Investigation on behavior and seismic performance of reduced beam sections

Ermeydan, İbrahim; Akgönen, Alirıza İlker

doi:10.7764/rdlc.21.2.427

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…Artificial intelligence-based models are increasingly being employed widely in almost every single field with developing technology. For example, it has been demonstrated that there are important contributions in modeling the steel connections of seismic performance used in civil engineering with artificial intelligence [29][30][31]. As another example, Guerra et al [32] performed a comparative study of swarm intelligence metaheuristics in the unscented Kalman filter-based neural training applied to the identification and control of robotic manipulators.…”

Section: Modelmentioning

confidence: 99%

A New Approach for Completing Missing Data Series in Pan Evaporation Using Multi-Meteorologic Phenomena

Dis

2023

Sustainability

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The most crucial losses in the hydrological cycle occur due to evaporation (EP). As a result, the accurate attainment of this complex phenomenon is critical in studies on irrigation, efficiency in the basins, dams, continuous hydrometeorological simulations, flood frequency, and water budget analysis. However, EP data sets are expensive, difficult to sustainably measure, and scarce, also, predictions are challenging tasks due to the wide range of parameters involved in these processes. In this study, the data gaps are filled with Class A evaporation pan observations through building a new meteorological station during seasons with no gauge measurements available for a three-year time period. These observations demonstrate high correlations with the readings from the Meteorology Airport Station, with a PCC of 0.75. After the continuous EP time series was completed over Kahramanmaras, these values were retrieved non-linearly via an artificial intelligence model using multi-meteorological parameters. In the study, the simulation performance is evaluated with the help of eight different statistical metrics in addition to graphical representations. The evaluation reveals that, when compared to the other EP functions, using both temperature and wind-driven simulations has the highest correlation (PCC = 0.94) and NSCE (0.87), as well as the lowest bias (PBias = −1.65%, MAE = 1.27 mm d−1, RMSD = 1.6 mm d−1, CRMSE = 24%) relative to the gauge measurements, while they give the opposite results in the solely precipitation-based models (PCC = 0.42, NSCE = 0.17, PBias = −6.44%, MAE = 3.58 mm d−1, RMSD = 4.2 mm d−1, CRMSE = 62%). It has been clearly seen that the temperature parameter is the most essential factor, while precipitation alone may be insufficient in EP predictions; additionally, wind speed and relative humidity would improve the prediction performance in artificial intelligence techniques.

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Section: Modelmentioning

confidence: 99%

A New Approach for Completing Missing Data Series in Pan Evaporation Using Multi-Meteorologic Phenomena

Dis

2023

Sustainability

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Impact of Probabilistic Modeling Alternatives on the Seismic Fragility Analysis of Reinforced Concrete Dual Wall–Frame Buildings towards Resilient Designs

Martinez,

Gallegos,

Araya-Letelier

et al. 2024

Sustainability

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Demands to advance toward more resilient and sustainable cities in terms of reducing casualties, economic losses, downtime, and environmental impacts derived from earthquake-induced damage are becoming more frequent. Indeed, accurate evaluations of the seismic performance of buildings via numerical simulations are crucial for the sustainable development of the built environment. Nevertheless, performance estimations could be influenced by alternative probabilistic methods that can be chosen throughout the procedure of building-specific risk assessment, specifically in the construction and validation of fragility functions. This study evaluates the numerical impacts of selecting different probabilistic models on seismic risk metrics for reinforced concrete dual wall–frame buildings. Specifically, alternative probabilistic models are implemented and evaluated for (i) the identification and elimination of unusual observations within the simulated data (i.e., outliers); (ii) the selection and implementation of different Probability Distribution Functions (PDFs) to estimate fragility functions at different limit states (LSs); and (iii) the application of goodness-of-fit tests and information criteria to assess the validity of proposed PDFs. According to the results, the risk measures showed large variability at the extreme building LS (collapse). On the other hand, for a lower LS (service level), the measures remain similar in all the cases despite the methods selected. Further, the variability observed in the collapse response is up to two times that after eliminating data outliers. Finally, the large variability obtained with the evaluated alternative probabilistic modeling methods suggests re-opening the technical discussion over the state of the practice often used in earthquake engineering to improve the decision-making process, mitigating earthquake-induced consequences in an environmentally, economically, and socially beneficial manner.

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Investigation on behavior and seismic performance of reduced beam sections

Cited by 2 publications

References 11 publications

A New Approach for Completing Missing Data Series in Pan Evaporation Using Multi-Meteorologic Phenomena

A New Approach for Completing Missing Data Series in Pan Evaporation Using Multi-Meteorologic Phenomena

Impact of Probabilistic Modeling Alternatives on the Seismic Fragility Analysis of Reinforced Concrete Dual Wall–Frame Buildings towards Resilient Designs

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