (>0.96) across all criteria. The META-DES model correctly predicted the failure mode of the DSWCs with an accuracy of 0.907 in the testing phase. The META-DES algorithm performed better than previous methods which are employed to identify the failure mode.

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Section: Experimental Databasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data-driven Dynamic-classifiers-based Seismic Failure Mode Detection of Deep Steel W-shape Columns

Barkhordari

Tehranizadeh

2023

Period. Polytech. Civil Eng.

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“…Therefore, seismic performance assessment methodologies of structures rely on the availability of numerical models that simulate inelastic local buckling. For this purpose, different model fidelities have been proposed throughout the years ranging from phenomenological (e.g., [2]) fiber-based (e.g., [3]) and continuum finite element [4][5] approaches.…”

Section: Introductionmentioning

confidence: 99%

“…In prior work [8] the importance of the mesh type and size, the residual stress pattern, the initial geometric imperfections, and constitutive model formulation have been stressed. A common simulation practice for simulating inelastic cyclic local buckling has been the use of shell elements [4][5][8][9]. However, the accuracy of inelastic strain demand predictions within the local buckling region is challenging to assess.…”

Section: Introductionmentioning

confidence: 99%

“…Prior related work has mostly focused on the ability of CFE models to capture the global behavior of steel members (e.g., deduced moment-chord rotation, axial shortening) [4][5]; however, the accurate prediction of local strain and deformation demands has been overlooked. This is important for predicting ductile fracture due to ultra-low cycle fatigue [6].…”

Section: Introductionmentioning

confidence: 99%

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Digital‐Image‐Correlation for Simulating Cyclic Local Buckling in Steel Beams

Ozden,

de Castro e Sousa,

Lignos

2023

ce papers

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Nonlinear continuum finite element (CFE) analyses rely on accurate multiaxial constitutive law formulations along with reliable imperfection patterns for simulating nonlinear geometric instabilities in steel members under mechanical loading. Validations of these models usually rely on conventional measurements of macroscopic parameters (e.g., displacement and strain field). It is common to use global deformation characteristics (e.g., deduced moment versus chord rotation) for model validation purposes of steel members exhibiting inelastic cyclic local buckling. The reason is that measurements from conventional strain gauge readings are not deemed to be reliable well before the onset of local buckling. This is potentially problematic when the accurate prediction of local strain demands is essential. This paper discusses the experimental results from a steel beam instrumented with a digital image correlation (DIC) system and how optical measurements acquired from DIC can be used to benchmark a CFE model representation of this beam. It is shown that while different CFE model types simulate accurately the moment rotation relationship of the steel beam, local strain demand predictions could vary considerably between model types. The results suggest that DIC measurements can inform the selection of proper imperfection patterns to be used in CFE models for the reliable estimation of inelastic strain demands.

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Finite Element Study of Seismic Compactness Limits for Tubular Columns in Seismic Frames

Constain-Montoya,

Sediek,

et al. 2024

Lecture Notes in Civil Engineering

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Measurement, Characterization, and Modeling of Initial Geometric Imperfections in Wide-Flange Steel Members Subjected to Combined Axial and Cyclic Lateral Loading

Cited by 6 publications

References 16 publications

Data-driven Dynamic-classifiers-based Seismic Failure Mode Detection of Deep Steel W-shape Columns

Data-driven Dynamic-classifiers-based Seismic Failure Mode Detection of Deep Steel W-shape Columns

Digital‐Image‐Correlation for Simulating Cyclic Local Buckling in Steel Beams

Finite Element Study of Seismic Compactness Limits for Tubular Columns in Seismic Frames

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