Particle swarm optimization technique-based prediction of peak ground acceleration of Iraq’s tectonic regions

Hason, Mahir M.; Hanoon, Ammar N.; Abdulhameed, Ali A.

doi:10.1016/j.jksues.2021.06.004

Cited by 15 publications

(9 citation statements)

References 32 publications

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“…It is simpler to build than other optimization methods since it makes use of probabilistic transition rules and does not start with a seed solution. the number of [1,34]. When combined with other optimization tools, it can build hybrid tools that have several advantages over other optimization methods.…”

Section: Pso Algorithmmentioning

confidence: 99%

The Behavior of Hybrid Fiber-Reinforced Concrete Elements: A New Stress-Strain Model Using an Evolutionary Approach

et al. 2022

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Several stress-strain models were used to predict the strengths of steel fiber reinforced concrete, which are distinctive of the material. However, insufficient research has been done on the influence of hybrid fiber combinations (comprising two or more distinct fibers) on the characteristics of concrete. For this reason, the researchers conducted an experimental program to determine the stress-strain relationship of 30 concrete samples reinforced with two distinct fibers (a hybrid of polyvinyl alcohol and steel fibers), with compressive strengths ranging from 40 to 120 MPa. A total of 80 % of the experimental results were used to develop a new empirical stress-strain model, which was accomplished through the application of the particle swarm optimization (PSO) tech-nique. It was discovered in this investigation that the new stress-strain model predictions are consistent with the remaining 20% of the experimental stress-strain curves obtained. Case studies of hybrid–fiber–reinforced concrete constructions were investigated in order to better understand the behavior of such elements. The data revealed that the proposed model has the highest absolute relative error (ARE) frequencies (ARE10 % and the lowest absolute relative error (ARE > 15 %) frequencies (ARE > 15 %).

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Section: Pso Algorithmmentioning

confidence: 99%

The Behavior of Hybrid Fiber-Reinforced Concrete Elements: A New Stress-Strain Model Using an Evolutionary Approach

et al. 2022

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“…In [151], a particle swarm optimization (PSO) model has been proposed to calculate the peak ground acceleration (PGA). PGA is among the main parameters pinpointing the EQ intensity.…”

Section: A Optimization and Seismic Signalsmentioning

confidence: 99%

Employing Remote Sensing, Data Communication Networks, AI, and Optimization Methodologies in Seismology

Abdalzaher

El-Sayed

Fouda

2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Seismology is among the intrinsic sciences that strictly affect human lives. Many research efforts are presented in the literature aiming at achieving risk mitigation and disaster management. More particularly, modern technologies have been employed in such a pivot. However, the day-to-day challenges and complexities of such natural science that face the stack holders still need more reliable and intelligent solutions. The solution can depend on a partial or integrated system of modern technologies. In this paper, we extensively survey the co-related modern technologies aiming to gather the major efforts exerted in this regard. It also outlines the desirability of seismology to modern technologies. Then, we present a detailed analysis of remote sensing and data communication networks (DCNs), which are considered the backend of seismic networks. Furthermore, for seismology, we depict both classical and non-classical approaches based on DCN principles, such as optical fiber-based acoustic sensors, social media, and the internet of things (IoT). Following that, a comprehensive description of the various optimization techniques utilized for seismic wave analysis and for prolonging network lifetime is offered. A description of the important functions that artificial intelligence (AI) can play in different fields of seismology is also included. Finally, we present some recommendations for stack holders to prevent natural calamities and preserve human lives.

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“…Hason et al [24], a delicate computing method called Molecule particle swarm optimization (PSO) is connected to develop figure models for crest ground speeding up (PGA). These models are produced taking after the Iraq structural locale, covering 187-chronicled records (2004-2020) from worldwide seismic stations to the closest epicenter remove.…”

Section: Related Workmentioning

confidence: 99%

Earthquake prediction in Iraq using machine learning techniques

Jarah

Hashim

Alasadi

2022

IJEECS

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This study deals with addressing the scientific achievements and the history of earthquake prediction in Iraq, in addition to attempting to discuss the possibility of machine learning to predict earthquakes from a theoretical perspective. The idea of predicting earthquakes gives at least a little time to protect people and reduce earthquake damage. In Iraq, we notice an increase in the occurrence of earthquakes, especially in the southern regions, where they form a strange phenomenon because they are plain areas and far from the seismic fault line, due to the errors that accompany excessive oil extraction and in random and unstudied ways, and geological studies raise fears in predicting an increase in earthquakes for the coming years. We have explored the possibility of applying machine learning technology to predict earthquakes in Iraq, and follow-up recording of tremors at different stations in Iraq through three centers of seismic sensor networks. In addition to the earthquake catalog in Iraq (1900-2019). This study may pave the way for more research to develop an integrated and accurate earthquake prediction system using machine-learning technologies.

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Particle swarm optimization technique-based prediction of peak ground acceleration of Iraq’s tectonic regions

Cited by 15 publications

References 32 publications

The Behavior of Hybrid Fiber-Reinforced Concrete Elements: A New Stress-Strain Model Using an Evolutionary Approach

The Behavior of Hybrid Fiber-Reinforced Concrete Elements: A New Stress-Strain Model Using an Evolutionary Approach

Employing Remote Sensing, Data Communication Networks, AI, and Optimization Methodologies in Seismology

Earthquake prediction in Iraq using machine learning techniques

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